#!/usr/bin/env python
"""
    This software was developed by the University of Tennessee as part of the
    Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
    project funded by the US National Science Foundation.

    If you use DANSE applications to do scientific research that leads to
    publication, we ask that you acknowledge the use of the software with the
    following sentence:

    "This work benefited from DANSE software developed under NSF award DMR-0520547."

    copyright 2008, University of Tennessee
"""

""" 
    Provide base functionality for all model components
    
    The following has changed since going from BaseComponent to BaseModel:
    
        - Arithmetic operation between models is no longer supported.
          It was found to be of little use and not very flexible.
        
        - Parameters are now stored as Parameter object to provide 
          the necessary extra information like limits, units, etc... 
"""

# imports   
import copy
   
class Parameter(object):
    """
        Parameter class
    """
    name  = ''
    value = 0.0
    def __init__(self, name, value):
        self.name = name
        self.value = value
        

class ParameterProperty(object):
    """
        Parameter property allow direct access to
        the parameter values
    """
    def __init__(self,name,**kw):
        self.name = name
       
    def __get__(self,instance,cls):
        return instance.parameters[self.name].value

    def __set__(self,instance,value):
        instance.parameters[self.name].value = value


   
from ModelAdaptor import ModelAdaptor

class BaseModel(ModelAdaptor):
    """ 
        Basic model component
    """
    ## Name of the model
    name = "BaseModel"
    
    def __init__(self):
        ## Dictionary of Parameter objects
        self.parameters = {}
        
        for item in self.params:
            self.parameters[item] = Parameter(item, self.params[item])

        

    # Evaluation methods to be implemented by the models
    def run(self, x=0):  return NotImplemented
    def runXY(self, x=0):  return NotImplemented
    
    def __call__(self, x=0):
        """     
            Evaluate the model. Equivalent to runXY(x)
            @param x: input value
            @return: value of the model
        """
        return self.runXY(x)
    
    def clone(self):
        """ Returns a new object identical to the current object """
        
        obj = copy.deepcopy(self)
        obj.params = copy.deepcopy(self.params)
        obj.details = copy.deepcopy(self.details)
    
        return obj
    
    def setParam(self, name, value):
        """ 
            Set the value of a model parameter
        
            @param name: name of the parameter
            @param value: value of the parameter
        """
        # Lowercase for case insensitivity
        name = name.lower()
        if name in self.parameters:
            print "found"
        return object.__setattr__(self, name, value)
        
    def getParam(self, name):
        """ 
            Set the value of a model parameter

            @param name: name of the parameter
            @param value: value of the parameter
        """
        # Lowercase for case insensitivity
        name = name.lower()
        return object.__getattribute__(self, name)
        
    def getParamList(self):
        """ 
            Return a list of all available parameters for the model 
        """
        param_list = self.params.keys()
        return param_list
        
    
if __name__ == "__main__":
    b = BaseModel() 
    print b.operateOn