- gui: package containing the graphical user interface (the GUI part of the MED module)
- res: resources files associated to the MEDOP package (icons, config files, data files, ...)
- exe: additional executable programs that can be launched from the MEDOP framework
+- tut: folder containing a set of tutorials use cases to learn medmem usage from using the python API
index.rst \
medop-specifications.rst \
medop-develguide.rst \
- medop-userguide.rst
+ medop-userguide-gui.rst
+ medop-userguide-api.rst
# Files on the development of the prototype (version 2010)
RSTFILES+=\
font-size: 70%;
margin-top: 5px;
}
+
+
+.tt {
+ font-family:"Courier New",Courier,monospace;
+}
+.strike {
+ text-decoration: line-through;
+}
+
+.bolditalic {
+ font-style:italic;
+ font-weight:bold
+}
+
+.underline {
+ text-decoration:underline;
+}
+
+.tag {
+ font-family:"Courier New",Courier,monospace;
+}
+
+.tagb {
+ font-family:"Courier New",Courier,monospace;
+ font-weight:bold
+}
+
+.todo {
+ background-color:yellow;
+}
+
+.warn {
+ background-color:#FFE4E4;
+}
+
+.info {
+ background-color:#EEEEEE;
+}
+
+.date {
+ font-family:"Courier New",Courier,monospace;
+ font-style:italic;
+}
+
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
-language = 'fr'
+language = 'en'
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
# Options for LaTeX output
# ------------------------
-# The paper size ('letter' or 'a4').
-latex_paper_size = 'a4'
-
-# The font size ('10pt', '11pt' or '12pt').
-latex_font_size = '10pt'
+latex_elements = {
+ # The paper size ('letterpaper' or 'a4paper').
+ 'papersize': 'a4paper',
+ # Select another font family
+ #'fontpkg':'\\usepackage{eucal}',
+ # get rid off blank pages
+ 'classoptions': ',openany,oneside',
+ 'babel' : '\\usepackage[english]{babel}',
+ # The font size ('10pt', '11pt' or '12pt').
+ #'pointsize': '10pt',
+}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, document class [howto/manual]).
('index', 'medop-alldoc.tex', 'Documentation du module MED', 'G. Boulant', 'manual'),
('medop-specifications', 'medop-specifications.tex', 'Module MED - Specifications', 'G. Boulant', 'manual'),
('medop-develguide', 'medop-develguide.tex', 'Module MED - Guide de developpement', 'G. Boulant', 'manual'),
- ('medop-userguide', 'medop-userguide.tex', 'Module MED - Guide d\'utilisation', 'G. Boulant', 'manual')
+ ('medop-userguide-gui', 'medop-userguide-gui.tex', 'Module MED - Guide d\'utilisation de l\'interface graphique', 'G. Boulant', 'howto'),
+ ('medop-userguide-api', 'medop-userguide-api.tex', 'MEDMEM library - Starter guide for users', 'G. Boulant', 'howto')
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
-#latex_logo = None
+latex_logo = os.path.join("@srcdir@","_static","head.png")
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
-#latex_use_parts = False
+latex_use_parts = True
# Additional stuff for the LaTeX preamble.
#latex_preamble = ''
# If false, no module index is generated.
latex_use_modindex = False
+
+# Definition of substitute variables for rst texts
+# ------------------------------------------------
+# The rst_epilog can be used to define variable in conf.py and use the
+# values of these variables in the rst text files.
+srcdir = '@srcdir@'
+rst_epilog = '.. |SPHINXDOC_SRCDIR| replace:: %s' % srcdir
interface graphique pour la réalisation des opérations de manipulation
les plus usuelles.
-.. warning:: La partie interface graphique du module MED (composant
- logiciel dénommé MEDOP) doit être considérée comme un
- prototype pour la version SALOME6.
+Documentation de référence
+==========================
-Documentation principale
-========================
+**Documentation d'utilisation**
+
+.. toctree::
+ :maxdepth: 1
+
+ medop-userguide-gui.rst
+ medop-userguide-api.rst
+
+**Documentation technique**
.. toctree::
:maxdepth: 1
medop-specifications.rst
medop-develguide.rst
- medop-userguide.rst
-Documentation du prototype 2010
-===============================
+**Documentation annexe**
+
+.. toctree::
+ :maxdepth: 1
+
+ medop-references.rst
+
+Archives documentaires
+======================
+
+**Documentation du prototype 2010**
Cette section décrit le prototype mis au point en 2010 pour les
analyses de conception (maquette 2010 sur base MEDMEM)
medop-prototype-develguide.rst
medop-prototype-medmem.rst
-Documentation annexe
-====================
+**Journal de travail**
.. toctree::
:maxdepth: 1
- medop-references.rst
medop-workingnotes-2010.rst
medop-workingnotes-2011.rst
medop-workingnotes-2012.rst
.. |XMED_USERGUIDE_PDF| replace:: version pdf
.. _XMED_USERGUIDE_PDF: _static/documents/xmed-userguide.pdf
+
+
+.. =========================================================
+.. Rendering roles
+.. =========================================================
+.. This role can be used to display monospace text (code)
+.. role:: tt
+ :class: tt
+
+.. role:: strike
+ :class: strike
+
+.. role:: bolditalic
+ :class: bolditalic
+
+.. role:: underline
+ :class: underline
+
+.. role:: tag
+ :class: tag
+
+.. role:: tagb
+ :class: tagb
+
+.. role:: todo
+ :class: todo
+
+.. role:: date
+ :class: date
+
+.. role:: warn
+ :class: warn
+
+.. role:: info
+ :class: info
--- /dev/null
+.. meta::
+ :description: introduction guide for users of the MEDMEM library
+ :keywords: mesh, field, med, MEDCoupling, MEDLoader
+ :author: Guillaume Boulant
+
+.. include:: medop-definitions.rst
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+MEDMEM library: Starter guide for users
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+This document illustrates how to start with the programming interface
+of the MEDMEM library. The users is someone who intends to create a
+data processing script involving meshes and fields.
+
+.. contents:: Sommaire
+ :local:
+ :backlinks: none
+ :depth: 2
+
+General overview
+================
+
+Definition of the MEDMEM library
+--------------------------------
+
+The MEDMEM library is designed to manipulate meshes and fields that
+conform to the MED data model. This library can be used in C++
+programs as in python scripts for data processing on meshes and
+fields. The library contains the data structure to describe meshes and
+fields as C++ objects (MEDCoupling package). It provides a set of
+functions to manage the persistency toward the med file format
+(MEDLoader package), and to process the data througt interpolation and
+localization algorithms (INTERP_KERNEL and REMAPPER packages), for
+example to perform field projections from a mesh to another.
+
+Installation of the MEDMEM library
+----------------------------------
+
+The MEDMEM library is part of the SALOME MED module and then is
+installed together with this module by the installation process of
+SALOME. Nevertheless, it is possible for low-weight deployment to
+install only the MEDMEM library from the source files embedded in the
+SALOME MED module. Keep in mind that the MEDMEM library is designed to
+be a self-consistent library with very few third party softwares (only
+med-file, glibc and mpi typically). In particular, it is strictly
+independant from the SALOME framework even if it distributed with
+SALOME for convenience reasons.
+
+Components of the MEDMEM library
+--------------------------------
+
+The MEDMEM library consists in a small set of atomic libraries files,
+in particular:
+
+* :tt:`medcoupling`: this library provides the data structures (C++
+ classes) to describe meshes and fields.
+* :tt:`medloader`: this library provides I/O functions to the MED file
+ format
+* :tt:`interpkernel`: this library provides the mathematical
+ structures and algorithms required med data processing, in
+ particular interpolation and localization.
+* :tt:`medcouplingremapper`: this library provides the functions for
+ fields projections and interpolation.
+
+The figure below represents the layer structure of the packages of the
+library:
+
+.. image:: images/medlayers_70pc.png
+ :align: center
+
+What we call MEDMEM library in this document is represented by the
+orange packages on this diagram. The white packages reprensent the old
+deprecated MEDMEM library. The blue packages represent the aditionnal
+components for field manipulation througth the user interface (TUI and
+GUI).
+
+The MEDMEM library comes also with this set of atomic libraries for
+advanced users/programmers:
+
+* :tt:`medcouplingcorba`: this library is designed for cross process
+ exchange of medcoupling objects.
+* :tt:`medpartitioner`: this library provides functions to split a MED
+ domain in several part in the perspective of parallel computing
+
+All these atomic C++ libraries are wrapped into a set of python
+modules (using the swig binding technology) so that all the data
+processing can be realized by scripting.
+
+.. warning:: It could happen that some parts of the C++ libraries are
+ not wrapped into python modules. This coverture will be
+ extend on demand and if the integrity of the concepts is
+ preserved.
+
+Main concepts of the MEDMEM library
+===================================
+
+.. warning:: TODO avec Antony. Présenter les structure de données de
+ MEDCoupling principalement. Describe the MEDMEM data
+ model, the typical content of a med file, the types of
+ cell that compose the meshes, the types of spatial
+ discretization of fields, ...
+
+Basic usages of the MEDMEM library
+==================================
+
+This section illustrates the usage of main features of the MEDMEM
+library using python examples. The usage of python is just to have a
+light syntax that makes more easy the first understanding.
+
+.. note:: All code examples here after are parts of the tutorial use
+ cases located in the folder :tt:`src/MEDOP/tut` in the MED
+ source directory. These use cases are all working executable
+ programs and they can be used to initiate your own script.
+
+Preparing the shell environment
+-------------------------------
+
+We make the hypothesis here that the MEDMEM library is installed using
+the SALOME procedure and then is located in the MED module
+installation directory. In addition to the MED library, the third
+party softwares required for executing the examples are: python, hdf5
+and med-fichier. Then, you should prepare your shell environment
+with a set of instructions that looks like::
+
+ #------ python ------
+ export PYTHONHOME=</path/to/python>
+ export PYTHONSTARTUP=${PYTHONHOME}/pythonrc.py
+ export PYTHON_INCLUDE=${PYTHONHOME}/include/python2.6
+ export PATH=${PYTHONHOME}/bin:${PATH}
+ export LD_LIBRARY_PATH=${PYTHONHOME}/lib:${LD_LIBRARY_PATH}
+
+ #------ hdf5 ------
+ HDF5HOME=</path/to/hdf5>
+ export PATH=${HDF5HOME}/bin:$PATH
+ export LD_LIBRARY_PATH=${HDF5HOME}/lib:${LD_LIBRARY_PATH}
+ export HDF5_DISABLE_VERSION_CHECK=1
+
+ #------ med ------
+ MED2HOME=</path/to/med>
+ export PATH=${MED2HOME}/bin:${PATH}
+ export LD_LIBRARY_PATH=${MED2HOME}/lib:${LD_LIBRARY_PATH}
+
+ #------ medmem ---
+ MED_ROOT_DIR=<path/to/salome_med_module>
+ export LD_LIBRARY_PATH=${MED_ROOT_DIR}/lib/salome:${LD_LIBRARY_PATH}
+ PYTHONPATH=${MED_ROOT_DIR}/lib/python2.6/site-packages/salome:${PYTHONPATH}
+ PYTHONPATH=${MED_ROOT_DIR}/bin/salome:${PYTHONPATH}
+ PYTHONPATH=${MED_ROOT_DIR}/lib/salome:${PYTHONPATH}
+ export PYTHONPATH
+
+Example 01: Explore a med file to get information concerning meshes and fields
+------------------------------------------------------------------------------
+
+:objectives: This example illustrates how to get information
+ concerning meshes and fields from a med file, using the
+ MEDLoader library.
+
+The loading of meshes and fields from a med file to a MEDCoupling data
+structure requires first the knowledge of metadata associated to these
+meshes and fields. You have to know the names of the meshes, so that
+you can specify the one you want to load, and then the names of the
+fields associated to one given mesh, the space discretizations used
+for each field, and the iterations available.
+
+The MEDLoader library can read these metadata without loading the
+physical data that compose the meshes and fields. This feature ensures
+the performance of the exploration process, in particular in the case
+of big meshes.
+
+This first instruction looks for meshes embedded in the med file
+(located by :tt:`filepath`) and returns the list of mesh names:
+
+.. include:: ../../tut/medloader/tutorial.py
+ :literal:
+ :start-after: # _T1A
+ :end-before: # _T1B
+
+.. WARNING: Note that the file path for the include directive must be
+ relative to this rst source file (i.e. as organized in the MED
+ source directory, and nevertheless the build procedure is realized
+ elsewhere.
+
+Then, you may select one of these names (or iterate on all names of
+the list) and read the list of fields defined on this mesh:
+
+.. include:: ../../tut/medloader/tutorial.py
+ :literal:
+ :start-after: # _T2A
+ :end-before: # _T2B
+
+A field name could identify several MEDCoupling fields, that differ by
+their spatial discretization on the mesh (values on cells, values on
+nodes, ...). This spatial discretization is specified by the
+TypeOfField that is an integer value in this list:
+
+* :tt:`0 = ON_CELLS` (physical values defined by cell)
+* :tt:`1 = ON_NODES` (physical values defined on nodes)
+* :tt:`2 = ON_GAUSS_PT` (physical values defined on Gauss points)
+* :tt:`3 = ON_GAUSS_NE`
+
+.. note:: This constant variables are defined by the MEDLoader module
+ (:tt:`from MEDLoader import ON_NODES`).
+
+As a consequence, before loading the physical values of a field, we
+have to determine the types of spatial discretization that come with
+this field name and to choose one of this types. The instruction below
+read all the spatial discretization types available for the field of
+name :tt:`fieldName` defined on the mesh of name :tt:`meshName`:
+
+.. include:: ../../tut/medloader/tutorial.py
+ :literal:
+ :start-after: # _T3A
+ :end-before: # _T3B
+
+Once you have selected the spatial discretization of interest (called
+:tt:`typeOfDiscretization` in the code below, that corresponds to an
+item of the list :tt:`listOfTypes`), you can extract the list of time
+iterations available for the identified field:
+
+.. include:: ../../tut/medloader/tutorial.py
+ :literal:
+ :start-after: # _T4A
+ :end-before: # _T4B
+
+The iterations can be weither a list of time steps for which the field
+is defined (a timeseries) or a list of frequency steps (spectral
+analysis). In any case, an iteration item consists in a couple of
+integers, the first defining the main iteration step and the second an
+iteration order in this step, that can be consider as a sub-iteration
+of the step. In most of cases, the iteration order is set to :tt:`-1`
+(no sub-iterations).
+
+The field values can now be read for one particular time step (or
+spectrum tic), defined by the pair (iteration number, iteration
+order). This is illustrated by the example here after.
+
+Example 02: Load a mesh and a field from a med file
+---------------------------------------------------
+
+:objectives: This illustrates how to load the physical data of a
+ specified mesh and a specified field.
+
+The metadata read from a med file are required to identify the list of
+meshes and fields in the med file. We assume in this example that the
+mesh and field to load are identified, i.e. we know the name of the
+mesh to load (:tt:`meshName`) and the characteristic properties of the
+field to load (:tt:`fieldName`, :tt:`typeOfDiscretization` and
+:tt:`iteration`). For example, the instruction below load the mesh of
+name :tt:`meshName`:
+
+.. include:: ../../tut/medloader/tutorial.py
+ :literal:
+ :start-after: # _T5A
+ :end-before: # _T5B
+
+and the instruction below load the field with name :tt:`fieldName`
+defined on this mesh at a particular iteration step characterized by
+the couple :tt:`(iterationNumber,iterationOrder)`:
+
+.. include:: ../../tut/medloader/tutorial.py
+ :literal:
+ :start-after: # _T6A
+ :end-before: # _T6B
+
+The variables :tt:`mesh` and :tt:`field` in this code example are instances of
+the MEDCoupling classes describing the meshes and fields.
+
+Note that the read functions required the parameter
+:tt:`dimrestriction`. This parameter discreminates the mesh dimensions you
+are interested to relatively to the maximal dimension of cells
+contained in the mesh (then its value could be 0, -1, -2 or -3
+depending on the max dimension of the mesh). A value of
+:tt:`dimrestriction=0` means "no restriction".
+
+Example 03: Manage the MEDCoupling data load from a med file
+------------------------------------------------------------
+
+:objectives: Some suggestions for the MEDCoupling objects management,
+ in a programming context.
+
+In a real programming case, it could be relevant to explore first the
+med file to load all metadata concerning the whole set of meshes and
+associated fields, and then to load the physical data only once when
+required by the program.
+
+Such a programming scenario required that you keep all metadata in
+data structures created in memory, so that you can manage the
+collection of meshes and fields. Nevertheless, the MEDMEM library
+does not provide such data structures.
+
+We suggest to work with a simple list concept to store the metadata
+for each mesh entry and each field entry. Note that a mesh entry is
+characterized by the mesh name only, while a field entry is
+charaterized by the following attributes:
+
+* :tt:`fieldName`: the name of the field
+* :tt:`meshName`: the name of the mesh that supports the field
+* :tt:`typeOfDiscretization`: the type of spatial discretization
+* :tt:`iteration`: a couple of integers :tt:`(iter,order)` that
+ characterizes the step in a serie (timeseries or spectrum).
+
+By default, we suggest to work with a simple map concept (dictionnary in a
+python context, map in a C++ context) to register the meshes and
+fields loaded from the med file for each metadata entry.
+
+Then, depending on the processing algorithm you intend to implement,
+you may dispatch the data in a tree structure that fit your specific
+case, for performance reasons. For example, the following code
+illustrates how to dispatch the metadata in a tree data structure
+where leaves are the physical data (field objects). We first have to
+define a tree structure (basic definition in htis simple case, but it
+works fine):
+
+.. include:: ../../tut/medloader/manage.py
+ :literal:
+ :start-after: # _T1A
+ :end-before: # _T1B
+
+Then, we can scan the med structure and dispatch the metadata in the
+tree structure:
+
+.. include:: ../../tut/medloader/manage.py
+ :literal:
+ :start-after: # _T2A
+ :end-before: # _T2B
+
+Finally (and afterwards), we can display on standard output the
+metadata registered in the tree structure:
+
+.. include:: ../../tut/medloader/manage.py
+ :literal:
+ :start-after: # _T3A
+ :end-before: # _T3B
+
+Example 04: Simple arithmetic operations with fields
+----------------------------------------------------
+
+:objectives: This example illustrates how to load field iterations
+ from a med file containing a field timeseries and shows
+ how to use these iterations in simple arithmetic
+ operations.
+
+We consider a med file :tt:`timeseries.med`, containing one single
+mesh named :tt:`Grid_80x80` that supports a field with values defined
+on nodes (:tt:`typeOfDiscretization=ON_NODES`) given for ten
+iterations.
+
+This first code block identifies the mesh and the field to consider in
+this example:
+
+.. include:: ../../tut/addfields/operations.py
+ :literal:
+ :start-after: # _T1A
+ :end-before: # _T1B
+
+The following instructions load the field, make a scaling on the
+physical values (multiply by 3) and then save the result in an output
+med file named :tt:`scaling.med`:
+
+.. include:: ../../tut/addfields/operations.py
+ :literal:
+ :start-after: # _T2A
+ :end-before: # _T2B
+
+Note the usage of the method :tt:`applyFunc` that takes in argument a
+string expression that defined the mathematical function to apply on
+the values of the fields. In this expression, the field is symbolized
+by the letter :tt:`f`.
+
+The following set of instructions makes the addition of iteration
+number 3 with iteration number 4 of the field. Note that this
+operation required first to load the mesh:
+
+.. include:: ../../tut/addfields/operations.py
+ :literal:
+ :start-after: # _T3A
+ :end-before: # _T3B
+
+Exemple 05: Compare fields load from different files
+----------------------------------------------------
+
+:objectives: Illustrates the usage of the function
+ changeUnderlyingMesh
+
+Exemple 06: Create a field from scratch on a spatial domain
+-----------------------------------------------------------
+
+:objectives: Illustrates the applyFunc method of fields
+
+Exemple 07: Manipulate structured mesh
+--------------------------------------
+
+:objectives: Illustrates the basic usage of the advanced interface of
+ MEDLoader.
+
+The MEDLoader frontal interface let you load unstructured meshes:
+
+.. include:: ../../tut/medloader/tutorial.py
+ :literal:
+ :start-after: # _T5A
+ :end-before: # _T5B
+
+That is to say that even if the mesh is a structured mesh (a grid mesh
+for example), then you will get a MEDCoupling unstructured mesh
+object.
+
+To manipulate structured mesh objects, you have to use the MEDLoader
+backend interface named :tt:`MEDFileMesh`, or its derivative
+:tt:`MEDFileUMesh` for unstructured meshes, and :tt:`MEDFileCMesh` for
+structured meshes (CMesh for Cartesian Mesh). The code below
+illustrates how to load a mesh using the :tt:`MEDFileMesh` interface,
+and to know if it is a structured mesh:
+
+.. include:: ../../tut/medloader/cmesh.py
+ :literal:
+ :start-after: # _T1A
+ :end-before: # _T1B
+
+This second example can be used in the case where you know in advance
+that it is a structured mesh:
+
+.. include:: ../../tut/medloader/cmesh.py
+ :literal:
+ :start-after: # _T2A
+ :end-before: # _T2B
+
+In any cases, you can also save the mesh in another file with the
+methode :tt:`write` of the :tt:`MEDFileMesh` object:
+
+.. include:: ../../tut/medloader/cmesh.py
+ :literal:
+ :start-after: # _T3A
+ :end-before: # _T3B
+
+Exemple 08: Make a projection of a field
+----------------------------------------
+
+:objectives: Make the projection of a field from a source mesh to a
+ target meshe. The source mesh and the target mesh are
+ two different mesh of the same geometry.
+
+The input data of this use case are:
+
+* a source mesh, and a field defined on this source mesh (left side of
+ the figure below)
+* a target mesh, on which we want to project the field (right side of
+ the figure below)
+
+.. note:: The two meshes are displayed side by side on the figure for
+ convenience reason, but in the real use case they stand at
+ the same location in 3D space (they describe the same
+ geometry).
+
+.. image:: images/medop_projection_inputs.png
+ :align: center
+
+The expected result is a field defined on the target mesh and which
+corresponds to a physical data equivalent to the source field,
+i.e. with conservation of some physical properties. This operation
+requires the usage of interpolation algorithms provided by the
+:tt:`medcouplingremapper` library:
+
+.. include:: ../../tut/projection/demomed/demo_loadsource.py
+ :literal:
+ :start-after: # _T1A
+ :end-before: # _T1B
+
+Some comments on this code:
+
+* The physical property to be preserved by this interpolation is
+ specified using the keyword :tt:`ConservativeVolumic`
+* The parameter :tt:`P0P0` given at the preparation step of the
+ remapper specifies that the interpolation is done from CELLS (P0) to
+ CELLS (P0).
+* The interpolation, strictly speaking, is performed by the
+ instruction :tt:`ftarget =
+ remap.transferField(fsource,defaultValue)`
+* In this instruction, the :tt:`defaultValue` is used to set the target value
+ in the case where there is no cell in the source mesh that overlap
+ the target mesh (for example when the source mesh correspond to a
+ geometrical sub-part of the target mesh).
+
+When executing the :tt:`remapper`, the result is a new field defined on
+the target mesh, as illustrated on the figure below:
+
+.. image:: images/medop_projection_result.png
+ :align: center
+
+Exemple 09: Make a partition of a mesh using a field
+----------------------------------------------------
+
+:objective: This illustrates how to make a mesh partition using the
+ value of a field defined on this mesh.
+
+The input data is a MEDCoupling scalar field (:tt:`field`) defined on
+a 3D mesh, and we want to use this field as a criterium to make a
+partition of the mesh, for example by creating the mesh surface that
+delimits the volumes where the field value is greater that a limit L
+(and conversely the volumes where the field value is lower).
+
+.. image:: images/partition_mesh.png
+ :align: center
+
+The code below shows the simplest way to extract the cells where
+:tt:`field>L` and to create the skin mesh:
+
+.. include:: ../../tut/medcoupling/partition.py
+ :literal:
+ :start-after: # _T1A
+ :end-before: # _T1B
+
+At the end, the variable :tt:`skin` is a 2D mesh that can be saved in
+a med file using the MEDLoader:
+
+.. image:: images/partition_skin.png
+ :align: center
+
+Advanced usages of the MEDMEM library
+=====================================
+
+This section could explain how to process the physical data
+(dataArray) and to manipulate the advanced concepts of the MEDMEM
+library.
+
+.. Exemple 01: Create a field from an image
+.. ----------------------------------------
+
--- /dev/null
+.. meta::
+ :keywords: maillage, champ, manipulation, guide utilisateur
+ :author: Guillaume Boulant
+
+.. include:: medop-definitions.rst
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+Module MED: Guide d'utilisation de l'interface graphique
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+Ce document est un guide rapide pour l'utilisation de l'interface
+graphique du module MED. Il montre comment utiliser le module sur la
+base de quelques exemples de référence, inspirés des cas d'utilisation
+identifiés lors de l'analyse des besoins en matière de manipulation de
+champs.
+
+.. warning:: Le document est autonome, mais il est vivement conseillé
+ de parcourir au préalable (ou en parallèle) :doc:`le document de
+ spécifications<medop-specifications>`, au moins pour fixer les
+ concepts et la terminologie.
+
+.. contents:: Sommaire
+ :local:
+ :backlinks: none
+
+Présentation générale du module MED
+===================================
+
+L'ergonomie générale d'utilisation du module de manipulation de champs
+est inspirée des logiciels comme octave ou scilab. Elle associe une
+interface graphique, pour sélectionner et préparer les données, avec
+une interface texte (la console python) pour le travail effectif sur
+les données.
+
+Pour cela, le module propose deux espaces utilisateurs qui sont
+symbolisés par les rectangles rouges et vert sur la capture d'écran
+ci-dessous:
+
+* **l'espace des données** (*dataspace*), dans lequel l'utilisateur
+ définit les sources de données med (*datasource*), c'est-à-dire les
+ fichiers med dans lesquels sont lus les champs et maillages. Cet
+ espace permet l'exploration des maillages et des champs fournis par
+ les différentes sources de données.
+* **l'espace de travail** (*workspace*), dans lequel l'utilisateur
+ peut déposer des champs sélectionnées dans l'espace source, pour
+ ensuite les travailler par exemple pour produire des nouveaux champs
+ au moyen des fonctions de manipulation fournies par l'interface
+ textuelle (console python TUI).
+
+.. image:: images/xmed-gui-withframe.png
+ :align: center
+
+L'utilisation type des fonctions de manipulation de champs suit un
+processus de la forme suivante:
+
+1. Chargement d'un fichier med dans l'espace de données (dataspace) et
+ exploration du contenu, composé de maillages et de champs définis
+ sur ces maillages et pouvant contenir un ou plusieurs pas de temps.
+2. Sélection (graphique) des champs à manipuler dans l'espace de
+ travail (workspace), avec la possibilité de préciser des
+ restrictions d'utilisation (pas de temps, composantes, groupe de
+ maille).
+3. Création de nouveaux champs par l'exécution d'opérations
+ algébriques (+,-,*,/) entre champs, l'application de fonctions
+ mathématiques standard (pow, sqrt, abs), ou encore l'initialisation
+ "from scratch" sur un maillage support.
+4. Contrôle visuel rapide des champs produits (avec les modules VISU
+ et/ou PARAVIS de SALOME, pilotés automatiquement depuis l'interface
+ utilisateur)
+5. Enregistrement d'une partie des champs produits dans un fichier med
+
+
+Tour rapide des fonctions du module MED
+=======================================
+
+Cette section présente des exemples d'utilisation du module XMED sous
+la forme de "storyboard", et illustre au passage les fonctions mises à
+disposition par le module.
+
+.. warning:: Cette section est en travaux. Tant que cet avis n'aura
+ pas disparu, veuillez en considérer le plan et le contenu encore
+ incomplets, temporaires et sujets à caution.
+
+Exemple 1: Explorer des sources de données
+------------------------------------------
+
+.. note:: Cet exemple présente les fonctions:
+
+ * ajouter une source de données
+ * fonctions "Extends field series", "Visualize"
+
+.. |ICO_DATASOURCE_ADD| image:: images/ico_datasource_add.png
+ :height: 16px
+
+.. |ICO_XMED| image:: images/ico_xmed.png
+ :height: 16px
+
+.. |ICO_DATASOURCE_EXPAND| image:: images/ico_datasource_expandfield.png
+ :height: 16px
+
+.. |ICO_DATASOURCE_VIEW| image:: images/ico_datasource_view.png
+ :height: 16px
+
+Au démarrage, le module de manipulation de champs, identifié par
+l'icône |ICO_XMED|, présente une interface vierge:
+
+.. image:: images/xmed-gui-start.png
+ :align: center
+ :width: 800px
+
+La première étape consiste à ajouter une ou plusieurs source de
+données med dans le "dataspace". Pour cela, on clique sur l'icône "Add
+datasource" |ICO_DATASOURCE_ADD| qui propose de sélectionner un
+fichier med:
+
+.. image:: images/xmed-gui-datasource-selectfile.png
+ :align: center
+ :width: 800px
+
+L'opération ajoute une nouvelle entrée (datasource) dans l'espace de
+données (dataspace). Le contenu peut être exploré en parcourant
+l'arborescence. La figure ci-dessous (image de gauche) montre le
+résultat du chargement du fichier ``timeseries.med`` contenant un
+maillage de nom ``Grid_80x80`` sur lequel est défini un champ au noeud
+de nom ``Pulse``. Par défaut, la composition du champs (en terme de
+pas de temps et de composantes) n'est pas affichée pour éviter
+l'encombrement visuel de l'arbre. On doit faire la demande explicite
+au moyen de la commande "Expand field timeseries"
+|ICO_DATASOURCE_EXPAND| disponible dans le menu contextuel associé aux
+champs. Le résultat est affiché sur l'image centrale. La liste des
+itérations du champ ``Pulse`` peut être consultée.
+
+.. |IMG_DATASOURCE_EXPLORE| image:: images/xmed-gui-datasource-explore-zoom.png
+ :height: 340px
+.. |IMG_DATASOURCE_MENUCON| image:: images/xmed-gui-datasource-menucontextuel-zoom.png
+ :height: 340px
+.. |IMG_DATASOURCE_EXPANDF| image:: images/xmed-gui-datasource-expand-zoom.png
+ :height: 340px
+
++--------------------------+--------------------------+--------------------------+
+| |IMG_DATASOURCE_EXPLORE| | |IMG_DATASOURCE_MENUCON| | |IMG_DATASOURCE_EXPANDF| |
++--------------------------+--------------------------+--------------------------+
+
+.. note:: En toute rigueur, le concept de *champ* dans le modèle MED
+ désigne une itération donnée. Un ensemble d'itérations est désigné
+ par le terme *série temporelle de champs*. Par abus de langage, et
+ s'il n'y a pas ambiguité, on utilisera le nom du champ pour
+ désigner à la fois le champs proprement dit ou la série temporelle
+ à laquelle il appartient.
+
+Enfin, il est possible au niveau du dataspace de visualiser la forme
+générale du champ au moyen d'une carte scalaire affichée dans le
+viewer de SALOME. Pour cela, on sélectionne le pas de temps à
+visualiser et on utilise la commande "Visualize" |ICO_DATASOURCE_VIEW|
+disponible dans le menu contextuel associé:
+
+.. image:: images/xmed-gui-datasource-visualize-zoom.png
+ :align: center
+ :width: 800px
+
+.. note:: Cette représentation graphique a pour objectif le contrôle
+ visuel rapide. Aussi, les fonctions du module VISU sont employées
+ par défaut, mais il est possible de faire l'affichage des cartes
+ scalaires au moyen du module PARAVIS (choix de préférence non
+ implémenté pour le moment, mais techniquement réalisable).
+
+Exemple 2: Rassembler des champs issus de différentes sources
+-------------------------------------------------------------
+
+.. note:: Cet exemple présente les fonctions:
+
+ * fonction "Use in workspace"
+ * fonction "Save"
+
+.. |ICO_DATASOURCE_USE| image:: images/ico_datasource_use.png
+ :height: 16px
+.. |ICO_WORKSPACE_SAVE| image:: images/ico_workspace_save.png
+ :height: 16px
+
+L'objectif est de récupérer des données issues de différents fichiers
+med, puis de les rassembler dans un même fichier en sortie.
+
+On commence par ajouter les sources de données med dans l'espace de
+données (dataspace). Dans l'exemple ci-dessous, l'espace de données
+contient deux sources de nom ``parametric_01.med`` et
+``smallmesh_varfiled.med``. La première source contient le maillage
+``Grid_80x80_01`` sur lequel est défini le champ ``StiffExp_01``. La
+deuxième source contient le maillage ``My2DMesh`` sur lequel sont
+définis deux champs de noms respectifs ``testfield1`` et
+``testfield2``:
+
+.. image:: images/xmed-userguide-example2-datasource.png
+ :align: center
+ :width: 800px
+
+Pour l'exemple, on souhaite rassembler les champs ``StiffExp_01`` et
+``testfield2`` dans un fichier de nom ``result.med``. La procédure
+consiste à importer les deux champs dans l'espace de travail
+(workspace), puis à sauvegarder l'espace de travail. Pour cela, on
+sélectionne les champs et on utilise la commande "Use in workspace"
+|ICO_DATASOURCE_USE| disponible dans le menu contextuel. Les deux
+champs sélectionnés apparaissent dans l'arborescence de l'espace de
+travail:
+
+.. image:: images/xmed-userguide-example2-workspace.png
+ :align: center
+ :width: 800px
+
+La sauvegarde de l'espace de travail est faite au moyen de la commande
+"Save workspace" |ICO_WORKSPACE_SAVE| disponible dans la barre
+d'outils du module. Une fenêtre de dialogue invite l'utilisateur à
+spécifier le nom du fichier de sauvegarde:
+
+.. image:: images/xmed-userguide-example2-workspace-save.png
+ :align: center
+ :width: 800px
+
+Ce fichier ``result.med`` peut ensuite être rechargé dans le module
+XMED (ou les modules VISU ou PARAVIS) pour vérifier la présence des
+champs sauvegardés.
+
+.. BUG: plantage à l'utilsation dans XMED d'un fichier rechargé
+.. (invalid mesh on field)
+
+.. _xmed.userguide.exemple3:
+
+Exemple 3: Appliquer une opération mathématique sur des champs
+--------------------------------------------------------------
+
+.. note:: Cet exemple présente les fonctions:
+
+ * exécution d'opérations mathématiques dans la console TUI
+ * fonction "put" pour référencer un champ de travail dans la liste
+ des champs persistant.
+ * fonction "Visualize" depuis le TUI.
+
+L'usage le plus courant du module de manipulation de champs est
+d'exécuter des opérations mathématiques dont les opérandes sont des
+champs ou des composantes de ces champs.
+
+On se place dans une situation où les sources de données sont définies
+dans le "dataspace" (dans l'exemple ci-après, une série temporelle de
+nom ``Pulse``, contenant 10 pas de temps, définis sur un maillage de
+nom ``Grid_80x80``, le tout issu du datasource ``timeseries.med``).
+
+Comme vu précedemment, pour manoeuvrer un champ dans l'espace de
+travail, on sélectionne ce champ, puis on exécute la commande "Use in
+workspace" |ICO_DATASOURCE_USE| du menu contextuel. Dans le cas
+présent, un seul champ est sélectionné (contre deux dans l'exemple
+précédent) et la commande ouvre alors une fenêtre de dialogue qui
+permet de préciser les données sur lesquelles on souhaite
+effectivement travailler et comment on veut les manoeuvrer:
+
+.. image:: images/xmed-gui-datasource-useinworkspace-alias.png
+ :align: center
+ :width: 800px
+
+.. note:: En l'état actuel du développement, l'interface propose
+ uniquement de définir le nom de la variable sous laquelle doit être
+ manoeuvré le champ dans la console de travail (TUI). Dans une
+ version ultérieure, il est prévue de pouvoir préciser la ou les
+ composante du champs à utiliser et un groupe de maille pour définir
+ une restriction géométrique. Inversement, il sera également
+ possible de choisir une série temporelle complète pour faire des
+ opérations globales sur l'ensemble des pas de temps.
+
+Aprés validation, le champ est placé dans l'arborescence du
+"workspace" et une variable de nom ``<alias>`` est créée
+automatiquement dans la console de travail pour désigner le
+champ. Dans cet exemple, ``<alias>`` vaut ``f3``, positionné ainsi par
+l'utilisateur pour rappeler que la variable correspond au pas de temps
+n°3:
+
+.. image:: images/xmed-gui-workspace.png
+ :align: center
+ :width: 800px
+
+La manipulation peut commencer. Dans l'exemple ci-dessous, on crée le
+champ ``r`` comme le résultat d'une transformation afine du champ
+``f3`` (multiplication du champ par le facteur 2.7 auquel on ajoute
+l'offset 5.2)::
+
+ >>> r=2.7*f3+5.2
+
+On peut poursuivre la manipulation du champs avec une variété
+d'opérations qui sont détaillées dans les spécifications du module
+(cf. :ref:`Spécification des opérations<xmed-specifications>`):
+
+ >>> r=f3/1000 # les valeurs de r sont celles du champ f3 réduites d'un facteur 1000
+ >>> r=1/f3 # les valeurs de r sont les inverses des valeurs de f3
+ >>> r=f3*f3 # les valeurs de r sont celles du champ f3 élevées au carré
+ >>> r=pow(f3,2) # même résultat
+ >>> r=abs(f3) # valeur absolue du champ f3
+ >>> ...
+
+Les opérations peuvent utiliser plusieurs opérandes de type champs. Si
+``f4`` désigne le pas de temps n°4 du champ ``Pulse``, alors on peut
+calculer toute combinaison algébrique des deux champs::
+
+ >>> r=f3+f4
+ >>> r=f3-f4
+ >>> r=f3/f4
+ >>> r=f3*f4
+
+Avec au besoin l'utilisation de variables scalaires::
+
+ >>> r=4*f3-f4/1000
+ >>> ...
+
+Dans ces exemples, la variable ``r`` désigne un champ de travail qui
+contient le résultat de l'opération. Par défaut, ce champ de travail
+n'est pas référencé dans l'arborescence du workspace. Si on souhaite
+tout de même le référencer, par exemple pour qu'il soit pris en compte
+dans la sauvegarde, alors on tape la commande::
+
+ >>> put(r)
+
+La fonction ``put`` a pour but de marquer le champ en argument comme
+persistent, puis de le ranger dans l'arborescence du "workspace" afin
+qu'il soit visible et sélectionnable. En effet, parmi tous les champs
+qui pourront être créés dans la console pendant la session de travail,
+tous n'ont pas besoin d'être sauvegardés. Certains sont même des
+variables temporaires qui servent à la construction des champs
+résultats finaux. C'est pourquoi, seuls les champs rangés dans
+l'arborescence du workspace sont enregistrés lors de la demande de
+sauvegarde du workspace.
+
+Les variables définies dans la console ont d'autres utilités. Tout
+d'abord, elles permettent d'imprimer les informations concernant le
+champ manoeuvré. Pour cela, on tape simplement le nom de la variable
+puis retour::
+
+ >>> f3
+ field name (id) = Pulse (3)
+ mesh name (id) = Grid_80x80 (0)
+ discretization = ON_NODES
+ (iter, order) = (3,-1)
+ data source = file:///home/gboulant/development/projets/salome/MEDOP/XMED/xmed/resources/datafiles/timeseries.med
+
+Elle peut également être utilisée comme argument des commandes de
+gestion disponibles dans l'interface textuelle (dont la liste
+détaillée est décrite à la section :ref:`Documentation de l'interface
+textuelle<xmed.userguide.tui>`). Par exemple, la fonction ``view``
+permet d'afficher la carte scalaire du champ dans le viewer::
+
+ >>> view(f3)
+
+Donne:
+
+.. image:: images/xmed-gui-workspace-view.png
+ :align: center
+ :width: 800px
+
+.. note:: On remarquera ici qu'il est facile de comparer deux pas de
+ temps d'un champ, par exemple en calculant la différence ``f3-f4``,
+ puis en affichant un aperçu de la carte scalaire résultat au moyen
+ de la fonction ``view``::
+
+ >>> view(f3-f4)
+
+On peut enfin tout simplement afficher les données du champs par la
+commande ``print``::
+
+ >>> print f3
+ Data content :
+ Tuple #0 : -0.6
+ Tuple #1 : -0.1
+ Tuple #2 : 0.4
+ Tuple #3 : -0.1
+ Tuple #4 : 0.4
+ ...
+ Tuple #6556 : 3.5
+ Tuple #6557 : 3.3
+ Tuple #6558 : 1.5
+ Tuple #6559 : 0.3
+ Tuple #6560 : 0.2
+
+Il est important de noter que les opérations entre champs ne peuvent
+être faites qu'entre champs définis sur le même maillage. Il s'agit là
+d'une spécification du modèle MED qui interdit d'envisager les
+opérations entre champs définis sur des maillages géométriquement
+différents. Techniquement, cela se traduit par l'obligation pour les
+objets informatique *champs* de partager le même objet informatique
+*maillage*.
+
+Dans l'hypothèse où on souhaite utiliser des champs définis sur des
+maillages différents, par exemple pour manoeuvrer les valeurs des
+champs à l'interface de deux maillages partageant une zone géométrique
+2D, il faut d'abord ramener tous les champs sur le même maillage de
+surface par une opération de projection.
+
+.. note:: Même si ceci est techniquement possible avec la bibliothèque
+ MEDCoupling, cet type d'opération de projection n'est pas encore
+ disponible dans le module de manipulation de champs (prévu en
+ 2012).
+
+Un autre besoin plus classique est l'utilisation de champs définis sur
+des maillages géométriquement identiques, mais techniquement
+différents, par exemple lorsqu'ils sont chargés de fichiers med
+différents. Pour traiter ce cas de figure, la bibliothèque MEDCoupling
+prévoit une fonction de "Changement du maillage support", dont
+l'utilisation au niveau du module de manipulation de champs est
+illustrée dans :ref:`l'exemple 4<xmed.userguide.exemple4>` ci-après.
+
+.. _xmed.userguide.exemple4:
+
+Exemple 4: Comparer des champs issues de différentes sources
+------------------------------------------------------------
+
+.. note:: Cet exemple présente les fonctions:
+
+ * Changement du maillage support "change underlying mesh"
+
+On se place ici dans le cas de figure où des champs ont été produits
+sur le même maillage, au sens géométrique, mais enregistrés dans des
+fichiers med différents. C'est le cas par exemple d'une étude
+paramétrique où plusieurs calculs sont effectués avec des variantes
+sur certains paramètres du modèle simulé, chaque calcul produisant un
+fichier med.
+
+Soit ``parametric_01.med`` et ``parametric_02.med`` deux fichiers med
+contenant les champs que l'on souhaite comparer, par exemple en
+calculant la différence des valeurs et en visualisant le résultat.
+
+Aprés le chargement des sources de données dans le module XMED,
+l'utilisateur se trouve en présence de deux maillages, au sens
+technique du terme cette fois-ci, c'est-à-dire que les champs sont
+associées à des objets informatiques maillage différents, bien que
+géométriquement identiques.
+
+Or, les fonctions de manipulation de champs ne permettent pas les
+opérations sur des champs dont les maillages supports sont différents
+(voir la remarque à la fin de :ref:`l'exemple
+3<xmed.userguide.exemple3>`).
+
+Pour résoudre ce cas de figure, le module de manipulation de champs
+met à disposition la fonction "Change underlying mesh" qui permet de
+remplacer le maillage support d'un champ par un autre à partir du
+moment où les deux maillages sont géométriquement identiques,
+c'est-à-dire que les noeuds ont les mêmes coordonnées spatiales.
+
+.. |ICO_DATASOURCE_CHG| image:: images/ico_datasource_changeUnderlyingMesh.png
+ :height: 16px
+
+Dans l'exemple proposé, l'utilisateur sélectionne le premier pas de
+temps du champ ``StiffExp_01`` du "datasource" ``parametric_01.med``,
+puis l'importe dans l'espace de travail au moyen de la commande "Use
+in workspace" |ICO_DATASOURCE_USE|. Il sélectionne ensuite le premier
+pas de temps du champs ``StiffExp_02`` du "datasource"
+``parametric_02.med``, mais l'importe dans l'espace de travail au
+moyen de la commande "Change underlying mesh" |ICO_DATASOURCE_CHG|. La
+fenêtre de dialogue ci-dessous s'affiche et invite l'utilisateur à
+choisir le nouveau maillage support par sélection dans l'arborescence
+du "dataspace":
+
+.. image:: images/xmed-gui-datasource-changeUnderlyingMesh.png
+ :align: center
+
+Dans cet exemple, on sélectionne le maillage ``Grid_80x80_01`` support
+du champ ``StiffExp_01``, avec lequel on souhaite faire la
+comparaison. Après validation, l'arborescence du workspace contient le
+champ ``StiffExp_02`` défini sur le maillage ``Grid_80x80_01``:
+
+.. image:: images/xmed-gui-datasource-changeUnderlyingMesh_wsview.png
+ :align: center
+
+.. note:: La fonction "Change underlying mesh" ne modifie pas le champ
+ sélectionné dans le "dataspace" (principe de base de fonctionnement
+ du dataspace), mais crée une copie du champ dans l'espace de travail
+ pour ensuite remplacer le maillage support. D'où le nom par défaut
+ pour le champ ``dup(<nom du champ sélectionné>)`` (dup pour
+ "duplicate").
+
+Il reste à associer une variable à ce champ pour le manipuler dans la
+console. Ceci peut être fait au moyen de la commande "Use in console",
+disponible dans le menu contextuel du workspace.
+
+En définitif, si ``f1`` désigne le champ issu du datasource
+``parametric_01.med`` et ``f2`` le champ issu du datasource
+``parametric_02.med`` par la procédure décrite ci-dessus, alors la
+comparaison des deux grandeurs peut être faite comme pour le cas de
+:ref:`l'exemple 3<xmed.userguide.exemple3>`::
+
+ >>> r=f1-f2
+ >>> view(r)
+
+.. note:: En remarque générale sur cet exemple, il convient de noter
+ les points suivants:
+
+ * l'égalité géométrique de deux maillages est établie à une marge
+ d'erreur prés qu'il est possible de définir techniquement, mais
+ qui n'est pas ajustable au niveau de l'interface du module de
+ manipulation de champs. Elle est fixée à une valeur standard qui
+ permet de traiter la plupart des cas utilisateur. On verra à
+ l'usage s'il est nécessaire de remonter ce paramètre au niveau de
+ l'interface.
+ * L'utilisateur doit faire la démande explicite de changer le
+ maillage support d'un champ, en prévision de la comparaison de
+ champs issus de datasource différentes. Il s'agit là d'un choix
+ fonctionnel délibéré pour que l'utilisateur garde trace des
+ modifications faites sur les données (pas de modification
+ automatiques à l'insu de l'utilisateur, même sous prétexte
+ d'amélioration de l'ergonomie).
+
+
+Exemple 5: Créer un champ sur un domaine spatial
+------------------------------------------------
+
+.. note:: Cet exemple présente les fonctions:
+
+ * initialisation par une fonction de la position spatiale
+ * initialisation sur un groupe de maille
+
+Le domaine géométrique de définition du champs à créer est spécifié
+ici par la donnée d'un groupe de mailles. Ce cas d'usage est
+typiquement prévu pour produire les conditions de chargement initial
+d'une structure, par exemple en définissant un champ sur une surface
+de la géométrie, identifiée par un nom de groupe de mailles.
+
+.. warning:: DEVELOPPEMENT EN COURS
+
+Exemple 6: Extraire une partie d'un champ
+-----------------------------------------
+
+.. note:: Cet exemple présente les fonctions:
+
+ * extraire une composante (ou un sous-ensemble des composantes)
+ * extraire un domaine géométrique (valeurs sur un groupe de maille)
+ * extraire un ou plusieurs pas de temps.
+
+.. warning:: DEVELOPPEMENT EN COURS
+
+ On doit illustrer ici les fonctions de restriction, qui
+ permettraient de récupérer certaines composantes uniquement. Le
+ principe est qu'on crée un nouveau champ qui est une restriction du
+ champ argument à une liste de composantes à spécifier (utiliser la
+ fonction __call__ des fieldproxy).
+
+Pour l'extraction des pas de temps, on peut se ramener au cas de
+l'exemple 2 avec une seule source de donnée.
+
+Exemple 7: Créer un champ à partir d'une image to[mp]ographique
+---------------------------------------------------------------
+
+.. note:: Cet exemple présente les fonctions:
+
+ * Création d'un champ sans datasource (ni maillage, ni champs), à
+ partir d'un fichier image
+
+En tomographie ou en topographie, les appareils de mesure produisent
+des images qui représentent une grandeur physique en niveaux de gris
+sur un plan de coupe donné. L'image ci-dessous représente par exemple
+une vue interne du corps humain faite par IRM:
+
+.. image:: images/xmed-irm.png
+ :align: center
+ :width: 600px
+
+Cette image est un ensemble de pixels organisés sur une grille
+cartesienne. Elle peut donc être modélisée sous la forme d'un champ
+scalaire dont les valeurs sont définies aux cellules d'un maillage
+réglés de même taille que l'image (en nombre de pixels):
+
+.. image:: images/xmed-irm-field.png
+ :align: center
+ :width: 600px
+
+Le module de manipulation de champ fournit un utilitaire appelé
+``image2med.py`` qui permet d'appliquer ce principe à la conversion
+d'un fichier image en fichier med contenant la représentation de
+l'image sous forme d'un champ scalaire (seul le niveau de gris est
+conservé)::
+
+ $ <xmed_root_dir>/bin/salome/xmed/image2med.py -i myimage.png -m myfield.med
+
+.. |ICO_IMAGESOURCE| image:: images/ico_imagesource.png
+ :height: 16px
+
+Cette opération de conversion peut être faite automatiquement dans
+l'interface graphique du module au moyen de la commande "Add Image
+Source" |ICO_IMAGESOURCE| disponible dans la barre d'outils. Cette
+commande ouvre la fenêtre suivante pour inviter l'utilisateur à
+choisir un fichier image:
+
+.. image:: images/medop_image2med_dialog.png
+ :align: center
+
+Le nom du fichier med résultat est proposé par défaut (changement de
+l'extention en ``*.med``) mais il peut être modifié. Enfin, on peut
+demander le chargement automatique du fichier med produit pour ajout
+dans l'espace de donnée. Les champs peuvent alors être manipulés comme
+dans les cas d'utilisation standard.
+
+Par exemple, l'image ci-dessous affiche le résultat de la différence
+entre deux images, ajoutée à l'image de référence: si i1 et i2
+désignent les champs créés à partir des deux images, on représente ``r
+= i1 + 5*(i2-i1)`` où le facteur 5 est arbitraire et sert à amplifier
+la zone d'intérêt (en haut de l'oeil gauche):
+
+.. image:: images/xmed-irm-diff.png
+ :align: center
+ :width: 600px
+
+L'exemple ci-dessous est le résultat du chargement d'une image
+tomographique issue du projet MAP (Charles Toulemonde,
+EDF/R&D/MMC). L'image tomographique:
+
+.. image:: images/champ_altitude_MAP.png
+ :align: center
+ :width: 600px
+
+Le résultat du chargement:
+
+.. image:: images/medop_image2med_tomographie.png
+ :align: center
+ :width: 800px
+
+Exemple 8: Continuer l'analyse dans PARAVIS
+-------------------------------------------
+
+.. note:: Cet exemple présente les fonctions:
+
+ * Export de champs vers le module PARAVIS.
+
+Les possibilités de représentation graphique des champs fournies par
+le module MED ont pour seul objectif le contrôle visuel rapide. Par
+défaut, le viewer de VISU est employé.
+
+Pour une analyse plus détaillées des champs, il est nécessaire de
+poursuivre le travail dans PARAVIS. Le module de manipulation de
+champs offre une fonction qui simplifie ce passage, en faisant le
+chargement automatique dans PARAVIS et en proposant une visualisation
+par défaut (carte de champs scalaire).
+
+Pour cela, il faut sélectionner dans l'espace de travail les champs à
+exporter, puis déclencher la fonction d'export depuis le menu
+contextuel associé:
+
+.. image:: images/medop_exportparavis.png
+ :align: center
+
+Les champs sélectionnés sont regroupés dans une entrée MED du
+navigateur PARAVIS, et le premier champ est affiché sous forme de
+carte de champ:
+
+.. image:: images/medop_exportparavis_result.png
+ :align: center
+ :width: 800px
+
+.. note:: La fonction d'export est une fonction de confort. La même
+ opération peut être faite manuellement en procédant d'abord à
+ l'enregistrement des champs sous forme de fichier MED, puis en
+ chargeant le fichier généré dans le module PARAVIS pour
+ visualisation.
+
+.. _xmed.userguide.tui:
+
+Utilisation de l'interface textuelle du module MED (TUI)
+========================================================
+
+Toutes les opérations menées au moyen de l'interface graphique peuvent
+être réalisées (avec plus ou moins de facilité) avec l'interface
+textuelle. Le module de manipulation de champs peut même être utilisé
+exclusivement en mode texte. Pour cela, on lance la commande::
+
+ $ <path/to/appli>/medop.sh
+
+Cette commande ouvre une console de commandes ``medop>``. Un fichier
+med peut être chargé et travaillé, par exemple pour créer des champs à
+partir des données du fichier.
+
+Que l'on soit en mode texte pur ou en mode graphique, un séquence de
+travail type dans la console peut ressembler au jeu d'instructions
+suivantes::
+
+ >>> load("/path/to/mydata.med")
+ >>> la
+ id=0 name = testfield1
+ id=1 name = testfield2
+ >>> f1=get(0)
+ >>> f2=get(1)
+ >>> ls
+ f1 (id=0, name=testfield1)
+ f2 (id=1, name=testfield2)
+ >>> r=f1+f2
+ >>> ls
+ f1 (id=0, name=testfield1)
+ f2 (id=1, name=testfield2)
+ r (id=2, name=testfield1+testfield2)
+ >>> r.update(name="toto")
+ >>> ls
+ f1 (id=0, name=testfield1)
+ f2 (id=1, name=testfield2)
+ r (id=2, name=toto)
+ >>> put(r)
+ >>> save("result.med")
+
+Les commandes principales sont:
+
+* ``load``: charge un fichier med dans la base de données (utile
+ uniquement en mode texte pur)::
+
+ >>> load("/path/to/datafile.med")
+
+* ``la``: affiche la liste de tous les champs chargés en base de données ("list all")
+* ``get``: définit un champ dans l'espace de travail à partir de son
+ identifiant (utile plutôt en mode texte pur car l'interface
+ graphique permet de faire cette opération par sélection d'un champ
+ dans le dataspace)::
+
+ >>> f=get(fieldId)
+
+* ``ls``: affiche la liste des champs présent dans l'espace de travail ("list")
+* ``put``: met un champ en référence dans l'*espace de gestion*::
+
+ >>> put(f)
+
+* ``save``: sauvegarde tous les champs référencés dans l'espace de
+ gestion dans un fichier med::
+
+ >>> save("/path/to/resultfile.med")
+
+.. note:: On peut faire à ce stade plusieurs remarques:
+
+ * la commande ``load`` charge uniquement les méta-informations
+ décrivant les maillage et les champs (noms, type de
+ discrétisation, liste des pas de temps). Les maillages et les
+ valeurs physiques des champs sont chargées ultérieurement (et
+ automatiquement) dés lors qu'elles sont requises par une
+ opération. Dans tous les cas, les données med (méta-informations
+ et valeurs) sont physiquement stockées au niveau de l'espace
+ *base de données*.
+ * la commande ``get`` définit en réalité un *manipulateur de champ*
+ dans l'espace de travail, c'est-à-dire une variable qui fait la
+ liaison avec le champ physique hébergé dans la base de
+ données. Les données physiques ne circulent jamais entre les
+ espaces, mais restent centralisées au niveau de la base de
+ données.
+
+Les commandes TUI suivantes nécessitent de travailler dans
+l'environnement graphique:
+
+* ``visu``: afficher une carte de champ pour contrôle visuel rapide
+ (pas de paramettrage possible)
+
+ >>> view(f)
+
+
+++ /dev/null
-.. meta::
- :keywords: maillage, champ, manipulation, guide utilisateur
- :author: Guillaume Boulant
-
-.. include:: medop-definitions.rst
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-Module MED: Guide d'utilisation de l'interface graphique
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-Ce document est un guide rapide pour l'utilisation de l'interface
-graphique du module MED. Il montre comment utiliser le module sur la
-base de quelques exemples de référence, inspirés des cas d'utilisation
-identifiés lors de l'analyse des besoins en matière de manipulation de
-champs.
-
-.. warning:: Le document est autonome, mais il est vivement conseillé
- de parcourir au préalable (ou en parallèle) :doc:`le document de
- spécifications<medop-specifications>`, au moins pour fixer les
- concepts et la terminologie.
-
-.. contents:: Sommaire
- :local:
- :backlinks: none
-
-Présentation générale du module MED
-===================================
-
-L'ergonomie générale d'utilisation du module de manipulation de champs
-est inspirée des logiciels comme octave ou scilab. Elle associe une
-interface graphique, pour sélectionner et préparer les données, avec
-une interface texte (la console python) pour le travail effectif sur
-les données.
-
-Pour cela, le module propose deux espaces utilisateurs qui sont
-symbolisés par les rectangles rouges et vert sur la capture d'écran
-ci-dessous:
-
-* **l'espace des données** (*dataspace*), dans lequel l'utilisateur
- définit les sources de données med (*datasource*), c'est-à-dire les
- fichiers med dans lesquels sont lus les champs et maillages. Cet
- espace permet l'exploration des maillages et des champs fournis par
- les différentes sources de données.
-* **l'espace de travail** (*workspace*), dans lequel l'utilisateur
- peut déposer des champs sélectionnées dans l'espace source, pour
- ensuite les travailler par exemple pour produire des nouveaux champs
- au moyen des fonctions de manipulation fournies par l'interface
- textuelle (console python TUI).
-
-.. image:: images/xmed-gui-withframe.png
- :align: center
-
-L'utilisation type des fonctions de manipulation de champs suit un
-processus de la forme suivante:
-
-1. Chargement d'un fichier med dans l'espace de données (dataspace) et
- exploration du contenu, composé de maillages et de champs définis
- sur ces maillages et pouvant contenir un ou plusieurs pas de temps.
-2. Sélection (graphique) des champs à manipuler dans l'espace de
- travail (workspace), avec la possibilité de préciser des
- restrictions d'utilisation (pas de temps, composantes, groupe de
- maille).
-3. Création de nouveaux champs par l'exécution d'opérations
- algébriques (+,-,*,/) entre champs, l'application de fonctions
- mathématiques standard (pow, sqrt, abs), ou encore l'initialisation
- "from scratch" sur un maillage support.
-4. Contrôle visuel rapide des champs produits (avec les modules VISU
- et/ou PARAVIS de SALOME, pilotés automatiquement depuis l'interface
- utilisateur)
-5. Enregistrement d'une partie des champs produits dans un fichier med
-
-
-Tour rapide des fonctions du module MED
-=======================================
-
-Cette section présente des exemples d'utilisation du module XMED sous
-la forme de "storyboard", et illustre au passage les fonctions mises à
-disposition par le module.
-
-.. warning:: Cette section est en travaux. Tant que cet avis n'aura
- pas disparu, veuillez en considérer le plan et le contenu encore
- incomplets, temporaires et sujets à caution.
-
-Exemple 1: Explorer des sources de données
-------------------------------------------
-
-.. note:: Cet exemple présente les fonctions:
-
- * ajouter une source de données
- * fonctions "Extends field series", "Visualize"
-
-.. |ICO_DATASOURCE_ADD| image:: images/ico_datasource_add.png
- :height: 16px
-
-.. |ICO_XMED| image:: images/ico_xmed.png
- :height: 16px
-
-.. |ICO_DATASOURCE_EXPAND| image:: images/ico_datasource_expandfield.png
- :height: 16px
-
-.. |ICO_DATASOURCE_VIEW| image:: images/ico_datasource_view.png
- :height: 16px
-
-Au démarrage, le module de manipulation de champs, identifié par
-l'icône |ICO_XMED|, présente une interface vierge:
-
-.. image:: images/xmed-gui-start.png
- :align: center
- :width: 800px
-
-La première étape consiste à ajouter une ou plusieurs source de
-données med dans le "dataspace". Pour cela, on clique sur l'icône "Add
-datasource" |ICO_DATASOURCE_ADD| qui propose de sélectionner un
-fichier med:
-
-.. image:: images/xmed-gui-datasource-selectfile.png
- :align: center
- :width: 800px
-
-L'opération ajoute une nouvelle entrée (datasource) dans l'espace de
-données (dataspace). Le contenu peut être exploré en parcourant
-l'arborescence. La figure ci-dessous (image de gauche) montre le
-résultat du chargement du fichier ``timeseries.med`` contenant un
-maillage de nom ``Grid_80x80`` sur lequel est défini un champ au noeud
-de nom ``Pulse``. Par défaut, la composition du champs (en terme de
-pas de temps et de composantes) n'est pas affichée pour éviter
-l'encombrement visuel de l'arbre. On doit faire la demande explicite
-au moyen de la commande "Expand field timeseries"
-|ICO_DATASOURCE_EXPAND| disponible dans le menu contextuel associé aux
-champs. Le résultat est affiché sur l'image centrale. La liste des
-itérations du champ ``Pulse`` peut être consultée.
-
-.. |IMG_DATASOURCE_EXPLORE| image:: images/xmed-gui-datasource-explore-zoom.png
- :height: 340px
-.. |IMG_DATASOURCE_MENUCON| image:: images/xmed-gui-datasource-menucontextuel-zoom.png
- :height: 340px
-.. |IMG_DATASOURCE_EXPANDF| image:: images/xmed-gui-datasource-expand-zoom.png
- :height: 340px
-
-+--------------------------+--------------------------+--------------------------+
-| |IMG_DATASOURCE_EXPLORE| | |IMG_DATASOURCE_MENUCON| | |IMG_DATASOURCE_EXPANDF| |
-+--------------------------+--------------------------+--------------------------+
-
-.. note:: En toute rigueur, le concept de *champ* dans le modèle MED
- désigne une itération donnée. Un ensemble d'itérations est désigné
- par le terme *série temporelle de champs*. Par abus de langage, et
- s'il n'y a pas ambiguité, on utilisera le nom du champ pour
- désigner à la fois le champs proprement dit ou la série temporelle
- à laquelle il appartient.
-
-Enfin, il est possible au niveau du dataspace de visualiser la forme
-générale du champ au moyen d'une carte scalaire affichée dans le
-viewer de SALOME. Pour cela, on sélectionne le pas de temps à
-visualiser et on utilise la commande "Visualize" |ICO_DATASOURCE_VIEW|
-disponible dans le menu contextuel associé:
-
-.. image:: images/xmed-gui-datasource-visualize-zoom.png
- :align: center
- :width: 800px
-
-.. note:: Cette représentation graphique a pour objectif le contrôle
- visuel rapide. Aussi, les fonctions du module VISU sont employées
- par défaut, mais il est possible de faire l'affichage des cartes
- scalaires au moyen du module PARAVIS (choix de préférence non
- implémenté pour le moment, mais techniquement réalisable).
-
-Exemple 2: Rassembler des champs issus de différentes sources
--------------------------------------------------------------
-
-.. note:: Cet exemple présente les fonctions:
-
- * fonction "Use in workspace"
- * fonction "Save"
-
-.. |ICO_DATASOURCE_USE| image:: images/ico_datasource_use.png
- :height: 16px
-.. |ICO_WORKSPACE_SAVE| image:: images/ico_workspace_save.png
- :height: 16px
-
-L'objectif est de récupérer des données issues de différents fichiers
-med, puis de les rassembler dans un même fichier en sortie.
-
-On commence par ajouter les sources de données med dans l'espace de
-données (dataspace). Dans l'exemple ci-dessous, l'espace de données
-contient deux sources de nom ``parametric_01.med`` et
-``smallmesh_varfiled.med``. La première source contient le maillage
-``Grid_80x80_01`` sur lequel est défini le champ ``StiffExp_01``. La
-deuxième source contient le maillage ``My2DMesh`` sur lequel sont
-définis deux champs de noms respectifs ``testfield1`` et
-``testfield2``:
-
-.. image:: images/xmed-userguide-example2-datasource.png
- :align: center
- :width: 800px
-
-Pour l'exemple, on souhaite rassembler les champs ``StiffExp_01`` et
-``testfield2`` dans un fichier de nom ``result.med``. La procédure
-consiste à importer les deux champs dans l'espace de travail
-(workspace), puis à sauvegarder l'espace de travail. Pour cela, on
-sélectionne les champs et on utilise la commande "Use in workspace"
-|ICO_DATASOURCE_USE| disponible dans le menu contextuel. Les deux
-champs sélectionnés apparaissent dans l'arborescence de l'espace de
-travail:
-
-.. image:: images/xmed-userguide-example2-workspace.png
- :align: center
- :width: 800px
-
-La sauvegarde de l'espace de travail est faite au moyen de la commande
-"Save workspace" |ICO_WORKSPACE_SAVE| disponible dans la barre
-d'outils du module. Une fenêtre de dialogue invite l'utilisateur à
-spécifier le nom du fichier de sauvegarde:
-
-.. image:: images/xmed-userguide-example2-workspace-save.png
- :align: center
- :width: 800px
-
-Ce fichier ``result.med`` peut ensuite être rechargé dans le module
-XMED (ou les modules VISU ou PARAVIS) pour vérifier la présence des
-champs sauvegardés.
-
-.. BUG: plantage à l'utilsation dans XMED d'un fichier rechargé
-.. (invalid mesh on field)
-
-.. _xmed.userguide.exemple3:
-
-Exemple 3: Appliquer une opération mathématique sur des champs
---------------------------------------------------------------
-
-.. note:: Cet exemple présente les fonctions:
-
- * exécution d'opérations mathématiques dans la console TUI
- * fonction "put" pour référencer un champ de travail dans la liste
- des champs persistant.
- * fonction "Visualize" depuis le TUI.
-
-L'usage le plus courant du module de manipulation de champs est
-d'exécuter des opérations mathématiques dont les opérandes sont des
-champs ou des composantes de ces champs.
-
-On se place dans une situation où les sources de données sont définies
-dans le "dataspace" (dans l'exemple ci-après, une série temporelle de
-nom ``Pulse``, contenant 10 pas de temps, définis sur un maillage de
-nom ``Grid_80x80``, le tout issu du datasource ``timeseries.med``).
-
-Comme vu précedemment, pour manoeuvrer un champ dans l'espace de
-travail, on sélectionne ce champ, puis on exécute la commande "Use in
-workspace" |ICO_DATASOURCE_USE| du menu contextuel. Dans le cas
-présent, un seul champ est sélectionné (contre deux dans l'exemple
-précédent) et la commande ouvre alors une fenêtre de dialogue qui
-permet de préciser les données sur lesquelles on souhaite
-effectivement travailler et comment on veut les manoeuvrer:
-
-.. image:: images/xmed-gui-datasource-useinworkspace-alias.png
- :align: center
- :width: 800px
-
-.. note:: En l'état actuel du développement, l'interface propose
- uniquement de définir le nom de la variable sous laquelle doit être
- manoeuvré le champ dans la console de travail (TUI). Dans une
- version ultérieure, il est prévue de pouvoir préciser la ou les
- composante du champs à utiliser et un groupe de maille pour définir
- une restriction géométrique. Inversement, il sera également
- possible de choisir une série temporelle complète pour faire des
- opérations globales sur l'ensemble des pas de temps.
-
-Aprés validation, le champ est placé dans l'arborescence du
-"workspace" et une variable de nom ``<alias>`` est créée
-automatiquement dans la console de travail pour désigner le
-champ. Dans cet exemple, ``<alias>`` vaut ``f3``, positionné ainsi par
-l'utilisateur pour rappeler que la variable correspond au pas de temps
-n°3:
-
-.. image:: images/xmed-gui-workspace.png
- :align: center
- :width: 800px
-
-La manipulation peut commencer. Dans l'exemple ci-dessous, on crée le
-champ ``r`` comme le résultat d'une transformation afine du champ
-``f3`` (multiplication du champ par le facteur 2.7 auquel on ajoute
-l'offset 5.2)::
-
- >>> r=2.7*f3+5.2
-
-On peut poursuivre la manipulation du champs avec une variété
-d'opérations qui sont détaillées dans les spécifications du module
-(cf. :ref:`Spécification des opérations<xmed-specifications>`):
-
- >>> r=f3/1000 # les valeurs de r sont celles du champ f3 réduites d'un facteur 1000
- >>> r=1/f3 # les valeurs de r sont les inverses des valeurs de f3
- >>> r=f3*f3 # les valeurs de r sont celles du champ f3 élevées au carré
- >>> r=pow(f3,2) # même résultat
- >>> r=abs(f3) # valeur absolue du champ f3
- >>> ...
-
-Les opérations peuvent utiliser plusieurs opérandes de type champs. Si
-``f4`` désigne le pas de temps n°4 du champ ``Pulse``, alors on peut
-calculer toute combinaison algébrique des deux champs::
-
- >>> r=f3+f4
- >>> r=f3-f4
- >>> r=f3/f4
- >>> r=f3*f4
-
-Avec au besoin l'utilisation de variables scalaires::
-
- >>> r=4*f3-f4/1000
- >>> ...
-
-Dans ces exemples, la variable ``r`` désigne un champ de travail qui
-contient le résultat de l'opération. Par défaut, ce champ de travail
-n'est pas référencé dans l'arborescence du workspace. Si on souhaite
-tout de même le référencer, par exemple pour qu'il soit pris en compte
-dans la sauvegarde, alors on tape la commande::
-
- >>> put(r)
-
-La fonction ``put`` a pour but de marquer le champ en argument comme
-persistent, puis de le ranger dans l'arborescence du "workspace" afin
-qu'il soit visible et sélectionnable. En effet, parmi tous les champs
-qui pourront être créés dans la console pendant la session de travail,
-tous n'ont pas besoin d'être sauvegardés. Certains sont même des
-variables temporaires qui servent à la construction des champs
-résultats finaux. C'est pourquoi, seuls les champs rangés dans
-l'arborescence du workspace sont enregistrés lors de la demande de
-sauvegarde du workspace.
-
-Les variables définies dans la console ont d'autres utilités. Tout
-d'abord, elles permettent d'imprimer les informations concernant le
-champ manoeuvré. Pour cela, on tape simplement le nom de la variable
-puis retour::
-
- >>> f3
- field name (id) = Pulse (3)
- mesh name (id) = Grid_80x80 (0)
- discretization = ON_NODES
- (iter, order) = (3,-1)
- data source = file:///home/gboulant/development/projets/salome/MEDOP/XMED/xmed/resources/datafiles/timeseries.med
-
-Elle peut également être utilisée comme argument des commandes de
-gestion disponibles dans l'interface textuelle (dont la liste
-détaillée est décrite à la section :ref:`Documentation de l'interface
-textuelle<xmed.userguide.tui>`). Par exemple, la fonction ``view``
-permet d'afficher la carte scalaire du champ dans le viewer::
-
- >>> view(f3)
-
-Donne:
-
-.. image:: images/xmed-gui-workspace-view.png
- :align: center
- :width: 800px
-
-.. note:: On remarquera ici qu'il est facile de comparer deux pas de
- temps d'un champ, par exemple en calculant la différence ``f3-f4``,
- puis en affichant un aperçu de la carte scalaire résultat au moyen
- de la fonction ``view``::
-
- >>> view(f3-f4)
-
-On peut enfin tout simplement afficher les données du champs par la
-commande ``print``::
-
- >>> print f3
- Data content :
- Tuple #0 : -0.6
- Tuple #1 : -0.1
- Tuple #2 : 0.4
- Tuple #3 : -0.1
- Tuple #4 : 0.4
- ...
- Tuple #6556 : 3.5
- Tuple #6557 : 3.3
- Tuple #6558 : 1.5
- Tuple #6559 : 0.3
- Tuple #6560 : 0.2
-
-Il est important de noter que les opérations entre champs ne peuvent
-être faites qu'entre champs définis sur le même maillage. Il s'agit là
-d'une spécification du modèle MED qui interdit d'envisager les
-opérations entre champs définis sur des maillages géométriquement
-différents. Techniquement, cela se traduit par l'obligation pour les
-objets informatique *champs* de partager le même objet informatique
-*maillage*.
-
-Dans l'hypothèse où on souhaite utiliser des champs définis sur des
-maillages différents, par exemple pour manoeuvrer les valeurs des
-champs à l'interface de deux maillages partageant une zone géométrique
-2D, il faut d'abord ramener tous les champs sur le même maillage de
-surface par une opération de projection.
-
-.. note:: Même si ceci est techniquement possible avec la bibliothèque
- MEDCoupling, cet type d'opération de projection n'est pas encore
- disponible dans le module de manipulation de champs (prévu en
- 2012).
-
-Un autre besoin plus classique est l'utilisation de champs définis sur
-des maillages géométriquement identiques, mais techniquement
-différents, par exemple lorsqu'ils sont chargés de fichiers med
-différents. Pour traiter ce cas de figure, la bibliothèque MEDCoupling
-prévoit une fonction de "Changement du maillage support", dont
-l'utilisation au niveau du module de manipulation de champs est
-illustrée dans :ref:`l'exemple 4<xmed.userguide.exemple4>` ci-après.
-
-.. _xmed.userguide.exemple4:
-
-Exemple 4: Comparer des champs issues de différentes sources
-------------------------------------------------------------
-
-.. note:: Cet exemple présente les fonctions:
-
- * Changement du maillage support "change underlying mesh"
-
-On se place ici dans le cas de figure où des champs ont été produits
-sur le même maillage, au sens géométrique, mais enregistrés dans des
-fichiers med différents. C'est le cas par exemple d'une étude
-paramétrique où plusieurs calculs sont effectués avec des variantes
-sur certains paramètres du modèle simulé, chaque calcul produisant un
-fichier med.
-
-Soit ``parametric_01.med`` et ``parametric_02.med`` deux fichiers med
-contenant les champs que l'on souhaite comparer, par exemple en
-calculant la différence des valeurs et en visualisant le résultat.
-
-Aprés le chargement des sources de données dans le module XMED,
-l'utilisateur se trouve en présence de deux maillages, au sens
-technique du terme cette fois-ci, c'est-à-dire que les champs sont
-associées à des objets informatiques maillage différents, bien que
-géométriquement identiques.
-
-Or, les fonctions de manipulation de champs ne permettent pas les
-opérations sur des champs dont les maillages supports sont différents
-(voir la remarque à la fin de :ref:`l'exemple
-3<xmed.userguide.exemple3>`).
-
-Pour résoudre ce cas de figure, le module de manipulation de champs
-met à disposition la fonction "Change underlying mesh" qui permet de
-remplacer le maillage support d'un champ par un autre à partir du
-moment où les deux maillages sont géométriquement identiques,
-c'est-à-dire que les noeuds ont les mêmes coordonnées spatiales.
-
-.. |ICO_DATASOURCE_CHG| image:: images/ico_datasource_changeUnderlyingMesh.png
- :height: 16px
-
-Dans l'exemple proposé, l'utilisateur sélectionne le premier pas de
-temps du champ ``StiffExp_01`` du "datasource" ``parametric_01.med``,
-puis l'importe dans l'espace de travail au moyen de la commande "Use
-in workspace" |ICO_DATASOURCE_USE|. Il sélectionne ensuite le premier
-pas de temps du champs ``StiffExp_02`` du "datasource"
-``parametric_02.med``, mais l'importe dans l'espace de travail au
-moyen de la commande "Change underlying mesh" |ICO_DATASOURCE_CHG|. La
-fenêtre de dialogue ci-dessous s'affiche et invite l'utilisateur à
-choisir le nouveau maillage support par sélection dans l'arborescence
-du "dataspace":
-
-.. image:: images/xmed-gui-datasource-changeUnderlyingMesh.png
- :align: center
-
-Dans cet exemple, on sélectionne le maillage ``Grid_80x80_01`` support
-du champ ``StiffExp_01``, avec lequel on souhaite faire la
-comparaison. Après validation, l'arborescence du workspace contient le
-champ ``StiffExp_02`` défini sur le maillage ``Grid_80x80_01``:
-
-.. image:: images/xmed-gui-datasource-changeUnderlyingMesh_wsview.png
- :align: center
-
-.. note:: La fonction "Change underlying mesh" ne modifie pas le champ
- sélectionné dans le "dataspace" (principe de base de fonctionnement
- du dataspace), mais crée une copie du champ dans l'espace de travail
- pour ensuite remplacer le maillage support. D'où le nom par défaut
- pour le champ ``dup(<nom du champ sélectionné>)`` (dup pour
- "duplicate").
-
-Il reste à associer une variable à ce champ pour le manipuler dans la
-console. Ceci peut être fait au moyen de la commande "Use in console",
-disponible dans le menu contextuel du workspace.
-
-En définitif, si ``f1`` désigne le champ issu du datasource
-``parametric_01.med`` et ``f2`` le champ issu du datasource
-``parametric_02.med`` par la procédure décrite ci-dessus, alors la
-comparaison des deux grandeurs peut être faite comme pour le cas de
-:ref:`l'exemple 3<xmed.userguide.exemple3>`::
-
- >>> r=f1-f2
- >>> view(r)
-
-.. note:: En remarque générale sur cet exemple, il convient de noter
- les points suivants:
-
- * l'égalité géométrique de deux maillages est établie à une marge
- d'erreur prés qu'il est possible de définir techniquement, mais
- qui n'est pas ajustable au niveau de l'interface du module de
- manipulation de champs. Elle est fixée à une valeur standard qui
- permet de traiter la plupart des cas utilisateur. On verra à
- l'usage s'il est nécessaire de remonter ce paramètre au niveau de
- l'interface.
- * L'utilisateur doit faire la démande explicite de changer le
- maillage support d'un champ, en prévision de la comparaison de
- champs issus de datasource différentes. Il s'agit là d'un choix
- fonctionnel délibéré pour que l'utilisateur garde trace des
- modifications faites sur les données (pas de modification
- automatiques à l'insu de l'utilisateur, même sous prétexte
- d'amélioration de l'ergonomie).
-
-
-Exemple 5: Créer un champ sur un domaine spatial
-------------------------------------------------
-
-.. note:: Cet exemple présente les fonctions:
-
- * initialisation par une fonction de la position spatiale
- * initialisation sur un groupe de maille
-
-Le domaine géométrique de définition du champs à créer est spécifié
-ici par la donnée d'un groupe de mailles. Ce cas d'usage est
-typiquement prévu pour produire les conditions de chargement initial
-d'une structure, par exemple en définissant un champ sur une surface
-de la géométrie, identifiée par un nom de groupe de mailles.
-
-.. warning:: DEVELOPPEMENT EN COURS
-
-Exemple 6: Extraire une partie d'un champ
------------------------------------------
-
-.. note:: Cet exemple présente les fonctions:
-
- * extraire une composante (ou un sous-ensemble des composantes)
- * extraire un domaine géométrique (valeurs sur un groupe de maille)
- * extraire un ou plusieurs pas de temps.
-
-.. warning:: DEVELOPPEMENT EN COURS
-
- On doit illustrer ici les fonctions de restriction, qui
- permettraient de récupérer certaines composantes uniquement. Le
- principe est qu'on crée un nouveau champ qui est une restriction du
- champ argument à une liste de composantes à spécifier (utiliser la
- fonction __call__ des fieldproxy).
-
-Pour l'extraction des pas de temps, on peut se ramener au cas de
-l'exemple 2 avec une seule source de donnée.
-
-Exemple 7: Créer un champ à partir d'une image to[mp]ographique
----------------------------------------------------------------
-
-.. note:: Cet exemple présente les fonctions:
-
- * Création d'un champ sans datasource (ni maillage, ni champs), à
- partir d'un fichier image
-
-En tomographie ou en topographie, les appareils de mesure produisent
-des images qui représentent une grandeur physique en niveaux de gris
-sur un plan de coupe donné. L'image ci-dessous représente par exemple
-une vue interne du corps humain faite par IRM:
-
-.. image:: images/xmed-irm.png
- :align: center
- :width: 600px
-
-Cette image est un ensemble de pixels organisés sur une grille
-cartesienne. Elle peut donc être modélisée sous la forme d'un champ
-scalaire dont les valeurs sont définies aux cellules d'un maillage
-réglés de même taille que l'image (en nombre de pixels):
-
-.. image:: images/xmed-irm-field.png
- :align: center
- :width: 600px
-
-Le module de manipulation de champ fournit un utilitaire appelé
-``image2med.py`` qui permet d'appliquer ce principe à la conversion
-d'un fichier image en fichier med contenant la représentation de
-l'image sous forme d'un champ scalaire (seul le niveau de gris est
-conservé)::
-
- $ <xmed_root_dir>/bin/salome/xmed/image2med.py -i myimage.png -m myfield.med
-
-.. |ICO_IMAGESOURCE| image:: images/ico_imagesource.png
- :height: 16px
-
-Cette opération de conversion peut être faite automatiquement dans
-l'interface graphique du module au moyen de la commande "Add Image
-Source" |ICO_IMAGESOURCE| disponible dans la barre d'outils. Cette
-commande ouvre la fenêtre suivante pour inviter l'utilisateur à
-choisir un fichier image:
-
-.. image:: images/medop_image2med_dialog.png
- :align: center
-
-Le nom du fichier med résultat est proposé par défaut (changement de
-l'extention en ``*.med``) mais il peut être modifié. Enfin, on peut
-demander le chargement automatique du fichier med produit pour ajout
-dans l'espace de donnée. Les champs peuvent alors être manipulés comme
-dans les cas d'utilisation standard.
-
-Par exemple, l'image ci-dessous affiche le résultat de la différence
-entre deux images, ajoutée à l'image de référence: si i1 et i2
-désignent les champs créés à partir des deux images, on représente ``r
-= i1 + 5*(i2-i1)`` où le facteur 5 est arbitraire et sert à amplifier
-la zone d'intérêt (en haut de l'oeil gauche):
-
-.. image:: images/xmed-irm-diff.png
- :align: center
- :width: 600px
-
-L'exemple ci-dessous est le résultat du chargement d'une image
-tomographique issue du projet MAP (Charles Toulemonde,
-EDF/R&D/MMC). L'image tomographique:
-
-.. image:: images/champ_altitude_MAP.png
- :align: center
- :width: 600px
-
-Le résultat du chargement:
-
-.. image:: images/medop_image2med_tomographie.png
- :align: center
- :width: 800px
-
-Exemple 8: Continuer l'analyse dans PARAVIS
--------------------------------------------
-
-.. note:: Cet exemple présente les fonctions:
-
- * Export de champs vers le module PARAVIS.
-
-Les possibilités de représentation graphique des champs fournies par
-le module MED ont pour seul objectif le contrôle visuel rapide. Par
-défaut, le viewer de VISU est employé.
-
-Pour une analyse plus détaillées des champs, il est nécessaire de
-poursuivre le travail dans PARAVIS. Le module de manipulation de
-champs offre une fonction qui simplifie ce passage, en faisant le
-chargement automatique dans PARAVIS et en proposant une visualisation
-par défaut (carte de champs scalaire).
-
-Pour cela, il faut sélectionner dans l'espace de travail les champs à
-exporter, puis déclencher la fonction d'export depuis le menu
-contextuel associé:
-
-.. image:: images/medop_exportparavis.png
- :align: center
-
-Les champs sélectionnés sont regroupés dans une entrée MED du
-navigateur PARAVIS, et le premier champ est affiché sous forme de
-carte de champ:
-
-.. image:: images/medop_exportparavis_result.png
- :align: center
- :width: 800px
-
-.. note:: La fonction d'export est une fonction de confort. La même
- opération peut être faite manuellement en procédant d'abord à
- l'enregistrement des champs sous forme de fichier MED, puis en
- chargeant le fichier généré dans le module PARAVIS pour
- visualisation.
-
-.. _xmed.userguide.tui:
-
-Utilisation de l'interface textuelle du moduel MED (TUI)
-========================================================
-
-Toutes les opérations menées au moyen de l'interface graphique peuvent
-être réalisées (avec plus ou moins de facilité) avec l'interface
-textuelle. Le module de manipulation de champs peut même être utilisé
-exclusivement en mode texte. Pour cela, on lance la commande::
-
- $ <path/to/appli>/medop.sh
-
-Cette commande ouvre une console de commandes ``medop>``. Un fichier
-med peut être chargé et travaillé, par exemple pour créer des champs à
-partir des données du fichier.
-
-Que l'on soit en mode texte pur ou en mode graphique, un séquence de
-travail type dans la console peut ressembler au jeu d'instructions
-suivantes::
-
- >>> load("/path/to/mydata.med")
- >>> la
- id=0 name = testfield1
- id=1 name = testfield2
- >>> f1=get(0)
- >>> f2=get(1)
- >>> ls
- f1 (id=0, name=testfield1)
- f2 (id=1, name=testfield2)
- >>> r=f1+f2
- >>> ls
- f1 (id=0, name=testfield1)
- f2 (id=1, name=testfield2)
- r (id=2, name=testfield1+testfield2)
- >>> r.update(name="toto")
- >>> ls
- f1 (id=0, name=testfield1)
- f2 (id=1, name=testfield2)
- r (id=2, name=toto)
- >>> put(r)
- >>> save("result.med")
-
-Les commandes principales sont:
-
-* ``load``: charge un fichier med dans la base de données (utile
- uniquement en mode texte pur)::
-
- >>> load("/path/to/datafile.med")
-
-* ``la``: affiche la liste de tous les champs chargés en base de données ("list all")
-* ``get``: définit un champ dans l'espace de travail à partir de son
- identifiant (utile plutôt en mode texte pur car l'interface
- graphique permet de faire cette opération par sélection d'un champ
- dans le dataspace)::
-
- >>> f=get(fieldId)
-
-* ``ls``: affiche la liste des champs présent dans l'espace de travail ("list")
-* ``put``: met un champ en référence dans l'*espace de gestion*::
-
- >>> put(f)
-
-* ``save``: sauvegarde tous les champs référencés dans l'espace de
- gestion dans un fichier med::
-
- >>> save("/path/to/resultfile.med")
-
-.. note:: On peut faire à ce stade plusieurs remarques:
-
- * la commande ``load`` charge uniquement les méta-informations
- décrivant les maillage et les champs (noms, type de
- discrétisation, liste des pas de temps). Les maillages et les
- valeurs physiques des champs sont chargées ultérieurement (et
- automatiquement) dés lors qu'elles sont requises par une
- opération. Dans tous les cas, les données med (méta-informations
- et valeurs) sont physiquement stockées au niveau de l'espace
- *base de données*.
- * la commande ``get`` définit en réalité un *manipulateur de champ*
- dans l'espace de travail, c'est-à-dire une variable qui fait la
- liaison avec le champ physique hébergé dans la base de
- données. Les données physiques ne circulent jamais entre les
- espaces, mais restent centralisées au niveau de la base de
- données.
-
-Les commandes TUI suivantes nécessitent de travailler dans
-l'environnement graphique:
-
-* ``visu``: afficher une carte de champ pour contrôle visuel rapide
- (pas de paramettrage possible)
-
- >>> view(f)
-
-
--- /dev/null
+This directory contains small use cases that are used in the userguide documentation
+to illustrate the basic usage of med coupling from the python programming interface.
--- /dev/null
+# You first have to set the shell environement for medcoupling
+
+all:
+ python addition.py
+ python operations.py
+
+clean:
+ rm -f addition.med scaling.med addition_01.med addition_02.med *~
--- /dev/null
+from MEDLoader import MEDLoader, ON_NODES
+
+medfilename = "timeseries.med" # med source filename
+meshname = "Grid_80x80" # name of the support mesh
+dimrestriction = 0 # 0=no restriction
+fieldname = "Pulse" # name of the field series
+
+# Load the support mesh
+mesh = MEDLoader.ReadUMeshFromFile(medfilename,meshname,dimrestriction)
+
+# Load the field at timestamps 3
+iteration, order = (3,-1) # timestamps to consider
+p3=MEDLoader.ReadField(ON_NODES,medfilename,meshname,dimrestriction,
+ fieldname,iteration,order)
+p3.setMesh(mesh)
+
+# Load the field at timestamps 4
+iteration, order = (4,-1) # timestamps to consider
+p4=MEDLoader.ReadField(ON_NODES,medfilename,meshname,dimrestriction,
+ fieldname,iteration,order)
+p4.setMesh(mesh)
+
+result = p3+p4
+result.setName("p3+p4")
+
+outfilename = "addition.med"
+MEDLoader.WriteField(outfilename,result,True)
+MEDLoader.WriteField(outfilename,p3,False)
+MEDLoader.WriteField(outfilename,p4,False)
+
--- /dev/null
+#!/usr/bin/env python
+# -*- coding: iso-8859-1 -*-
+
+# This script illustrates the basic usage of MEDCoupling and MEDLoader
+# to read fields in a med file and make basics operation on the values
+# (gboulant - 11/07/2011)
+
+
+# _T1A
+medfilename = "timeseries.med" # med source filename
+meshName = "Grid_80x80" # name of the support mesh
+dimrestriction = 0 # 0=no restriction
+fieldName = "Pulse" # name of the field series
+# _T1B
+
+# ==============================================================
+# Make a scaling. This does not require the loading of the mesh
+# _T2A
+from MEDLoader import MEDLoader, ON_NODES
+
+iteration, order = (3,-1) # timestamps to consider
+field=MEDLoader.ReadField(ON_NODES,
+ medfilename, meshName, dimrestriction,
+ fieldName, iteration, order)
+
+field.applyFunc("f*3")
+outfilename = "scaling.med"
+MEDLoader.WriteField(outfilename,field,True)
+# _T2B
+
+
+# ==============================================================
+# Make an addition. This requires to load first the mesh
+
+# _T3A
+# Load the support mesh
+mesh = MEDLoader.ReadUMeshFromFile(medfilename, meshName, dimrestriction)
+
+# Load the field at timestamps 3
+iteration, order = (3,-1)
+p3=MEDLoader.ReadField(ON_NODES,
+ medfilename,meshName,dimrestriction,
+ fieldName,iteration,order)
+# Associate the mesh
+p3.setMesh(mesh)
+
+# Load the field at timestamps 4
+iteration, order = (4,-1)
+p4=MEDLoader.ReadField(ON_NODES,
+ medfilename, meshName, dimrestriction,
+ fieldName, iteration, order)
+# Associate the mesh
+p4.setMesh(mesh)
+
+# Make the addition
+result = p3+p4
+result.setName("p3+p4")
+
+# We can finally save the result together with the operandes fields
+outfilename = "addition.med"
+MEDLoader.WriteField(outfilename,result,True)
+MEDLoader.WriteField(outfilename,p3,False)
+MEDLoader.WriteField(outfilename,p4,False)
+# _T3B
+
+# ==============================================================
+# Make an addition. A different way that loads fields all at once
+mesh = MEDLoader.ReadUMeshFromFile(medfilename, meshName, dimrestriction)
+timestamps = [(3,-1),(4,-1)]
+p3,p4 = MEDLoader.ReadFieldsOnSameMesh(ON_NODES,medfilename,
+ meshName,dimrestriction,
+ fieldName,timestamps)
+p3.setMesh(mesh)
+p4.setMesh(mesh)
+result = p3+p4
+result.setName("p3+p4")
+
+outfilename = "addition_01.med"
+MEDLoader.WriteField(outfilename,result,True)
--- /dev/null
+Note that this test scripts required the PIL python library
+
--- /dev/null
+# This configuration comes in extension with the standard configuration
+# defined by the file envlight.sh for image processing and math
+# processing.
+
+#------ Python Imaging Library ------
+PILDIR=/opt/programs/salome/workspace/prerequisites/install/Pil-117-py266-tcl859-tk859
+export PATH=${PILDIR}/bin:$PATH
+export PYTHONPATH=${PILDIR}/lib/python2.6/site-packages:${PYTHONPATH}
+
+#------ Numpy ------
+NUMPY_DIR=/opt/programs/salome/workspace/prerequisites/install/Numpy-151-py266-la331
+export PATH=${NUMPY_DIR}/bin:${PATH}
+export PYTHONPATH=${NUMPY_DIR}/lib/python2.6/site-packages:${PYTHONPATH}
+
+# ------ Scipy ------
+SCIPY_DIR=/opt/programs/salome/workspace/prerequisites/install/Scipy-090-py266-la331-sw204-nu151
+export PYTHONPATH=${SCIPY_DIR}/lib/python2.6/site-packages:${PYTHONPATH}
+
+# WARN: Matplot, sip and pyqt are requires for the plotter used in
+# lagrange.py (could be optional)
+
+# ------ Matplot ----
+MATPLOT_DIR=/opt/programs/salome/workspace/prerequisites/install/Matplotlib-110-py266-set06c11-num151
+export PYTHONPATH=${MATPLOT_DIR}/lib/python2.6/site-packages:${PYTHONPATH}
+
+#------ sip ------
+SIPDIR=/opt/programs/salome/workspace/prerequisites/install/Sip-4132-py266
+export PATH=${SIPDIR}/bin:${PATH}
+export PYTHONPATH=${SIPDIR}/lib/python2.6/site-packages:${PYTHONPATH}
+export LD_LIBRARY_PATH=${SIPDIR}/lib/python2.6/site-packages:${LD_LIBRARY_PATH}
+
+PYQTDIR=/opt/programs/salome/workspace/prerequisites/install/Pyqt-491p1-py266-qt463p2-sip4132
+#export PYQT_SIPS=${PYQTDIR}/share/sip
+#export PYUIC=${PYQTDIR}/bin/pyuic4
+export PYTHONPATH=${PYQTDIR}/lib/python2.6/site-packages:${PYTHONPATH}
+export PATH=${PYQTDIR}/bin:${PATH}
--- /dev/null
+P5
+# CREATOR: GIMP PNM Filter Version 1.1
+6 6
+255
+¿¿ÿÿ¿¿¿¿ÿÿ¿¿ÿÿ\ 4\aÿÿÿÿ\ 3\ 4ÿÿ¿¿ÿÿ¿¿¿¿ÿÿ¿¿
\ No newline at end of file
--- /dev/null
+# -*- coding: iso-8859-1 -*-
+# This illustrates how to make a mesh partition using the value of a
+# field defined on this mesh (for example to extract the cells where
+# the field takes a value greater than a threshold L.
+# (Anthony Geay, nov. 2012)
+
+# WRN: this use case does not require a med input file because the
+# data (mesh and field) to work with are created from scratch.
+
+from MEDCoupling import *
+
+# =======================================================
+# Creation of the input data (mesh and field)
+# =======================================================
+#
+# We prepare the input field from scratch instead of load it from a
+# file, but there is no difference
+from MEDCouplingDataForTest import MEDCouplingDataForTest
+m3D=MEDCouplingDataForTest.build3DTargetMesh_1()
+m3D.setName("m3D")
+a=DataArrayDouble.New([1.,1.,1.,-1.,-1.,-1.,-1.,-1.])
+field=MEDCouplingFieldDouble.New(ON_CELLS,ONE_TIME)
+field.setMesh(m3D)
+field.setArray(a)
+field.checkCoherency()
+field.setName("f")
+
+# Save the field (and associated mesh)
+from MEDLoader import MEDLoader
+MEDLoader.WriteField("partition_input.med",field,True)
+
+# =======================================================
+# Determine the border skin mesh
+# =======================================================
+#
+# We have to determine the 2D mesh that delimits the volume where the
+# field is greater than a threshold L from the volume where the field
+# is lower than this threshold (in this example L=0).
+#
+# WRN: This works in SALOME V660 only
+#
+# _T1A
+L=0.
+arr = field.getArray()
+ids = arr.getIdsInRange(L,1e300)
+m3DSub = field.getMesh()[ids]
+skin = m3DSub.computeSkin()
+MEDLoader.WriteUMesh("partition_skin.med",skin,True);
+# _T1B
+
+# =======================================================
+# Compare to the result in SALOME V650
+# =======================================================
+# SALOME V650 requires a more complicated syntax.
+m2D,desc,descI,revDesc,revDescI=m3DSub.buildDescendingConnectivity()
+numberOf3DVolSharing=revDescI.deltaShiftIndex()
+ids2D=numberOf3DVolSharing.getIdsEqual(1)
+skin_V650=m2D[ids2D]
+# We can check if the two skins are identical
+print "Are two meshes equal between V660 and V650 ?",skin.isEqual(skin_V650,1e-12)
--- /dev/null
+#!/usr/bin/env python
+
+
+class Function:
+ def __init__(self, **kwargs):
+ self.kwargs = kwargs
+
+ def function(self, x, **kwargs):
+ # This should be implemented in a derived class
+ raise Runtime("function is not implemented yet")
+
+ def __call__(self, x):
+ # The argument can be a scalar or a list, we have to check
+ # that first.
+ if isIterable(x):
+ y = map(self,x)
+ else:
+ y = self.function(x, **self.kwargs)
+ return y
+
+def isIterable(x):
+ """
+ This returns True if the parameter is an iterable, a list or an
+ array, or any collection object.
+ """
+ try:
+ len(x)
+ return True
+ except TypeError, e:
+ return False
+
+#
+# =====================================================
+# Implementation of standard functions. All function are normalized
+# function: the xrange is [0,1], the yrange is [0,1]
+import numpy
+from scipy.constants import pi
+
+# Note that in this situation, the create another constructor because
+# the parameters can be deduced from one single parameter xlimit. The
+# constructor must create a kwargs dictionary that map the arguments
+# of the method "function"
+class FuncConique(Function):
+ def __init__(self,xlimit):
+ a = 1./(xlimit*xlimit-2*xlimit+1)
+ b = -2.*a
+ c = a
+ d = 1/(xlimit*xlimit)
+ # We call the super constructor to redefine the kwarg
+ # attribute, so that it fits with the arguments of the method
+ # "function":
+ Function.__init__(self,xlimit=xlimit,a=a,b=b,c=c,d=d)
+ # NOTE: Instead of calling the super constructor, we could
+ # redefine directly the kwargs attribute:
+ #self.kwargs = {"xlimit":xlimit,
+ # "a":a, "b":b,
+ # "c":c, "d":d}
+
+ def function(self,x,xlimit,a,b,c,d):
+ if x<xlimit:
+ y=d*x*x
+ else:
+ y=a*x*x+b*x+c
+ return y
+
+
+class FuncChapeau(Function):
+ def function(self,x,xlimit):
+ if x<xlimit:
+ y=x/xlimit
+ else:
+ y=(x-1)/(xlimit-1)
+ return y
+
+class FuncStiffExp(Function):
+ """
+ xlimit : the x position of the top of the function
+ stiffness : the higher it is, the stiffer the function is
+ """
+ def function(self,x,xlimit,stiffness):
+ if x<xlimit:
+ y=numpy.exp(stiffness*(x-xlimit))
+ else:
+ y=numpy.exp(-stiffness*(x-xlimit))
+ return y
+
+class FuncCosinus(Function):
+ def __init__(self,nbPeriods):
+ # The pulsation w must be choosen so that w*xmax=n*2pi where
+ # xmax=1 and n is an integer that corresponds to the number of
+ # oscilations on the xrange [0,xmax].
+ w=nbPeriods*2*pi
+ Function.__init__(self,w=w)
+
+ def function(self,x,w):
+ y=numpy.cos(w*x)
+ return y
+
+class FuncStiffPulse(Function):
+ def __init__(self,xlimit, stiffness, nbPeriods):
+ self.stiffexp=FuncStiffExp(xlimit=xlimit,stiffness=stiffness)
+ self.cosinus=FuncCosinus(nbPeriods=nbPeriods)
+ Function.__init__(self)
+
+ def function(self,x):
+ y=self.stiffexp(x)*numpy.abs(self.cosinus(x))
+ return y
+
+class FuncHeaviside(Function):
+ def function(self,x,xlimit):
+ if x<xlimit:
+ y=0
+ else:
+ y=1
+ return y
+
+class FuncPorte(Function):
+ def function(self,x,xinf,xsup):
+ if x<xinf or x>xsup:
+ y=0
+ else:
+ y=1
+ return y
+
+import lagrange
+class FuncLagrange(Function):
+ def __init__(self,points):
+ """
+ @points : a dictionary whose keys are x values and values are
+ y values to be considered as fixed points for interpolation.
+ """
+ Function.__init__(self)
+ self.polynom = lagrange.lagrange(points)
+
+ def function(self,x):
+ return self.polynom(x)
+
+#
+# =====================================================
+# Unit tests functions
+# =====================================================
+#
+class MyFunction(Function):
+ def function(self,x,a,b):
+ y=a*x+b
+ return y
+
+def TEST_Function():
+ # The parameters of the constructor of MyFunction must be
+ # consistent with the kwargs parameters of the method function of
+ # the class MyFunction (it must map exactly).
+ f=MyFunction(a=3.,b=7.)
+
+ x=2
+ y_ref = 3.*x+7.
+ y_res = f(x)
+ print y_ref
+ print y_res
+ if y_ref != y_res:
+ print "ERR"
+ else:
+ print "OK"
+
+def TEST_Function_withIterable():
+ f=MyFunction(a=3.,b=1.)
+
+ arrX = [0., 1., 2., 3.]
+ arrY = f(arrX)
+
+ arrY_ref = [1., 4., 7., 10.]
+ print "arrY res =%s"%arrY
+ print "arrY ref =%s"%arrY_ref
+
+def TEST_FuncConique():
+ f=FuncConique(xlimit=0.3)
+ from plotter import plot
+ plot(f)
+
+def TEST_FuncChapeau():
+ f=FuncChapeau(xlimit=0.3)
+ from plotter import plot
+ plot(f)
+
+def TEST_FuncStiffExp():
+ f=FuncStiffExp(xlimit=0.3,stiffness=20.)
+ from plotter import plot
+ plot(f)
+
+def TEST_FuncCosinus():
+ f=FuncCosinus(nbPeriods=20)
+ from plotter import plot
+ plot(f, step=0.001)
+
+def TEST_FuncStiffPulse():
+ f=FuncStiffPulse(xlimit=0.3,stiffness=50,nbPeriods=15)
+ from plotter import plot
+ plot(f, step=0.001)
+
+def TEST_FuncHeaviside():
+ f=FuncHeaviside(xlimit=0.3)
+ from plotter import plot
+ plot(f)
+
+def TEST_FuncPorte():
+ f=FuncPorte(xinf=0.3,xsup=0.4)
+ from plotter import plot
+ plot(f)
+
+def TEST_customize_01():
+ f=FuncStiffPulse(xlimit=0.3,stiffness=40,nbPeriods=20)
+
+ # One can customize the final function as follow (in this example,
+ # a linear transform)
+ def myfunc(x):
+ y=5*f(x)+2
+ return y
+
+ from plotter import plot
+ plot(myfunc, step=0.001)
+
+def TEST_customize_02():
+ f=FuncHeaviside(xlimit=0.3)
+
+ # One can customize the final function as follow (in this example,
+ # reverse of heaviside)
+ def myfunc(x):
+ y=1-f(x)
+ return y
+
+ from plotter import plot
+ plot(myfunc)
+
+def TEST_FuncLagrange():
+ points = {0.:5, 0.2:10, 0.9:10, 0.6:21, 1:8}
+ f=FuncLagrange(points)
+ from plotter import plot
+ plot(f)
+
+if __name__ == "__main__":
+ TEST_Function()
+ TEST_Function_withIterable()
+ #TEST_FuncConique()
+ #TEST_FuncChapeau()
+ #TEST_FuncStiffExp()
+ #TEST_FuncCosinus()
+ #TEST_FuncStiffPulse()
+ #TEST_FuncHeaviside()
+ #TEST_FuncPorte()
+ #TEST_customize_01()
+ #TEST_customize_02()
+ #TEST_FuncLagrange()
--- /dev/null
+#!/usr/bin/env python
+import scipy
+def lagrange(points):
+ '''
+ This returns a polynom that fits the points specified in the input
+ dictionary (lagrange interpolation). In this dictionary, the keys
+ are x value and the values are y corresponding values
+ (i.e. y=polynom(x)). The polynom is a scipy polynom and then is a
+ callable (can be used as a function).
+ '''
+ tmp = scipy.poly1d([0])
+ result=scipy.poly1d([0])
+
+ for i in points.keys():
+ numerator=scipy.poly1d([1])
+ denom = 1.0
+ for j in points.keys():
+ if (i != j):
+ tmp = scipy.poly1d([1,-j])
+ numerator = numerator * tmp
+ denom = denom * (i - j)
+ tmp = (numerator/denom) * points.get(i)
+ result = result + tmp
+
+ return result
+
+def points_usingfunction(arrX,function):
+ points={}
+ for x in arrX:
+ points[x] = function(x)
+ return points
+
+def points_usingarray(arrX,arrY):
+ points={}
+ for i in range(len(arrX)):
+ x=arrX[i]
+ y=arrY[i]
+ points[x] = y
+ return points
+
+def sortdict(points):
+ # Sort this dictionary by keys and returns 2 lists, the list of X
+ # and the list of Y, the whole ordered by X
+ keys = points.keys()
+ keys.sort()
+ return keys, [points[key] for key in keys]
+
+import pylab
+import numpy
+def plot(function, start=0., stop=1., step=0.01):
+ """
+ The parameter function must be a callable.
+ """
+ arrX=numpy.arange(start, stop, step, dtype='float64')
+ # function is a callable
+ arrY=map(function,arrX)
+ pylab.plot(arrX, arrY)
+ pylab.show()
+
+
+# ---
+# The points does not need to be ordered by x values
+def TEST_lagrange_01():
+ input = {0.:5, 0.2:10, 0.9:10, 0.6:21, 1:8}
+ polynom = lagrange(input)
+ print polynom
+ plot(function=polynom, start=0., stop=1., step=0.001)
+
+def TEST_lagrange_02():
+ input = {0.:0., 0.5:1., 1.:0.}
+ polynom = lagrange(input)
+ print polynom
+ plot(function=polynom, start=0., stop=1., step=0.001)
+
+# ---
+# One can create the input dictionary from arrays
+def TEST_lagrange_usingarrays_01():
+ arrX = [0., 0.2, 0.9, 0.6, 1]
+ arrY = [5, 10, 10, 21, 8]
+ input = points_usingarray(arrX,arrY)
+ polynom = lagrange(input)
+ print polynom
+ plot(function=polynom, start=0., stop=1., step=0.001)
+
+# Another example using numpy
+def TEST_lagrange_usingarrays_02():
+ arrX=numpy.arange(start=0., stop=1., step=0.1, dtype='float64')
+ arrY=numpy.zeros(len(arrX), dtype='float64')
+ arrY[3]=2
+ input = points_usingarray(arrX,arrY)
+ polynom = lagrange(input)
+ print polynom
+ plot(function=polynom, start=0., stop=1., step=0.001)
+
+# ---
+# One can create the input dictionary from a function applied to an
+# array of X values
+
+# simple method for mathematical functions
+def TEST_lagrange_usingfunction_01():
+ arrX=numpy.arange(start=0., stop=1., step=0.1, dtype='float64')
+ arrY=numpy.cos(10*arrX)
+ input = points_usingarray(arrX,arrY)
+ polynom = lagrange(input)
+ print polynom
+ plot(function=polynom, start=0., stop=1., step=0.001)
+
+# General method
+xlimit=0.8
+def chapeau(x):
+ if x<xlimit:
+ y=x
+ else:
+ y=2*xlimit-x
+ return y
+
+def TEST_lagrange_usingfunction_01():
+ arrX=numpy.arange(start=0., stop=1., step=0.1, dtype='float64')
+ input = points_usingfunction(arrX,chapeau)
+ polynom = lagrange(input)
+ print polynom
+ plot(function=polynom, start=0., stop=1., step=0.001)
+
+
+if __name__ == "__main__":
+ #TEST_lagrange_01()
+ TEST_lagrange_02()
+ #TEST_lagrange_usingarrays_01()
+ #TEST_lagrange_usingarrays_02()
+ #TEST_lagrange_usingfunction_01()
+ #TEST_lagrange_usingfunction_01()
--- /dev/null
+#!/usr/bin/env python
+
+import pylab
+import numpy
+def plot(function, start=0., stop=1., step=0.01):
+ """
+ The parameter function must be a callable.
+ """
+ arrX=numpy.arange(start, stop, step, dtype='float64')
+ # function is a callable
+ arrY=map(function,arrX)
+ pylab.plot(arrX, arrY)
+ pylab.show()
--- /dev/null
+import CORBA
+import PARAVIS_Gen_idl
+import SALOME_ContainerManager_idl
+
+from SALOME_NamingServicePy import SALOME_NamingServicePy_i
+
+from MEDCouplingCorba import *
+
+def createALocalMesh():
+ targetCoords=[ 0., 0., 0., 50., 0., 0. , 200., 0., 0. , 0., 50., 0., 50., 50., 0. , 200., 50., 0., 0., 200., 0., 50., 200., 0. , 200., 200., 0. ,
+ 0., 0., 50., 50., 0., 50. , 200., 0., 50. , 0., 50., 50., 50., 50., 50. , 200., 50., 50., 0., 200., 50., 50., 200., 50. , 200., 200., 50. ,
+ 0., 0., 200., 50., 0., 200. , 200., 0., 200. , 0., 50., 200., 50., 50., 200. , 200., 50., 200., 0., 200., 200., 50., 200., 200. , 200., 200., 200. ];
+ targetConn=[0,1,4,3,9,10,13,12, 1,2,5,4,10,11,14,13, 3,4,7,6,12,13,16,15, 4,5,8,7,13,14,17,16,
+ 9,10,13,12,18,19,22,21, 10,11,14,13,19,20,23,22, 12,13,16,15,21,22,25,24, 13,14,17,16,22,23,26,25];
+ targetMesh=MEDCouplingUMesh.New();
+ targetMesh.setMeshDimension(3);
+ targetMesh.setName("MyMesh3D");
+ targetMesh.setDescription("build3DMesh");
+ targetMesh.allocateCells(12);
+ for i in xrange(8):
+ targetMesh.insertNextCell(NORM_HEXA8,8,targetConn[8*i:8*(i+1)]);
+ pass
+ targetMesh.finishInsertingCells();
+ myCoords=DataArrayDouble.New();
+ myCoords.setValues(targetCoords,27,3);
+ targetMesh.setCoords(myCoords)
+ myCoords.setName("check in case")
+ return targetMesh;
+
+def createALocalField1():
+ m=createALocalMesh()
+ field=MEDCouplingFieldDouble.New(ON_CELLS,ONE_TIME)
+ field.setMesh(m)
+ da=DataArrayDouble.New()
+ da.setValues([1.,11.,101.,1001., 2.,12.,102.,1002., 3.,13.,103.,1003., 4.,14.,104.,1004., 5.,15.,105.,1005., 6.,16.,106.,1006., 7.,17.,107.,1007., 8.,18.,108.,1008.,],8,4)
+ field.setArray(da)
+ field.setName("vitoo")
+ field.setTime(4.5,3,4)
+ return field
+
+def createALocalField2():
+ m=createALocalMesh()
+ field=MEDCouplingFieldDouble.New(ON_NODES,ONE_TIME)
+ field.setMesh(m)
+ da=DataArrayDouble.New()
+ da.setValues([float(3*i) for i in xrange(27)],27,1)
+ field.setArray(da)
+ field.setName("vitooNode")
+ field.setTime(4.7,9,14)
+ return field
+
+def createALocalMultiField3():
+ fName="FieldOverTimeCorba"
+ m=createALocalMesh()
+ nbOfFields=100
+ fs=nbOfFields*[None]
+ for i in xrange(nbOfFields):
+ fs[i]=MEDCouplingFieldDouble.New(ON_CELLS,ONE_TIME)
+ fs[i].setMesh(m)
+ da=DataArrayDouble.New()
+ da.setValues([0.,1.,2.+i,3.,4.,5.,7.],8,1)
+ fs[i].setArray(da)
+ fs[i].setName(fName)
+ fs[i].setTime(1.2+i,9,14)
+ pass
+ ret=MEDCouplingFieldOverTime.New(fs);
+ return ret
+
+def createALocalCMesh4():
+ mesh=MEDCouplingCMesh.New();
+ coordsX=DataArrayDouble.New();
+ arrX=[ -1., 1., 2., 4. ]
+ coordsX.setValues(arrX,4,1);
+ coordsY=DataArrayDouble.New();
+ arrY=[ -2., 4., 8. ]
+ coordsY.setValues(arrY,3,1);
+ coordsZ=DataArrayDouble.New();
+ arrZ=[ -3., 3., 6., 12., 17. ]
+ coordsZ.setValues(arrZ,5,1);
+ mesh.setCoords(coordsX,coordsY,coordsZ);
+ mesh.setName("CMeshSample")
+ return mesh
+
+def createALocalField5():
+ m=createALocalCMesh4()
+ field=MEDCouplingFieldDouble.New(ON_CELLS,ONE_TIME)
+ field.setMesh(m)
+ da=DataArrayDouble.New()
+ field.setTime(14.5,0,0)
+ da.setValues([float(7*i) for i in xrange(24)],24,1)
+ field.setName("MeshOnCMesh");
+ field.setArray(da)
+ return field;
+
+
+######
+
+orb = CORBA.ORB_init([], CORBA.ORB_ID)
+poa=orb.resolve_initial_references("RootPOA");
+mgr=poa._get_the_POAManager();
+mgr.activate();
+
+###### Searching for
+
+naming_service = SALOME_NamingServicePy_i(orb)
+rp=SALOME_ContainerManager_idl._0_Engines.ResourceParameters("","","",["PARAVIS"],1,10,10,1,1,"first",[])
+cp=SALOME_ContainerManager_idl._0_Engines.ContainerParameters("","get","",1,False,"",rp)
+sm=naming_service.Resolve("/ContainerManager")
+cont=sm.GiveContainer(cp)
+paraviz=naming_service.Resolve("/Containers/%s/FactoryServer/PARAVIS_inst_1"%(cont.getHostName()))
+
+######
+
+meshCorba=MEDCouplingUMeshServant._this(createALocalMesh())
+ior=orb.object_to_string(meshCorba)
+print "mesh : ",ior
+
+f1=MEDCouplingFieldDoubleServant._this(createALocalField1())
+ior2=orb.object_to_string(f1)
+print "Field on cell ",ior2
+
+f2=MEDCouplingFieldDoubleServant._this(createALocalField2())
+ior3=orb.object_to_string(f2)
+print "Field on node ",ior3
+
+fs3=MEDCouplingFieldOverTimeServant._this(createALocalMultiField3())
+fs3.Register()
+ior4=orb.object_to_string(fs3)
+print "Fields over time ",ior4
+
+m2=MEDCouplingCMeshServant._this(createALocalCMesh4())
+ior5=orb.object_to_string(m2)
+print "CMesh 2 : ",ior5
+
+f5=MEDCouplingFieldDoubleServant._this(createALocalField5())
+ior6=orb.object_to_string(f5)
+print "Field on cell CMesh ",ior6
+
+script="""
+src1 = ParaMEDCorbaPluginSource()
+src1.IORCorba = '%s'
+asc=GetAnimationScene()
+rw=GetRenderView()
+dr=Show()\ndr.Visibility = 1
+"""
+
+content=script%(ior4)
+paraviz.ExecuteScript(content)
--- /dev/null
+#!/usr/bin/env python
+# -*- coding: iso-8859-1 -*-
+
+# This script illustrates the basic usage of MEDCoupling and MEDLoader
+# to generate test data files for various cases of med operation. It
+# illustrates also the usage of numpy to specify the values of the
+# fields when defined on a cartesian mesh (grid).
+# (gboulant - 11/07/2011)
+
+import MEDCoupling as MC
+import MEDLoader as ML
+
+import numpy
+
+#
+# ===============================================================
+# Helper functions to create meshes
+# ===============================================================
+#
+
+def createGridMesh(meshName, nbCellsX, nbCellsY):
+ """
+ The mesh is created using MEDCoupling. The code below creates a
+ cartesian mesh as a grid with nbCellsX segments in the X direction
+ and nbCellsY in the Y direction (nb. cells = nbCellsX * nbCellsY)
+ """
+ print "Creating grid mesh of size %sx%s"%(nbCellsX, nbCellsY)
+ cmesh=MC.MEDCouplingCMesh.New();
+
+ # Create X coordinates
+ nbNodesX = nbCellsX+1
+ stepX = 0.1
+ arrX = [float(i * stepX) for i in range(nbNodesX)]
+ coordsX=MC.DataArrayDouble.New()
+ coordsX.setValues(arrX,nbNodesX,1)
+
+ # Create Y coordinates
+ nbNodesY = nbCellsY+1
+ stepY = 0.1
+ arrY=[float(i * stepY) for i in range(nbNodesY)]
+ coordsY=MC.DataArrayDouble.New()
+ coordsY.setValues(arrY,nbNodesY,1)
+
+ # Create the grid
+ cmesh.setCoords(coordsX,coordsY)
+ cmesh.setName(meshName)
+
+ return cmesh
+
+def unstructuredMesh(cartesianMesh):
+ """
+ Convert the cartesian mesh in unstructured mesh for the need of
+ write function of MEDLoader
+ """
+ print "Creating unstructured mesh from %s"%(cartesianMesh.getName())
+ umesh=cartesianMesh.buildUnstructured();
+ umesh.setName(cartesianMesh.getName())
+ return umesh
+
+#
+# ===============================================================
+# Creating a cartesian mesh
+# ===============================================================
+#
+# The size is the number of discrete values in a direction, and then
+# corresponds to the number of cells in that direction.
+size=80
+#size=512
+
+
+# >>>
+# WARNING: remember the problem of tics and spaces. The parameter
+# "size" is considered to be a number of cells (intervals). The number
+# of nodes in that direction is size+1.
+# <<<
+
+nbCellsX = size
+nbNodesX = nbCellsX+1
+
+nbCellsY = size # The size could be different than the X size
+nbNodesY = nbCellsY+1
+
+meshName = "Grid_%sx%s"%(nbCellsX, nbCellsY)
+cmesh = createGridMesh(meshName, nbCellsX, nbCellsY)
+umesh = unstructuredMesh(cmesh)
+medFileName="gendata.med"
+ML.MEDLoader.WriteUMesh(medFileName,umesh,True);
+
+#
+# ===============================================================
+# Creating a scalar field, working with numpy
+# ===============================================================
+#
+
+def createField(fieldName,gridMesh,
+ numpy2Darray,typeOfField=MC.ON_CELLS,
+ iteration=0):
+ """
+ The number of values for the fields is deduced from the sizes of
+ the numpy array. If typeOfField is ON_CELLS, the size is considered
+ as the number of cells, otherwise it's considered as the number of
+ nodes. In any case, it must be consistent with the dimensions of
+ the numpy 2D array.
+ """
+ print "Creating field %s with iteration=%s"%(fieldName,iteration)
+
+ # The sizes are deduced from the numpy array. Note that if
+ # typeOfField is ON_CELLS, then the size should correspond to the
+ # number of cells, while if typeOfField is ON_NODES, then the size
+ # should correspond to the number of nodes
+ [sizeX,sizeY] = numpy2Darray.shape
+
+ # We first have to reshape the 2D numpy array in a 1D vector that
+ # concatenate all the rows
+ data=numpy2Darray.reshape(1,sizeX*sizeY)[0]
+ # Then, we can create a simple list as required by the MEDCoupling
+ # DataArrayDouble. Note also the usage of float type because
+ # MEDCoupling works only with real numbers
+ listdata=list(data)
+
+ # Create the field using the list obtained from the numpy array
+ field = MC.MEDCouplingFieldDouble.New(typeOfField,MC.ONE_TIME);
+ field.setName(fieldName);
+ field.setMesh(gridMesh);
+ field.setIteration(iteration)
+ field.setTimeValue(float(iteration))
+
+ nbComponents=1 # Only one single component for a scalar field
+ nbCells=sizeX*sizeY
+ dataArray=MC.DataArrayDouble.New();
+ dataArray.setValues(listdata,nbCells,nbComponents)
+ field.setArray(dataArray);
+
+ return field
+
+def writeField(fieldName, numpy2Darray,
+ typeOfField=MC.ON_CELLS,
+ iteration=0):
+
+ field = createField(fieldName, umesh, numpy2Darray,
+ typeOfField, iteration)
+ createFromScratch=False
+ ML.MEDLoader.WriteField(medFileName,field,createFromScratch)
+
+
+def createTestNumpy2DArray(sizeX, sizeY):
+ """
+ This illustrates how to create a numpy 2D array for input of the
+ createField function.
+ """
+ rows=[]
+ for irow in range(sizeY):
+ row = numpy.arange(start = irow*sizeY,
+ stop = irow*sizeY+sizeX,
+ step = 1,
+ dtype='float64')
+ rows.append(row)
+
+ numpy2Darray = numpy.vstack(rows)
+ return numpy2Darray
+
+def createTestFieldOnCells():
+ # Test field on cells
+ numpy2Darray = createTestNumpy2DArray(sizeX=nbCellsX, sizeY=nbCellsY)
+ writeField("FieldOnCells", numpy2Darray,
+ typeOfField=MC.ON_CELLS)
+
+def createTestFieldOnNodes():
+ # Test field on nodes
+ numpy2Darray = createTestNumpy2DArray(sizeX=nbNodesX, sizeY=nbNodesY)
+ writeField("FieldOnNodes", numpy2Darray,
+ typeOfField=MC.ON_NODES)
+
+
+#
+# =================================================
+# Creating a time series
+# =================================================
+#
+
+# -------------------------------------------------
+# Simple demo of the principles
+# -------------------------------------------------
+
+# In these functions, (x,y) are the indexes of the element in the
+# numpy array. Note that theses indexes maps the indexes of the
+# cartesian mesh.
+
+# A function can be a simple python function ...
+def f1(x,y):
+ z = 10*x
+ print "x=%s\ny=%s\nz=%s"%(x,y,z)
+ return z
+
+# ... but also a more sophisticated callable object, for example to
+# defines some parameters
+class Function(object):
+ def __init__(self, sizeX, sizeY, param):
+ self.sizeX = sizeX
+ self.sizeY = sizeY
+ self.param = param
+
+ def function(self, x,y):
+ z = self.param*x
+ print "x=%s\ny=%s\nz=%s"%(x,y,z)
+ return z
+
+ def __call__(self, x,y):
+ return self.function(x,y)
+
+fOnNodes=Function(sizeX=nbNodesX, sizeY=nbNodesY, param=10)
+fOnCells=Function(sizeX=nbCellsX, sizeY=nbCellsY, param=3)
+
+def createFunctionField_01():
+ sizeX=nbNodesX
+ sizeY=nbNodesY
+ typeOfField=MC.ON_NODES
+ f=fOnNodes
+ numpy2Darray = numpy.fromfunction(f,(sizeX,sizeY),dtype='float64')
+ writeField("FieldOnNodesUsingFunc", numpy2Darray,typeOfField)
+
+ f=fOnCells
+ sizeX=nbCellsX
+ sizeY=nbCellsY
+ typeOfField=MC.ON_CELLS
+ numpy2Darray = numpy.fromfunction(f,(sizeX,sizeY),dtype='float64')
+ writeField("FieldOnCellsUsingFunc", numpy2Darray,typeOfField)
+
+
+# -------------------------------------------------
+# Using the pyfunctions package to generate data
+# -------------------------------------------------
+
+def createNumpy2DArrayWithFunc(sizeX, sizeY, function):
+ """
+ @function : a callable than can be used as a function of X.
+ Typically function should be an instance of Function object
+ defined in pyfunctions.functions.
+ """
+
+ # X coordinates should range between 0 and 1 to use the normalized
+ # functions. We have to generate sizeX points:
+ step=1./sizeX
+ arrX=[float(i * step) for i in range(sizeX)]
+
+ values = function(arrX)
+
+ # Then on can create the base row for the numpy 2D array
+ rowX = numpy.array(values)
+ # and replicate this row along the Y axis
+ rows=[]
+ for irow in range(sizeY):
+ rows.append(rowX)
+
+ numpy2Darray = numpy.vstack(rows)
+ return numpy2Darray
+
+from pyfunctions.functions import FuncStiffPulse
+def createNumpy2DArrayWithFuncStiff(sizeX, sizeY):
+ f=FuncStiffPulse(xlimit=0.3,stiffness=30,nbPeriods=10)
+ return createNumpy2DArrayWithFunc(sizeX, sizeY, f)
+
+def createFunctionField_02():
+ sizeX=nbCellsX
+ sizeY=nbCellsY
+ typeOfField=MC.ON_CELLS
+ numpy2Darray = createNumpy2DArrayWithFuncStiff(sizeX,sizeY)
+ writeField("FieldOnCellsUsingFunc02", numpy2Darray,typeOfField)
+
+ sizeX=nbNodesX
+ sizeY=nbNodesY
+ typeOfField=MC.ON_NODES
+ numpy2Darray = createNumpy2DArrayWithFuncStiff(sizeX,sizeY)
+ writeField("FieldOnNodesUsingFunc02", numpy2Darray,typeOfField)
+
+#
+# =================================================
+# Functions to create custom fields for MEDOP tests
+# =================================================
+#
+def createTimeSeries():
+ """
+ Create a single med file with a single mesh and a field defined on
+ several time steps (time series).
+ """
+ meshName = "Grid_%sx%s"%(nbCellsX, nbCellsY)
+ cmesh = createGridMesh(meshName, nbCellsX, nbCellsY)
+ umesh = unstructuredMesh(cmesh)
+ medFileName="timeseries.med"
+ ML.MEDLoader.WriteUMesh(medFileName,umesh,True);
+
+ sizeX=nbNodesX
+ sizeY=nbNodesY
+ typeOfField=MC.ON_NODES
+
+ nbIterations=10
+ pulseStiffNess = 20
+ pulseNbPeriods = 10
+ for iteration in range(nbIterations):
+ xlimit = float(iteration)/float(nbIterations)
+ f=FuncStiffPulse(xlimit,stiffness=pulseStiffNess,nbPeriods=pulseNbPeriods)
+ numpy2Darray = createNumpy2DArrayWithFunc(sizeX,sizeY,f)
+ field = createField("Pulse",umesh,numpy2Darray,typeOfField,iteration)
+ ML.MEDLoader.WriteField(medFileName,field,False)
+
+from pyfunctions.functions import FuncStiffExp
+def createParametrics():
+ """
+ Create 2 med files containing each a mesh (identical) and a field
+ defined on this mesh in each file.
+ """
+ meshName = "Grid_%sx%s_01"%(nbCellsX, nbCellsY)
+ cmesh = createGridMesh(meshName, nbCellsX, nbCellsY)
+ umesh = unstructuredMesh(cmesh)
+
+ sizeX=nbNodesX
+ sizeY=nbNodesY
+ typeOfField=MC.ON_NODES
+
+ medFileName="parametric_01.med"
+ ML.MEDLoader.WriteUMesh(medFileName,umesh,True);
+ f=FuncStiffExp(xlimit=0.3,stiffness=30)
+ numpy2Darray = createNumpy2DArrayWithFunc(sizeX,sizeY,f)
+ fieldName = "StiffExp_01"
+ field = createField(fieldName,umesh, numpy2Darray,typeOfField)
+ ML.MEDLoader.WriteField(medFileName,field,False)
+
+ medFileName="parametric_02.med"
+ umesh.setName("Grid_%sx%s_02"%(nbCellsX, nbCellsY))
+ ML.MEDLoader.WriteUMesh(medFileName,umesh,True);
+ f=FuncStiffExp(xlimit=0.4,stiffness=30)
+ numpy2Darray = createNumpy2DArrayWithFunc(sizeX,sizeY,f)
+ fieldName = "StiffExp_02"
+ field = createField(fieldName,umesh, numpy2Darray,typeOfField)
+ ML.MEDLoader.WriteField(medFileName,field,False)
+
+def createParametrics_demo():
+ """
+ Create 2 med files containing each a mesh (identical) and a field
+ defined on this mesh in each file.
+ """
+ meshName = "mesh1"
+ cmesh = createGridMesh(meshName, nbCellsX, nbCellsY)
+ umesh = unstructuredMesh(cmesh)
+
+ sizeX=nbNodesX
+ sizeY=nbNodesY
+ typeOfField=MC.ON_NODES
+
+ listIteration = [0,1,2,3,4]
+
+ medFileName="parametric_01.med"
+ ML.MEDLoader.WriteUMesh(medFileName,umesh,True);
+ fieldName = "field1"
+ for iteration in listIteration:
+ #f=FuncStiffPulse(xlimit=0.3+0.1*iteration,stiffness=10,nbPeriods=5)
+ f=FuncStiffExp(xlimit=0.3+0.1*iteration,stiffness=10)
+ numpy2Darray = createNumpy2DArrayWithFunc(sizeX,sizeY,f)
+ field = createField(fieldName,umesh, numpy2Darray,typeOfField,iteration)
+ ML.MEDLoader.WriteField(medFileName,field,False)
+
+ medFileName="parametric_02.med"
+ umesh.setName("mesh2")
+ ML.MEDLoader.WriteUMesh(medFileName,umesh,True);
+ fieldName = "field2"
+ for iteration in listIteration:
+ #f=FuncStiffPulse(xlimit=0.3+0.1*iteration,stiffness=10,nbPeriods=6)
+ f=FuncStiffExp(xlimit=0.3+0.1*iteration,stiffness=15)
+ numpy2Darray = createNumpy2DArrayWithFunc(sizeX,sizeY,f)
+ field = createField(fieldName,umesh, numpy2Darray,typeOfField,iteration)
+ ML.MEDLoader.WriteField(medFileName,field,False)
+
+
+
+#
+# =================================================
+# Main runner
+# =================================================
+#
+if __name__ == "__main__":
+ #createTestFieldOnCells()
+ #createTestFieldOnNodes()
+ #createFunctionField_01()
+ #createFunctionField_02()
+ #createTimeSeries()
+ createParametrics_demo()
--- /dev/null
+#!/usr/bin/env python
+# -*- coding: iso-8859-1 -*-
+
+# This use case illustrates the usage of PIL (Python Imaging Library)
+# combined with the MEDCoupling and MEDLoader modules to save an image
+# as a field in a med file.
+# (gboulant - 27/06/2011)
+
+import MEDCoupling as MC
+import MEDLoader as ML
+
+#
+# ===============================================================
+# We first get data from the test image to render as a field
+# ===============================================================
+#
+import scipy, numpy
+# The data field array may be created from the lena image
+#image = scipy.lena()
+# We could either read a real image using the PIL python package.
+from scipy.misc import pilutil
+image = pilutil.imread("images/avatar.png",True)
+
+
+#from PIL import Image
+#im=Image.open("images/irm.png")
+#im=Image.open("images/lena.png")
+#image=pilutil.fromimage(im,True)
+#image=numpy.asarray(im)
+#print image
+
+dim = len(image.shape)
+print "Image space dimension = %d"%dim
+sizeX = image.shape[1]
+sizeY = image.shape[0]
+
+# The sizes defined the number of pixel in a direction, then the
+# number of cells to create in the mesh in that direction.
+
+# We must reshape the matrix of pixel in a 1D vector that concatenates
+# all the rows, and then convert this vector in a simple list of
+# double as required by the MEDCoupling field specification.
+import numpy
+imageDataNArray = image.reshape(1,sizeX*sizeY)[0]
+print imageDataNArray
+
+imageDataNArrayDouble = numpy.array(imageDataNArray, dtype='float64')
+imageDataArrayDouble = list(imageDataNArrayDouble)
+
+#
+# ===============================================================
+# Creating a cartesian mesh with a grid of the size of the image
+# ===============================================================
+#
+
+# >>>
+# WARNING: remember the problem of tics and spaces. The data values
+# are considered as values defined on cells. With size values in a
+# direction, we have to create size+1 mesh nodes in that direction.
+# <<<
+
+# The mesh is created using MEDCoupling
+cmesh=MC.MEDCouplingCMesh.New();
+cmesh.setName("imagemesh")
+
+# We use an arbitrary step between cells (the value does not matter)
+stepX = 0.1
+nbNodesX = sizeX+1
+arrX = [float(i * stepX) for i in range(nbNodesX)]
+coordsX=MC.DataArrayDouble.New()
+coordsX.setValues(arrX,nbNodesX,1)
+
+stepY = 0.1
+nbNodesY = sizeY+1
+arrY=[float(i * stepY) for i in range(nbNodesY)]
+coordsY=MC.DataArrayDouble.New()
+coordsY.setValues(arrY,nbNodesY,1)
+
+cmesh.setCoords(coordsX,coordsY)
+print "Imagem mesh dimension: %d"%cmesh.getSpaceDimension()
+
+# WARN: In the current state of development of MEDLoader, only
+# unstructured meshes are supported for writting function in med
+# files. We just have to convert the cartesian mesh in an unstructured
+# mesh before creating the field.
+umesh=cmesh.buildUnstructured();
+umesh.setName("imagemesh")
+
+#
+# ===============================================================
+# Creating a scalar field on the mesh using image data
+# ===============================================================
+#
+
+# Create the field using MEDCoupling
+field = MC.MEDCouplingFieldDouble.New(MC.ON_CELLS,MC.ONE_TIME);
+field.setName("imagefield");
+field.setMesh(umesh);
+# OPTIONAL: We set an arbitrary time step for test purpose
+field.setIteration(3);
+field.setOrder(0)
+
+dataArray=MC.DataArrayDouble.New();
+nbCells = sizeX*sizeY
+nbComponents=1 # For a scalar field
+
+# This example shows haw to initialize all cell with the same
+# value. Just create an array of size nbCells
+# dataArray.setValues(nbCells*[3.4],nbCells,nbComponents)
+
+dataArray.setValues(imageDataArrayDouble,nbCells,nbComponents)
+field.setArray(dataArray);
+
+# The MEDLoader can be used to save all the stuff in a med file. You
+# just have to specify the field and the MEDLoader will save the
+# underlying mesh.
+createFromScratch=True
+ML.MEDLoader.WriteField("fieldimage.med",field,createFromScratch)
--- /dev/null
+#!/usr/bin/env python
+# -*- coding: iso-8859-1 -*-
+
+# This simple use case illustrates the basic usage of MEDCoupling and
+# MEDLoader to create a cartesian mesh, define a field on this mesh,
+# and save all the stuff in a med file.
+# (gboulant - 27/06/2011)
+
+import MEDCoupling as MC
+import MEDLoader as ML
+
+#
+# ===============================================================
+# Creating a 512x512 cartesian mesh
+# ===============================================================
+#
+# The size is the number of discrete values in a direction, and then
+# corresponds to the number of cells in that direction.
+size=8
+#size=512
+
+# The mesh is created using MEDCoupling. The code below creates a
+# cartesian mesh as a sizexsize grid
+
+# >>>
+# WARNING: remember the problem of tics and spaces. The data values
+# are considered as values defined on cells. With size values in a
+# direction, we have to create size+1 mesh nodes in that direction.
+# <<<
+
+cmesh=MC.MEDCouplingCMesh.New();
+cmesh.setName("512x512 cartesian mesh")
+
+sizeX = size
+nbNodesX = sizeX+1
+stepX = 0.1
+arrX = [float(i * stepX) for i in range(nbNodesX)]
+print "Size of arrX = %d"%len(arrX)
+
+coordsX=MC.DataArrayDouble.New()
+coordsX.setValues(arrX,nbNodesX,1)
+
+sizeY = size
+nbNodesY = sizeY+1
+stepY = 0.1
+arrY=[float(i * stepY) for i in range(nbNodesY)]
+coordsY=MC.DataArrayDouble.New()
+coordsY.setValues(arrY,sizeY,1)
+
+cmesh.setCoords(coordsX,coordsY)
+print cmesh.getSpaceDimension()
+#print cmesh
+
+# WARN: In the current state of development of MEDLoader, only
+# unstructured meshes are supported for writting function in med
+# files. We just have to convert the cartesian mesh in an unstructured
+# mesh before creating the field.
+umesh=cmesh.buildUnstructured();
+umesh.setName("512x512 unstructured mesh")
+
+# This can be used to save the mesh only (can be visualize using
+# SMESH).
+meshFileName = "umesh.med"
+ML.MEDLoader.WriteUMesh(meshFileName,umesh,True);
+
+# Alternatively, you can use a MEDFileMesh to write the mesh in a
+# file.
+medFileCMesh = ML.MEDFileCMesh.New()
+medFileCMesh.setMesh(cmesh)
+medFileCMesh.setName(cmesh.getName())
+meshFileName = "cmesh.med"
+mode = 2
+medFileCMesh.write(meshFileName,mode)
+
+#
+# ===============================================================
+# Creating a scalar field on the 512x512 mesh
+# ===============================================================
+#
+# For the simple test, we create a field that varies in space as
+# field(x,y)=x+y where x and y are coordinates on the mesh
+
+# --- Field on cells
+
+# Create the field
+field = MC.MEDCouplingFieldDouble.New(MC.ON_CELLS);
+field.setName("AnalyticField_onCells");
+field.setMesh(umesh);
+
+nbComponents=1 # Only one single component for a scalar field
+fillFunction="x+y"
+field.fillFromAnalytic(nbComponents,fillFunction);
+
+# The MEDLoader can be used to save all the stuff in a med file. You
+# just have to specify the field and the MEDLoader will save the
+# underlying mesh.
+createFromScratch=True
+ML.MEDLoader.WriteField("fieldtest.med",field,createFromScratch)
+
+# --- Field on nodes
+
+field = MC.MEDCouplingFieldDouble.New(MC.ON_NODES);
+field.setName("AnalyticField_onNodes");
+field.setMesh(umesh);
+field.fillFromAnalytic(nbComponents,fillFunction);
+createFromScratch=False
+ML.MEDLoader.WriteField("fieldtest.med",field,createFromScratch)
+
+
+#
+# ===============================================================
+# Creating a scalar field, working with numpy
+# ===============================================================
+#
+
+# We start by creating a numpy matrix
+import numpy
+rows=[]
+for irow in range(sizeY):
+ row = numpy.arange(irow*sizeY,irow*sizeY+sizeX,dtype='float64')
+ rows.append(row)
+
+marray = numpy.vstack(rows)
+
+# Then, we can reshape the matrix in a 1D vector that concatenate all
+# the rows
+data=marray.reshape(1,sizeX*sizeY)[0]
+# Finally, we can create a simple list as required by the MEDCoupling
+# DataArrayDouble. Note also the usage of float type because
+# MEDCoupling works only with real numbers
+listdata=list(data)
+
+# Create the field using the list obtained from the numpy array
+fieldWithNumpy = MC.MEDCouplingFieldDouble.New(MC.ON_CELLS);
+fieldWithNumpy.setName("Numpy Field");
+fieldWithNumpy.setMesh(umesh);
+
+nbCells=sizeX*sizeY
+dataArray=MC.DataArrayDouble.New();
+dataArray.setValues(listdata,nbCells,nbComponents)
+fieldWithNumpy.setArray(dataArray);
+
+createFromScratch=False
+ML.MEDLoader.WriteField("fieldtest.med",fieldWithNumpy,createFromScratch)
+
+
--- /dev/null
+#!/usr/bin/env python
+# -*- coding: iso-8859-1 -*-
+#
+# This script illustrates how to create a matrix of pixels from data
+# read in an image file. At the end, the cells of the matrix
+# corresponds to the cells of a cartesian mesh that could hold a field
+# whose value is the value of the pixel.
+# (gboulant - 13/11/2011)
+
+from PIL import Image
+from PIL import ImageOps
+import numpy
+
+def image2matrix():
+ # Load the image
+ #img=Image.open("images/avatar.png")
+ img=Image.open("images/tests.pgm")
+
+ # Get a grayscale version
+ imgbw=ImageOps.grayscale(img)
+
+ # Save the image (optionnal)
+ imgbw.save(fp="testsbw.pgm")
+
+ # Get the data
+ imgdata=imgbw.getdata()
+ width,height=imgbw.size
+ print list(imgdata)
+ print width,height
+
+ # Convert the data in a matrix using numpy
+ tab=numpy.array(imgdata,dtype='float64')
+ print list(tab)
+ print tab
+ nbRows=height
+ nbCols=width
+ matrix=numpy.reshape(tab,(nbRows,nbCols))
+ # Note that in the reshape function, the height (sizeY) of the image
+ # is specified first, because it corresponds to the number of rows.
+ print matrix
+ print list(matrix)
+
+import MEDCoupling as MC
+import MEDLoader as ML
+def createMesh(meshname, sizeX, sizeY):
+ """
+ Creating a cartesian mesh with a grid of the size of the image.
+ sizeX and sizeY should be respectively the width and heigth of the
+ image.
+ """
+ # >>>
+ # WARNING: remember the problem of tics and spaces. The data values
+ # are considered as values defined on cells. With size values in a
+ # direction, we have to create size+1 mesh nodes in that direction.
+ # <<<
+
+ # The mesh is created using MEDCoupling
+ cmesh=MC.MEDCouplingCMesh.New();
+ cmesh.setName(meshname)
+
+ # We use an arbitrary step between cells (the value does not matter)
+ stepX = 0.1
+ nbNodesX = sizeX+1
+ arrX = [float(i * stepX) for i in range(nbNodesX)]
+ coordsX=MC.DataArrayDouble.New()
+ coordsX.setValues(arrX,nbNodesX,1)
+
+ # For the Y dimension, we have to reverse the coordinates (the
+ # first pixel is at y=height and not at y=0).
+ stepY = 0.1
+ nbNodesY = sizeY+1
+ lengthY = sizeY*stepY
+ arrY=[float(lengthY - i * stepY) for i in range(nbNodesY)]
+ coordsY=MC.DataArrayDouble.New()
+ coordsY.setValues(arrY,nbNodesY,1)
+
+ cmesh.setCoords(coordsX,coordsY)
+ print "Imagem mesh dimension: %d"%cmesh.getSpaceDimension()
+
+ # WARN: In the current state of development of MEDLoader, only
+ # unstructured meshes are supported for writting function in med
+ # files. We just have to convert the cartesian mesh in an unstructured
+ # mesh before creating the field.
+ umesh=cmesh.buildUnstructured();
+ umesh.setName("imagemesh")
+
+ return umesh
+
+def createField(fieldname, mesh, image):
+ """
+ Creating a scalar field on the mesh using image data
+ """
+ # Create the field using MEDCoupling
+ field = MC.MEDCouplingFieldDouble.New(MC.ON_CELLS,MC.ONE_TIME);
+ field.setName(fieldname);
+ field.setMesh(mesh);
+ # OPTIONAL: We set an arbitrary time step for test purpose
+ field.setIteration(0);
+ field.setOrder(0)
+
+ imagedata=list(image.getdata())
+ width,height=image.size
+ nbCells = width*height
+ dataArray=MC.DataArrayDouble.New();
+ nbComponents=1 # For a scalar field
+
+ dataArray.setValues(imagedata,nbCells,nbComponents)
+ field.setArray(dataArray);
+
+ return field
+
+def image2med():
+ img=Image.open("images/avatar.png")
+ #img=Image.open("images/irm.png")
+ imgbw=ImageOps.grayscale(img)
+ # We keep only the grayscale. Maybe, it could be usefull to get
+ # the RGB scales each on one component of the field.
+
+ width,height=imgbw.size
+ mesh=createMesh("mesh",width,height)
+ field=createField("field",mesh,imgbw)
+
+ createFromScratch=True
+ ML.MEDLoader.WriteField("image.med",field,createFromScratch)
+
+
+# ===================================================================
+
+if __name__ == "__main__":
+ #image2matrix()
+ image2med()
--- /dev/null
+all:
+ python tutorial.py
+ python explore.py
+ python manage.py
+ python testamel.py
+ python cmesh
+
+clean:
+ rm -f *~ *.pyc output.med
--- /dev/null
+#!/usr/bin/env python
+
+from MEDLoader import MEDLoader
+
+import os
+filename = "madnex_field.med"
+filepath = os.path.join(os.path.abspath(os.path.dirname(__file__)),filename)
+
+meshName="my_field_RG"
+fieldName="my_field"
+itNumber=0
+itOrder=0
+
+# Load as an unstructured mesh
+meshDimRelToMax = 0 # 0 = no restriction
+umesh = MEDLoader.ReadUMeshFromFile(filepath,meshName,meshDimRelToMax)
+print "umesh is structured: %s"%umesh.isStructured()
+
+# Load as a structured mesh explicitly
+# _T2A
+from MEDLoader import MEDFileCMesh
+medfile = MEDFileCMesh.New(filepath,meshName)
+cmesh = medfile.getMesh()
+# Note that the getMesh method is a short way to the method:
+#cmesh = medfile.getGenMeshAtLevel(0,False)
+print "cmesh is structured: %s"%cmesh.isStructured()
+# _T2B
+
+# Load and let MEDLoader decide what is nature of the mesh
+# _T1A
+from MEDLoader import MEDFileMesh
+medfile = MEDFileMesh.New(filepath,meshName)
+print medfile.advancedRepr()
+meshDimRelToMax = 0 # 0 = no restriction
+mesh = medfile.getGenMeshAtLevel(meshDimRelToMax)
+print "mesh is structured: %s"%mesh.isStructured()
+# _T1B
+
+
+# Write the mesh to another file
+# _T3A
+outputfilepath="output.med"
+mode=0
+medfile.write(outputfilepath,mode)
+# _T3B
+
+# test to reload the mesh
+medfile = MEDFileCMesh.New(outputfilepath,meshName)
+cmesh = medfile.getMesh()
+print "cmesh is structured: %s"%cmesh.isStructured()
+
+# Q: Is it possible to know if a mesh is structured or unstructured
+# without loading the mesh.
--- /dev/null
+#!/usr/bin/env python
+
+from MEDLoader import MEDLoader
+
+import os
+#filename = "madnex_field.med"
+filename = "timeseries.med"
+filepath = os.path.join(os.path.abspath(os.path.dirname(__file__)),filename)
+
+# Read the source meshes
+meshNames = MEDLoader.GetMeshNames(filepath)
+
+# Set to True if the meshes and fields data must be loaded. Otherwise,
+# only theire descriptions will be loaded.
+READ_PHYSICAL_DATA=False
+
+for meshName in meshNames:
+
+ print "%s"%meshName
+
+ # At this step, one can load the mesh of name meshName (but it is
+ # not an obligation to continue to explore the metadata)
+ meshDimRelToMax = 0 # 0 = no restriction
+ if READ_PHYSICAL_DATA:
+ mesh = MEDLoader.ReadUMeshFromFile(filepath,meshName,meshDimRelToMax)
+ # Note that the read function required the parameter
+ # meshDimRelToMax. This parameter discreminates the meshdim you
+ # are interested to relatively to the maximal dimension of cells
+ # contained in the mesh in file (then its value could be 0, -1, -2
+ # or -3 depending on the max dimension of the mesh. 0 means "no
+ # restriction".
+
+ # Read the names of the fields that rely on this mesh
+ fieldNames = MEDLoader.GetAllFieldNamesOnMesh(filepath,meshName)
+
+ for fieldName in fieldNames:
+
+ print " %s"%fieldName
+
+ # A field name could identify several MEDCoupling fields, that
+ # differ by their spatial discretization on the mesh (values on
+ # cells, values on nodes, ...). This spatial discretization is
+ # specified by the TypeOfField that is an integer value in this
+ # list:
+ # 0 = ON_CELLS
+ # 1 = ON_NODES
+ # 2 = ON_GAUSS_PT
+ # 3 = ON_GAUSS_NE
+ #
+ # As a consequence, before loading values of a field, we have
+ # to determine the types of spatial discretization defined for
+ # this field and to chooose one.
+
+ listOfTypes = MEDLoader.GetTypesOfField(filepath,meshName,fieldName)
+ for typeOfDiscretization in listOfTypes:
+ print " %s"%typeOfDiscretization
+
+ # Then, we can get the iterations associated to this field on
+ # this type of spatial discretization:
+ fieldIterations = MEDLoader.GetFieldIterations(typeOfDiscretization,
+ filepath,
+ meshName,
+ fieldName)
+
+ # Then, we can access to the physical data for each
+ # iteration of this field
+ for fieldIteration in fieldIterations:
+ itNumber = fieldIteration[0]
+ itOrder = fieldIteration[1]
+ print " (%s,%s)"%(itNumber,itOrder)
+
+ if READ_PHYSICAL_DATA:
+ medCouplingField = MEDLoader.ReadField(typeOfDiscretization,
+ filepath,
+ meshName,
+ meshDimRelToMax,
+ fieldName,
+ itNumber,
+ itOrder)
+ print medCouplingField
--- /dev/null
+#!/usr/bin/env python
+
+# _T1A
+import collections
+def tree():
+ return collections.defaultdict(tree)
+
+fieldTree = tree()
+meshDict = {}
+# _T1B
+
+import os
+filename = "timeseries.med"
+filepath = os.path.join(os.path.abspath(os.path.dirname(__file__)),filename)
+
+# _T2A
+from MEDLoader import MEDLoader
+meshNames = MEDLoader.GetMeshNames(filepath)
+
+meshDimRelToMax = 0 # 0 = no restriction
+
+for meshName in meshNames:
+ mesh = MEDLoader.ReadUMeshFromFile(filepath,meshName,meshDimRelToMax)
+ meshDict[meshName] = mesh
+
+ fieldNames = MEDLoader.GetAllFieldNamesOnMesh(filepath,meshName)
+ for fieldName in fieldNames:
+ listOfTypes = MEDLoader.GetTypesOfField(filepath,meshName,fieldName)
+ for typeOfDiscretization in listOfTypes:
+ fieldIterations = MEDLoader.GetFieldIterations(typeOfDiscretization,
+ filepath,
+ meshName,
+ fieldName)
+ for fieldIteration in fieldIterations:
+ itNumber = fieldIteration[0]
+ itOrder = fieldIteration[1]
+
+ field = MEDLoader.ReadField(typeOfDiscretization,
+ filepath,
+ meshName,
+ meshDimRelToMax,
+ fieldName,
+ itNumber,
+ itOrder)
+
+ fieldTree\
+ [meshName]\
+ [fieldName]\
+ [typeOfDiscretization]\
+ [itNumber][itOrder] = field
+# _T2B
+
+# Q: use a list of structures whose an attribute could be a
+# MEDCoupling field? Or a tree that you cross using attribute and
+# whose leaves are the MEDCoupling fields?
+# R: I think that the default structure should be a simple list that
+# store objects whith properties that corresponds to the metadata (and
+# if loaded the MEDCouplingField or Mesh). Then for specific request,
+# a BTree could be create to organize the search (for example if we
+# request all the fields for a given iteration step, then we should
+# use the iteration step as a first classifaction switch of the tree
+
+print fieldTree.keys()
+
+# _T3A
+for meshName in fieldTree.keys():
+ print "%s"%meshName
+ for fieldName in fieldTree[meshName].keys():
+ print " %s"%fieldName
+ for fieldType in fieldTree[meshName][fieldName].keys():
+ print " %s"%fieldType
+ for itNumber in fieldTree[meshName][fieldName][fieldType].keys():
+ for itOrder in fieldTree[meshName][fieldName][fieldType][itNumber].keys():
+ print " (%s,%s)"%(itNumber,itOrder)
+ print fieldTree[meshName][fieldName][fieldType][itNumber][itOrder]
+# _T3B
--- /dev/null
+# -*- coding: iso-8859-1 -*-
+# This illustrates the use of getValueOn in the case of hexahedron
+# meshes (for which a temporary limitation implies the usage of the
+# method simplexize that split the hexahedron into simplex,
+# i.e. triangles and tetrahedrons).
+#
+# (gboulant, nov. 2012)
+from MEDLoader import *
+from MEDCoupling import *
+
+import os
+filename = "timeseries.med"
+filepath = os.path.join(os.path.abspath(os.path.dirname(__file__)),filename)
+
+rmedfilename = filepath
+
+# Load the meshe data
+meshname = "Grid_80x80"
+fieldname = "Pulse"
+dimrestriction = 0 # no restriction
+msource = MEDLoader.ReadUMeshFromFile(rmedfilename,meshname,dimrestriction)
+
+# WARN: In the current version of MEDCoupling, the getValueOn works
+# only with simplex cells (triangles, tetrahedron). This is not a
+# technical problem, but a question of specification of the
+# interpolation to be performed in the case of other cells, in
+# particular in the case of hexahedron meshes.
+#
+# A temporary solution (with good numerical results) is to split
+# hexahedrons into simplex (before the association of the mesh to the
+# field) using the method simplexize.
+policy = 0
+msource.simplexize(policy)
+
+# Load the field data at iteration 0
+iteration = 0
+order = -1
+fieldOnNodes = MEDLoader.ReadField(ON_NODES,rmedfilename,
+ meshname,dimrestriction,
+ fieldname,iteration,order)
+
+
+fieldOnNodes.setMesh(msource)
+
+# Get the value of field at coordinates x,y
+x=0.5
+y=0.5
+fieldValue = fieldOnNodes.getValueOn([x,y])
+print fieldValue
--- /dev/null
+#!/usr/bin/env python
+# This script illustrates the basic features of MEDLoader
+# (gboulant, 17 nov 2012)
+import os
+filename = "timeseries.med"
+filepath = os.path.join(os.path.abspath(os.path.dirname(__file__)),filename)
+
+# _T1A
+from MEDLoader import MEDLoader
+meshNames = MEDLoader.GetMeshNames(filepath)
+# _T1B
+meshName=meshNames[0]
+# _T2A
+fieldNames = MEDLoader.GetAllFieldNamesOnMesh(filepath,meshName)
+# _T2B
+fieldName=fieldNames[0]
+# _T3A
+listOfTypes = MEDLoader.GetTypesOfField(filepath,meshName,fieldName)
+# _T3B
+typeOfDiscretization=listOfTypes[0]
+# _T4A
+fieldIterations = MEDLoader.GetFieldIterations(typeOfDiscretization,
+ filepath,
+ meshName,
+ fieldName)
+# _T4B
+
+iteration = fieldIterations[0]
+iterationNumber = iteration[0]
+iterationOrder = iteration[1]
+
+dimrestriction = 0
+# _T5A
+mesh = MEDLoader.ReadUMeshFromFile(filepath, meshName, dimrestriction)
+# _T5B
+# _T6A
+field = MEDLoader.ReadField(typeOfDiscretization,
+ filepath, meshName, dimrestriction,
+ fieldName, iterationNumber, iterationOrder)
+# _T6B
--- /dev/null
+<VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian">
+ <UnstructuredGrid>
+ <Piece NumberOfPoints="2325" NumberOfCells="9330">
+ <PointData>
+
+ </PointData>
+ <CellData>
+ <DataArray type="Float32" Name="PID" NumberOfComponents="1" format="ascii" RangeMin="0" RangeMax="2">
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10 10 10 10 10 10
+ </DataArray>
+ </Cells>
+ </Piece>
+ </UnstructuredGrid>
+</VTKFile>
--- /dev/null
+# You first have to set the shell environement for medcoupling
+# (see medcoupling/env.sh)
+
+all:
+ python simpledemo.py
+
+clean:
+ rm merge.med *~
--- /dev/null
+from MEDLoader import *
+
+it,dt = (-1,-1)
+mesh = MEDLoader.ReadUMeshFromFile("field1.med","DomainMesh_1",0)
+f1 = MEDLoader.ReadField(ON_CELLS,"field1.med","DomainMesh_1",0,"field1",it,dt)
+f1.setMesh(mesh)
+
+mesh = MEDLoader.ReadUMeshFromFile("field2.med","DomainMesh_2",0)
+f2 = MEDLoader.ReadField(ON_CELLS,"field2.med","DomainMesh_2",0,"field2",it,dt)
+f2.setMesh(mesh)
+
+mesh = MEDLoader.ReadUMeshFromFile("field3.med","DomainMesh_3",0)
+f3 = MEDLoader.ReadField(ON_CELLS,"field3.med","DomainMesh_3",0,"field3",it,dt)
+f3.setMesh(mesh)
+
+#mesh = MEDLoader.ReadUMeshFromFile("field4.med","DomainMesh_4",0)
+#f4 = MEDLoader.ReadField(ON_CELLS,"field4.med","DomainMesh_4",0,"field4",it,dt)
+#f4.setMesh(mesh)
+
+fmerge=MEDCouplingFieldDouble.MergeFields([f1,f2,f3])
+fmerge.setName("merge")
+
+MEDLoader.WriteField("merge.med",fmerge,True)
+
--- /dev/null
+# You first have to set the shell environement for medcoupling
+# (see medcoupling/env.sh)
+
+all:
+ python splitdemo.py
+
+clean:
+ rm out*.vtu DomainMesh.vtu Domain.vtu *~
--- /dev/null
+from MEDLoader import *
+
+fns=['DomainMesh_1.med', 'DomainMesh_2.med','DomainMesh_3.med', 'DomainMesh_4.med']
+outs=["out1.vtu","out2.vtu","out3.vtu","out4.vtu"]
+trs=[(-0.714,-0.714,0.),(0.,0.,0.),(-0.577,-0.577,-0.577),(-0.577,0.3,-0.577)]
+scal=0.5
+fs=4*[None]
+dds=4*[None]
+mns=4*[None]
+i=0
+
+splitview=True
+
+for fn in fns:
+ m=MEDFileMesh.New(fn)
+ m=m.getMeshAtLevel(0)
+ m.writeVTK(outs[i])
+ mns[i]=m.deepCpy()
+ if splitview:
+ m.translate([scal*elt for elt in trs[i]])
+ f=MEDCouplingFieldDouble.New(ON_CELLS,NO_TIME)
+ f.setMesh(m)
+ da=DataArrayDouble.New()
+ da.alloc(m.getNumberOfCells(),1)
+ da.fillWithValue(i)
+ dds[i]=da.deepCpy()
+ f.setArray(da)
+ fs[i]=f
+ i+=1
+ pass
+
+
+f=MEDCouplingFieldDouble.MergeFields(fs)
+f.setName("PID")
+
+MEDCouplingFieldDouble.WriteVTK("Domain.vtu",[f])
+dd=DataArrayDouble.Aggregate(dds)
+m=MEDCouplingUMesh.MergeUMeshes(mns)
+if splitview:
+ m.translate([4.*elt for elt in [0.577,0.577,0.577]])
+f2=MEDCouplingFieldDouble.New(ON_CELLS,NO_TIME)
+f2.setMesh(m)
+f2.setArray(dd)
+f2.setName("PID")
+MEDCouplingFieldDouble.WriteVTK('DomainMesh.vtu',[f2])
+#m=MEDCouplingUMesh.MergeUMeshes(mns)
+#m.writeVTK("DomainMesh.vtu")
--- /dev/null
+from MEDLoader import *
+
+fns=['DomainMesh_1.med', 'DomainMesh_2.med','DomainMesh_3.med']
+outs=["out1.vtu","out2.vtu","out3.vtu","out4.vtu"]
+trs=[(-0.714,-0.714,0.),(0.,0.,0.),(-0.577,-0.577,-0.577)]
+scal=0.5
+fs=3*[None]
+dds=3*[None]
+mns=3*[None]
+i=0
+
+splitview=False
+
+for fn in fns:
+ m=MEDFileMesh.New(fn)
+ m=m.getMeshAtLevel(0)
+ m.writeVTK(outs[i])
+ mns[i]=m.deepCpy()
+ if splitview:
+ m.translate([scal*elt for elt in trs[i]])
+ f=MEDCouplingFieldDouble.New(ON_CELLS,NO_TIME)
+ f.setMesh(m)
+ da=DataArrayDouble.New()
+ da.alloc(m.getNumberOfCells(),1)
+ da.fillWithValue(i)
+ dds[i]=da.deepCpy()
+ f.setArray(da)
+ fs[i]=f
+ i+=1
+ pass
+
+
+f=MEDCouplingFieldDouble.MergeFields(fs)
+f.setName("PID")
+
+MEDCouplingFieldDouble.WriteVTK("Domain.vtu",[f])
+dd=DataArrayDouble.Aggregate(dds)
+m=MEDCouplingUMesh.MergeUMeshes(mns)
+if splitview:
+ m.translate([3.*elt for elt in [0.577,0.577,0.577]])
+f2=MEDCouplingFieldDouble.New(ON_CELLS,NO_TIME)
+f2.setMesh(m)
+f2.setArray(dd)
+f2.setName("PID")
+MEDCouplingFieldDouble.WriteVTK('DomainMesh.vtu',[f2])
+#m=MEDCouplingUMesh.MergeUMeshes(mns)
+#m.writeVTK("DomainMesh.vtu")
--- /dev/null
+DBRep_DrawableShape
+
+CASCADE Topology V1, (c) Matra-Datavision
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++1 0
\ No newline at end of file
--- /dev/null
+# You first have to set the shell environement for medcoupling
+# (see medcoupling/env.sh)
+
+all:
+ python demo_loadsource.py
+ python demo_createsource.py
+
+clean:
+ rm -f createsource_fieldtarget.med loadsource_fieldtarget.med *~
--- /dev/null
+#
+# Another possibility (to illustrate the creation of values) is to
+# create the source field using the function fillFromAnalytic
+#
+from MEDLoader import *
+from MEDCouplingRemapper import *
+
+# Read the source mesh and create a field on it
+msource = MEDLoader.ReadUMeshFromFile("meshsource.med","meshsource",0)
+
+equation = "319.*cos(((x)*(x)*3+(y-0.52)*(y-0.52)+(z-0.1)*(z-0.1))*7)"
+fsource=msource.fillFromAnalytic(ON_CELLS,1,equation)
+fsource.setName("Temperature")
+fsource.setNature(ConservativeVolumic)
+
+# Read the target mesh
+mtarget = MEDLoader.ReadUMeshFromFile("meshtarget.med","meshtarget",0)
+
+# Remapper of type P0P0 (interpolation from cells to cells)
+remap = MEDCouplingRemapper()
+remap.prepare(msource,mtarget,"P0P0")
+
+defaultValue = 1e100
+ftarget = remap.transferField(fsource,defaultValue)
+ftarget.setName("Temperature")
+
+outfilename = "createsource_fieldtarget.med"
+MEDLoader.WriteField(outfilename,ftarget,True)
+
--- /dev/null
+# _T1A
+from MEDLoader import MEDLoader, ON_CELLS
+from MEDCouplingRemapper import MEDCouplingRemapper, ConservativeVolumic
+
+# Read the source mesh and the source field
+it,dt = (-1,-1)
+msource = MEDLoader.ReadUMeshFromFile("fieldsource.med","meshsource",0)
+fsource = MEDLoader.ReadField(ON_CELLS,"fieldsource.med","meshsource",0,
+ "Temperature",it,dt)
+fsource.setMesh(msource)
+fsource.setNature(ConservativeVolumic)
+
+# Read the target mesh
+mtarget = MEDLoader.ReadUMeshFromFile("meshtarget.med","meshtarget",0)
+
+# Remapper of type P0P0 (interpolation from cells to cells)
+remap = MEDCouplingRemapper()
+remap.prepare(msource,mtarget,"P0P0")
+
+defaultValue = 1e100
+ftarget = remap.transferField(fsource,defaultValue)
+ftarget.setName("Temperature")
+
+outfilename = "loadsource_fieldtarget.med"
+MEDLoader.WriteField(outfilename,ftarget,True)
+# _T1B
--- /dev/null
+# You first have to set the shell environement for medcoupling
+# (see medcoupling/env.sh)
+
+all:
+ python projection.py
+
+clean:
+ rm MeshTarget.vtu MeshSource.vtu FieldTarget.vtu *~
--- /dev/null
+from MEDLoader import *
+from MEDCouplingRemapper import *
+
+ms=MEDFileMesh.New("MeshSource.med") ; ms=ms.getMeshAtLevel(0)
+mt=MEDFileMesh.New("MeshTarget.med") ; mt=mt.getMeshAtLevel(0)
+fs=ms.fillFromAnalytic(ON_CELLS,1,"319.*cos(((x)*(x)*3+(y-0.52)*(y-0.52)+(z-0.1)*(z-0.1))*7)")
+fs.setNature(ConservativeVolumic)
+fs.setName("Temperature")
+MEDCouplingFieldDouble.WriteVTK("MeshSource.vtu",[fs])
+
+mt2=mt.deepCpy()
+mt2.translate([0.4,0.,0.])
+mt2.writeVTK("MeshTarget.vtu")
+#
+remap=MEDCouplingRemapper()
+remap.prepare(ms,mt,"P0P0")
+ft=remap.transferField(fs,1e100)
+ft.setName("Temperature")
+ft.getMesh().translate([0.4,0.,0.])
+MEDCouplingFieldDouble.WriteVTK("FieldTarget.vtu",[ft])
+
+
+# Image illustrating the starting point:
+# - load MeshSource.vtu (contains mesh+field)
+# - load MeshTarget.vtu (contains only the mesh with translation)
+#
+# Image illustrating the result point:
+# - load MeshSource.vtu
+# - load FieldTarget.vtu
+
