import adao
import daEficas
import daYacsSchemaCreator
- import daCore.AssimilationStudy
+ import daCore.Aidsm
import daYacsSchemaCreator.infos_daComposant as infos
except:
logging.fatal("Import of ADAO python modules failed !" +
traceback.print_exc()
sys.exit(1)
+#===============================================================================
+
#----------- Templates Part ---------------#
+
begin_catalog_file = """# -*- coding: utf-8 -*-
#
# Copyright (C) 2008-2021 EDF R&D
JdC = JDC_CATA (
code = '%s',
execmodul = None,
- regles = ( AU_MOINS_UN ('ASSIMILATION_STUDY','CHECKING_STUDY'), AU_PLUS_UN ('ASSIMILATION_STUDY','CHECKING_STUDY')),
+ regles = ( AU_MOINS_UN ('ASSIMILATION_STUDY','OPTIMIZATION_STUDY','REDUCTION_STUDY','CHECKING_STUDY'), AU_PLUS_UN ('ASSIMILATION_STUDY','OPTIMIZATION_STUDY','REDUCTION_STUDY','CHECKING_STUDY')),
)
VERSION_CATALOGUE='%s'
Observers = F_Observers("f")
)
+OPTIMIZATION_STUDY = PROC(nom="OPTIMIZATION_STUDY",
+ op=None,
+ repetable = "n",
+ StudyName = SIMP(statut="o", typ = "TXM", defaut="ADAO Calculation Case"),
+ StudyRepertory = SIMP(statut="f", typ = "Repertoire", validators=FunctionVal(ChDir), min=1, max=1),
+ Debug = SIMP(statut="f", typ = "I", into=(0, 1), defaut=0),
+ ExecuteInContainer = SIMP(statut="f", typ = "TXM", min=1, max=1, defaut = "No", into=("No", "Mono", "Multi")),
+ AlgorithmParameters = F_AlgorithmParameters("o",({optim_names}), AlgorithmParametersInNS),
+ Background = F_Background("o", BackgroundInNS),
+ BackgroundError = F_BackgroundError("f", BackgroundErrorInNS),
+ Observation = F_Observation("o", ObservationInNS),
+ ObservationError = F_ObservationError("f", ObservationErrorInNS),
+ ObservationOperator = F_ObservationOperator("o"),
+ EvolutionModel = F_EvolutionModel("f"),
+ EvolutionError = F_EvolutionError("f", EvolutionErrorInNS),
+ ControlInput = F_ControlInput("f"),
+ UserDataInit = F_Init("f"),
+ UserPostAnalysis = F_UserPostAnalysis("o"),
+ InputVariables = F_variables("f"),
+ OutputVariables = F_variables("f"),
+ Observers = F_Observers("f")
+ )
+
+REDUCTION_STUDY = PROC(nom="REDUCTION_STUDY",
+ op=None,
+ repetable = "n",
+ StudyName = SIMP(statut="o", typ = "TXM", defaut="ADAO Calculation Case"),
+ StudyRepertory = SIMP(statut="f", typ = "Repertoire", validators=FunctionVal(ChDir), min=1, max=1),
+ Debug = SIMP(statut="f", typ = "I", into=(0, 1), defaut=0),
+ ExecuteInContainer = SIMP(statut="f", typ = "TXM", min=1, max=1, defaut = "No", into=("No", "Mono", "Multi")),
+ AlgorithmParameters = F_AlgorithmParameters("o",({reduc_names}), AlgorithmParametersInNS),
+ Background = F_Background("o", BackgroundInNS),
+ BackgroundError = F_BackgroundError("o", BackgroundErrorInNS),
+ Observation = F_Observation("o", ObservationInNS),
+ ObservationError = F_ObservationError("o", ObservationErrorInNS),
+ ObservationOperator = F_ObservationOperator("o"),
+ EvolutionModel = F_EvolutionModel("f"),
+ EvolutionError = F_EvolutionError("f", EvolutionErrorInNS),
+ ControlInput = F_ControlInput("f"),
+ UserDataInit = F_Init("f"),
+ UserPostAnalysis = F_UserPostAnalysis("o"),
+ InputVariables = F_variables("f"),
+ OutputVariables = F_variables("f"),
+ Observers = F_Observers("f")
+ )
+
CHECKING_STUDY = PROC(nom="CHECKING_STUDY",
op=None,
repetable = "n",
#----------- End of Templates Part ---------------#
-
+#===============================================================================
#----------- Begin generation script -----------#
# Generates into a string
mem_file = io.StringIO()
-# Start file
+# Initial step: On ouvre le fichier
from time import strftime
mem_file.write(unicode(begin_catalog_file, 'utf-8').format(**{'date':strftime("%Y-%m-%d %H:%M:%S")}))
-# Step initial: on obtient la liste des algos
+# Step 0: on obtient la liste des algos
algos_names = ""
+optim_names = ""
+reduc_names = ""
check_names = ""
decl_algos = ""
-assim_study_object = daCore.AssimilationStudy.AssimilationStudy()
+algal_names = ""
+assim_study_object = daCore.Aidsm.Aidsm()
algos_list = assim_study_object.get_available_algorithms()
del assim_study_object
for algo_name in algos_list:
if algo_name in infos.AssimAlgos:
logging.debug("An assimilation algorithm is found: " + algo_name)
algos_names += "\"" + algo_name + "\", "
- elif algo_name in infos.CheckAlgos:
+ if algo_name in infos.OptimizationAlgos:
+ logging.debug("An optimization algorithm is found: " + algo_name)
+ optim_names += "\"" + algo_name + "\", "
+ if algo_name in infos.ReductionAlgos:
+ logging.debug("A reduction algorithm is found: " + algo_name)
+ reduc_names += "\"" + algo_name + "\", "
+ if algo_name in infos.CheckAlgos:
logging.debug("A checking algorithm is found: " + algo_name)
check_names += "\"" + algo_name + "\", "
- else:
- logging.debug("This algorithm is not considered: " + algo_name)
+ if algo_name in infos.AssimAlgos+infos.OptimizationAlgos+infos.ReductionAlgos+infos.CheckAlgos:
+ # Pour filtrer sur les algorithmes vraiment interfacés, car il peut y en avoir moins que "algos_list"
+ algal_names += "\"" + algo_name + "\", "
-# Step 1: A partir des infos, on cree les fonctions qui vont permettre
-# d'entrer les donnees utilisateur
+# Step 1: A partir des infos, on crée les fonctions qui vont permettre
+# d'entrer les données utilisateur
for data_input_name in infos.DataTypeDict:
logging.debug('A data input Type is found: ' + data_input_name)
data_name = data_input_name
data_default = ""
ms_default = ""
- # On recupere les differentes facon d'entrer les donnees
+ # On récupère les différentes façon d'entrer les données
for basic_type in infos.DataTypeDict[data_input_name]:
data_into += "\"" + basic_type + "\", "
- # On choisit le default
+ # On choisit le défaut
data_default = "\"" + infos.DataTypeDefaultDict[data_input_name] + "\""
if data_input_name in infos.DataSValueDefaultDict:
ms_default = " defaut=\"" + infos.DataSValueDefaultDict[data_input_name] + "\","
'data_into' : data_into,
'data_default' : data_default,
'ms_default' : ms_default,
- 'algos_names' : algos_names+check_names,
+ #~ 'algos_names' : algal_names,
}))
-# Step 2: On cree les fonctions qui permettent de rentrer les donnees des algorithmes
+# Step 2: On crée les fonctions qui permettent de rentrer les données des algorithmes
for assim_data_input_name in infos.AssimDataDict:
logging.debug("An input function data input is found: " + assim_data_input_name)
# assim_name = assim_data_input_name
'default_choice' : default_choice,
}))
-# Step 3: On ajoute les fonctions representant les options possibles
+# Step 3: On ajoute les fonctions représentant les options possibles
for opt_name in infos.OptDict:
logging.debug("An optional node is found: " + opt_name)
data_name = opt_name
'data_into' : data_into,
'data_default' : data_default,
'ms_default' : ms_default,
- 'algos_names' : algos_names+check_names,
+ #~ 'algos_names' : algal_names,
}))
-# Step 4: On ajoute la methode optionnelle init
-# TODO uniformiser avec le step 3
+# Step 3bis: On ajoute la méthode optionnelle init
+# TODO si possible uniformiser avec le step 3
mem_file.write(unicode(init_method, 'utf-8'))
-# Step 5: Add observers
+# Step 4: On ajoute les observers
decl_choices = ""
for obs_var in infos.ObserversList:
decl_choices += observers_choice.format(**{'var_name':obs_var})
'decl_choices' : decl_choices,
}))
-# Step 5: Add algorithmic choices
-
-all_names = eval((algos_names+check_names))
+# Step 5: On ajoute les choix algorithmiques
+all_names = eval((algal_names))
all_algo_defaults = ""
for algo in all_names:
- assim_study_object = daCore.AssimilationStudy.AssimilationStudy()
+ assim_study_object = daCore.Aidsm.Aidsm()
assim_study_object.setAlgorithmParameters(Algorithm=algo)
par_dict = assim_study_object.get("AlgorithmRequiredParameters",False)
par_keys = sorted(par_dict.keys())
})
mem_file.write(unicode(algo_choices, 'utf-8').format(**{'all_algo_defaults':unicode(all_algo_defaults, 'utf-8')}))
-# Final step: Add algorithm and assim_study
+# Step 6: On ajoute l'algorithme et le assim_study
mem_file.write(unicode(assim_study, 'utf-8').format(**{
'algos_names':algos_names,
+ 'optim_names':optim_names,
+ 'reduc_names':reduc_names,
'check_names':check_names,
'decl_algos':decl_algos,
}))
-# Write file
+# Final step: On écrit le fichier
if sys.version_info.major > 2:
with open(os.path.join(args.catalog_path, args.catalog_name), "w", encoding='utf8') as final_file:
final_file.write(mem_file.getvalue())
with open(os.path.join(args.catalog_path, args.catalog_name), "wr") as final_file:
final_file.write(mem_file.getvalue().encode('utf-8'))
mem_file.close()
+
+#----------- End generation script -----------#
+
+#===============================================================================
