2 Copyright (C) 2008-2015 EDF R&D
4 This file is part of SALOME ADAO module.
6 This library is free software; you can redistribute it and/or
7 modify it under the terms of the GNU Lesser General Public
8 License as published by the Free Software Foundation; either
9 version 2.1 of the License, or (at your option) any later version.
11 This library is distributed in the hope that it will be useful,
12 but WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
14 Lesser General Public License for more details.
16 You should have received a copy of the GNU Lesser General Public
17 License along with this library; if not, write to the Free Software
18 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
20 See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
22 Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
24 .. index:: single: TUI
25 .. index:: single: API/TUI
26 .. index:: single: adaoBuilder
29 ================================================================================
30 **[DocR]** Textual Application Programming Interface for the user (API/TUI)
31 ================================================================================
35 in its present version, this text programming interface (TUI) is experimental,
36 and so changes can be required in forthcoming versions.
38 This section presents advanced usage of the ADAO module using its text
39 programming interface (API/TUI). This interface gives ability to create a
40 calculation object in a similar way than the case building obtained through the
41 graphical interface (GUI). When one wants to elaborate "by hand" the TUI
42 calculation case, it is recommended to extensively use all the ADAO module
43 documentation, and to go back if necessary to the graphical interface (GUI), to
44 get all the elements allowing to correctly set the commands. The general used
45 notions and terms are defined in :ref:`section_theory`.
47 .. _subsection_tui_creating:
49 Creation of ADAO TUI calculation case and examples
50 --------------------------------------------------
52 .. _subsection_tui_example:
54 A simple setup example of an ADAO TUI calculation case
55 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
57 To introduce the TUI interface, lets begin by a simple but complete example of
58 ADAO calculation case. All the data are explicitly defined inside the script in
59 order to make the reading easier. The whole set of commands is the following
62 from numpy import array
64 case = adaoBuilder.New()
65 case.set( 'AlgorithmParameters', Algorithm='3DVAR' )
66 case.set( 'Background', Vector=[0, 1, 2] )
67 case.set( 'BackgroundError', ScalarSparseMatrix=1.0 )
68 case.set( 'Observation', Vector=array([0.5, 1.5, 2.5]) )
69 case.set( 'ObservationError', DiagonalSparseMatrix='1 1 1' )
70 case.set( 'ObservationOperator', Matrix='1 0 0;0 2 0;0 0 3' )
71 case.set( 'Observer', Variable="Analysis", Template="ValuePrinter" )
74 The result of running these commands in SALOME (either as a SALOME "*shell*"
75 command, in the Python command window of the interface, or by the script
76 execution entry of the menu) is the following::
78 Analysis [ 0.25000264 0.79999797 0.94999939]
80 Detailed setup of an ADAO TUI calculation case
81 +++++++++++++++++++++++++++++++++++++++++++++++
83 More details are given here on the successive steps of the setup of an ADAO TUI
84 calculation case. The commands themselves are detailed just after in the
85 :ref:`subsection_tui_commands`.
87 The creation and initialisation of a study are done using the following
88 commands, the ``case`` object name of the ADAO TUI calculation case being let
89 free to the user choice::
91 from numpy import array
93 case = adaoBuilder.New()
95 It is recommended to import by default the ``numpy`` module or some of its
96 embedded constructors such as the ``array`` one, to make easier its upcoming use
99 Thereafter, the case has to be build by preparing and storing the data that
100 define the study. The commands order does not matter, it is sufficient that all
101 the concepts, required by the algorithm used, are present. The user can refer to
102 the :ref:`section_reference` and its subparts to get details about commands by
103 algorithm. Here, we define successively the chosen data assimilation or
104 optimization algorithm and its parameters, then the *a priori* state
105 :math:`\mathbf{x}^b` (named ``Background``) and its errors covariance
106 :math:`\mathbf{B}` (named ``BackgroundError``), and after that, the observation
107 :math:`\mathbf{y}^o` (named ``Observation``) and its errors covariance
108 :math:`\mathbf{R}` (named ``ObservationError``)::
110 case.set( 'AlgorithmParameters', Algorithm='3DVAR' )
112 case.set( 'Background', Vector=[0, 1, 2] )
113 case.set( 'BackgroundError', ScalarSparseMatrix=1.0 )
115 case.set( 'Observation', Vector=array([0.5, 1.5, 2.5]) )
116 case.set( 'ObservationError', DiagonalSparseMatrix='1 1 1' )
118 As a remark, vector or matrix inputs can be given as objects of type ``str``,
119 ``list`` or ``tuple`` of Python, or of type ``array`` or ``matrix`` of Numpy.
120 For these last two cases, one has only to import Numpy module before.
122 After that, one has to define the operators :math:`H` of observation and
123 possibly :math:`M` of evolution. In all cases, linear or non-linear, they can be
124 defined as functions. In the simple case of a linear operator, one can also
125 define it using the matrix that corresponds to the linear operator. In the most
126 simple present case of a linear operator, we use the following syntax for an
127 operator from :math:`\mathbf{R}^3` into itself::
129 case.ObservationOperator(Matrix = "1 0 0;0 2 0;0 0 3")
131 In the most frequent case of a non-linear operator of :math:`\mathbf{R}^n` into
132 :math:`\mathbf{R}^p`, it has to be previously available as a Python function,
133 known in the current name space, which takes a ``numpy`` vector (or an ordered
134 list) of size :math:`n` as input and which returns as output a ``numpy`` vector
135 of size :math:`p`. When the non-linear operator is the only one to be defined by
136 the keyword "*OneFunction*", its adjoint is directly established by numerical
137 calculations and it can be parametrized by the keyword "*Parameters*". The
138 following example shows a ``simulation`` function (which realizes here the same
139 linear operator than above) and record it in the ADAO case::
143 "Simulation function H to perform Y=H(X)"
144 __x = numpy.matrix(numpy.ravel(numpy.matrix(x))).T
145 __H = numpy.matrix("1 0 0;0 2 0;0 0 3")
148 case.set( 'ObservationOperator',
149 OneFunction = simulation,
150 Parameters = {"DifferentialIncrement":0.01},
153 To obtain intermediary or final results of the case, one can add some
154 "*observer*", that link a script to execute with an intermediate or final
155 calculation variable. The reader can go the description of the way of
156 :ref:`section_advanced_observer`, and to the :ref:`section_reference` in order
157 to know what are the observable quantities. This link between an "*observer*"
158 and an observable quantity is done in a similar way than the calculation data
161 case.set( 'Observer', Variable="Analysis", Template="ValuePrinter" )
163 Finally, when all the required information are available in the ADAO calculation
164 case named ``case``, it can be executed in a very simple way in the environment
165 of the Python interpreter::
169 At the end, we get a very compact script previously proposed in
170 :ref:`subsection_tui_example`.
172 Using more complex calculation data or information
173 ++++++++++++++++++++++++++++++++++++++++++++++++++
175 Such an interface being written in Python, it is possible to use all the power
176 of the language to enter more complex data than explicit declaration.
178 The registering of input data supports various variable types, but in addition,
179 these inputs can come from variables currently available in the name space of the
180 script. It is then easy to use previously calculated variables or obtained by
181 importing "user" scripts. If for example the observations are available as a
182 list in an external Python file named ``observations.py`` under the name
183 ``table``, the registering of the observations in the ADAO TUI calculation
184 case can be done by the following operations::
186 from observations import table
187 case.set( 'Observation', Vector=table )
189 The first line imports the ``table`` variable from the external file, and the
190 second one register directly this table as the "*Observation*" data.
192 The simplicity of this recording demonstrates the ease of obtaining
193 computational data from external sources, files or computing flows achievable in
194 Python. As usual, it is recommended to the user to check its data before saving
195 them in the ADAO TUI calculation case to avoid errors complicated to correct.
197 Obtain and use the results of calculation in a richer way
198 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
200 Similarly, it is possible to obtain and process the results of calculation in a
201 richer way, following up on post-processing after the TUI calculation.
203 The variables of calculation results, or the internal variables coming from
204 optimization or data assimilation, are available through the ``get`` method of
205 the ADAO TUI calculation case, which send back an object of list type of the
206 required variable. The reader can go to the :ref:`section_ref_output_variables`
207 for a detailed description on this subject.
209 For instance, we give some script lines that allow to get the number of
210 iterations of the optimization and the optimal value, and its size::
213 print " Number of iterations :", len(case.get("CostFunctionJ"))
214 Xa = case.get("Analysis")
215 print " Optimal analysis :", Xa[-1]
216 print " Size of the analysis :", len(Xa[-1])
219 These lines can be very simply added to the initial example of ADAO TUI
220 calculation case given in :ref:`subsection_tui_example`.
222 As well as for data entry, the simplicity of results achievement makes it easy
223 to consider post-processing chains in SALOME, to use for example visualization
224 with MatPlotLib or PARAVIS [PARAVIS]_, mesh adaptation with HOMARD [HOMARD]_, or
225 for other calculations.
227 .. _subsection_tui_commands:
229 Set of available commands in text user interface TUI
230 ----------------------------------------------------
232 In the TUI interface of ADAO module, we follow usual Python conventions and
233 recommendations to make the distinction between public objects, and private or
234 reserved ones because of implementation details. In practice, every object or
235 function name beginning with at least one "**_**" sign is private in the usual
236 programming sense ("*private*"). Nevertheless, the absence of such a sign at the
237 beginning of a name does not designate it as public. In general, in Python, and
238 unlike other languages, you can access private objects or functions. This can
239 sometimes be useful, but such use in your codes will lead to crashes without
240 warning in future versions. It is strongly recommended not to do so.
242 To clarify and facilitate the use of the module for scripting, **this section
243 therefore defines the application programming interface (API) for textual user
244 interface (TUI) by a comprehensive and restricted manner**. Use in scripts of
245 ADAO objects or functions other than those defined here is strongly discouraged,
246 as this will likely lead to crashes without warning in future versions.
248 Equivalent syntax calls for commands
249 ++++++++++++++++++++++++++++++++++++
251 The definition of data during the ADAO TUI calculation case creation supports
252 **two completely equivalent syntaxes**. One can:
254 - either use the ``set`` command and as the first argument the concept ``XXXXX``
255 on which to apply the command whose arguments follow,
256 - or use the command ``setXXXXX`` containing the arguments of the command to
259 To illustrate this equivalence, we take the example of two commands that lead to
262 case.set( 'Background', Vector=[0, 1, 2] )
266 case.setBackground( Vector=[0, 1, 2] )
268 The choice of one or the other syntaxes is freely left to the user, according to
269 its context of use. In the following, for clarity, we define the controls
270 according to the second syntax.
272 Creating a calculation case in TUI text interface
273 +++++++++++++++++++++++++++++++++++++++++++++++++
275 The creation and the initialisation of a calculation case in TUI text interface
276 are done by importing the interface module "*adaoBuilder*" and by by invoking
277 its method "*New()*" as illustrated in the following lines (the ``case`` object
278 name being let free to the user choice)::
280 from numpy import array
282 case = adaoBuilder.New()
284 It is recommended by default to always import the ``numpy`` module (or some of
285 its embedded constructors such as the ``array`` one) to make easier its upcoming
288 Defining the calculation data
289 +++++++++++++++++++++++++++++
291 The following commands are used to define the data of an ADAO TUI calculation
292 case. The pseudo-type of the arguments is similar and consistent with those of
293 the inputs in GUI interface, as described in section of
294 :ref:`section_reference_entry` and in particular by the
295 :ref:`section_ref_entry_types`. The verification of the adequacy of variables is
296 done either on their definition, or at runtime.
298 In each command, the boolean keyword "*Stored*" indicates whether you optionally
299 want to store the quantity defined, for disposal during calculation or at the
300 output. The default is not to store, and it is recommended to keep this default.
301 Indeed, for a TUI calculation case, the quantity given in entries are often
302 available in the current name space of the case.
304 The available commands are:
306 .. index:: single: setBackground
308 **setBackground** (*Vector, VectorSerie, Script, Stored*)
309 This command allows to set the background :math:`\mathbf{x}^b`. Depending on
310 the algorithm, it can be defined as a simple vector by "*Vector*", or as a
311 vector list by "*VectorSerie*". If it is defined by a script in the
312 "*Script*" keyword, the vector is of type "*Vector*" (by default) or
313 "*VectorSerie*" according to whether one of these variables is positioned to
316 .. index:: single: setBackgroundError
318 **setBackgroundError** (*Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored*)
319 This command allows to set the matrix :math:`\mathbf{B}` of background error
320 covariance. The matrix may be completely defined by the "*Matrix*" keyword,
321 or in a sparse way, by a diagonal matrix whose unique variance is given on
322 the diagonal by "*ScalarSparseMatrix*", or by a diagonal matrix which one
323 gives the vector of variances located on the diagonal by
324 "*DiagonalSparseMatrix*". If it is defined by a script in "*Script*", the
325 matrix is of type "*Matrix*" (by default), "*ScalarSparseMatrix*" or
326 "*DiagonalSparseMatrix*" according to whether one of these variables is
327 positioned to "*True*".
329 .. index:: single: setCheckingPoint
331 **setCheckingPoint** (*Vector, VectorSerie, Script, Stored*)
332 This command allows to set a current point :math:`\mathbf{x}` used in a
333 checking algorithm. Depending on the algorithm, it can be defined as a
334 simple vector by "*Vector*", or as a vector list by "*VectorSerie*". If it
335 is defined by a script in the "*Script*" keyword, the vector is of type
336 "*Vector*" (by default) or "*VectorSerie*" according to whether one of these
337 variables is positioned to "*True*".
339 .. index:: single: setControlModel
341 **setControlModel** (*Matrix, OneFunction, ThreeFunctions, Parameters, Script, Stored*)
342 This command allows to set the control operator :math:`O`, which represents
343 an external linear input control of the evolution or observation operator.
344 One can refer to the :ref:`section_ref_operator_control`. Its value is
345 defined as an object of type function or of type "*Matrix*". For the
346 function case, various functional forms may be used, as described in the
347 :ref:`section_ref_operator_requirements`, and entered by "*OneFunction*" or
348 "*ThreeFunctions*" keywords. If it is defined by a script in the "*Script*"
349 keyword, the operator is of type "*Matrix*", "*OneFunction*" or
350 "*ThreeFunctions*" according to whether one of these variables is positioned
351 to "*True*". The control parameters of the adjoint numerical approximation,
352 in the "*OneFunction*"case, can be given by a dictionary through the
353 "*Parameters*" keyword. Potential entries of this dictionary are
354 "*DifferentialIncrement*", "*CenteredFiniteDifference*" (similar to the one
355 of graphical interface).
357 .. index:: single: setControlInput
359 **setControlInput** (*Vector, VectorSerie, Script, Stored*)
360 This command allows to set the control vector :math:`\mathbf{u}`. Depending
361 on the algorithm, it can be defined as a simple vector by "*Vector*", or as
362 a vector list by "*VectorSerie*". If it is defined by a script in the
363 "*Script*" keyword, the vector is of type "*Vector*" (by default) or
364 "*VectorSerie*" according to whether one of these variables is positioned to
367 .. index:: single: setEvolutionError
369 **setEvolutionError** (*Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored*)
370 This command allows to set the matrix :math:`\mathbf{Q}` of evolution error
371 covariance. The matrix may be completely defined by the "*Matrix*" keyword,
372 or in a sparse way, by a diagonal matrix whose unique variance is given on
373 the diagonal by "*ScalarSparseMatrix*", or by a diagonal matrix which one
374 gives the vector of variances located on the diagonal by
375 "*DiagonalSparseMatrix*". If it is defined by a script in "*Script*", the
376 matrix is of type "*Matrix*" (by default), "*ScalarSparseMatrix*" or
377 "*DiagonalSparseMatrix*" according to whether one of these variables is
378 positioned to "*True*".
380 .. index:: single: setEvolutionModel
382 **setEvolutionModel** (*Matrix, OneFunction, ThreeFunctions, Parameters, Script, Stored*)
383 This command allows to set the evolution operator :math:`M`, which describes
384 an elementary evolution step. Its value is defined as an object of type
385 function or of type "*Matrix*". For the function case, various functional
386 forms may be used, as described in the
387 :ref:`section_ref_operator_requirements`, and entered by "*OneFunction*" or
388 "*ThreeFunctions*" keywords. If it is defined by a script in the "*Script*"
389 keyword, the operator is of type "*Matrix*", "*OneFunction*" or
390 "*ThreeFunctions*" according to whether one of these variables is positioned
391 to "*True*". The control parameters of the adjoint numerical approximation,
392 in the "*OneFunction*"case, can be given by a dictionary through the
393 "*Parameters*" keyword. Potential entries of this dictionary are
394 "*DifferentialIncrement*", "*CenteredFiniteDifference*" (similar to the one
395 of graphical interface).
397 .. index:: single: setObservation
399 **setObservation** (*Vector, VectorSerie, Script, Stored*)
400 This command allows to set the observation vector :math:`\mathbf{y}^o`.
401 Depending on the algorithm, it can be defined as a simple vector by
402 "*Vector*", or as a vector list by "*VectorSerie*". If it is defined by a
403 script in the "*Script*" keyword, the vector is of type "*Vector*" (by
404 default) or "*VectorSerie*" according to whether one of these variables is
405 positioned to "*True*".
407 .. index:: single: setObservationError
409 **setObservationError** (*Matrix, ScalarSparseMatrix, DiagonalSparseMatrix, Script, Stored*)
410 This command allows to set the matrix :math:`\mathbf{R}` of observation
411 error covariance. The matrix may be completely defined by the "*Matrix*"
412 keyword, or in a sparse way, by a diagonal matrix whose unique variance is
413 given on the diagonal by "*ScalarSparseMatrix*", or by a diagonal matrix
414 which one gives the vector of variances located on the diagonal by
415 "*DiagonalSparseMatrix*". If it is defined by a script in "*Script*", the
416 matrix is of type "*Matrix*" (by default), "*ScalarSparseMatrix*" or
417 "*DiagonalSparseMatrix*" according to whether one of these variables is
418 positioned to "*True*".
420 .. index:: single: setObservationOperator
422 **setObservationOperator** (*Matrix, OneFunction, ThreeFunctions, Parameters, Script, Stored*)
423 This command allows to set the evolution operator :math:`H`, which
424 transforms the input parameters :math:`\mathbf{x}` in results
425 :math:`\mathbf{y}` that are compared to observations :math:`\mathbf{y}^o`.
426 Its value is defined as an object of type function or of type "*Matrix*".
427 For the function case, various functional forms may be used, as described in
428 the :ref:`section_ref_operator_requirements`, and entered by "*OneFunction*"
429 or "*ThreeFunctions*" keywords. If it is defined by a script in the
430 "*Script*" keyword, the operator is of type "*Matrix*", "*OneFunction*" or
431 "*ThreeFunctions*" according to whether one of these variables is positioned
432 to "*True*". The control parameters of the adjoint numerical approximation,
433 in the "*OneFunction*"case, can be given by a dictionary through the
434 "*Parameters*" keyword. Potential entries of this dictionary are
435 "*DifferentialIncrement*", "*CenteredFiniteDifference*" (similar to the one
436 of graphical interface).
438 .. index:: single: set
440 **set** (*Concept,...*)
441 This command allows to have an equivalent syntax for all the commands of
442 these section. Its first argument is the name of the concept to be defined
443 (for example "*Background*" or "*ObservationOperator*"), on which the
444 following arguments, which are the same as in the individual previous
445 commands, are applied. When using this command, it is required to name the
446 arguments (for example "*Vector=...*").
448 Setting the calculation, outputs, etc.
449 ++++++++++++++++++++++++++++++++++++++
451 .. index:: single: setAlgorithmParameters
453 **setAlgorithmParameters** (*Algorithm, Parameters, Script*)
454 This command allows to choose the calculation or the verification algorithm
455 by the argument "*Algorithm*" in the form of an algorithm name (it is useful
456 to refer to the :ref:`section_reference_assimilation` and to the
457 :ref:`section_reference_checking`) and to define the calculation parameters
458 by the argument "*Parameters*". In the case of a definition by "*Script*",
459 the file must contain the two variables "*Algorithm*" and "*Parameters*" (or
460 "*AlgorithmParameters*" equivalently).
462 .. index:: single: setDebug
465 This command enables the detailed information mode when running.
467 .. index:: single: setNoDebug
470 This command disables the detailed information mode when running.
472 .. index:: single: setObserver
474 **setObserver** (*Variable, Template, String, Script, Info*)
475 This command allows to set an *observer* on the current or final calculation
476 variable. Reference should be made to the description of the
477 ':ref:`ref_observers_requirements` for their list and content, and to the
478 :ref:`section_reference` to know what are the observable quantities. One
479 defines as "*String*" the *observer* body, using a string including if
480 necessary line breaks. It is recommended to use the patterns available by
481 the argument "*Template*". In the case of a definition as "*Script*", the
482 file must contain only the body of the function, as described in the
483 :ref:`ref_observers_requirements`.
485 Perform the calculation
486 +++++++++++++++++++++++
488 .. index:: single: executePythonScheme
490 **executePythonScheme** ()
491 This command launches the complete calculation in the environment of the
492 current Python interpreter, without interaction with YACS [YACS]_. The
493 standard output and standard error are those of the Python interpreter. If
494 necessary, the internal parallelism, of the algorithms in ADAO and of the
495 simulation code used, is available.
497 .. index:: single: execute
500 This command is a user shorthand for "*executePythonScheme*".
502 Get the calculation results separately
503 ++++++++++++++++++++++++++++++++++++++
505 .. index:: single: get
508 This command explicitly extract the variables available at the output of
509 calculation case for use in the rest of the scripting, such as
510 visualization. Its argument the name of a variable "*Concept*" and returns
511 back the quantity as a list (even if there is only one specimen) of this
512 base variable. For a list of variables and use them, the user has to refer
513 to the :ref:`subsection_r_o_v_Inventaire` and more generally to the
514 :ref:`section_ref_output_variables` and to the individual documentations of
517 More advanced examples of ADAO TUI calculation case
518 ---------------------------------------------------
520 We propose here more comprehensive examples of ADAO TUI calculation, by giving
521 the purpose of the example and a set of commands that can achieve this goal.
523 Independent holding of the results of a calculation case
524 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
526 The objective is to perform in TUI the setting of data for an ADAO calculation
527 case, its execution, and then the retrieving of the results to follow on a
528 independent holding of these results (this last step not being described here,
529 because it depends on the the user).
531 The hypothesis of the user case are the following ones. It is assumed:
533 #. that we want to adjust 3 parameters ``alpha``, ``beta`` and ``gamma`` in a bounded domain,
534 #. that we dispose of observations named ``observations``,
535 #. 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,
536 #. 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.
538 In order to try in a simple way this example of TUI calculation case, we choose
539 for example the following entries, perfectly arbitrary, by building the
540 observations by simulation in order to set a twin experiments case::
543 # Artificial building of an example of user data
544 # ----------------------------------------------
549 alphamin, alphamax = 0., 10.
550 betamin, betamax = 3, 13
551 gammamin, gammamax = 1.5, 15.5
554 "Simulation function H to perform Y=H(X)"
556 __x = numpy.matrix(numpy.ravel(numpy.matrix(x))).T
557 __H = numpy.matrix("1 0 0;0 2 0;0 0 3; 1 2 3")
560 # Observations obtained by simulation
561 # -----------------------------------
562 observations = simulation((2, 3, 4))
564 The set of commands that can be used is the following::
571 Xb = (alpha, beta, gamma)
573 (alphamin, alphamax),
575 (gammamin, gammamax))
579 case = adaoBuilder.New()
581 'AlgorithmParameters',
585 "MaximumNumberOfSteps":100,
586 "StoreSupplementaryCalculations":[
589 "SimulatedObservationAtOptimum",
593 case.set( 'Background', Vector = numpy.array(Xb), Stored = True )
594 case.set( 'Observation', Vector = numpy.array(observations) )
595 case.set( 'BackgroundError', ScalarSparseMatrix = 1.0e10 )
596 case.set( 'ObservationError', ScalarSparseMatrix = 1.0 )
598 'ObservationOperator',
599 OneFunction = simulation,
600 Parameters = {"DifferentialIncrement":0.0001},
602 case.set( 'Observer', Variable="CurrentState", Template="ValuePrinter" )
605 # Independent holding
606 # -------------------
607 Xbackground = case.get("Background")
608 Xoptimum = case.get("Analysis")[-1]
609 FX_at_optimum = case.get("SimulatedObservationAtOptimum")[-1]
610 J_values = case.get("CostFunctionJ")[:]
612 print "Number of internal iterations...: %i"%len(J_values)
613 print "Initial state...................:",numpy.ravel(Xbackground)
614 print "Optimal state...................:",numpy.ravel(Xoptimum)
615 print "Simulation at optimal state.....:",numpy.ravel(FX_at_optimum)
618 The command set execution gives the following result::
620 CurrentState [ 5. 7. 9.]
621 CurrentState [ 0. 3. 1.5]
622 CurrentState [ 1.40006418 3.86705307 3.7061137 ]
623 CurrentState [ 1.42580231 3.68474804 3.81008738]
624 CurrentState [ 1.60220353 3.0677108 4.06146069]
625 CurrentState [ 1.72517855 3.03296953 4.04915706]
626 CurrentState [ 2.00010755 3. 4.00055409]
627 CurrentState [ 1.99995528 3. 3.99996367]
628 CurrentState [ 2.00000007 3. 4.00000011]
629 CurrentState [ 2. 3. 4.]
631 Number of internal iterations...: 10
632 Initial state...................: [ 5. 7. 9.]
633 Optimal state...................: [ 2. 3. 4.]
634 Simulation at optimal state.....: [ 2. 6. 12. 20.]
636 As it should be in twin experiments, it is found that we get correctly the
637 parameters that were used to artificially build the observations.
639 .. Réconciliation de courbes à l'aide de MedCoupling
640 .. +++++++++++++++++++++++++++++++++++++++++++++++++
642 .. Utilisation de fonctions de surveillance de type "observer"
643 .. +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
645 .. Suivre d'un recalage à l'aide de MatPlotLib
646 .. +++++++++++++++++++++++++++++++++++++++++++
648 .. Equivalences entre l'interface graphique (GUI) et l'interface textuelle (TUI)
649 .. -----------------------------------------------------------------------------
651 .. [HOMARD] For more information on HOMARD, see the *HOMARD module* and its integrated help available from the main menu *Help* of the SALOME platform.
653 .. [PARAVIS] For more information on PARAVIS, see the *PARAVIS module* and its integrated help available from the main menu *Help* of the SALOME platform.
655 .. [YACS] For more information on YACS, see the *YACS module* and its integrated help available from the main menu *Help* of the SALOME platform.