source: sasview/sansmodels/src/sans/models/dispersion_models.py @ fb071900

"""
    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
"""

"""
    Class definitions for python dispersion model for 
    model parameters. These classes are bridges to the C++
    dispersion object.
    
    The ArrayDispersion class takes in numpy arrays only.
    
    Usage:
    These classes can be used to set the dispersion model of a SANS model
    parameter:
    
        cyl = CylinderModel()
        cyl.set_dispersion('radius', GaussianDispersion())
    
    
    After the dispersion model is set, you can access it's
    parameter through the dispersion dictionary:
    
        cyl.dispersion['radius']['width'] = 5.0
    
    TODO: For backward compatibility, the model parameters are still kept in
    a dictionary. The next iteration of refactoring work should involve moving
    away from value-based parameters to object-based parameter. We want to
    store parameters as objects so that we can unify the 'params' and 'dispersion'
    dictionaries into a single dictionary of parameter objects that hold the
    complete information about the parameter (units, limits, dispersion model, etc...).  
    
    
"""
import sans_extension.c_models as c_models 



class DispersionModel:
    """
        Python bridge class for a basic dispersion model 
        class with a constant parameter value distribution
    """
    def __init__(self):
        self.cdisp = c_models.new_dispersion_model()
        
    def set_weights(self, values, weights):
        """
            Set the weights of an array dispersion
        """
        message = "set_weights is not available for DispersionModel.\n"
        message += "  Solution: Use an ArrayDispersion object"
        raise "RuntimeError", message
        
class GaussianDispersion(DispersionModel):
    """
        Python bridge class for a dispersion model based 
        on a Gaussian distribution.
    """
    def __init__(self):
        self.cdisp = c_models.new_gaussian_model()
        
    def set_weights(self, values, weights):
        """
            Set the weights of an array dispersion
        """
        message = "set_weights is not available for GaussianDispersion.\n"
        message += "  Solution: Use an ArrayDispersion object"
        raise "RuntimeError", message
    
class SchulzDispersion(DispersionModel):
    """
        Python bridge class for a dispersion model based 
        on a Schulz distribution.
    """
    def __init__(self):
        self.cdisp = c_models.new_schulz_model()
        
    def set_weights(self, values, weights):
        """
            Set the weights of an array dispersion
        """
        message = "set_weights is not available for SchulzDispersion.\n"
        message += "  Solution: Use an ArrayDispersion object"
        raise "RuntimeError", message
    
class LogNormalDispersion(DispersionModel):
    """
        Python bridge class for a dispersion model based 
        on a Log Normal distribution.
    """
    def __init__(self):
        self.cdisp = c_models.new_lognormal_model()
        
    def set_weights(self, values, weights):
        """
            Set the weights of an array dispersion
        """
        message = "set_weights is not available for LogNormalDispersion.\n"
        message += "  Solution: Use an ArrayDispersion object"
        raise "RuntimeError", message
        
class ArrayDispersion(DispersionModel):
    """
        Python bridge class for a dispersion model based on arrays.
        The user has to set a weight distribution that
        will be used in the averaging the model parameter
        it is applied to. 
    """
    def __init__(self):
        self.cdisp = c_models.new_array_model()
        
    def set_weights(self, values, weights):
        """
            Set the weights of an array dispersion
            Only accept numpy arrays.
            @param values: numpy array of values
            @param weights: numpy array of weights for each value entry
        """
        if len(values) != len(weights):
            raise ValueError, "ArrayDispersion.set_weights: given arrays are of different lengths"
        
        c_models.set_dispersion_weights(self.cdisp, values, weights)
 
models = {"gaussian":GaussianDispersion, "array":ArrayDispersion,
          "schulz":SchulzDispersion, "lognormal":LogNormalDispersion}