From bb442fb00def8af0416025ee176de011f7e50e68 Mon Sep 17 00:00:00 2001 From: Jean-Philippe ARGAUD Date: Wed, 20 Oct 2021 20:18:23 +0200 Subject: [PATCH] Documentation review and update --- doc/en/advanced.rst | 17 +- doc/en/conf.py | 1 + doc/en/images/adao_viewer.png | Bin 35540 -> 35630 bytes doc/en/index.rst | 10 +- doc/en/ref_algorithm_3DVAR.rst | 2 + doc/en/ref_algorithm_4DVAR.rst | 2 +- doc/en/ref_algorithm_EnsembleKalmanFilter.rst | 2 +- doc/en/ref_algorithm_ExtendedKalmanFilter.rst | 4 +- doc/en/ref_algorithm_KalmanFilter.rst | 4 +- .../ref_algorithm_UnscentedKalmanFilter.rst | 2 +- doc/en/ref_operator_requirements.rst | 11 + doc/en/snippets/EstimationOf_Parameters.rst | 10 + ...stimationOf.rst => EstimationOf_State.rst} | 0 doc/en/theory.rst | 248 ++++++++++++++-- doc/en/tui.rst | 21 +- doc/en/tutorials_in_python.rst | 16 +- doc/en/tutorials_in_salome.rst | 9 +- doc/fr/advanced.rst | 19 +- doc/fr/conf.py | 1 + doc/fr/images/adao_viewer.png | Bin 34977 -> 35290 bytes doc/fr/index.rst | 11 +- doc/fr/ref_algorithm_3DVAR.rst | 2 + doc/fr/ref_algorithm_4DVAR.rst | 2 +- doc/fr/ref_algorithm_EnsembleKalmanFilter.rst | 2 +- doc/fr/ref_algorithm_ExtendedKalmanFilter.rst | 4 +- doc/fr/ref_algorithm_KalmanFilter.rst | 4 +- .../ref_algorithm_UnscentedKalmanFilter.rst | 2 +- doc/fr/ref_operator_requirements.rst | 13 +- doc/fr/scripts/simple_3DVAR.png | Bin 38721 -> 40873 bytes .../scripts/simple_NonLinearLeastSquares.png | Bin 38721 -> 40873 bytes ...tionOf.rst => EstimationOf_Parameters.rst} | 2 +- doc/fr/snippets/EstimationOf_State.rst | 10 + doc/fr/theory.rst | 266 ++++++++++++++++-- doc/fr/tui.rst | 23 +- doc/fr/tutorials_in_python.rst | 17 +- doc/fr/tutorials_in_salome.rst | 127 +++++---- src/daComposant/daCore/Aidsm.py | 3 +- 37 files changed, 690 insertions(+), 177 deletions(-) create mode 100644 doc/en/snippets/EstimationOf_Parameters.rst rename doc/en/snippets/{EstimationOf.rst => EstimationOf_State.rst} (100%) rename doc/fr/snippets/{EstimationOf.rst => EstimationOf_Parameters.rst} (94%) create mode 100644 doc/fr/snippets/EstimationOf_State.rst diff --git a/doc/en/advanced.rst b/doc/en/advanced.rst index 42ed950..9bd408e 100644 --- a/doc/en/advanced.rst +++ b/doc/en/advanced.rst @@ -390,14 +390,16 @@ Getting more information when running a calculation --------------------------------------------------- .. index:: single: Logging +.. index:: single: Debug +.. index:: single: setDebug When running a calculation, useful data and messages are logged. There are two ways to obtain theses information. The first one, and the preferred way, is to use the built-in variable "*Debug*" -available in every ADAO case. It is available through the edition GUI of the -module. Setting it to "*1*" will send messages in the log window of the YACS -scheme execution. +available in every ADAO case. It can be reached in the module's graphical user +interface (GUI) as well as in the textual interface (TUI). Setting it to "*1*" +will send messages in the log window of the YACS scheme execution. The second one consist in using the "*logging*" native module of Python (see the Python documentation http://docs.python.org/library/logging.html for more @@ -415,10 +417,11 @@ following Python lines:: The standard logging module default level is "*WARNING*", the default level in the ADAO module is "*INFO*". -It is also recommended to include some logging or debug mechanisms in the -simulation code, and use them in conjunction with the two previous methods. But -be careful not to store too big variables because it cost time, whatever logging -level is chosen (that is, even if these variables are not printed). +It is also recommended to include logging monitoring or debugging mechanisms in +the user's physical simulation code, and to exploit them in conjunction with +the previous two methods. But be careful not to store "too big" variables +because it cost time or memory, whatever logging level is chosen (that is, even +if these variables are not printed). .. _subsection_ref_parallel_df: diff --git a/doc/en/conf.py b/doc/en/conf.py index 3098191..690ac9a 100644 --- a/doc/en/conf.py +++ b/doc/en/conf.py @@ -88,6 +88,7 @@ exclude_patterns = [ pygments_style = None templates_path = ['_templates'] exclude_trees = ['snippets',] +numfig = True # -- Options for HTML output --------------------------------------------------- diff --git a/doc/en/images/adao_viewer.png b/doc/en/images/adao_viewer.png index 547633fcb8a583a903bb4603104526460b7a7c0f..3af5d5dbcdca543d2c4cce12fc26319d2e3b4408 100644 GIT binary patch literal 35630 zcmX6_2RvKf`_^xl4zot>s!=uD+Pl@FMyL@x)ZTkjbl9_KrFKy(2x5c~+S+@It*E_c zYX47u|J#0qn~QtTIq&;E&+|S<#2Yn5lH2sRiHL|ulwQBmBqF*NNJR9X81aALOyZ5& z>)_>@^$W-gBBJuRJ7=bZuOFDd)`Sod`Ee2vy$>cLIsvENuMiP=J|QAndrL$l@r{Ux 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It uses information coming from experimental measurements or observations, and from numerical *a priori* models, including information about their errors. Parts of the framework are also known under the names of -*parameter estimation*, *inverse problems*, *Bayesian estimation*, *optimal -interpolation*, *field reconstruction*, etc. The ADAO module currently offers -more than one hundred different algorithmic methods and allows the study of -about 350 distinct applied problems. More details can be found in the section -:ref:`section_theory`. +*inverse problems*, *parameter estimation*, *Bayesian estimation*, *optimal +interpolation*, *field reconstruction*, etc. More details can be found in the +section :ref:`section_theory`. The ADAO module currently offers more than one +hundred different algorithmic methods and allows the study of about 350 +distinct applied problems. The documentation for this module is divided into several major categories, related to the theoretical documentation (indicated in the section title by diff --git a/doc/en/ref_algorithm_3DVAR.rst b/doc/en/ref_algorithm_3DVAR.rst index 7aae57a..55f77e3 100644 --- a/doc/en/ref_algorithm_3DVAR.rst +++ b/doc/en/ref_algorithm_3DVAR.rst @@ -77,6 +77,8 @@ initialization point for the minimization, but it is not recommended. .. include:: snippets/CostDecrementTolerance.rst +.. include:: snippets/EstimationOf_Parameters.rst + .. include:: snippets/GradientNormTolerance.rst .. include:: snippets/InitializationPoint.rst diff --git a/doc/en/ref_algorithm_4DVAR.rst b/doc/en/ref_algorithm_4DVAR.rst index f873df5..95b70ed 100644 --- a/doc/en/ref_algorithm_4DVAR.rst +++ b/doc/en/ref_algorithm_4DVAR.rst @@ -73,7 +73,7 @@ its application domain is similar to the one of Kalman filters, specially the .. include:: snippets/CostDecrementTolerance.rst -.. include:: snippets/EstimationOf.rst +.. include:: snippets/EstimationOf_State.rst .. include:: snippets/GradientNormTolerance.rst diff --git a/doc/en/ref_algorithm_EnsembleKalmanFilter.rst b/doc/en/ref_algorithm_EnsembleKalmanFilter.rst index c4cedef..ad8eaf3 100644 --- a/doc/en/ref_algorithm_EnsembleKalmanFilter.rst +++ b/doc/en/ref_algorithm_EnsembleKalmanFilter.rst @@ -96,7 +96,7 @@ obtain less costly data assimilation with hopefully the same quality. .. ------------------------------------ .. .. include:: snippets/Header2Algo03AdOp.rst -.. include:: snippets/EstimationOf.rst +.. include:: snippets/EstimationOf_State.rst .. include:: snippets/InflationFactor.rst diff --git a/doc/en/ref_algorithm_ExtendedKalmanFilter.rst b/doc/en/ref_algorithm_ExtendedKalmanFilter.rst index 39fdb62..5521a0c 100644 --- a/doc/en/ref_algorithm_ExtendedKalmanFilter.rst +++ b/doc/en/ref_algorithm_ExtendedKalmanFilter.rst @@ -43,7 +43,7 @@ the state and **P** the state error covariance : :align: center :width: 100% .. centered:: - **Timeline of steps in extended Kalman filter assimilation** + **Timeline of steps in extended Kalman filter data assimilation** We notice that there is no analysis performed at the initial time step (numbered 0 in the time indexing) because there is no forecast at this time @@ -90,7 +90,7 @@ by default), and the extended Kalman filter conducted without any constraint .. include:: snippets/ConstrainedBy.rst -.. include:: snippets/EstimationOf.rst +.. include:: snippets/EstimationOf_State.rst StoreSupplementaryCalculations .. index:: single: StoreSupplementaryCalculations diff --git a/doc/en/ref_algorithm_KalmanFilter.rst b/doc/en/ref_algorithm_KalmanFilter.rst index 13bff48..10eb7bb 100644 --- a/doc/en/ref_algorithm_KalmanFilter.rst +++ b/doc/en/ref_algorithm_KalmanFilter.rst @@ -47,7 +47,7 @@ the state and **P** the state error covariance : :align: center :width: 100% .. centered:: - **Timeline of steps in Kalman filter assimilation** + **Timeline of steps in Kalman filter data assimilation** We notice that there is no analysis performed at the initial time step (numbered 0 in the time indexing) because there is no forecast at this time @@ -82,7 +82,7 @@ with the help of the :ref:`section_ref_algorithm_LinearityTest`. .. ------------------------------------ .. .. include:: snippets/Header2Algo03AdOp.rst -.. include:: snippets/EstimationOf.rst +.. include:: snippets/EstimationOf_State.rst StoreSupplementaryCalculations .. index:: single: StoreSupplementaryCalculations diff --git a/doc/en/ref_algorithm_UnscentedKalmanFilter.rst b/doc/en/ref_algorithm_UnscentedKalmanFilter.rst index ab77057..207f5ce 100644 --- a/doc/en/ref_algorithm_UnscentedKalmanFilter.rst +++ b/doc/en/ref_algorithm_UnscentedKalmanFilter.rst @@ -85,7 +85,7 @@ by default), and the "unscented" Kalman filter conducted without any constraint .. include:: snippets/ConstrainedBy.rst -.. include:: snippets/EstimationOf.rst +.. include:: snippets/EstimationOf_State.rst .. include:: snippets/AlphaBeta.rst diff --git a/doc/en/ref_operator_requirements.rst b/doc/en/ref_operator_requirements.rst index 9fd0b52..e73f54f 100644 --- a/doc/en/ref_operator_requirements.rst +++ b/doc/en/ref_operator_requirements.rst @@ -87,6 +87,17 @@ following figure: .. centered:: **Choosing graphically an operator functional representation** +In ADAO textual interface (TUI), in the specific case illustrated above, the +same approach is taken by writing : +:: + + ... + case.set( 'ObservationOperator', + OneFunction = True, + Script = 'scripts_for_JDC.py' + ) + ... + .. _section_ref_operator_one: First functional form: one direct operator only diff --git a/doc/en/snippets/EstimationOf_Parameters.rst b/doc/en/snippets/EstimationOf_Parameters.rst new file mode 100644 index 0000000..70664c4 --- /dev/null +++ b/doc/en/snippets/EstimationOf_Parameters.rst @@ -0,0 +1,10 @@ +.. index:: single: EstimationOf + +EstimationOf + *Predefined name*. This key allows to choose the type of estimation to be + performed. It can be either state-estimation, with a value of "State", or + parameter-estimation, with a value of "Parameters". The default choice is + "Parameters". + + Example: + ``{"EstimationOf":"Parameters"}`` diff --git a/doc/en/snippets/EstimationOf.rst b/doc/en/snippets/EstimationOf_State.rst similarity index 100% rename from doc/en/snippets/EstimationOf.rst rename to doc/en/snippets/EstimationOf_State.rst diff --git a/doc/en/theory.rst b/doc/en/theory.rst index b90c4cd..8fe356d 100644 --- a/doc/en/theory.rst +++ b/doc/en/theory.rst @@ -31,6 +31,7 @@ .. index:: single: true state .. index:: single: observation .. index:: single: a priori +.. index:: single: EstimationOf **Data Assimilation** is a general well established framework for computing the optimal estimate of the true state of a system, over time if necessary. It uses @@ -55,12 +56,21 @@ section `Going further in the state estimation by optimization methods`_, but they are far more general and can be used without data assimilation concepts. Two main types of applications exist in data assimilation, which are covered by -the same formalism: **fields reconstruction** and **parameters identification**. -These are also referred to as **state estimation** and **parameters estimation** -respectively. Before introducing the `Simple description of the data -assimilation methodological framework`_ in a next section, these two types of -applications are briefly described. At the end, some references allow `Going -further in the data assimilation framework`_. +the same formalism: **fields reconstruction** (see `Fields reconstruction or +measures interpolation`_) and **parameters identification** (see `Parameters +identification, models adjustment, or calibration`_). These are also referred +to as **state estimation** and **parameters estimation** respectively, and one +can if necessary elaborate joint estimation of both (see `Joint state and +parameter estimation in dynamics`_). In ADAO, some algorithms can be used +either in state estimation or in parameter estimation. This is done simply by +changing the required option "*EstimationOf*" in the algorithm parameters. +Before introducing the `Simple description of the data assimilation +methodological framework`_ in a next section, these two types of applications +are briefly described. At the end, some detailed information allow `Going +further in the data assimilation framework`_ and `Going further in the state +estimation by optimization methods`_, as well as `Going further in data +assimilation for dynamics`_ and having an `Overview of reduction methods and +of reduced optimization`_. Fields reconstruction or measures interpolation ----------------------------------------------- @@ -69,6 +79,7 @@ Fields reconstruction or measures interpolation .. index:: single: measures interpolation .. index:: single: fields interpolation .. index:: single: state estimation +.. index:: single: background **Fields reconstruction (or interpolation)** consists in finding, from a restricted set of real measures, the physical field which is the most @@ -96,8 +107,8 @@ One must therefore make the reconstruction of a field at any point in space, in a "consistent" manner with the evolution equations and with the measures of the previous time steps. -Parameters identification, models adjustment, calibration ---------------------------------------------------------- +Parameters identification, models adjustment, or calibration +------------------------------------------------------------ .. index:: single: parameters identification .. index:: single: parameters adjustment @@ -157,14 +168,16 @@ Without going into the advanced methods to solve this problem, we can mention the conceptually very simple approach of considering the vector of states to be interpolated as *augmented* by the vector of parameters to be calibrated. It can be noted that we are in *state estimation* or *reconstruction of fields*, -and that in the temporal case, the evolution of the parameters to estimate is -simply the identity. The assimilation or optimization algorithms can then be -applied to the augmented vector. Valid for moderate nonlinearities in the -simulation, this simple method extends the optimization space, and thus leads -to larger problems, but it is often possible to reduce the representation to -numerically computable cases. Without exhaustiveness, the separated variables -optimization, the reduced rank filtering, or the specific treatment of -covariance matrices, are common techniques to avoid this dimension problem. +and that in the temporal case of parameters identification, the evolution of +the parameters to estimate is simply the identity. The assimilation or +optimization algorithms can then be applied to the augmented vector. Valid for +moderate nonlinearities in the simulation, this simple method extends the +optimization space, and thus leads to larger problems, but it is often possible +to reduce the representation to numerically computable cases. Without +exhaustiveness, the separated variables optimization, the reduced rank +filtering, or the specific treatment of covariance matrices, are common +techniques to avoid this dimension problem. In the temporal case, we will see +below indications for a `Joint state and parameter estimation in dynamics`_. To go further, we refer to the mathematical methods of optimization and augmentation developed in many books or specialized articles, finding their @@ -286,11 +299,12 @@ extended to dynamic or time-related problems, called respectively "*4D-Var*" and "*Kalman filter (KF)*" and their derivatives. They have to take into account an evolution operator to establish an analysis at the right time steps of the gap between observations and simulations, and to have, at every moment, -the propagation of the background through the evolution model. In the same way, -these methods can be used in case of non linear observation or evolution -operators. Many other variants have been developed to improve the numerical -quality of the methods or to take into account computer requirements such as -calculation size and time. +the propagation of the background through the evolution model. The next section +provides information on `Going further in data assimilation for dynamics`_. In +the same way, these methods can be used in case of non linear observation or +evolution operators. Many other variants have been developed to improve the +numerical quality of the methods or to take into account computer requirements +such as calculation size and time. A schematic view of Data Assimilation and Optimization approaches ----------------------------------------------------------------- @@ -484,8 +498,196 @@ it is available in the ADAO module: The reader interested in the subject of optimization can look at [WikipediaMO]_ as a general entry point. -Reduction methods and reduced optimization ------------------------------------------- +Going further in data assimilation for dynamics +----------------------------------------------- + +.. index:: single: dynamic (system) +.. index:: single: system dynamic +.. index:: single: temporal evolution +.. index:: single: ODE (Ordinary Differential Equation) +.. index:: single: EstimationOf + +We can analyze a system in temporal evolution (dynamics) with the help of data +assimilation, in order to explicitly take into account the flow of time in the +estimation of states or parameters. We briefly introduce here the problematic, +and some theoretical or practical tools, to facilitate the user treatment of +such situations. It is nevertheless indicated that the variety of physical and +user problems is large, and that it is therefore recommended to adapt the +treatment to the constraints, whether they are physical, numerical or +computational. + +General form of dynamic systems ++++++++++++++++++++++++++++++++ + +Systems in temporal evolution can be studied or represented using dynamic +systems. In this case, it is easy to conceive the analysis of their behavior +with the help of data assimilation (it is even in this precise case that the +data assimilation approach was initially widely developed). + +We formalize the numerical simulation framework in a simple way. A simple +dynamic system dynamic system on the state :math:`\mathbf{x}` can be described +in the form: + +.. math:: \forall t \in \mathbb{R}^{+}, \frac{d\mathbf{x}}{dt} = \mathcal{D}(\mathbf{x},\mathbf{u},t) + +where :math:`\mathbf{x}` is the unknown state vector, :math:`\mathbf{u}` is a +known external control vector, and :math:`\mathcal{D}` is the (possibly +non-linear) operator of the system dynamics. It is an Ordinary Differential +Equation (ODE), of the first order, on the state. In discrete time, this +dynamical system can be written in the following form: + +.. math:: \forall n \in \mathbb{N}, \mathbf{x}_{n+1} = M(\mathbf{x}_{n},\mathbf{u}_{n},t_n\rightarrow t_{n+1}) + +for an indexing :math:`t_n` of discrete times with :math:`n\in\mathbf{N}`. +:math:`M` is the discrete evolution operator obtained from :math:`\mathcal{D}`. +Usually, we omit the time notation in the evolution operator :math:`M`. +Approximating the :math:`\mathcal{D}` operator by :math:`M` introduces (or +adds, if it already exists) a :math:`\epsilon` model error. + +We can then characterize two types of estimates in dynamics, which we describe +hereafter on the discrete time dynamical system: `State estimation in +dynamics`_ and `Parameter estimation in dynamics`_. Combined, the two types can +be used to make a `Joint state and parameter estimation in dynamics`_. In ADAO, +some algorithms can be used either in state estimation or in parameter +estimation. This is done simply by changing the required option +"*EstimationOf*" in the algorithm parameters. + +State estimation in dynamics +++++++++++++++++++++++++++++ + +The state estimation can be conducted by data assimilation on the discrete time +version of the dynamical system, written in the following form: + +.. math:: \mathbf{x}_{n+1} = M(\mathbf{x}_{n},\mathbf{u}_{n}) + \mathbf{\epsilon}_{n} + +.. math:: \mathbf{y}_{n} = H(\mathbf{x}_{n}) + \mathbf{\nu}_{n} + +where :math:`\mathbf{x}` is the system state to be estimated, +:math:`\mathbf{x}_{n}` and :math:`\mathbf{y}_{n}` are respectively the +(computed) unobserved and (measured) observed state of the system, :math:`M` +and :math:`H` are the incremental evolution and observation operators, +respectively, :math:`\mathbf{\epsilon}_{n}` and :math:`\mathbf{\nu}_{n}` are +the evolution and observation noise or error, respectively, and +:math:`\mathbf{u}_{n}` is a known external control. The two operators :math:`M` +and :math:`H` are directly usable in data assimilation with ADAO. + +Parameter estimation in dynamics +++++++++++++++++++++++++++++++++ + +The parameter estimation can be written a differently to be solved by data +assimilation. Still on the discrete time version of the dynamical system, we +look for a nonlinear :math:`G` mapping, parameterized by :math:`\mathbf{a}`, +between inputs :math:`\mathbf{x}_{n}` and measurements :math:`\mathbf{y}_{n}` +at each step :math:`t_n`, the error to be controlled as a function of +parameters :math:`\mathbf{y}_{n}` being +:math:`\mathbf{y}_{n}-G(\mathbf{x}_{n},\mathbf{a})`. We can proceed by +optimization on this error, with regularization, or by filtering by writing the +problem represented in state estimation: + +.. math:: \mathbf{a}_{n+1} = \mathbf{a}_{n} + \mathbf{\epsilon}_{n} + +.. math:: \mathbf{y}_{n} = G(\mathbf{x}_{n},\mathbf{a}_{n}) + \mathbf{\nu}_{n} + +where, this time, the choices of the evolution and observation error models +:math:`\mathbf{\epsilon}_{n}` and :math:`\mathbf{\nu}_{n}` condition the +performance of convergence and observation tracking. The estimation of the +parameters :math:`\mathbf{a}` is done by using pairs +:math:`(\mathbf{x}_{n},\mathbf{y}_{n})` of corresponding inputs and outputs. + +In this case of parameter estimation, in order to apply data assimilation +methods, we therefore impose the hypothesis that the evolution operator is the +identity (*Note: it is therefore not used, but must be declared in ADAO, for +example as a 1 matrix*), and the observation operator is :math:`G`. + +Joint state and parameter estimation in dynamics +++++++++++++++++++++++++++++++++++++++++++++++++ + +A special case concerns the joint estimation of state and parameters used in a +dynamic system. One seeks to jointly estimate the state :math:`\mathbf{x}` +(which depends on time) and the parameters :math:`\mathbf{a}` (which does not +depend on time). There are several ways to deal with this problem, but the most +general one is to use a state vector augmented by the parameters, and to extend +the operators accordingly. + +To do this, using the notations of the previous two subsections, we define the +auxiliary variable :math:`\mathbf{w}` such that: + +.. math:: \mathbf{w} = \left[ + \begin{array}{c} + \mathbf{x} \\ + \mathbf{a} + \end{array} + \right] + = \left[ + \begin{array}{c} + \mathbf{w}_{|x} \\ + \mathbf{w}_{|a} + \end{array} + \right] + +and the operators of evolution :math:`\tilde{M}` and observation +:math:`\tilde{H}` associated to the augmented problem: + +.. math:: \tilde{M}(\mathbf{w},\mathbf{u}) = \left[ + \begin{array}{c} + M(\mathbf{w}_{|x},\mathbf{u}) \\ + \mathbf{w}_{|a} + \end{array} + \right] + = \left[ + \begin{array}{c} + M(\mathbf{x},\mathbf{u}) \\ + \mathbf{a} + \end{array} + \right] + +.. math:: \tilde{H}(\mathbf{w}) = \left[ + \begin{array}{c} + H(\mathbf{w}_{|x}) \\ + G(\mathbf{w}_{|x},\mathbf{w}_{|a}) + \end{array} + \right] + = \left[ + \begin{array}{c} + H(\mathbf{x}) \\ + G(\mathbf{x},\mathbf{a}) + \end{array} + \right] + +With these notations, by extending the noise variables +:math:`\mathbf{\epsilon}` and :math:`\mathbf{\nu}` appropriately, the joint +state :math:`\mathbf{x}` and parameters :math:`\mathbf{a}` discrete-time +estimation problem, using the joint variable :math:`\mathbf{w}`, is then +written: + +.. math:: \mathbf{w}_{n+1} = \tilde{M}(\mathbf{w}_{n},\mathbf{u}_{n}) + \mathbf{\epsilon}_{n} + +.. math:: \mathbf{y}_{n} = \tilde{H}(\mathbf{w}_{n}) + \mathbf{\nu}_{n} + +The incremental evolution and observation operators are therefore respectively +the augmented operators :math:`\tilde{M}` and :math:`\tilde{H}`, and are +directly usable in data assimilation with ADAO. + +Conceptual scheme for data assimilation in dynamics ++++++++++++++++++++++++++++++++++++++++++++++++++++ + +To complete the description, we can represent the data assimilation process in +a dynamics specific way using a temporal scheme, which describes the action of +the evolution (:math:`M` or :math:`\tilde{M}`) and observation (:math:`H` or +:math:`\tilde{H}`) operators during the discrete simulation. A possible +representation is as follows: + + .. _schema_d_AD_temporel: + .. image:: images/schema_temporel_KF.png + :align: center + :width: 100% + .. centered:: + **Timeline of steps for data assimilation operators in dynamics** + +The concepts described in this diagram can be directly and simply used in ADAO. + +Overview of reduction methods and of reduced optimization +--------------------------------------------------------- .. index:: single: reduction .. index:: single: reduction methods diff --git a/doc/en/tui.rst b/doc/en/tui.rst index 6dd382a..c1e2ebe 100644 --- a/doc/en/tui.rst +++ b/doc/en/tui.rst @@ -30,16 +30,23 @@ **[DocR]** Textual User Interface for ADAO (TUI/API) ================================================================================ +.. |eficas_totui| image:: images/eficas_totui.png + :align: middle + :scale: 50% + 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`. As in the graphical -interface, we point out that the TUI approach is intended to create and manage -a single calculation case. +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 "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`. As in the graphical interface, we point out +that the TUI approach is intended to create and manage a single calculation +case. .. _subsection_tui_creating: diff --git a/doc/en/tutorials_in_python.rst b/doc/en/tutorials_in_python.rst index 1adb80b..e944503 100644 --- a/doc/en/tutorials_in_python.rst +++ b/doc/en/tutorials_in_python.rst @@ -27,6 +27,10 @@ **[DocU]** Tutorials on using the ADAO module in Python ================================================================================ +.. |eficas_totui| image:: images/eficas_totui.png + :align: middle + :scale: 50% + This section presents some examples on using the ADAO module in Python. The first one shows how to build a very simple data assimilation case defining explicitly all the required input data through the textual user interface (TUI) @@ -37,8 +41,16 @@ definitions of Python interface, but external files can use other languages. These examples are intentionally described in the same way than for the :ref:`section_tutorials_in_salome` because they are similar to the ones that -can be treated in the graphical user interface in SALOME. The mathematical -notations used afterward are explained in the section :ref:`section_theory`. +can be treated in the graphical user interface in SALOME. 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. The mathematical notations used +afterward are explained in the section :ref:`section_theory`. + +Other simple examples, and their accompanying illustrations, are included at +the end of the reference documentation of some algorithms. It is the case, in a +non-limitative way, of :ref:`section_ref_algorithm_3DVAR`, +:ref:`section_ref_algorithm_KalmanFilter` and +:ref:`section_ref_algorithm_ExtendedBlue`. .. _section_tutorials_in_python_explicit: diff --git a/doc/en/tutorials_in_salome.rst b/doc/en/tutorials_in_salome.rst index 30fa1d2..ab604ed 100644 --- a/doc/en/tutorials_in_salome.rst +++ b/doc/en/tutorials_in_salome.rst @@ -29,16 +29,19 @@ .. |eficas_new| image:: images/eficas_new.png :align: middle - :scale: 50% + :scale: 75% .. |eficas_save| image:: images/eficas_save.png :align: middle - :scale: 50% + :scale: 75% .. |eficas_saveas| image:: images/eficas_saveas.png + :align: middle + :scale: 75% +.. |eficas_totui| image:: images/eficas_totui.png :align: middle :scale: 50% .. |eficas_yacs| image:: images/eficas_yacs.png :align: middle - :scale: 50% + :scale: 75% This section presents some examples on using the ADAO module in SALOME. The first one shows how to build a very simple data assimilation case defining diff --git a/doc/fr/advanced.rst b/doc/fr/advanced.rst index cd30130..b1cd72c 100644 --- a/doc/fr/advanced.rst +++ b/doc/fr/advanced.rst @@ -422,14 +422,17 @@ Obtenir plus d'information lors du déroulement d'un calcul ---------------------------------------------------------- .. index:: single: Logging +.. index:: single: Debug +.. index:: single: setDebug Lors du déroulement d'un calcul, des données et messages utiles sont disponibles. Il y a deux manières d'obtenir ces informations. La première, et la manière préférentielle, est d'utiliser la variable interne -"*Debug*" disponible dans chaque cas ADAO. Elle est atteignable dans l'interface -graphique d'édition du module. La mettre à "*1*" permet d'envoyer des messages -dans la fenêtre de sortie de l'exécution dans YACS ("*YACS Container Log*"). +"*Debug*" disponible dans chaque cas ADAO. Elle est atteignable dans +l'interface graphique d'édition (GUI) du module comme dans l'interface +textuelle (TUI). La mettre à "*1*" permet d'envoyer des messages dans la +fenêtre de sortie de l'exécution dans YACS ("*YACS Container Log*"). La seconde consiste à utiliser le module Python natif "*logging*" (voir la documentation Python http://docs.python.org/library/logging.html pour de plus @@ -450,11 +453,11 @@ Le niveau par défaut standard de surveillance par logging est "*WARNING*", le niveau par défaut dans le module ADAO est "*INFO*". Il est aussi recommandé d'inclure de la surveillance par logging ou des -mécanismes de débogage dans le code de simulation, et de les utiliser en -conjonction avec les deux méthodes précédentes. Néanmoins, il convient d'être -prudent dans le stockage de "grosses" variables car cela coûte du temps, -quel que soit le niveau de surveillance choisi (c'est-à-dire même si ces -variables ne sont pas affichées). +mécanismes de débogage dans le code de simulation physique de l'utilisateur, et +de les exploiter en conjonction avec les deux méthodes précédentes. Néanmoins, +il convient d'être prudent dans le stockage de "grosses" variables car cela +coûte du temps ou de la mémoire, quel que soit le niveau de surveillance choisi +(c'est-à-dire même si ces variables ne sont pas affichées). .. _subsection_ref_parallel_df: diff --git a/doc/fr/conf.py b/doc/fr/conf.py index cd93c05..c9a5781 100644 --- a/doc/fr/conf.py +++ b/doc/fr/conf.py @@ -88,6 +88,7 @@ exclude_patterns = [ pygments_style = None templates_path = ['_templates'] exclude_trees = ['snippets',] +numfig = True # -- Options for HTML output --------------------------------------------------- diff --git a/doc/fr/images/adao_viewer.png b/doc/fr/images/adao_viewer.png index 780f506ffd5119dbd652846a10d38bfd18e80a6b..e9829d38829e33fe875e80092cda1cc73b483603 100644 GIT binary patch literal 35290 zcmXVY2Rz&B_rH64+e_VQwMJXTwQJYj+-gx2HG&}Awnpu}*S)t^DQa(OlSu592raeu zh*S_ojfmKL{vO}||I@yt4O!3goO9mi{eB-GUg>Jwy7}-X6&2Mj%@@xNsHlDmp`!X# zmikw4PtuL%>)_@$yQex&si>;sY0q9?{r}ImFAQ|3r~-JYsQw9~qB;S0{j*9%<^6<; 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self.__objname = __names[0] else: self.__objname = "ADD" -- 2.39.2