emlib/emlib.py
def Calibrate(self,Calibration,Observation,runs=None,TimeSeries=None,Algorithm=None,start=None,end=None,dt=None):
        """
        Wrapper to calibrate model via supplied Monte Carlo algorithm.

        :param Calibration: Model Coefficients
        :type Calibration: emlib.Calibration
        :param Observation: What really happend
        :type Observation: emlib.Observation
        :param maxruns: Maximum times to integrate
        :type maxruns: int
        :param TimeSeries: Optional dtInput Table
        :type TimeSeries: emlib.TimeSeries
        :param Algorithm: Calibration Function
        :type Algorithm: **func**
        :param start: Optinal simulation start
        :type start: datetime.date,int
        :param end: optional simulation end
        :type end: datetime.date,int

        :returns: Model Calibration
        :rtype: emlib.Calibration

        This function will integrate the current model *maxruns* times using the supplied **Algorithm**.  If no algorithm is supplied :func:`GF_BruteForceMSE` is assumed.

        :Example:

        >>> bestCalibration = Model.Calibrate(startingCalibration,
                                              Observation, runs=5000)

        .. note::  Supplying a large *maxruns* may hang the terminal while the calibrator executes.  Using CTRL+C will break out of the program but all progress calibrating will be lost.

        """
        if not Algorithm:
            emlog.warn("No fitness method provided, assuming GF_BruteForceMSE")
            return GF_BruteForceMSE(self, Calibration, Observation, runs, TimeSeries, start, end, dt)
        else:
            emlog.debug("Applying fitness function:"+str(Algorithm))

            return Algorithm(self, Calibration, Observation, runs, TimeSeries, start, end, dt)

        Wrapper to calibrate model via supplied Monte Carlo algorithm.

        :param Calibration: Model Coefficients
        :type Calibration: emlib.Calibration
        :param Observation: What really happend
        :type Observation: emlib.Observation
        :param maxruns: Maximum times to integrate
        :type maxruns: int
        :param TimeSeries: Optional dtInput Table
        :type TimeSeries: emlib.TimeSeries
        :param Algorithm: Calibration Function
        :type Algorithm: **func**
        :param start: Optinal simulation start
        :type start: datetime.date,int
        :param end: optional simulation end
        :type end: datetime.date,int

        :returns: Model Calibration
        :rtype: emlib.Calibration

        This function will integrate the current model *maxruns* times using the supplied **Algorithm**.  If no algorithm is supplied :func:`GF_BruteForceMSE` is assumed.

        :Example:

        >>> bestCalibration = Model.Calibrate(startingCalibration,
                                              Observation, runs=5000)

        .. note::  Supplying a large *maxruns* may hang the terminal while the calibrator executes.  Using CTRL+C will break out of the program but all progress calibrating will be lost.

        
Wrapper to calibrate model via supplied Monte Carlo algorithm.
Wrapper to calibrate model via supplied Monte Carlo algorithm .
def Calibrate(self,Calibration,Observation,runs=None,TimeSeries=None,Algorithm=None,start=None,end=None,dt=None):
        if not Algorithm:
            emlog.warn("No fitness method provided, assuming GF_BruteForceMSE")
            return GF_BruteForceMSE(self, Calibration, Observation, runs, TimeSeries, start, end, dt)
        else:
            emlog.debug("Applying fitness function:"+str(Algorithm))
            return Algorithm(self, Calibration, Observation, runs, TimeSeries, start, end, dt)
def Calibrate ( self , Calibration , Observation , runs = None , TimeSeries = None , Algorithm = None , start = None , end = None , dt = None ) : if not Algorithm : emlog . warn ( "No fitness method provided, assuming GF_BruteForceMSE" ) return GF_BruteForceMSE ( self , Calibration , Observation , runs , TimeSeries , start , end , dt ) else : emlog . debug ( "Applying fitness function:" + str ( Algorithm ) ) return Algorithm ( self , Calibration , Observation , runs , TimeSeries , start , end , dt )
Wrapper to calibrate model via supplied Monte Carlo algorithm.
Wrapper to calibrate model via supplied Monte Carlo algorithm .
"""
        Wrapper to calibrate model via supplied Monte Carlo algorithm.

        :param Calibration: Model Coefficients
        :type Calibration: emlib.Calibration
        :param Observation: What really happend
        :type Observation: emlib.Observation
        :param maxruns: Maximum times to integrate
        :type maxruns: int
        :param TimeSeries: Optional dtInput Table
        :type TimeSeries: emlib.TimeSeries
        :param Algorithm: Calibration Function
        :type Algorithm: **func**
        :param start: Optinal simulation start
        :type start: datetime.date,int
        :param end: optional simulation end
        :type end: datetime.date,int

        :returns: Model Calibration
        :rtype: emlib.Calibration

        This function will integrate the current model *maxruns* times using the supplied **Algorithm**.  If no algorithm is supplied :func:`GF_BruteForceMSE` is assumed.

        :Example:

        >>> bestCalibration = Model.Calibrate(startingCalibration,
                                              Observation, runs=5000)

        .. note::  Supplying a large *maxruns* may hang the terminal while the calibrator executes.  Using CTRL+C will break out of the program but all progress calibrating will be lost.

        """
None
emlib.Calibration
This function will integrate the current model *maxruns* times using the supplied **Algorithm**.  If no algorithm is supplied :func:`GF_BruteForceMSE` is assumed.
self
Calibration
None
Observation
None
runs
TimeSeries
Optional dtInput Table
Optional dtInput Table
Algorithm
None
start
Optinal simulation start
Optinal simulation start
end
optional simulation end
optional simulation end
dt
maxruns
Maximum times to integrate
Maximum times to integrate
Example
>>> bestCalibration = Model.Calibrate(startingCalibration,
Observation, runs=5000)

:  Supplying a large *maxruns* may hang the terminal while the calibrator executes.  Using CTRL+C will break out of the program but all progress calibrating will be lost.
>>> bestCalibration = Model . Calibrate ( startingCalibration Observation runs = 5000 ) : Supplying a large * maxruns * may hang the terminal while the calibrator executes . Using CTRL + C will break out of the program but all progress calibrating will be lost .