source: sasview/fittingview/src/sans/perspectives/fitting/fitproblem.py @ 850525c

################################################################################
#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. 
#
#See the license text in license.txt
#
#copyright 2009, University of Tennessee
################################################################################
import copy 
from sans.models.qsmearing import smear_selection


class FitProblemComponent(object):
    """
    Inferface containing information to store data, model, range of data, etc...
    and retreive this information. This is an inferface 
    for a fitProblem i.e relationship between data and model.
    """
    def enable_smearing(self, flag=False):
        """
        :param flag: bool.When flag is 1 get the computer smear value. When
        flag is 0 ingore smear value.
        """
    def get_smearer(self):
        """
        return smear object
        """
    def save_model_name(self, name):
        """
        """  
    def get_name(self):
        """
        """
    def set_model(self, model):
        """ 
        associates each model with its new created name
        :param model: model selected
        :param name: name created for model
        """
    def get_model(self):
        """
        :return: saved model
        """
    def set_theory_data(self, data):
        """ 
        save a copy of the data select to fit
        :param data: data selected
        """
    def get_theory_data(self):
        """
        :return: list of data dList
        """
    def set_fit_data(self, data):
        """ 
         Store of list of data and create  by create new fitproblem of each data
         id , if there was existing information about model, this information
         get copy to the new fitproblem
        :param data: list of data selected
        """   
    def get_fit_data(self):
        """
        """
    def set_model_param(self, name, value=None):
        """ 
        Store the name and value of a parameter of this fitproblem's model
        :param name: name of the given parameter
        :param value: value of that parameter
        """
    def get_model_param(self):
        """ 
        return list of couple of parameter name and value
        """
    def schedule_tofit(self, schedule=0):
        """
        set schedule to true to decide if this fit  must be performed
        """
    def get_scheduled(self):
        """
        return true or false if a problem as being schedule for fitting
        """
    def set_range(self, qmin=None, qmax=None):
        """
        set fitting range 
        """
    def get_range(self):
        """
        :return: fitting range
        """
    def set_weight(self, flag=None):
        """
        set fitting range 
        """
    def get_weight(self):
        """
        get fitting weight
        """
    def clear_model_param(self):
        """
        clear constraint info
        """
    def set_fit_tab_caption(self, caption):
        """
        store the caption of the page associated with object
        """
    def get_fit_tab_caption(self):
        """
        Return the caption of the page associated with object
        """
        
   
class FitProblemDictionary(FitProblemComponent, dict):
    """
    This module implements a dictionary of fitproblem objects
    """
    def __init__(self):
        FitProblemComponent.__init__(self)
        dict.__init__(self)
        ## the current model
        self.model = None
        ## if 1 this fit problem will be selected to fit , if 0 
        ## it will not be selected for fit
        self.schedule = 0
        ##list containing parameter name and value
        self.list_param = []
        ## fitting range
        self.qmin = None
        self.qmax = None
        self._smear_on = False
        self.scheduled = 0
        self.fit_tab_caption = ''
 
    def enable_smearing(self, flag=False, fid=None):
        """
        :param flag: bool.When flag is 1 get the computer smear value. When
        flag is 0 ingore smear value.
        """
        self._smear_on = flag
        if fid is None:
            for value in self.itervalues():
                value.enable_smearing(flag)
        else:
            if fid in self.iterkeys():
                self[fid].enable_smearing(flag)
        
    def set_smearer(self, smearer, fid=None):
        """
        save reference of  smear object on fitdata
        :param smear: smear object from DataLoader
        """
        if fid is None:
            for value in self.itervalues():
                value.set_smearer(smearer)
        else:
            if fid in self.iterkeys():
                self[fid].set_smearer(smearer)
                
    def get_smearer(self, fid=None):
        """
        return smear object
        """
        result = []
        if fid is None:
            for value in self.itervalues():
                result.append(value.get_smearer())
        else:
            if fid in self.iterkeys():
                result.append(self[fid].get_smearer())
        return result
    
    def save_model_name(self, name, fid=None):
        """
        """  
        if fid is None:
            for value in self.itervalues():
                value.save_model_name(name)
        else:
            if fid in self.iterkeys():
                self[fid].save_model_name(name)
                
    def get_name(self, fid=None):
        """
        """
        result = []
        if fid is None:
            for value in self.itervalues():
                result.append(value.get_name())
        else:
            if fid in self.iterkeys():
                result.append(self[fid].get_name())
        return result
    
    def set_model(self, model, fid=None):
        """ 
        associates each model with its new created name
        :param model: model selected
        :param name: name created for model
        """
        self.model = model
        if fid is None:
            for value in self.itervalues():
                value.set_model(self.model)
        else:
            if fid in self.iterkeys():
                self[fid].set_model(self.model)
      
    def get_model(self, fid):
        """
        :return: saved model
        """
        if fid in self.iterkeys():
            self[fid].get_model()
       
    def set_fit_tab_caption(self, caption):
        """
        store the caption of the page associated with object
        """
        self.fit_tab_caption = caption
    
    def get_fit_tab_caption(self):
        """
        Return the caption of the page associated with object
        """
        return self.fit_tab_caption
    
    def set_theory_data(self, fid, data=None):
        """ 
        save a copy of the data select to fit
        :param data: data selected
        """
        if fid in self.iterkeys():
            self[fid].set_theory_data(data)
            
    def get_theory_data(self, fid):
        """
        :return: list of data dList
        """
        if fid in self.iterkeys():
            return self[fid].get_theory_data()
            
    def add_data(self, data):
        """
        Add data to the current dictionary of fitproblem. if data id does not
        exist create a new fit problem.
        :note: only data changes in the fit problem
        """
        if data.id not in self.iterkeys():
            self[data.id] = FitProblem()
        self[data.id].set_fit_data(data)
        
    def set_fit_data(self, data):
        """ 
        save a copy of the data select to fit
        :param data: data selected
        
        """
        self.clear()
        if data is None:
            data = []
        for d in data:
            if (d is not None):
                if (d.id not in self.iterkeys()):
                    self[d.id] = FitProblem()
                self[d.id].set_fit_data(d)
                self[d.id].set_model(self.model)
                self[d.id].set_range(self.qmin, self.qmax)
                #self[d.id].set_smearer(self[d.id].get_smearer())
    def get_fit_data(self, fid):
        """
       
        return data for the given fitproblem id
        :param fid: is key representing a fitproblem. usually extract from data
                    id
        """
        if fid in self.iterkeys():
            return self[fid].get_fit_data()
   
    def set_model_param(self, name, value=None, fid=None):
        """ 
        Store the name and value of a parameter of this fitproblem's model
        :param name: name of the given parameter
        :param value: value of that parameter
        """
        if fid is None:
            for value in self.itervalues():
                value.set_model_param(name, value)
        else:
            if fid in self.iterkeys():
                self[fid].set_model_param(name, value)
                
    def get_model_param(self, fid):
        """ 
        return list of couple of parameter name and value
        """
        if fid in self.iterkeys():
            return self[fid].get_model_param()
      
    def schedule_tofit(self, schedule=0):
        """
        set schedule to true to decide if this fit  must be performed
        """
        self.scheduled = schedule
        for value in self.itervalues():
            value.schedule_tofit(schedule)
      
    def get_scheduled(self):
        """
        return true or false if a problem as being schedule for fitting
        """
        return self.scheduled
    
    def set_range(self, qmin=None, qmax=None, fid=None):
        """
        set fitting range 
        """
        self.qmin = qmin
        self.qmax = qmax
        if fid is None:
            for value in self.itervalues():
                value.set_range(self.qmin, self.qmax)
        else:
            if fid in self.iterkeys():
                self[fid].value.set_range(self.qmin, self.qmax)
        
    def get_range(self, fid):
        """
        :return: fitting range
        """
        if fid in self.iterkeys():
            return self[fid].get_range()
        
    def set_weight(self,  is2d, flag=None, fid=None):
        """
        fit weight
        """
        if fid is None:
            for value in self.itervalues():
                value.set_weight(flag=flag, is2d=is2d)
        else:
            if fid in self.iterkeys():
                self[fid].set_weight(flag=flag, is2d=is2d)
                
    def get_weight(self, fid=None):
        """
        return fit weight
        """
        if fid in self.iterkeys():
            return self[fid].get_weight()
                 
    def clear_model_param(self, fid=None):
        """
        clear constraint info
        """
        if fid is None:
            for value in self.itervalues():
                value.clear_model_param()
        else:
            if fid in self.iterkeys():
                self[fid].clear_model_param()
                
    def get_fit_problem(self):
        """
        return fitproblem contained in this dictionary
        """
        return self.itervalues()
    
    def set_result(self, result):
        """
        set a list of result
        """
        self.result = result
                
    def get_result(self):
        """
        get result 
        """
        return self.result
    

class FitProblem(FitProblemComponent):
    """  
    FitProblem class allows to link a model with the new name created in _on_model,
    a name theory created with that model  and the data fitted with the model.
    FitProblem is mostly used  as value of the dictionary by fitting module.
    """
    def __init__(self):
        FitProblemComponent.__init__(self)
        """
        contains information about data and model to fit
        """
        ## data used for fitting
        self.fit_data = None
        self.theory_data = None
        ## the current model
        self.model = None
        ## if 1 this fit problem will be selected to fit , if 0 
        ## it will not be selected for fit
        self.schedule = 0
        ##list containing parameter name and value
        self.list_param = []
        ## smear object to smear or not data1D
        self.smearer_compute_count = 0
        self.smearer_enable = False
        self.smearer_computer_value = None
        ## fitting range
        self.qmin = None
        self.qmax = None
        # fit weight
        self.weight = None
        
        
    def enable_smearing(self, flag=False):
        """
        :param flag: bool.When flag is 1 get the computer smear value. When
        flag is 0 ingore smear value.
        """
        self.smearer_enable = flag
        
    def set_smearer(self, smearer):
        """
        save reference of  smear object on fitdata
        
        :param smear: smear object from DataLoader
        
        """
        self.smearer_computer_value = smearer
       
    def get_smearer(self):
        """
        return smear object
        """
        if not self.smearer_enable:
            return None
        if self.smearer_computer_value is None and \
            self.smearer_compute_count > 1:
            #smeari_selection should be call only once per fitproblem
            self.smearer_computer_value = smear_selection(self.fit_data,
                                                           self.model)
            self.smearer_compute_count += 1
        return self.smearer_computer_value
    
    def save_model_name(self, name):
        """
        """  
        self.name_per_page= name
        
    def get_name(self):
        """
        """
        return self.name_per_page
    
    def set_model(self,model):
        """ 
        associates each model with its new created name
        :param model: model selected
        :param name: name created for model
        """
        self.model= model
        
    def get_model(self):
        """
        :return: saved model
        """
        return self.model
  
    def set_theory_data(self, data):
        """ 
        save a copy of the data select to fit
        
        :param data: data selected
        
        """
        self.theory_data = copy.deepcopy(data)
        
  
         
    def get_theory_data(self):
        """
        :return: theory generated with the current model and data of this class
        """
        return self.theory_data

    def set_fit_data(self, data):
        """ 
        Store data associated with this class
        :param data: list of data selected
        """
        self.fit_data = copy.deepcopy(data)
        
    def get_fit_data(self):
        """
        :return: data associate with this class
        """
        return self.fit_data
    
    def set_weight(self, is2d, flag=None):
        """
        Received flag and compute error on data.
        :param flag: flag to transform error of data.
        :param is2d: flag to distinguish 1D to 2D Data
        """
        from .utils import get_weight
        self.weight = get_weight(data=self.fit_data, is2d=is2d, flag=flag)
        if is2d:
            self.fit_data.err_data = self.weight
        else:
            self.fit_data.dy = self.weight

    def get_weight(self):
        """
        returns weight array
        """
        return self.weight
        
    def set_model_param(self,name,value=None):
        """ 
        Store the name and value of a parameter of this fitproblem's model
        :param name: name of the given parameter
        :param value: value of that parameter
        """
        self.list_param.append([name,value])
        
    def get_model_param(self):
        """ 
        return list of couple of parameter name and value
        """
        return self.list_param
        
    def schedule_tofit(self, schedule=0):
        """
        set schedule to true to decide if this fit  must be performed
        """
        self.schedule = schedule
        
    def get_scheduled(self):
        """
        return true or false if a problem as being schedule for fitting
        """
        return self.schedule
    
    def set_range(self, qmin=None, qmax=None):
        """
        set fitting range
        :param qmin: minimum value to consider for the fit range
        :param qmax: maximum value to consider for the fit range
        """
        self.qmin = qmin
        self.qmax = qmax
        
    def get_range(self):
        """
        :return: fitting range
        
        """
        return self.qmin, self.qmax
    
    def clear_model_param(self):
        """
        clear constraint info
        """
        self.list_param = []
        
    def set_fit_tab_caption(self, caption):
        """
        """
        self.fit_tab_caption = str(caption)
        
    def get_fit_tab_caption(self):
        """
        """
        return self.fit_tab_caption