def __init__(self):
"""
Constructor.
@ In, None
@ Out, None
"""
BaseType.__init__(self)
self.name = 'DataObject'
self.printTag = self.name
self._sampleTag = 'RAVEN_sample_ID' # column name to track samples
self.protectedTags = ['RAVEN_parentID', 'RAVEN_isEnding'
] # list(str) protected RAVEN variable names,
# should not be avail to user as var names
self._inputs = [] # list(str) if input variables
self._outputs = [] # list(str) of output variables
self._metavars = [] # list(str) of POINTWISE metadata variables
self._orderedVars = [] # list(str) of vars IN ORDER of their index
self._meta = {} # dictionary to collect meta until data is collapsed
self._selectInput = None # if not None, describes how to collect input data from history
self._selectOutput = None # if not None, describes how to collect output data from history
self._pivotParams = {
} # independent dimensions as keys, values are the vars that depend on them
self._fromVarToIndex = {
} # mapping between variables and indexes ({var:index}).
# "index" here refers to dimensional variables (e.g. time, x, y, z etc)
self._aliases = {} # variable aliases
self._data = None # underlying data structure
self._collector = None # object used to collect samples
self._inputKDTree = None # for finding outputs given inputs (pointset only?)
self._scaleFactors = None # scaling factors inputs as {var:(mean,scale)}
self.hierarchical = False # this flag controls the printing/plotting of the dataobject
def __init__(self):
"""
Init of Base class
@ In, None
@ Out, None
"""
BaseType.__init__(self)
## Use the class name as the type, so as we extend this class, this is
## automatically updated to be the correct value. Honestly, we shouldn't
## need this as we can just reference the class name wherever this is used.
## Otherwise, if you don't agree with that sentiment, then this should at
## least propogate itself up the hierarchy
self.type = self.__class__.__name__
# outstreaming options
self.options = {}
# counter
self.counter = 0
# overwrite outstream?
self.overwrite = True
# outstream types available
self.availableOutStreamType = []
# number of agregated outstreams
self.numberAggregatedOS = 1
# optional sub directory for printing and plotting
self.subDirectory = None
self.printTag = 'OUTSTREAM MANAGER'
self.filename = ''
def __init__(self):
"""
Default Constructor that will initialize member variables with reasonable
defaults or empty lists/dictionaries where applicable.
@ In, None
@ Out, None
"""
BaseType.__init__(self)
Assembler.__init__(self)
self.counter = 0 # Counter of the samples performed (better the input generated!!!). It is reset by calling the function self.initialize
self.auxcnt = 0 # Aux counter of samples performed (for its usage check initialize method)
self.limit = sys.maxsize # maximum number of Samples (for example, Monte Carlo = Number of HistorySet to run, DET = Unlimited)
self.toBeSampled = {
} # Sampling mapping dictionary {'Variable Name':'name of the distribution'}
self.dependentSample = {
} # Sampling mapping dictionary for dependent variables {'Variable Name':'name of the external function'}
self.distDict = {
} # Contains the instance of the distribution to be used, it is created every time the sampler is initialized. keys are the variable names
self.funcDict = {
} # Contains the instance of the function to be used, it is created every time the sampler is initialized. keys are the variable names
self.values = {
} # for each variable the current value {'var name':value}
self.inputInfo = {
} # depending on the sampler several different type of keywarded information could be present only one is mandatory, see below
self.initSeed = None # if not provided the seed is randomly generated at the istanciation of the sampler, the step can override the seed by sending in another seed
self.inputInfo[
'SampledVars'] = self.values # this is the location where to get the values of the sampled variables
self.inputInfo['SampledVarsPb'] = {
} # this is the location where to get the probability of the sampled variables
self.inputInfo[
'PointProbability'] = None # this is the location where the point wise probability is stored (probability associated to a sampled point)
self.inputInfo['crowDist'] = {
} # Stores a dictionary that contains the information to create a crow distribution. Stored as a json object
self.constants = {} # In this dictionary
self.reseedAtEachIteration = False # Logical flag. True if every newer evaluation is performed after a new reseeding
self.FIXME = False # FIXME flag
self.printTag = self.type # prefix for all prints (sampler type)
self.restartData = None # presampled points to restart from
self.restartTolerance = 1e-15 # strictness with which to find matches in the restart data
self._endJobRunnable = sys.maxsize # max number of inputs creatable by the sampler right after a job ends (e.g., infinite for MC, 1 for Adaptive, etc)
######
self.variables2distributionsMapping = {
} # for each variable 'varName' , the following informations are included: 'varName': {'dim': 1, 'reducedDim': 1,'totDim': 2, 'name': 'distName'} ; dim = dimension of the variable; reducedDim = dimension of the variable in the transformed space; totDim = total dimensionality of its associated distribution
self.distributions2variablesMapping = {
} # for each variable 'distName' , the following informations are included: 'distName': [{'var1': 1}, {'var2': 2}]} where for each var it is indicated the var dimension
self.NDSamplingParams = {
} # this dictionary contains a dictionary for each ND distribution (key). This latter dictionary contains the initialization parameters of the ND inverseCDF ('initialGridDisc' and 'tolerance')
######
self.addAssemblerObject('Restart', '-n', True)
#used for PCA analysis
self.variablesTransformationDict = {
} # for each variable 'modelName', the following informations are included: {'modelName': {latentVariables:[latentVar1, latentVar2, ...], manifestVariables:[manifestVar1,manifestVar2,...]}}
self.transformationMethod = {
} # transformation method used in variablesTransformation node {'modelName':method}
self.entitiesToRemove = [
] # This variable is used in order to make sure the transformation info is printed once in the output xml file.
def __init__(self):
"""
This is the basic method initialize the metric object
@ In, none
@ Out, none
"""
BaseType.__init__(self)
self.type = self.__class__.__name__
self.name = self.__class__.__name__
def __init__(self):
"""
This is the basic method initialize the metric object
@ In, none
@ Out, none
"""
BaseType.__init__(self)
self.type = self.__class__.__name__
self.name = self.__class__.__name__
self.acceptsProbability = False #If True the metric needs to be able to handle (value,probability) where value and probability are lists
self.acceptsDistribution = False #If True the metric needs to be able to handle a passed in Distribution
def __init__(self):
"""
Constructor
@ In, None
@ Out, None
"""
BaseType.__init__(self) # Base Class
self.database = None # Database object
self.databaseDir = '' # Database directory. Default = working directory.
self.workingDir = '' #
self.printTag = 'DATABASE' # For printing verbosity labels
self.variables = None # if not None, list of specific variables requested to be stored by user
def __init__(self):
"""
Constructor
@ In, None
@ Out, None
"""
BaseType.__init__(self)
self.__file = None # when open, refers to open file, else None
self.__path = '' # file path
self.__base = '' # file base
self.__ext = None # file extension
self.__linkedModel = None # hard link to a certain Code subtype (e.g. RELAP-7, MooseBasedApp, etc,)
self.type = None # type ("type" in the input) to label a file to any particular subcode in the code interface
self.perturbable = False # is this file perturbable by a sampling strategy?
def __init__(self):
"""
Constructor
@ In, None
@ Out, None
"""
# Base Class
BaseType.__init__(self)
# Database object
self.database = None
# Database directory. Default = working directory.
self.databaseDir = ''
self.workingDir = ''
self.printTag = 'DATABASE'
def __init__(self):
"""
Default Constructor that will initialize member variables with reasonable
defaults or empty lists/dictionaries where applicable.
@ In, None
@ Out, None
"""
#FIXME: Since the similarity of this class with the base sampler, we should merge this
BaseType.__init__(self)
Assembler.__init__(self)
self.counter = {} # Dict containing counters used for based and derived class
self.counter['mdlEval'] = 0 # Counter of the model evaluation performed (better the input generated!!!). It is reset by calling the function self.initialize
self.counter['varsUpdate'] = 0 # Counter of the optimization iteration.
self.limit = {} # Dict containing limits for each counter
self.limit['mdlEval'] = sys.maxsize # Maximum number of the loss function evaluation
self.limit['varsUpdate'] = sys.maxsize # Maximum number of the optimization iteration.
self.initSeed = None # Seed for random number generators
self.optVars = None # Decision variables for optimization
self.optVarsInit = {} # Dict containing upper/lower bounds and initial of each decision variables
self.optVarsInit['upperBound'] = {} # Dict containing upper bounds of each decision variables
self.optVarsInit['lowerBound'] = {} # Dict containing lower bounds of each decision variables
self.optVarsInit['initial'] = {} # Dict containing initial values of each decision variables
self.optVarsInit['ranges'] = {} # Dict of the ranges (min and max) of each variable's domain
self.optVarsHist = {} # History of normalized decision variables for each iteration
self.nVar = 0 # Number of decision variables
self.objVar = None # Objective variable to be optimized
self.optType = None # Either maximize or minimize
self.optTraj = None # Identifiers of parallel optimization trajectories
self.thresholdTrajRemoval = None # Threshold used to determine the convergence of parallel optimization trajectories
self.paramDict = {} # Dict containing additional parameters for derived class
self.absConvergenceTol = 0.0 # Convergence threshold (absolute value)
self.relConvergenceTol = 1.e-3 # Convergence threshold (relative value)
self.solutionExport = None #This is the data used to export the solution (it could also not be present)
self.values = {} # for each variable the current value {'var name':value}
self.inputInfo = {} # depending on the optimizer several different type of keywarded information could be present only one is mandatory, see below
self.inputInfo['SampledVars' ] = self.values # this is the location where to get the values of the sampled variables
self.constants = {} # dictionary of constants variables
self.FIXME = False # FIXME flag
self.printTag = self.type # prefix for all prints (optimizer type)
self._endJobRunnable = sys.maxsize # max number of inputs creatable by the optimizer right after a job ends
self.constraintFunction = None # External constraint function, could be not present
self.mdlEvalHist = None # Containing information of all model evaluation
self.objSearchingROM = None # ROM used internally for fast loss function evaluation
self.addAssemblerObject('Restart' ,'-n',True)
self.addAssemblerObject('TargetEvaluation','1')
self.addAssemblerObject('Function','-1')
def __init__(self,runInfoDict):
"""
Constructor
@ In, runInfoDict, dict, the dictionary containing the runInfo (read in the XML input file)
@ Out, None
"""
BaseType.__init__(self)
Assembler.__init__(self)
#if alias are defined in the input it defines a mapping between the variable names in the framework and the one for the generation of the input
#self.alias[framework variable name] = [input code name]. For Example, for a MooseBasedApp, the alias would be self.alias['internal_variable_name'] = 'Material|Fuel|thermal_conductivity'
self.alias = {'input':{},'output':{}}
self.subType = ''
self.runQueue = []
self.printTag = 'MODEL'
self.createWorkingDir = False
def __init__(self,runInfoDict):
"""
Constructor
@ In, None
@ Out, None
"""
BaseType.__init__(self)
self.database = None # Database object
self.exist = False # does it exist?
self.built = False # is it built?
self.filename = "" # filename
self.workingDir = runInfoDict['WorkingDir']
self.databaseDir = self.workingDir # Database directory. Default = working directory.
self.printTag = 'DATABASE' # For printing verbosity labels
self.variables = None # if not None, list of specific variables requested to be stored by user
def __init__(self,runInfoDict):
"""
Constructor
@ In, runInfoDict, dict, the dictionary containing the runInfo (read in the XML input file)
@ Out, None
"""
BaseType.__init__(self)
self.workingDir = runInfoDict['WorkingDir']
self.__functionFile = '' # function file name
self.__actionDictionary = {} # action dictionary
# dictionary of implemented actions
self.__actionImplemented = {'residuumSign':False,'supportBoundingTest':False,'residuum':False,'gradient':False}
self.__inputVariables = [] # list of variables' names' given in input (xml)
self.__inputFromWhat = {} # dictionary of input data type
self.__inputFromWhat['dict'] = self.__inputFromDict
#self.__inputFromWhat['Data'] = self.__inputFromData
self.printTag = 'FUNCTIONS'
def getInputSpecification(cls):
"""
Method to get a reference to a class that specifies the input data for class "cls".
@ In, cls, the class for which we are retrieving the specification
@ Out, inputSpecification, InputData.ParameterInput, class to use for specifying the input of cls.
"""
spec = BaseType.getInputSpecification()
spec.addParam('dir', param_type=InputTypes.StringType, required=False)
return spec
def __init__(self):
"""
Constructor
@ In, None
@ Out, None
"""
BaseType.__init__(self)
self.parList = [
] # List of list [[role played in the step, class type, specialization, global name (user assigned by the input)]]
self.sleepTime = 0.005 # Waiting time before checking if a run is finished
#If a step possess re-seeding instruction it is going to ask to the sampler to re-seed according
# re-seeding = a number to be used as a new seed
# re-seeding = 'continue' the use the already present random environment
#If there is no instruction (self.initSeed = None) the sampler will reinitialize
self.initSeed = None
self._knownAttribute += [
'sleepTime', 're-seeding', 'pauseAtEnd', 'fromDirectory'
]
self._excludeFromModelValidation = ['SolutionExport']
self.printTag = 'STEPS'