Merge pull request #56 from force-h2020/remove-braindump

Remove braindump directory

braindump/datasource.py

-class DataSource():
-    """Base class that performs calculation or extraction of information
-    """
-    __metaclass__ = abc.ABCMeta
-
-    def __init__(self, name):
-        self.name = name
-
-    @abstractclassmethod
-    def provides(cls):
-        return [CUBA.key]
-
-    def execute(self, parameters):
-        """Performs the evaluation and returns a list of Result.
-        """
-
-
-class Simulator(DataSource):
-    pass
-
-
-class Database(DataSource):
-    pass
-
-
-# Represents the result of a simulator.
-# It contains the resulting cuba key, the associated uncertainty and the
-# originating simulator.
-# Difference between uncertainty and quality: uncertainty is a numerical value
-# of the value, as in the case of an experimental simulation.
-# quality is the level of accuracy of the (e.g.c omputational) method, as
-# the importance and reliability of that value. It should be an enumeration
-# value such as HIGH, MEDIUM, POOR
-Result = namedtuple("Result", "cuba_key value uncertainty originator quality")
-

braindump/kpi_calculator.py

-class KPICalculator():
-    """Base class that defines the equaation to compute the KPIs values from
-    the results of the simulators
-
-    input: a list of SimulatorResult
-    output: a list of KPIs.
-    """
-    __metaclass__ = abc.ABCMeta
-
-    def execute(self, datasource_results)
-        """Returns the KPIResult"""
-
-
-KPIResult = namedtuple("KPIResult", "name cuba_key value uncertainty quality")
-
-

braindump/main.py

-from force_bdss import *
-
-wf=Workflow()
-wf.set_mco(Dakota())
-wf.set_datasources([
-    ViscositySimulator(),
-    CostExtractor(),
-])
-wf.objectives([
-    Objective(),
-    Objective()
-    ])
-
-
-wf.set_parameters({
-    "material_1": {
-        CUBA.FORMULA: "H2O",
-        CUBA.CONCENTRATION: Range(0, 100)
-    },
-    "material_2": {
-        CUBA.FORMULA: "glycol",
-        CUBA.CONCENTRATION: Formula("material_1",
-            lambda material_1: 100 - material_1[CUBA.CONCENTRATION])
-    }
-})
-
-wf.set_result_callback(callback)
-
-# constraints vs computed value
-# e.g. constraint = value from 0 to 100
-# computed value = 100 - other value
-# TODO: Study dakota better to understand how it works and what kind of
-# interface it expects
-
-pareto_front = wf.execute()
-
-plot(pareto_front)
-

braindump/mco.py

-class MCO():
-    """Receives a list of KPIResult to decide the next step in the parameter
-    space"""
-    __metaclass__ = abc.ABCMeta
-    starting_point
-    variable_constraints
-    objectives
-
-    def get_next_parameters(kpi_results):
-        pass
-
-
-class Dakota(MCO):
-    def __init__(self, options):
-        pass
-
-
-class DakotaInput():
-    """Read the parameters from the dakota input file and returns
-    the parameters for further consumption"""
-    def parse(filename):
-        """Returns the parameters"""
-
-
-class DakotaOutput():
-    """Writes the KPIs to the file for consumption by Dakota"""

braindump/workflow.py

-# This is a prototype for the Workflow in FORCE
-import abc
-
-
-class Workflow():
-    # calculates the KPIs from the list of the cuba keys coming from the
-    # simulators
-    mco = None
-    data_sources = None
-
-
-
-
-class ViscosityCalculator(Simulator):
-    pass
-
-
-class CostExtractor(Database):
-    provides_kpi = []
-
-
-class Objective()
-    kpi = None
-    optimization_type = MAXIMIZE
-