7. [DocU] Advanced usage of the ADAO module and interoperability¶
This section presents advanced methods to use the ADAO module, how to get more
information during calculation, or how to use it without the graphical user
interface (GUI). It requires to know how to find files or commands included
inside the whole SALOME installation. All the names to be replaced by user are
indicated by the syntax
7.1. Converting and executing an ADAO command file (JDC) using a Shell script¶
It is possible to convert and execute an ADAO command file (JDC, or “.comm/.py”
files pair, which resides in
<ADAO JDC file directory>) automatically by
using a template Shell script containing all the required steps. If the SALOME
main launching command , named
salome, is not available in a classical
terminal, the user has to know where are the main SALOME launching files, and in
salome one. The directory in which this script resides is
<SALOME main installation dir> and has to be replaced by
the good one in the Shell file template.
When an ADAO command file is build by the ADAO graphical editor and saved, if it
is named for example “AdaoStudy1.comm”, then a companion file named
“AdaoStudy1.py” is automatically created in the same directory. It is named
<ADAO Python file> in the template, and it is converted to YACS as an
<ADAO YACS xml scheme> as a “.xml” file named “AdaoStudy1.xml”. After that,
this last one can be executed in console mode using the standard YACS console
command (see YACS documentation for more information).
In all launching command Shell files examples, we choose to start and stop the SALOME application server in the same script. It is not mandatory, but it is useful to avoid stalling SALOME sessions.
The simplest example consist in only launching the given YACS sheme, which was
previously generated by the user in the graphical interface. In this case, after
having replaced the strings between
<...> symbols, one needs only to save
the following Shell script:
#!/bin/bash USERDIR="<ADAO JDC file directory>" SALOMEDIR="<SALOME main installation directory>" $SALOMEDIR/salome start -k -t $SALOMEDIR/salome shell -- "driver $USERDIR/<ADAO YACS xml scheme>" $SALOMEDIR/salome shell killSalome.py
It is then required to change it to be in executable mode.
A more complete example consist in launching execution of a YACS scheme given by
the user, having previously verified its availability. For that, replacing the
<SALOME main installation directory>, one needs only to save the
following Shell script:
#!/bin/bash if (test $# != 1) then echo -e "\nUsage: $0 <ADAO YACS xml scheme>\n" exit else USERFILE="$1" fi if (test ! -e $USERFILE) then echo -e "\nError : the XML file named $USERFILE does not exist.\n" exit else SALOMEDIR="<SALOME main installation directory>" $SALOMEDIR/salome start -k -t $SALOMEDIR/salome shell -- "driver $USERFILE" $SALOMEDIR/salome shell killSalome.py fi
An another example consist in adding the conversion of the ADAO command file
(JDC, or “.comm/.py” files pair) in an associated YACS scheme (“.xml” file). At
the end of the script, one choose also to remove the
<ADAO YACS xml scheme>
because it is a generated file. For that, after having carefully replaced the
<SALOME main installation directory>, one needs only to save the
following Shell script:
#!/bin/bash if (test $# != 1) then echo -e "\nUsage: $0 <ADAO .comm/.py case>\n" exit else D=`dirname $1` F=`basename -s .comm $1` F=`basename -s .py $F` USERFILE="$D/$F" fi if (test ! -e $USERFILE.py) then echo -e "\nError : the PY file named $USERFILE.py does not exist.\n" exit else SALOMEDIR="<SALOME main installation directory>" $SALOMEDIR/salome start -k -t $SALOMEDIR/salome shell -- "python $SALOMEDIR/bin/AdaoYacsSchemaCreator.py $USERFILE.py $USERFILE.xml" $SALOMEDIR/salome shell -- "driver $USERFILE.xml" $SALOMEDIR/salome shell killSalome.py rm -f $USERFILE.xml fi
In all cases, the standard output and errors come in the launching terminal.
7.2. Running an ADAO calculation scheme in YACS using the text user mode (YACS TUI)¶
This section describes how to execute in TUI (Text User Interface) YACS mode a YACS calculation scheme, obtained in the graphical interface by using the ADAO “Export to YACS” function. It uses the standard YACS TUI mode, which is briefly recalled here (see YACS documentation for more information) through a simple example. As described in documentation, a XML scheme can be loaded in a Python. We give here a whole sequence of command lines to test the validity of the scheme before executing it, adding some initial supplementary ones to explicitly load the types catalog to avoid weird difficulties:
import pilot import SALOMERuntime import loader SALOMERuntime.RuntimeSALOME_setRuntime() r = pilot.getRuntime() xmlLoader = loader.YACSLoader() xmlLoader.registerProcCataLoader() try: catalogAd = r.loadCatalog("proc", "<ADAO YACS xml scheme>") r.addCatalog(catalogAd) except: pass try: p = xmlLoader.load("<ADAO YACS xml scheme>") except IOError,ex: print("IO exception:",ex) logger = p.getLogger("parser") if not logger.isEmpty(): print("The imported file has errors :") print(logger.getStr()) if not p.isValid(): print("The schema is not valid and can not be executed") print(p.getErrorReport()) info=pilot.LinkInfo(pilot.LinkInfo.ALL_DONT_STOP) p.checkConsistency(info) if info.areWarningsOrErrors(): print("The schema is not consistent and can not be executed") print(info.getGlobalRepr()) e = pilot.ExecutorSwig() e.RunW(p) if p.getEffectiveState() != pilot.DONE: print(p.getErrorReport())
This method allows for example to edit the YACS XML scheme in TUI, or to gather results for further use.
7.3. Running an ADAO calculation in R environment using the TUI ADAO interface¶
To extend the analysis and treatment capacities, it is possible to use ADAO
calculations in R environment (see [R] for more details). It is available
in SALOME by launching the R interpreter in the shell “
Moreover, the package “rPython” (or the more recent “reticulate” one) has
to be available, it can be installed by the user if required by the following R
# # IMPORTANT: to be run in R interpreter # ------------------------------------- install.packages("rPython")
One will refer to the [GilBellosta15] documentation for more information on this package.
The ADAO calculations defined in text interface (API/TUI, see [DocR] Textual User Interface for ADAO (TUI/API)) can be interpreted from the R environment, using some data and information from R. The approach is illustrated in the example A simple setup example of an ADAO TUI calculation case, suggested in the API/TUI interface description. In the R interpreter, one can run the following commands, directly coming from the simple example:
# # IMPORTANT: to be run in R interpreter # ------------------------------------- library(rPython) python.exec(" from numpy import array from adao import adaoBuilder case = adaoBuilder.New() case.set( 'AlgorithmParameters', Algorithm='3DVAR' ) case.set( 'Background', Vector=[0, 1, 2] ) case.set( 'BackgroundError', ScalarSparseMatrix=1.0 ) case.set( 'Observation', Vector=array([0.5, 1.5, 2.5]) ) case.set( 'ObservationError', DiagonalSparseMatrix='1 1 1' ) case.set( 'ObservationOperator', Matrix='1 0 0;0 2 0;0 0 3' ) case.set( 'Observer', Variable='Analysis', Template='ValuePrinter' ) case.execute() ")
giving the result:
Analysis [ 0.25000264 0.79999797 0.94999939]
In writing the ADAO calculations run from R, one must take close attention to the good use of single and double quotes, that should not collide between the two languages.
The data can come from the R environment and should be stored in properly assigned variables to be used later in Python for ADAO. One will refer to the [GilBellosta15] documentation for the implementation work. We can transform the above example to use data from R to feed the three variables of background, observation and observation operator. We get in the end the optimal state also in a R variable. The other lines are identical. The example thus becomes:
# # IMPORTANT: to be run in R interpreter # ------------------------------------- # # R variables # ----------- xb <- 0:2 yo <- c(0.5, 1.5, 2.5) h <- '1 0 0;0 2 0;0 0 3' # # Python code # ----------- library(rPython) python.assign( "xb", xb ) python.assign( "yo", yo ) python.assign( "h", h ) python.exec(" from numpy import array from adao import adaoBuilder case = adaoBuilder.New() case.set( 'AlgorithmParameters', Algorithm='3DVAR' ) case.set( 'Background', Vector=xb ) case.set( 'BackgroundError', ScalarSparseMatrix=1.0 ) case.set( 'Observation', Vector=array(yo) ) case.set( 'ObservationError', DiagonalSparseMatrix='1 1 1' ) case.set( 'ObservationOperator', Matrix=str(h) ) case.set( 'Observer', Variable='Analysis', Template='ValuePrinter' ) case.execute() xa = list(case.get('Analysis')[-1]) ") # # R variables # ----------- xa <- python.get("xa")
One notices the explicit
list type conversions to ensure that
the data are transmitted as known standard types from “rPython” package.
Moreover, it is the data that can be transferred between the two languages, not
functions or methods. It is therefore necessary to prepare generically in Python
the functions to execute required by ADAO, and to forward them correctly the
data available in R.
The most comprehensive cases, proposed in More advanced examples of ADAO TUI calculation case, can be executed in the same way, and they give the same result as in the standard Python interface.
7.4. Using the ADAO EFICAS graphical interface as an ADAO TUI command¶
To make it easier to quickly edit an ADAO command file with ADAO EFICAS (JDC, or pair of files “.comm/.py”, that are together in a user study directory), you can launch the graphical user interface from the Python interpreter. To do this, in a Python interpreter obtained from the “SALOME shell”, the following commands are used:
from adao import adaoBuilder adaoBuilder.Gui()
As a reminder, the easiest way to get a Python interpreter included in a “SALOME shell” session is to run the following command in a terminal:
$SALOMEDIR/salome shell -- python
<SALOME main installation directory>.
If necessary, explicit messages can be used to identify the required environment variables that are missing. However, this command should not be run in the SALOME Python console (because in this case it is enough to activate the module since we already are in the graphical environment…) or in an independant Python install, but it can be run in a “SALOME shell” session obtained from the “Tools/Extensions” menu of SALOME.
7.5. Change the default execution mode of nodes in YACS¶
Various reasons may lead to want to change the default mode of node execution in YACS (see 1 for the correct use of these possibilities). This may be for performance reasons, or for example for reasons of resource conflicts.
One may want to use this change in execution mode to extend the use of local computing resources or to set remote calculations for a node that requires it. This is particularly the case for a node that should use a simulation resource available on a cluster, for example.
In addition, the various calculations that are carried out (user-provided operators, results retrieval functions, etc.) may also present conflicts if they are performed in a single process, and in particular in the main process of SALOME. This is the default YACS operating mode for performance and simplicity reasons. However, it is recommended to change this functioning when encountering execution instabilities or error messages in the graphical interface.
In any case, in the YACS schema being edited, it is sufficient to change the execution mode of the node(s) that require it. They have to be executed in a new container created for the occasion (it is not enough to use the default container, it is explicitly necessary to create a new one) and whose properties are adapted to the intended use. The procedure is therefore as follows:
Create a new YACS container, using the context menu in the tree view of the YACS schema (usually on the left),
Adapt the characteristics of the container, for example by selecting a “type” property with the value “multi” for a truly parallel execution, or by choosing a remote computing resource defined by the “Resource” property, or by using advanced parameters,
Graphically select in the central view the node whose execution mode you want to change,
In the panel to the right of the node entries, unfold the execution choices (named “Execution Mode”), check the “Container” box instead of the “YACS” default, and choose the newly created container (it is usually named “container0”),
Save the modified schema
This can be repeated for each node that requires it, by reusing the same new container for all nodes, or by creating a new container for each node.
A more generic way to impose a global execution in a separate container is to use a variable named “ExecuteInContainer”. This variable is available for ADAO cases through graphical user interface (GUI) or the textual one (it is for example available by default in the List of commands and keywords for data assimilation or optimisation case).
This change in execution mode is extremely powerful and flexible. It is therefore recommended that the user both use it, and at the same time be attentive to the interaction of the different choices he makes, to avoid, for example, an unintended performance deterioration, or computer conflicts that are complicated to diagnose.
7.6. Getting information on special variables during the ADAO calculation¶
Some special internal optimization variables, used during calculations, can be monitored during the ADAO calculation. These variables can be printed, plotted, saved, etc. This can be done using “observer”, that are commands gathered in scripts, each associated with one variable.
Some templates are available when editing the ADAO case in graphical editor. These simple scripts can be customized by the user, either at the embedded edition stage, or at the edition stage before execution, to improve the tuning of the ADAO calculation.
To implement these “observer” efficiently, one can look to the Requirements for functions describing an “observer”.
7.7. Getting more information when running a calculation¶
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 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 information on this module). Everywhere in the YACS scheme, mainly through the scripts entries, the user can set the logging level in accordance to the needs of detailed information. The different logging levels are: “DEBUG”, “INFO”, “WARNING”, “ERROR”, “CRITICAL”. All the information flagged with a certain level will be printed for whatever activated level above this particular one (included). The easiest way is to change the log level by using the following Python lines:
import logging logging.getLogger().setLevel(logging.DEBUG)
The standard logging module default level is “WARNING”, the default level in the ADAO module is “INFO”.
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).
7.8. Accelerating numerical derivatives calculations by using a parallel mode¶
When setting an operator, as described in Requirements for functions describing an operator, the user can choose a functional form “ScriptWithOneFunction”. This form explicitly leads to approximate the tangent and adjoint operators (if they are required) by a finite differences calculation. It requires several calls to the direct operator (which is the user defined function), at least as many times as the dimension of the state vector. This are these calls that can potentially be executed in parallel.
Under some conditions (described right after), it is then possible to accelerate the numerical derivatives calculations by using a parallel mode for the finite differences approximation. When setting up an ADAO case, it is done by adding the optional keyword “EnableMultiProcessing”, set to “1” or “True”. This keyword is included in the “SCRIPTWITHONEFUNCTION” command in the operator definition by graphical interface, or in the “Parameters” accompanying the command “OneFunction” by textual interaface. By default, this parallel mode is disabled (“EnableMultiProcessing=0”). The parallel mode will only use local resources (both multi-cores or multi-processors) of the computer on which execution is running, requiring by default as many resources as available. If necessary, one can reduce the available ressources by limiting the possible number of parallel processes using the keyword “NumberOfProcesses”, set to desired maximum number (or to “0” for automatic control, which is the default value).
The main conditions to perform parallel calculations come from the user defined function, that represents the direct operator. This function has at least to be “thread safe” to be executed in Python parallel environment (notions out of scope of this paragraph). It is not obvious to give general rules, so it’s recommended, for the user who enable this internal parallelism, to carefully verify his function and the obtained results.
From a user point of view, some conditions, that have to be met to set up parallel calculations for tangent and the adjoint operators approximations, are the following ones:
The dimension of the state vector is more than 2 or 3.
Unitary calculation of user defined direct function “last for long time”, that is, more than few minutes.
The user defined direct function does not already use parallelism (or parallel execution is disabled in the user calculation).
The user defined direct function avoids read/write access to common resources, mainly stored data, output files or memory capacities.
The “observer” added by the user avoid read/write access to common resources, such as files or memory.
If these conditions are satisfied, the user can choose to enable the internal parallelism for the numerical derivative calculations. Despite the simplicity of activating, by setting one variable only, the user is urged to verify the results of its calculations. One must at least doing them one time with parallelism enabled, and an another time with parallelism disabled, to compare the results. If it does fail somewhere, you have to know that this parallel scheme is working for complex codes, like Code_Aster in SalomeMeca [SalomeMeca] for example. So, if it does not work in your case, check your operator function before and during enabling parallelism…
In case of doubt, it is recommended NOT TO ACTIVATE this parallelism.
It is also recalled that one have to choose the type “multi” for the default container in order to launch the scheme, to allow a really parallel execution.
7.9. Convergence control for calculation cases and iterative algorithms¶
There are many reasons to want to control the convergence of available calculation cases or algorithms in ADAO. For example, one may want repeatability of optimal solutions, certified quality, stability of optimal search conditions in studies, saving of global computation time, etc. Moreover, we notice that the methods used in ADAO are frequently iterative, reinforcing the interest of this convergence control.
By default, the available calculation cases or algorithms in ADAO give access to multiple ways to control their convergence, specially adapted to each method. These controls are derived from classical optimization theory and from the possibilities of each algorithm. The default values of the controls are chosen to ensure an optimal search for high quality simulation functions with “standard” behavior (regularity, physical and numerical quality…), which is not necessarily the main property of real simulations due to various constraints. It is therefore quite normal to adapt the convergence criteria to the study cases encountered, but it is an expert approach to establish the correct adaptation.
There are fairly generic ways to control the optimal search and the convergence of algorithms. We indicate here the most useful ones, in a non-exhaustive way, and with the significant restriction that there are many exceptions to the recommendations made. To go further, this generic information must be completed by the information specific to each algorithm or calculation case, indicated in the documentation of the different [DocR] Data assimilation or optimization calculation cases.
A first way is to limit the default number of iterations in the iterative search processes. Even if this is not the best theoretical way to control the algorithm, it is very effective in a real study process. For this purpose, the keyword “MaximumNumberOfIterations” exists in all cases of calculations that support it, and its default value is usually set to an equivalent of infinity so that it is not the stopping criterion. This is the case for calculations based on variational methods such as Calculation algorithm “3DVAR”, Calculation algorithm “4DVAR” and Calculation algorithm “NonLinearLeastSquares”, but this is also the case for other ones like the Calculation algorithm “DerivativeFreeOptimization” or Calculation algorithm “QuantileRegression”. In practice, a value between 10 and 30 is recommended to make this control parameter effective and still obtain an optimal search of good quality. For an optimal search of sufficient quality, this restriction should not be set too strictly, i.e. a 30 iteration limit should be more favorable than a 10 iteration limit.
A second way to control convergence is to adapt the relative decrement tolerance in the minimization of the cost functional considered. This tolerance is controlled by the keyword “CostDecrementTolerance” in the algorithms that support it. The default value is rather strict, it is chosen for a theoretical convergence control when the numerical simulations are of high numerical quality. In practice, it can be adapted without hesitation to be between and . This adaptation allows in particular to reduce or avoid the difficulties of optimal search which are manifested by many successive iterations on almost identical states.
A third way to improve convergence is to adapt the default setting of the finite difference approximation, primarily for the observation operator and a single-operator representation. The control of this property is done with the keyword “DifferentialIncrement” which sets the definition using the First functional form: one direct operator only. Its default value is (or 1%), and it can usually be adjusted between and (although it is wise to check carefully the relevance of its value, it is easy in ADAO to change this parameter). The convergence criterion must then be adjusted so that it does not exceed the order of magnitude of this approximation. In practice, it is sufficient to set the “CostDecrementTolerance” criterion to approximately the same precision (i.e. with an order of magnitude more or less) as the “DifferentialIncrement” criterion. This way of improvement is also to be completed with analyses using the Checking algorithm “LinearityTest” and Checking algorithm “GradientTest”.
From experience, it is a priori not recommended to use other means to control convergence, even if they exist. These parameter adjustments are simple to implement, and it is favorable to try them (in twin experiments or not) because they solve many problems encountered in practice.
For more information on YACS, see the YACS module and its integrated help available from the main menu Help of the SALOME platform.