Allow initial background calculation

[modules/adao.git] / doc / en / tui.rst

..
   Copyright (C) 2008-2016 EDF R&D

   This file is part of SALOME ADAO module.

   This library is free software; you can redistribute it and/or
   modify it under the terms of the GNU Lesser General Public
   License as published by the Free Software Foundation; either
   version 2.1 of the License, or (at your option) any later version.

   This library is distributed in the hope that it will be useful,
   but WITHOUT ANY WARRANTY; without even the implied warranty of
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
   Lesser General Public License for more details.

   You should have received a copy of the GNU Lesser General Public
   License along with this library; if not, write to the Free Software
   Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA

   See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com

   Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D

.. index:: single: TUI
.. index:: single: API/TUI
.. index:: single: adaoBuilder
.. _section_tui:

================================================================================
**[DocR]** Textual Application Programming Interface for the user (API/TUI)
================================================================================

.. warning::

  in its present version, this text programming interface (TUI) is experimental,
  and so changes can be required in forthcoming versions.

This section presents advanced usage of the ADAO module using its text
programming interface (API/TUI). This interface gives ability to create a
calculation object in a similar way than the case building obtained through the
graphical interface (GUI). When one wants to elaborate "by hand" 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 :ref:`section_theory`.

.. _subsection_tui_creating:

Creation of ADAO TUI calculation case and examples
--------------------------------------------------

.. _subsection_tui_example:

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::

    from numpy import array
    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 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]

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
:ref:`subsection_tui_commands`.

The creation and initialisation 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
    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 :ref:`section_reference` and its subparts 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
:math:`\mathbf{x}^b` (named ``Background``) and its errors covariance
:math:`\mathbf{B}` (named ``BackgroundError``), and after that, the observation
:math:`\mathbf{y}^o` (named ``Observation``) and its errors  covariance
:math:`\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 :math:`H` of observation and
possibly :math:`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 :math:`\mathbf{R}^3` into itself::

    case.ObservationOperator(Matrix = "1 0 0;0 2 0;0 0 3")

In the most frequent case of a non-linear operator of :math:`\mathbf{R}^n` into
:math:`\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 :math:`n` as input and which returns as output a ``numpy`` vector
of size :math:`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)"
        __x = numpy.matrix(numpy.ravel(numpy.matrix(x))).T
        __H = numpy.matrix("1 0 0;0 2 0;0 0 3")
        return __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
:ref:`section_advanced_observer`, and to the :ref:`section_reference` 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
:ref:`subsection_tui_example`.

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
``table``, the registering of the observations in the ADAO TUI calculation
case can be done by the following operations::

    from observations import table
    case.set( 'Observation', Vector=table )

The first line imports the ``table`` 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.

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 :ref:`section_ref_output_variables`
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 :", len(case.get("CostFunctionJ"))
    Xa = case.get("Analysis")
    print "    Optimal analysis     :", Xa[-1]
    print "    Size of the analysis :", len(Xa[-1])
    print

These lines can be very simply added to the initial example of ADAO TUI
calculation case given in :ref:`subsection_tui_example`.

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.

.. _subsection_tui_commands:

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.

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.

Creating a calculation case in TUI text interface
+++++++++++++++++++++++++++++++++++++++++++++++++

The creation and the initialisation 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
    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.

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
:ref:`section_reference_entry` and in particular by the
:ref:`section_ref_entry_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:

.. index:: single: setBackground

**setBackground** (*Vector, VectorSerie, Script, Stored*)
    This command allows to set the background :math:`\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*".

.. index:: single: setBackgroundError

**setBackgroundError** (*Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored*)
    This command allows to set the matrix :math:`\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*".

.. index:: single: setCheckingPoint

**setCheckingPoint** (*Vector, VectorSerie, Script, Stored*)
    This command allows to set a current point :math:`\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*".

.. index:: single: setControlModel

**setControlModel** (*Matrix, OneFunction, ThreeFunctions, Parameters, Script, Stored*)
    This command allows to set the control operator :math:`O`, which represents
    an external linear input control of the evolution or observation operator.
    One can refer to the :ref:`section_ref_operator_control`. 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
    :ref:`section_ref_operator_requirements`, 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).

.. index:: single: setControlInput

**setControlInput** (*Vector, VectorSerie, Script, Stored*)
    This command allows to set the control vector :math:`\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*".

.. index:: single: setEvolutionError

**setEvolutionError** (*Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored*)
    This command allows to set the matrix :math:`\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*".

.. index:: single: setEvolutionModel

**setEvolutionModel** (*Matrix, OneFunction, ThreeFunctions, Parameters, Script, Stored*)
    This command allows to set the evolution operator :math:`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
    :ref:`section_ref_operator_requirements`, 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).

.. index:: single: setObservation

**setObservation** (*Vector, VectorSerie, Script, Stored*)
    This command allows to set the observation vector :math:`\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*".

.. index:: single: setObservationError

**setObservationError** (*Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored*)
    This command allows to set the matrix :math:`\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*".

.. index:: single: setObservationOperator

**setObservationOperator** (*Matrix, OneFunction, ThreeFunctions, AppliedInXb, Parameters, Script, Stored*)
    This command allows to set the evolution operator :math:`H`, which
    transforms the input parameters :math:`\mathbf{x}` in results
    :math:`\mathbf{y}` that are compared to observations :math:`\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 :ref:`section_ref_operator_requirements`, 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 :math:`H` operator evaluated in :math:`\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).

.. index:: single: set

**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=...*").

Setting the calculation, outputs, etc.
++++++++++++++++++++++++++++++++++++++

.. index:: single: setAlgorithmParameters

**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 :ref:`section_reference_assimilation` and to the
    :ref:`section_reference_checking`) 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).

.. index:: single: setDebug

**setDebug** ()
    This command enables the detailed information mode when running.

.. index:: single: setNoDebug

**setNoDebug** ()
    This command disables the detailed information mode when running.

.. index:: single: setObserver

**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
    ':ref:`ref_observers_requirements` for their list and content, and to the
    :ref:`section_reference` 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
    :ref:`ref_observers_requirements`.

Perform the calculation
+++++++++++++++++++++++

.. index:: single: executePythonScheme

**executePythonScheme** ()
    This command launches the complete calculation in the environment of the
    current Python interpreter, without interaction with YACS [YACS]_. The
    standard output and standard error are those of the Python interpreter. If
    necessary, the internal parallelism, of the algorithms in ADAO and of the
    simulation code used, is available.

.. index:: single: execute

**execute** ()
    This command is a user shorthand for "*executePythonScheme*".

Get the calculation results separately
++++++++++++++++++++++++++++++++++++++

.. index:: single: get

**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 :ref:`subsection_r_o_v_Inventaire` and more generally to the
    :ref:`section_ref_output_variables` and to the individual documentations of
    the algorithms.

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.

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 the user).

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

#. that we want to adjust 3 parameters ``alpha``, ``beta`` and ``gamma`` in a bounded domain,
#. that we dispose of observations named ``observations``,
#. 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,
#. 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 choose
for example the following entries, perfectly arbitrary, by building the
observations by simulation in order to set a twin experiments case::

    #
    # 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)"
        import numpy
        __x = numpy.matrix(numpy.ravel(numpy.matrix(x))).T
        __H = numpy.matrix("1 0 0;0 2 0;0 0 3; 1 2 3")
        return __H * __x
    #
    # Observations obtained by simulation
    # -----------------------------------
    observations = simulation((2, 3, 4))

The set of commands that can be used is the following::

    import numpy
    import adaoBuilder
    #
    # Formatting entries
    # ------------------
    Xb = (alpha, beta, gamma)
    Bounds = (
        (alphamin, alphamax),
        (betamin,  betamax ),
        (gammamin, gammamax))
    #
    # TUI ADAO
    # --------
    case = adaoBuilder.New()
    case.set(
        'AlgorithmParameters',
        Algorithm = '3DVAR',
        Parameters = {
            "Bounds":Bounds,
            "MaximumNumberOfSteps":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()
    #
    # Independent holding
    # -------------------
    Xbackground   = case.get("Background")
    Xoptimum      = case.get("Analysis")[-1]
    FX_at_optimum = case.get("SimulatedObservationAtOptimum")[-1]
    J_values      = case.get("CostFunctionJ")[:]
    print
    print "Number of internal iterations...: %i"%len(J_values)
    print "Initial state...................:",numpy.ravel(Xbackground)
    print "Optimal state...................:",numpy.ravel(Xoptimum)
    print "Simulation at optimal state.....:",numpy.ravel(FX_at_optimum)
    print

The command set execution gives the following result::

    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.]
    Optimal state...................: [ 2.  3.  4.]
    Simulation at optimal state.....: [  2.   6.  12.  20.]

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

.. Réconciliation de courbes à l'aide de MedCoupling
.. +++++++++++++++++++++++++++++++++++++++++++++++++

.. Utilisation de fonctions de surveillance de type "observer"
.. +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

.. Suivre d'un recalage à l'aide de MatPlotLib
.. +++++++++++++++++++++++++++++++++++++++++++

.. Equivalences entre l'interface graphique (GUI) et l'interface textuelle (TUI)
.. -----------------------------------------------------------------------------

.. [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.