Custom Methods

Custom smoothing and forecasting methods must either inherit from pycast.methods.BaseMethod or pycast.methods.BaseForecastingMethod and implement the following functions:

• __init__(self, *args, **kwargs)
• execute(self, timeSeries)
• get_parameter_intervals(self)

Code to start with

To implement your custom method, it is recommended to start with the following example:

#!/usr/bin/env python
# -*- coding: UTF-8 -*-

#Copyright (c) 2012-2015 Christian Schwarz
#
#Permission is hereby granted, free of charge, to any person obtaining
#a copy of this software and associated documentation files (the
#"Software"), to deal in the Software without restriction, including
#without limitation the rights to use, copy, modify, merge, publish,
#distribute, sublicense, and/or sell copies of the Software, and to
#permit persons to whom the Software is furnished to do so, subject to
#the following conditions:
#
#The above copyright notice and this permission notice shall be
#included in all copies or substantial portions of the Software.
#
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
#EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
#MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
#NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
#LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
#WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

from pycast.methods import BaseMethod
from pycast.common.timeseries import TimeSeries

class CustomSmoothingMethod(BaseMethod):
## Alternative:
### class CustomForecastingMethod(BaseMethod):
"""This is your custom Method"""

    def __init__(self, *args, **kwargs):
        """Initializes the BaseMethod.

        :param List args:    Arguments that are required for initialization.
        :param Dictionary hasToBeSorted:    Keyword arguments that are required for initialization.
        """
        super(BaseMethod, self).__init__(requiredParameters, hasToBeSorted, hasToBeNormalized)

        ## YOUR CUSTOM CODE HERE

    def _get_parameter_intervals(self):
        """Returns the intervals for the methods parameter.

        Only parameters with defined intervals can be used for optimization!

        :return:    Returns a dictionary containing the parameter intervals, using the parameter
            name as key, while the value hast the following format:

            [minValue, maxValue, minIntervalClosed, maxIntervalClosed]

                - minValue
                    Minimal value for the parameter

                - maxValue
                    Maximal value for the parameter

                - minIntervalClosed
                    :py:const:`True`, if minValue represents a valid value for the parameter.
                    :py:const:`False` otherwise.

                - maxIntervalClosed:
                    :py:const:`True`, if maxValue represents a valid value for the parameter.
                    :py:const:`False` otherwise.

        :rtype:     Dictionary
        """
        parameterIntervals = {}

        ## YOUR METHOD SPECIFIC CODE HERE!

        return parameterIntervals

    def execute(self, timeSeries):
    """Executes the BaseMethod on a given TimeSeries object.

    :param TimeSeries timeSeries: TimeSeries object that fullfills all requirements (normalization, sortOrder).

    :return:    Returns a TimeSeries object containing the smoothed/forecasted values.
    :rtype:     TimeSeries

    :raise:    Raises a :py:exc:`NotImplementedError` if the child class does not overwrite this function.
    """
    ## YOUR METHOD SPECIFIC CODE HERE!