Source code for sas.sascalc.calculator.BaseComponent

#!/usr/bin/env python

"""
Provide base functionality for all model components
"""

# imports
import copy
from collections import OrderedDict

import numpy as np
#TO DO: that about a way to make the parameter
#is self return if it is fittable or not

[docs]class BaseComponent: """ Basic model component Since version 0.5.0, basic operations are no longer supported. """
[docs] def __init__(self): """ Initialization""" ## Name of the model self.name = "BaseComponent" ## Parameters to be accessed by client self.params = {} self.details = {} ## Dictionary used to store the dispersity/averaging # parameters of dispersed/averaged parameters. self.dispersion = {} # string containing information about the model such as the equation #of the given model, exception or possible use self.description = '' #list of parameter that can be fitted self.fixed = [] #list of non-fittable parameter self.non_fittable = [] ## parameters with orientation self.orientation_params = [] ## magnetic parameters self.magnetic_params = [] ## store dispersity reference self._persistency_dict = {} ## independent parameter name and unit [string] self.input_name = "Q" self.input_unit = "A^{-1}" ## output name and unit [string] self.output_name = "Intensity" self.output_unit = "cm^{-1}" self.is_multiplicity_model = False self.is_structure_factor = False self.is_form_factor = False
[docs] def __str__(self): """ :return: string representatio """ return self.name
[docs] def is_fittable(self, par_name): """ Check if a given parameter is fittable or not :param par_name: the parameter name to check """ return par_name.lower() in self.fixed
#For the future #return self.params[str(par_name)].is_fittable()
[docs] def run(self, x): """ run 1d """ return NotImplemented
[docs] def runXY(self, x): """ run 2d """ return NotImplemented
[docs] def calculate_ER(self): """ Calculate effective radius """ return NotImplemented
[docs] def calculate_VR(self): """ Calculate volume fraction ratio """ return NotImplemented
[docs] def evalDistribution(self, qdist): """ Evaluate a distribution of q-values. * For 1D, a numpy array is expected as input: :: evalDistribution(q) where q is a numpy array. * For 2D, a list of numpy arrays are expected: [qx_prime,qy_prime], where 1D arrays, :: qx_prime = [ qx[0], qx[1], qx[2], ....] and :: qy_prime = [ qy[0], qy[1], qy[2], ....] Then get :: q = np.sqrt(qx_prime^2+qy_prime^2) that is a qr in 1D array; :: q = [q[0], q[1], q[2], ....] .. note:: Due to 2D speed issue, no anisotropic scattering is supported for python models, thus C-models should have their own evalDistribution methods. The method is then called the following way: :: evalDistribution(q) where q is a numpy array. :param qdist: ndarray of scalar q-values or list [qx,qy] where qx,qy are 1D ndarrays """ if qdist.__class__.__name__ == 'list': # Check whether we have a list of ndarrays [qx,qy] if len(qdist)!=2 or \ qdist[0].__class__.__name__ != 'ndarray' or \ qdist[1].__class__.__name__ != 'ndarray': msg = "evalDistribution expects a list of 2 ndarrays" raise RuntimeError(msg) # Extract qx and qy for code clarity qx = qdist[0] qy = qdist[1] # calculate q_r component for 2D isotropic q = np.sqrt(qx**2+qy**2) # vectorize the model function runXY v_model = np.vectorize(self.runXY, otypes=[float]) # calculate the scattering iq_array = v_model(q) return iq_array elif qdist.__class__.__name__ == 'ndarray': # We have a simple 1D distribution of q-values v_model = np.vectorize(self.runXY, otypes=[float]) iq_array = v_model(qdist) return iq_array else: mesg = "evalDistribution is expecting an ndarray of scalar q-values" mesg += " or a list [qx,qy] where qx,qy are 2D ndarrays." raise RuntimeError(mesg)
[docs] def clone(self): """ Returns a new object identical to the current object """ obj = copy.deepcopy(self) return self._clone(obj)
[docs] def _clone(self, obj): """ Internal utility function to copy the internal data members to a fresh copy. """ obj.params = copy.deepcopy(self.params) obj.details = copy.deepcopy(self.details) obj.dispersion = copy.deepcopy(self.dispersion) obj._persistency_dict = copy.deepcopy( self._persistency_dict) return obj
[docs] def set_dispersion(self, parameter, dispersion): """ model dispersions """ ##Not Implemented return None
[docs] def getProfile(self): """ Get SLD profile : return: (z, beta) where z is a list of depth of the transition points beta is a list of the corresponding SLD values """ #Not Implemented return None, None
[docs] def setParam(self, name, value): """ Set the value of a model parameter :param name: name of the parameter :param value: value of the parameter """ # Look for dispersion parameters toks = name.split('.') if len(toks)==2: for item in self.dispersion.keys(): if item.lower()==toks[0].lower(): for par in self.dispersion[item]: if par.lower() == toks[1].lower(): self.dispersion[item][par] = value return else: # Look for standard parameter for item in self.params.keys(): if item.lower()==name.lower(): self.params[item] = value return raise ValueError("Model does not contain parameter %s" % name)
[docs] def getParam(self, name): """ Set the value of a model parameter :param name: name of the parameter """ # Look for dispersion parameters toks = name.split('.') if len(toks)==2: for item in self.dispersion.keys(): if item.lower()==toks[0].lower(): for par in self.dispersion[item]: if par.lower() == toks[1].lower(): return self.dispersion[item][par] else: # Look for standard parameter for item in self.params.keys(): if item.lower()==name.lower(): return self.params[item] raise ValueError("Model does not contain parameter %s" % name)
[docs] def getParamList(self): """ Return a list of all available parameters for the model """ list = _ordered_keys(self.params) # WARNING: Extending the list with the dispersion parameters list.extend(self.getDispParamList()) return list
[docs] def getDispParamList(self): """ Return a list of all available parameters for the model """ list = [] for item in _ordered_keys(self.dispersion): for p in _ordered_keys(self.dispersion[item]): if p not in ['type']: list.append('%s.%s' % (item.lower(), p.lower())) return list
# Old-style methods that are no longer used
[docs] def setParamWithToken(self, name, value, token, member): """ set Param With Token """ return NotImplemented
[docs] def getParamWithToken(self, name, token, member): """ get Param With Token """ return NotImplemented
[docs] def getParamListWithToken(self, token, member): """ get Param List With Token """ return NotImplemented
[docs] def __add__(self, other): """ add """ raise ValueError("Model operation are no longer supported")
[docs] def __sub__(self, other): """ sub """ raise ValueError("Model operation are no longer supported")
[docs] def __mul__(self, other): """ mul """ raise ValueError("Model operation are no longer supported")
[docs] def __div__(self, other): """ div """ raise ValueError("Model operation are no longer supported")
[docs]def _ordered_keys(d): keys = list(d.keys()) if not isinstance(d, OrderedDict): keys.sort() return keys