9. [DocR] Textual User Interface for ADAO (TUI/API)

This section presents advanced usage of the ADAO module using its text programming interface (API/TUI). This interface gives user ability to create a calculation object in a similar way than the case building obtained through the graphical interface (GUI). A scripted form of a case built in the GUI can be obtained directly using the TUI export button eficas_totui integrated in the interface, but more complicated or integrated cases can be build only using TUI approach. When one wants to elaborate directly the TUI calculation case, it is recommended to extensively use all the ADAO module documentation, and to go back if necessary to the graphical interface (GUI), to get all the elements allowing to correctly set the commands. The general used notions and terms are defined in [DocT] A brief introduction to Data Assimilation and Optimization. As in the graphical interface, we point out that the TUI approach is intended to create and manage a single calculation case.

9.1. Creation of ADAO TUI calculation case and examples

9.1.1. A simple setup example of an ADAO TUI calculation case

To introduce the TUI interface, lets begin by a simple but complete example of ADAO calculation case. All the data are explicitly defined inside the script in order to make the reading easier. The whole set of commands is the following one:

# -*- coding: utf-8 -*-
#
from numpy import array
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.execute()

The result of running these commands in SALOME (either as a SALOME “shell” command, in the SALOME Python command window of the interface, or by the script execution entry of the menu) is the following:

Analysis [0.25000264 0.79999797 0.94999939]

9.1.2. Detailed setup of an ADAO TUI calculation case

More details are given here on the successive steps of the setup of an ADAO TUI calculation case. The commands themselves are detailed just after in the Set of available commands in text user interface TUI. We keep the indication [...] of preceding or following lines to emphasize that these commands can be inserted in the middle of a complete stream of Python scripting for a real study.

The creation and initialization of a study are done using the following commands, the case object name of the ADAO TUI calculation case being let free to the user choice:

[...]
from numpy import array
from adao import adaoBuilder
case = adaoBuilder.New()
[...]

It is recommended to import by default the numpy module or some of its embedded constructors such as the array one, to make easier its upcoming use in the commands.

Thereafter, the case has to be build by preparing and storing the data that define the study. The commands order does not matter, it is sufficient that all the concepts, required by the algorithm used, are present. The user can refer to the [DocR] Reference description of the ADAO commands and keywords and its sub-parts to get details about commands by algorithm. Here, we define successively the chosen data assimilation or optimization algorithm and its parameters, then the a priori state \mathbf{x}^b (named Background) and its errors covariance \mathbf{B} (named BackgroundError), and after that, the observation \mathbf{y}^o (named Observation) and its errors covariance \mathbf{R} (named ObservationError):

[...]
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' )
[...]

As a remark, vector or matrix inputs can be given as objects of type str, list or tuple of Python, or of type array or matrix of Numpy. For these last two cases, one has only to import Numpy module before.

After that, one has to define the operators H of observation and possibly M of evolution. In all cases, linear or non-linear, they can be defined as functions. In the simple case of a linear operator, one can also define it using the matrix that corresponds to the linear operator. In the most simple present case of a linear operator, we use the following syntax for an operator from \mathbf{R}^3 into itself:

[...]
case.set( 'ObservationOperator', Matrix = "1 0 0;0 2 0;0 0 3")
[...]

In the most frequent case of a non-linear operator of \mathbf{R}^n into \mathbf{R}^p, it has to be previously available as a Python function, known in the current name space, which takes a Numpy vector (or an ordered list) of size n as input and which returns as output a Numpy vector of size p. When the non-linear operator is the only one to be defined by the keyword “OneFunction”, its adjoint is directly established by numerical calculations and it can be parametrized by the keyword “Parameters”. The following example shows a simulation function (which realizes here the same linear operator than above) and record it in the ADAO case:

[...]
import numpy
def simulation(x):
    "Simulation function H to perform Y=H(X)"
    import numpy
    __x = numpy.ravel(x)
    __H = numpy.diag([1.,2.,3.])
    return numpy.dot(__H, __x)
#
case.set( 'ObservationOperator',
    OneFunction = simulation,
    Parameters  = {"DifferentialIncrement":0.01},
    )
[...]

To obtain intermediary or final results of the case, one can add some “observer”, that link a script to execute with an intermediate or final calculation variable. The reader can go the description of the way of Getting information on special variables during the ADAO calculation, and to the [DocR] Reference description of the ADAO commands and keywords in order to know what are the observable quantities. This link between an “observer” and an observable quantity is done in a similar way than the calculation data definition:

[...]
case.set( 'Observer', Variable="Analysis", Template="ValuePrinter" )
[...]

Finally, when all the required information are available in the ADAO calculation case named case, it can be executed in a very simple way in the environment of the Python interpreter:

[...]
case.execute()
[...]

At the end, we get a very compact script previously proposed in A simple setup example of an ADAO TUI calculation case.

9.1.3. Using more complex calculation data or information

Such an interface being written in Python, it is possible to use all the power of the language to enter more complex data than explicit declaration.

The registering of input data supports various variable types, but in addition, these inputs can come from variables currently available in the name space of the script. It is then easy to use previously calculated variables or obtained by importing “user” scripts. If for example the observations are available as a list in an external Python file named observations.py under the name someTable, the registering of the observations in the ADAO TUI calculation case can be done by the following operations:

[...]
from observations import someTable
case.set( 'Observation', Vector=someTable )
[...]

The first line imports the someTable variable from the external file, and the second one register directly this table as the “Observation” data.

The simplicity of this recording demonstrates the ease of obtaining computational data from external sources, files or computing flows achievable in Python. As usual, it is recommended to the user to check its data before saving them in the ADAO TUI calculation case to avoid errors complicated to correct.

9.1.4. Obtain and use the results of calculation in a richer way

Similarly, it is possible to obtain and process the results of calculation in a richer way, following up on post-processing after the TUI calculation.

The variables of calculation results, or the internal variables coming from optimization or data assimilation, are available through the get method of the ADAO TUI calculation case, which send back an object of list type of the required variable. The reader can go to the Variables and information available at the output for a detailed description on this subject.

For instance, we give some script lines that allow to get the number of iterations of the optimization and the optimal value, and its size:

[...]
print("")
print("    Number of iterations : %i"%len(case.get("CostFunctionJ")))
Xa = case.get("Analysis")
print("    Optimal analysis     : %s"%(Xa[-1],))
print("    Size of the analysis : %i"%len(Xa[-1]))
print("")
[...]

These lines can be very simply added to the initial example of ADAO TUI calculation case given in A simple setup example of an ADAO TUI calculation case.

As well as for data entry, the simplicity of results achievement makes it easy to consider post-processing chains in SALOME, to use for example visualization with MatPlotLib or PARAVIS [PARAVIS], mesh adaptation with HOMARD [HOMARD], or for other calculations.

9.2. Set of available commands in text user interface TUI

In the TUI interface of ADAO module, we follow usual Python conventions and recommendations to make the distinction between public objects, and private or reserved ones because of implementation details. In practice, every object or function name beginning with at least one “_” sign is private in the usual programming sense (“private”). Nevertheless, the absence of such a sign at the beginning of a name does not designate it as public. In general, in Python, and unlike other languages, you can access private objects or functions. This can sometimes be useful, but such use in your codes will lead to crashes without warning in future versions. It is strongly recommended not to do so.

To clarify and facilitate the use of the module for scripting, this section therefore defines the application programming interface (API) for textual user interface (TUI) by a comprehensive and restricted manner. Use in scripts of ADAO objects or functions other than those defined here is strongly discouraged, as this will likely lead to crashes without warning in future versions.

9.2.1. Equivalent syntax calls for commands

The definition of data during the ADAO TUI calculation case creation supports two completely equivalent syntaxes. One can:

  • either use the set command and as the first argument the concept XXXXX on which to apply the command whose arguments follow,

  • or use the command setXXXXX containing the arguments of the command to apply.

To illustrate this equivalence, we take the example of two commands that lead to the same result:

case.set( 'Background', Vector=[0, 1, 2] )

and:

case.setBackground( Vector=[0, 1, 2] )

The choice of one or the other syntaxes is freely left to the user, according to its context of use. In the following, for clarity, we define the controls according to the second syntax.

9.2.2. Creating a calculation case in TUI text interface

The creation and the initialization of a calculation case in TUI text interface are done by importing the interface module “adaoBuilder” and by by invoking its method “New()” as illustrated in the following lines (the case object name being let free to the user choice):

[...]
from numpy import array
from adao import adaoBuilder
case = adaoBuilder.New()
[...]

It is recommended by default to always import the numpy module (or some of its embedded constructors such as the array one) to make easier its upcoming use in the commands.

9.2.3. Defining the calculation data

The following commands are used to define the data of an ADAO TUI calculation case. The pseudo-type of the arguments is similar and consistent with those of the inputs in GUI interface, as described in section of [DocR] General entries and outputs and in particular by the List of possible input types. The verification of the adequacy of variables is done either on their definition, or at runtime.

In each command, the boolean keyword “Stored” indicates whether you optionally want to store the quantity defined, for disposal during calculation or at the output. The default is not to store, and it is recommended to keep this default. Indeed, for a TUI calculation case, the quantity given in entries are often available in the current name space of the case.

The available commands are:

setBackground (Vector, VectorSerie, Script, DataFile, ColNames, ColMajor, Stored)

This command allows to set the background \mathbf{x}^b. Depending on the algorithm, it can be defined as a simple vector by “Vector”, or as a vector list by “VectorSerie”. If it is defined by a script in the “Script” keyword, the vector is of type “Vector” (by default) or “VectorSerie” according to whether one of these variables is positioned to “True”. If there is a data file given by “DataFile” (selecting, in rows by default or in lines as chosen by “ColMajor”, all the variables by default or those from the list “ColNames”), the vector is of type “Vector”.

setBackgroundError (Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored)

This command allows to set the matrix \mathbf{B} of background error covariance. The matrix may be completely defined by the “Matrix” keyword, or in a sparse way, by a diagonal matrix whose unique variance is given on the diagonal by “ScalarSparseMatrix”, or by a diagonal matrix which one gives the vector of variances located on the diagonal by “DiagonalSparseMatrix”. If it is defined by a script in “Script”, the matrix is of type “Matrix” (by default), “ScalarSparseMatrix” or “DiagonalSparseMatrix” according to whether one of these variables is positioned to “True”.

setCheckingPoint (Vector, VectorSerie, Script, DataFile, ColNames, ColMajor, Stored)

This command allows to set a current point \mathbf{x} used in a checking algorithm. Depending on the algorithm, it can be defined as a simple vector by “Vector”, or as a vector list by “VectorSerie”. If it is defined by a script in the “Script” keyword, the vector is of type “Vector” (by default) or “VectorSerie” according to whether one of these variables is positioned to “True”. If there is a data file given by “DataFile” (selecting, in rows by default or in lines as chosen by “ColMajor”, all the variables by default or those from the list “ColNames”), the vector is of type “Vector”.

setControlModel (Matrix, OneFunction, ThreeFunctions, Parameters, Script, ExtraArguments, Stored)

This command allows to set the control operator O, which represents an external linear input control of the evolution or observation operator. One can refer to the Special case of controlled evolution or observation operator. Its value is defined as an object of type function or of type “Matrix”. For the function case, various functional forms may be used, as described in the Requirements for functions describing an operator, and entered by “OneFunction” or “ThreeFunctions” keywords. If it is defined by a script in the “Script” keyword, the operator is of type “Matrix”, “OneFunction” or “ThreeFunctions” according to whether one of these variables is positioned to “True”. The control parameters of the adjoint numerical approximation, in the “OneFunction”case, can be given by a dictionary through the “Parameters” keyword. Potential entries of this dictionary are “DifferentialIncrement”, “CenteredFiniteDifference” (similar to the one of graphical interface). If the operator requires some complementary fixed arguments, they can be given through the variable “ExtraArguments” as a named parameters dictionary.

setControlInput (Vector, VectorSerie, Script, DataFile, ColNames, ColMajor, Stored)

This command allows to set the control vector \mathbf{u}. Depending on the algorithm, it can be defined as a simple vector by “Vector”, or as a vector list by “VectorSerie”. If it is defined by a script in the “Script” keyword, the vector is of type “Vector” (by default) or “VectorSerie” according to whether one of these variables is positioned to “True”. If there is a data file given by “DataFile” (selecting, in rows by default or in lines as chosen by “ColMajor”, all the variables by default or those from the list “ColNames”), the vector is of type “Vector”.

setEvolutionError (Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored)

This command allows to set the matrix \mathbf{Q} of evolution error covariance. The matrix may be completely defined by the “Matrix” keyword, or in a sparse way, by a diagonal matrix whose unique variance is given on the diagonal by “ScalarSparseMatrix”, or by a diagonal matrix which one gives the vector of variances located on the diagonal by “DiagonalSparseMatrix”. If it is defined by a script in “Script”, the matrix is of type “Matrix” (by default), “ScalarSparseMatrix” or “DiagonalSparseMatrix” according to whether one of these variables is positioned to “True”.

setEvolutionModel (Matrix, OneFunction, ThreeFunctions, Parameters, Script, ExtraArguments, Stored)

This command allows to set the evolution operator M, which describes an elementary evolution step. Its value is defined as an object of type function or of type “Matrix”. For the function case, various functional forms may be used, as described in the Requirements for functions describing an operator, and entered by “OneFunction” or “ThreeFunctions” keywords. If it is defined by a script in the “Script” keyword, the operator is of type “Matrix”, “OneFunction” or “ThreeFunctions” according to whether one of these variables is positioned to “True”. The control parameters of the adjoint numerical approximation, in the “OneFunction”case, can be given by a dictionary through the “Parameters” keyword. Potential entries of this dictionary are “DifferentialIncrement”, “CenteredFiniteDifference” (similar to the one of graphical interface). If the operator requires some complementary fixed arguments in addition to the state \mathbf{x}, they can be given through the variable “ExtraArguments” as a named parameters dictionary.

setObservation (Vector, VectorSerie, Script, DataFile, ColNames, ColMajor, Stored)

This command allows to set the observation vector \mathbf{y}^o. Depending on the algorithm, it can be defined as a simple vector by “Vector”, or as a vector list by “VectorSerie”. If it is defined by a script in the “Script” keyword, the vector is of type “Vector” (by default) or “VectorSerie” according to whether one of these variables is positioned to “True”. If there is a data file given by “DataFile” (selecting, in rows by default or in lines as chosen by “ColMajor”, all the variables by default or those from the list “ColNames”), the vector is of type “Vector”.

setObservationError (Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored)

This command allows to set the matrix \mathbf{R} of observation error covariance. The matrix may be completely defined by the “Matrix” keyword, or in a sparse way, by a diagonal matrix whose unique variance is given on the diagonal by “ScalarSparseMatrix”, or by a diagonal matrix which one gives the vector of variances located on the diagonal by “DiagonalSparseMatrix”. If it is defined by a script in “Script”, the matrix is of type “Matrix” (by default), “ScalarSparseMatrix” or “DiagonalSparseMatrix” according to whether one of these variables is positioned to “True”.

setObservationOperator (Matrix, OneFunction, ThreeFunctions, AppliedInXb, Parameters, Script, ExtraArguments, Stored)

This command allows to set the evolution operator H, which transforms the input parameters \mathbf{x} in results \mathbf{y} that are compared to observations \mathbf{y}^o. Its value is defined as an object of type function or of type “Matrix”. For the function case, various functional forms may be used, as described in the Requirements for functions describing an operator, and entered by “OneFunction” or “ThreeFunctions” keywords. If it is defined by a script in the “Script” keyword, the operator is of type “Matrix”, “OneFunction” or “ThreeFunctions” according to whether one of these variables is positioned to “True”. When the H operator evaluated in \mathbf{x}^b is available, it can be given using “AppliedInXb” and will be considered as a vector. The control parameters of the adjoint numerical approximation, in the “OneFunction”case, can be given by a dictionary through the “Parameters” keyword. Potential entries of this dictionary are “DifferentialIncrement”, “CenteredFiniteDifference” (similar to the one of graphical interface). If the operator requires some complementary fixed arguments in addition to the state \mathbf{x}, they can be given through the variable “ExtraArguments” as a named parameters dictionary.

set (Concept,…)

This command allows to have an equivalent syntax for all the commands of these section. Its first argument is the name of the concept to be defined (for example “Background” or “ObservationOperator”), on which the following arguments, which are the same as in the individual previous commands, are applied. When using this command, it is required to name the arguments (for example “Vector=…”).

9.2.4. Setting the calculation, outputs, etc.

setAlgorithmParameters (Algorithm, Parameters, Script)

This command allows to choose the calculation or the verification algorithm by the argument “Algorithm” in the form of an algorithm name (it is useful to refer to the [DocR] Data assimilation or optimization calculation cases and to the [DocR] Checking cases) and to define the calculation parameters by the argument “Parameters”. In the case of a definition by “Script”, the file must contain the two variables “Algorithm” and “Parameters” (or “AlgorithmParameters” equivalently).

setName (String)

This command allows to set a short title for the calculation case.

setDirectory (String)

This command allows to set the execution standard directory.

setDebug ()

This command enables the detailed information mode when running.

setNoDebug ()

This command disables the detailed information mode when running.

setObserver (Variable, Template, String, Script, Info)

This command allows to set an observer on the current or final calculation variable. Reference should be made to the description of the ‘Requirements for functions describing an “observer” for their list and content, and to the [DocR] Reference description of the ADAO commands and keywords to know what are the observable quantities. One defines as “String” the observer body, using a string including if necessary line breaks. It is recommended to use the patterns available by the argument “Template”. In the case of a definition as “Script”, the file must contain only the body of the function, as described in the Requirements for functions describing an “observer”. The “Info” variable contains an information string or can be void.

setUserPostAnalysis (Template, String, Script)

This command allows to define the treatment of parameters or results after the calculation algorithm has been performed. Its value is defined either as a predefined pattern name, or as a script file name, or as a string. This allows to produce directly post-processing code in an ADAO case. It is possible to use patterns available by argument “Template” (which can be “AnalysisPrinter”, “AnalysisSaver” and “AnalysisPrinterAndSaver”). In the case of a definition by “Script”, the specified file must contain only the commands that could have been put after the execution of the calculation. We refer to the description of Requirements to describe a post-processing after an ADAO calculation for the list of templates and their format. Important note: this processing is only performed when the case is executed in TUI or exported to YACS.

9.2.5. Perform the calculation

execute (Executor, SaveCaseInFile, nextStep)

This command launches the complete calculation in the execution environment chosen by the keyword Executor, which is defined by default with respect of the launching environment. This environment can be the current Python interpreter, without interaction with YACS (using the value “Python”), or the one of YACS (using the value “YACS[YACS]). If a file is given in the keyword SaveCaseInFile, it will be used to save the associated version of commands file for the given execution environment. The boolean keyword “nextStep” indicates whether the run starts from the result of the previous run without storing it (value “True”) or not (value “False”, by default). During the execution, the usual outputs (standard and error) are the one of the chosen environment. If necessary (and if possible), the ADAO algorithms internal parallelism, the parallelism of YACS, and the internal parallelism of the simulation code(s) used, are available.

9.2.6. Get the calculation results separately

get (Concept)

This command explicitly extract the variables available at the output of calculation case for use in the rest of the scripting, such as visualization. Its argument the name of a variable “Concept” and returns back the quantity as a list (even if there is only one specimen) of this base variable. For a list of variables and use them, the user has to refer to the Inventory of potentially available information at the output and more generally to the Variables and information available at the output and to the individual documentations of the algorithms.

9.2.7. Saving, loading or converting calculation case commands

The saving or loading of a calculation case deals with quantities and actions that are linked by the previous commands, excepted case external operations (such as, for example, post-processing that can be developped after the calculation cas). The registered or loaded commands remain fully compatible with these Python external case operations.

load (FileName, Content, Object, Formater)

This command allows to read or load a calculation case, from a file named “FileName” or a content in memory by “Content” or “Object”. The “Formater” keyword can indicate “TUI” for commands of textual application programming interface (default), and “COM” for commands of COMM type coming from EFICAS interface for ADAO.

dump (FileName, Formater)

This command allows to save, in a file named “FileName”, the commands of the current calculation case. The “Formater” keyword can indicate “TUI” for commands of textual application programming interface (default), and “YACS” for commands of type YACS.

convert (FileNameFrom, ContentFrom, ObjectFrom, FormaterFrom, FileNameTo, FormaterTo)

This command allows to convert directly from a known format to an another one the commands establishing the current calculation case. Some formats are only available as input or as output.

In addition, simple information about the case study as defined by the user can be obtained by using the Python “print” command directly on the case, at any stage during its design. For example:

# -*- coding: utf-8 -*-
#
from numpy import array
from adao import adaoBuilder
case = adaoBuilder.New()
case.set( 'AlgorithmParameters', Algorithm='3DVAR' )
case.set( 'Background',          Vector=[0, 1, 2] )
#
print(case)

which result is here:

================================================================================
ADAO Study report
================================================================================

Summary build with ADAO version 9.12.0

  - AlgorithmParameters command has been set with values:
        Algorithm = '3DVAR'

  - Background command has been set with values:
        Vector = [0, 1, 2]


9.3. More advanced examples of ADAO TUI calculation case

We propose here more comprehensive examples of ADAO TUI calculation, by giving the purpose of the example and a set of commands that can achieve this goal.

9.3.1. Independent holding of the results of a calculation case

The objective is to perform in TUI the setting of data for an ADAO calculation case, its execution, and then the retrieving of the results to follow on a independent holding of these results (this last step not being described here, because it depends on the user).

The hypothesis of the user case are the following ones. It is assumed:

  1. that we want to adjust 3 parameters alpha, beta and gamma in a bounded domain,

  2. that we dispose of observations named observations,

  3. that the user have a Python function of physical simulation named simulation, previously (well) tested, which transforms the 3 parameters in results similar to the observations,

  4. that the independent holding, that the user want to elaborate, is represented here by the simple printing of the initial state, of the optimal state, of the simulation in that point, of the intermediate state and of the number of optimization iteration.

In order to try in a simple way this example of TUI calculation case, we set ourselves in a twin experiments case (for information, see the approach To test a data assimilation chain: the twin experiments). For that, we choose for example the following entries, perfectly arbitrary, by building the observations by simulation. Then we solve the adjustment problem through the command set execution that follows. Finally, the whole problem is set and solved by the following script:

# -*- coding: utf-8 -*-
#
import numpy
from adao import adaoBuilder
#
# =============================================================
# PROBLEM SETTINGS
#
# Artificial building of an example of user data
# ----------------------------------------------
alpha = 5.
beta = 7
gamma = 9.0
#
alphamin, alphamax = 0., 10.
betamin,  betamax  = 3, 13
gammamin, gammamax = 1.5, 15.5
#
def simulation(x):
    "Simulation function H to perform Y=H(X)"
    __x = numpy.ravel(x)
    __H = numpy.array([[1,0,0],[0,2,0],[0,0,3],[1,2,3]])
    return numpy.dot(__H, __x)
#
# Observations obtained by simulation
# -----------------------------------
Xtrue = (2, 3, 4)
observations = simulation(Xtrue)
#
# =============================================================
# SOLVING THE PROBLEM
#
# Formatting entries
# ------------------
Xb = (alpha, beta, gamma)
Bounds = (
    (alphamin, alphamax),
    (betamin,  betamax ),
    (gammamin, gammamax))
#
# ADAO TUI
# --------
case = adaoBuilder.New()
case.set(
    'AlgorithmParameters',
    Algorithm = '3DVAR',
    Parameters = {
        "Bounds":Bounds,
        "MaximumNumberOfIterations":100,
        "StoreSupplementaryCalculations":[
            "CostFunctionJ",
            "CurrentState",
            "SimulatedObservationAtOptimum",
            ],
        }
    )
case.set( 'Background', Vector = numpy.array(Xb), Stored = True )
case.set( 'Observation', Vector = numpy.array(observations) )
case.set( 'BackgroundError', ScalarSparseMatrix = 1.0e10 )
case.set( 'ObservationError', ScalarSparseMatrix = 1.0 )
case.set(
    'ObservationOperator',
    OneFunction = simulation,
    Parameters  = {"DifferentialIncrement":0.0001},
    )
case.set( 'Observer', Variable="CurrentState", Template="ValuePrinter" )
case.execute()
#
# Getting variables of interest
# -----------------------------
Xbackground   = case.get("Background")
Xoptimum      = case.get("Analysis")[-1]
FX_at_optimum = case.get("SimulatedObservationAtOptimum")[-1]
J_values      = case.get("CostFunctionJ")[:]
#
# =============================================================
# INDEPENDENT HOLDING OF RESULTS
#
print("")
print("Number of internal iterations...: %i"%len(J_values))
print("Initial state...................: %s"%(numpy.ravel(Xbackground),))
print("Idealized state.................: %s"%(numpy.ravel(Xtrue)*1.,))
print("Optimal state...................: %s"%(numpy.ravel(Xoptimum),))
print("Simulation at optimal state.....: %s"%(numpy.ravel(FX_at_optimum),))
print("")

The command set execution gives the following results:

CurrentState [5. 7. 9.]
CurrentState [0.  3.  1.5]
CurrentState [1.40006418 3.86705307 3.7061137 ]
CurrentState [1.42580231 3.68474804 3.81008738]
CurrentState [1.60220353 3.0677108  4.06146069]
CurrentState [1.72517855 3.03296953 4.04915706]
CurrentState [2.00010755 3.         4.00055409]
CurrentState [1.99995528 3.         3.99996367]
CurrentState [2.00000007 3.         4.00000011]
CurrentState [2. 3. 4.]

Number of internal iterations...: 10
Initial state...................: [5. 7. 9.]
Idealized state.................: [2. 3. 4.]
Optimal state...................: [2. 3. 4.]
Simulation at optimal state.....: [ 2.  6. 12. 20.]

As it should be in twin experiments, when we trust mainly in observations, it is found that we get correctly the parameters that were used to artificially build the observations.

HOMARD

For more information on HOMARD, see the HOMARD module and its integrated help available from the main menu Help of the SALOME platform.

PARAVIS

For more information on PARAVIS, see the PARAVIS module and its integrated help available from the main menu Help of the SALOME platform.

YACS

For more information on YACS, see the YACS module and its integrated help available from the main menu Help of the SALOME platform.