From f603eb64ffb79368b17f2380c6cba9cc9e366266 Mon Sep 17 00:00:00 2001 From: Jean-Philippe ARGAUD Date: Mon, 14 Nov 2011 14:11:29 +0100 Subject: [PATCH] =?utf8?q?Am=C3=A9lioration=20de=20la=20bibliographie=20et?= =?utf8?q?=20des=20exemples?= MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit --- doc/bibliography.rst | 25 +++++++++++++++++++++++++ doc/examples.rst | 33 +++------------------------------ doc/index.rst | 21 +-------------------- 3 files changed, 29 insertions(+), 50 deletions(-) create mode 100644 doc/bibliography.rst diff --git a/doc/bibliography.rst b/doc/bibliography.rst new file mode 100644 index 0000000..c682554 --- /dev/null +++ b/doc/bibliography.rst @@ -0,0 +1,25 @@ +.. _section_bibliography: + +================================================================================ +Bibliography +================================================================================ + +.. [Argaud09] Argaud J.-P., Bouriquet B., Hunt J., *Data Assimilation from Operational and Industrial Applications to Complex Systems*, Mathematics Today, pp.150-152, October 2009 + +.. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999, http://www.ecmwf.int/newsevents/training/rcourse_notes/pdf_files/Assim_concepts.pdf + +.. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2004-2008, http://cerea.enpc.fr/HomePages/bocquet/assim.pdf + +.. [Byrd95] Byrd R. H., Lu P., Nocedal J., *A Limited Memory Algorithm for Bound Constrained Optimization*, SIAM Journal on Scientific and Statistical Computing, 16(5), pp.1190-1208, 1995 + +.. [Ide97] Ide K., Courtier P., Ghil M., Lorenc A. C., *Unified notation for data assimilation: operational, sequential and variational*, Journal of the Meteorological Society of Japan, 75(1B), pp.181-189, 1997 + +.. [Kalnay03] Kalnay E., *Atmospheric Modeling, Data Assimilation and Predictability*, Cambridge University Press, 2003 + +.. [Tarantola87] Tarantola A., *Inverse Problem: Theory Methods for Data Fitting and Parameter Estimation*, Elsevier, 1987 + +.. [Talagrand97] Talagrand O., *Assimilation of Observations, an Introduction*, Journal of the Meteorological Society of Japan, 75(1B), pp.191-209, 1997 + +.. [WikipediaDA] Wikipedia/Data_assimilation: http://en.wikipedia.org/wiki/Data_assimilation + +.. [Zhu97] Zhu C., Byrd R. H., Nocedal J., *L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, Vol 23(4), pp.550-560, 1997 diff --git a/doc/examples.rst b/doc/examples.rst index b0e8bac..6982776 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -207,8 +207,6 @@ the user can choose to split the file in several ones, or to mix explicit data definition in the ADAO GUI and implicit data definition by external files. The present script looks like:: - #-*-coding:iso-8859-1-*- - # import numpy # # Definition of the Background as a vector @@ -305,8 +303,6 @@ file, using the mandatory variable name "*AlgorithmParameters*" for the dictionary. All the keys inside the dictionary are optional, they all have default values, and can exist without being used. For example:: - #-*-coding:iso-8859-1-*- - # AlgorithmParameters = { "Minimizer" : "CG", # Possible choice : "LBFGSB", "TNC", "CG", "BFGS" "MaximumNumberOfSteps" : 10, @@ -339,7 +335,8 @@ previously, all the numerical values of this example are arbitrary. The objective is to set up the input and output definitions of a physical case by external python scripts, using a general non-linear operator, adding control on parameters and so on... The complete framework scripts can be found in the -ADAO examples standard directory. +ADAO skeletons examples directory under the name +"*External_data_definition_by_scripts*". Experimental set up +++++++++++++++++++ @@ -357,7 +354,7 @@ The background state :math:`\mathbf{x}^b`, which represent some *a priori* knowledge of the true state, is build as a normal random perturbation of 20% the true state :math:`\mathbf{x}^t` for each component, which is: - ``Xb = Xt + normal(0,20%*Xt)`` + ``Xb = Xt + normal(0, 20%*Xt)`` To describe the background error covariances matrix :math:`\mathbf{B}`, we make as previously the hypothesis of uncorrelated errors (that is, a diagonal matrix, @@ -401,8 +398,6 @@ We first define the true state :math:`\mathbf{x}^t` and some convenient matrix building function, in a Python script file named ``Physical_data_and_covariance_matrices.py``:: - #-*-coding:iso-8859-1-*- - # import numpy # def True_state(): @@ -426,9 +421,6 @@ perturbation of the true state, adding at the end of the script the definition of a *required ADAO variable* in order to export the defined value. It is done in a Python script file named ``Script_Background_xb.py``:: - #-*-coding:iso-8859-1-*- - # - import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH") from Physical_data_and_covariance_matrices import True_state import numpy # @@ -447,9 +439,6 @@ In the same way, we define the background error covariance matrix background of the true state, using the convenient function already defined. It is done in a Python script file named ``Script_BackgroundError_B.py``:: - #-*-coding:iso-8859-1-*- - # - import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH") from Physical_data_and_covariance_matrices import True_state, Simple_Matrix # xt, names = True_state() @@ -471,8 +460,6 @@ of the ADAO examples standard directory. It can be used in the case only the non-linear direct physical simulation exists. The script is partly reproduced here for convenience:: - #-*-coding:iso-8859-1-*- - # def FunctionH( XX ): """ Direct non-linear simulation operator """ # @@ -557,9 +544,6 @@ an input parameter, found under the key "value", in a variable named The following example Python script file named ``Script_ObservationOperator_H.py``, illustrates the case:: - #-*-coding:iso-8859-1-*- - # - import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH") import Physical_simulation_functions import numpy, logging # @@ -631,9 +615,6 @@ In this twin experiments framework, the observation :math:`\mathbf{y}^o` and its error covariances matrix :math:`\mathbf{R}` can be generated. It is done in two Python script files, the first one being named ``Script_Observation_yo.py``:: - #-*-coding:iso-8859-1-*- - # - import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH") from Physical_data_and_covariance_matrices import True_state from Physical_simulation_functions import FunctionH # @@ -647,9 +628,6 @@ Python script files, the first one being named ``Script_Observation_yo.py``:: and the second one named ``Script_ObservationError_R.py``:: - #-*-coding:iso-8859-1-*- - # - import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH") from Physical_data_and_covariance_matrices import True_state, Simple_Matrix from Physical_simulation_functions import FunctionH # @@ -668,8 +646,6 @@ assimilation algorithm. For example, if we use the standard 3DVAR algorithm, the following parameters can be defined in a Python script file named ``Script_AlgorithmParameters.py``:: - #-*-coding:iso-8859-1-*- - # # Creating the required ADAO variable # ----------------------------------- AlgorithmParameters = { @@ -688,9 +664,6 @@ use a intermediary Python script file in order to extract these results. The following example Python script file named ``Script_UserPostAnalysis.py``, illustrates the fact:: - #-*-coding:iso-8859-1-*- - # - import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH") from Physical_data_and_covariance_matrices import True_state import numpy # diff --git a/doc/index.rst b/doc/index.rst index c438e78..fe3a54c 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -38,29 +38,10 @@ condensed notation, consisting in the use of a space to separate values and a using examples advanced + bibliography Indices and tables ================================================================================ * :ref:`genindex` * :ref:`search` - -.. [Argaud09] Argaud J.-P., Bouriquet B., Hunt J., *Data Assimilation from Operational and Industrial Applications to Complex Systems*, Mathematics Today, pp.150-152, October 2009 - -.. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999, http://www.ecmwf.int/newsevents/training/rcourse_notes/pdf_files/Assim_concepts.pdf - -.. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2004-2008, http://cerea.enpc.fr/HomePages/bocquet/assim.pdf - -.. [Byrd95] Byrd R. H., Lu P., Nocedal J., *A Limited Memory Algorithm for Bound Constrained Optimization*, SIAM Journal on Scientific and Statistical Computing, 16(5), pp.1190-1208, 1995 - -.. [Ide97] Ide K., Courtier P., Ghil M., Lorenc A. C., *Unified notation for data assimilation: operational, sequential and variational*, Journal of the Meteorological Society of Japan, 75(1B), pp.181-189, 1997 - -.. [Kalnay03] Kalnay E., *Atmospheric Modeling, Data Assimilation and Predictability*, Cambridge University Press, 2003 - -.. [Tarantola87] Tarantola A., *Inverse Problem: Theory Methods for Data Fitting and Parameter Estimation*, Elsevier, 1987 - -.. [Talagrand97] Talagrand O., *Assimilation of Observations, an Introduction*, Journal of the Meteorological Society of Japan, 75(1B), pp.191-209, 1997 - -.. [WikipediaDA] Wikipedia/Data_assimilation: http://en.wikipedia.org/wiki/Data_assimilation - -.. [Zhu97] Zhu C., Byrd R. H., Nocedal J., *L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, Vol 23(4), pp.550-560, 1997 -- 2.39.2