Source code for sas.qtgui.Perspectives.Fitting.ModelThread

"""
    Calculation thread for modeling
"""

import time
import numpy
import math
from sas.sascalc.data_util.calcthread import CalcThread
from sas.sascalc.fit.MultiplicationModel import MultiplicationModel
import sas.qtgui.Utilities.LocalConfig as LocalConfig

[docs]class Calc2D(CalcThread): """ Compute 2D model This calculation assumes a 2-fold symmetry of the model where points are computed for one half of the detector and I(qx, qy) = I(-qx, -qy) is assumed. """ def __init__(self, data, model, smearer, qmin, qmax, page_id, state=None, weight=None, fid=None, toggle_mode_on=False, completefn=None, updatefn=None, update_chisqr=True, source='model', yieldtime=0.04, worktime=0.04, exception_handler=None, ): CalcThread.__init__(self, completefn, updatefn, yieldtime, worktime, exception_handler=exception_handler) self.qmin = qmin self.qmax = qmax self.weight = weight self.fid = fid #self.qstep = qstep self.toggle_mode_on = toggle_mode_on self.data = data self.page_id = page_id self.state = None # the model on to calculate self.model = model self.smearer = smearer self.starttime = 0 self.update_chisqr = update_chisqr self.source = source
[docs] def compute(self): """ Compute the data given a model function """ self.starttime = time.time() # Determine appropriate q range if self.qmin is None: self.qmin = 0 if self.qmax is None: if self.data is not None: newx = math.pow(max(math.fabs(self.data.xmax), math.fabs(self.data.xmin)), 2) newy = math.pow(max(math.fabs(self.data.ymax), math.fabs(self.data.ymin)), 2) self.qmax = math.sqrt(newx + newy) if self.data is None: msg = "Compute Calc2D receive data = %s.\n" % str(self.data) raise ValueError(msg) # Define matrix where data will be plotted radius = numpy.sqrt((self.data.qx_data * self.data.qx_data) + \ (self.data.qy_data * self.data.qy_data)) # For theory, qmax is based on 1d qmax # so that must be mulitified by sqrt(2) to get actual max for 2d index_model = (self.qmin <= radius) & (radius <= self.qmax) index_model = index_model & self.data.mask index_model = index_model & numpy.isfinite(self.data.data) if self.smearer is not None: # Set smearer w/ data, model and index. fn = self.smearer fn.set_model(self.model) fn.set_index(index_model) # Calculate smeared Intensity #(by Gaussian averaging): DataLoader/smearing2d/Smearer2D() value = fn.get_value() else: # calculation w/o smearing value = self.model.evalDistribution([ self.data.qx_data[index_model], self.data.qy_data[index_model] ]) output = numpy.zeros(len(self.data.qx_data)) # output default is None # This method is to distinguish between masked #point(nan) and data point = 0. output = output / output # set value for self.mask==True, else still None to Plottools output[index_model] = value elapsed = time.time() - self.starttime res = dict(image = output, data = self.data, page_id = self.page_id, model = self.model, state = self.state, toggle_mode_on = self.toggle_mode_on, elapsed = elapsed, index = index_model, fid = self.fid, qmin = self.qmin, qmax = self.qmax, weight = self.weight, update_chisqr = self.update_chisqr, source = self.source) if LocalConfig.USING_TWISTED: return res else: self.completefn(res)
[docs]class Calc1D(CalcThread): """ Compute 1D data """ def __init__(self, model, page_id, data, fid=None, qmin=None, qmax=None, weight=None, smearer=None, toggle_mode_on=False, state=None, completefn=None, update_chisqr=True, source='model', updatefn=None, yieldtime=0.01, worktime=0.01, exception_handler=None, ): """ """ CalcThread.__init__(self, completefn, updatefn, yieldtime, worktime, exception_handler=exception_handler) self.fid = fid self.data = data self.qmin = qmin self.qmax = qmax self.model = model self.weight = weight self.toggle_mode_on = toggle_mode_on self.state = state self.page_id = page_id self.smearer = smearer self.starttime = 0 self.update_chisqr = update_chisqr self.source = source self.out = None self.index = None
[docs] def compute(self): """ Compute model 1d value given qmin , qmax , x value """ self.starttime = time.time() output = numpy.zeros((len(self.data.x))) index = (self.qmin <= self.data.x) & (self.data.x <= self.qmax) intermediate_results = None # If we use a smearer, also return the unsmeared model unsmeared_output = None unsmeared_data = None unsmeared_error = None ##smearer the ouput of the plot if self.smearer is not None: first_bin, last_bin = self.smearer.get_bin_range(self.qmin, self.qmax) mask = self.data.x[first_bin:last_bin+1] unsmeared_output = numpy.zeros((len(self.data.x))) return_data = self.model.calculate_Iq(mask) if isinstance(return_data, tuple): # see sasmodels beta_approx: SasviewModel.calculate_Iq # TODO: implement intermediate results in smearers return_data, _ = return_data unsmeared_output[first_bin:last_bin+1] = return_data output = self.smearer(unsmeared_output, first_bin, last_bin) # Rescale data to unsmeared model # Check that the arrays are compatible. If we only have a model but no data, # the length of data.y will be zero. if isinstance(self.data.y, numpy.ndarray) and output.shape == self.data.y.shape: unsmeared_data = numpy.zeros((len(self.data.x))) unsmeared_error = numpy.zeros((len(self.data.x))) unsmeared_data[first_bin:last_bin+1] = self.data.y[first_bin:last_bin+1]\ * unsmeared_output[first_bin:last_bin+1]\ / output[first_bin:last_bin+1] unsmeared_error[first_bin:last_bin+1] = self.data.dy[first_bin:last_bin+1]\ * unsmeared_output[first_bin:last_bin+1]\ / output[first_bin:last_bin+1] unsmeared_output=unsmeared_output[index] unsmeared_data=unsmeared_data[index] unsmeared_error=unsmeared_error else: return_data = self.model.calculate_Iq(self.data.x[index]) if isinstance(return_data, tuple): # see sasmodels beta_approx: SasviewModel.calculate_Iq return_data, intermediate_results = return_data output[index] = return_data if intermediate_results: if isinstance(intermediate_results, list): # the model returns an ordered dictionary if len(intermediate_results) == 2: intermediate_results = { "P(Q)": intermediate_results[0], "S(Q)": intermediate_results[1] } else: # the model returns a callable which is then used to retrieve the data try: intermediate_results = intermediate_results() except: intermediate_results = {} else: # TODO: this conditional branch needs refactoring sq_values = None pq_values = None s_model = None p_model = None if isinstance(self.model, MultiplicationModel): s_model = self.model.s_model p_model = self.model.p_model elif hasattr(self.model, "calc_composition_models"): results = self.model.calc_composition_models(self.data.x[index]) if results is not None: pq_values, sq_values = results if pq_values is None or sq_values is None: if p_model is not None and s_model is not None: sq_values = numpy.zeros((len(self.data.x))) pq_values = numpy.zeros((len(self.data.x))) sq_values[index] = s_model.evalDistribution(self.data.x[index]) pq_values[index] = p_model.evalDistribution(self.data.x[index]) if pq_values is not None and sq_values is not None: intermediate_results = { "P(Q)": pq_values, "S(Q)": sq_values } else: intermediate_results = {} elapsed = time.time() - self.starttime res = dict(x = self.data.x[index], y = output[index], page_id = self.page_id, state = self.state, weight = self.weight, fid = self.fid, toggle_mode_on = self.toggle_mode_on, elapsed = elapsed, index = index, model = self.model, data = self.data, update_chisqr = self.update_chisqr, source = self.source, unsmeared_output = unsmeared_output, unsmeared_data = unsmeared_data, unsmeared_error = unsmeared_error, intermediate_results = intermediate_results) if LocalConfig.USING_TWISTED: return res else: self.completefn(res)
[docs] def results(self): """ Send resuts of the computation """ return [self.out, self.index]