Source code for sas.sasgui.perspectives.fitting.model_thread

"""
Calculation thread for modeling
"""

import time
import math

import numpy as np

from sas.sascalc.data_util.calcthread import CalcThread
from sas.sascalc.fit.MultiplicationModel import MultiplicationModel

[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 = max(math.fabs(self.data.xmax), math.fabs(self.data.xmin)) newy = max(math.fabs(self.data.ymax), math.fabs(self.data.ymin)) self.qmax = math.sqrt(newx**2 + newy**2) 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 = np.sqrt(self.data.qx_data**2 + self.data.qy_data**2) # 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 &= self.data.mask index_model &= np.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] ]) # Initialize output to NaN so masked elements do not get plotted. output = np.empty_like(self.data.qx_data) # output default is None # This method is to distinguish between masked #point(nan) and data point = 0. output[:] = np.NaN # set value for self.mask==True, else still None to Plottools output[index_model] = value elapsed = time.time() - self.starttime self.complete(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, #qstep=self.qstep, update_chisqr=self.update_chisqr, source=self.source)
[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 = np.zeros((len(self.data.x))) index = (self.qmin <= self.data.x) & (self.data.x <= self.qmax) # 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 = np.zeros((len(self.data.x))) unsmeared_output[first_bin:last_bin+1] = self.model.evalDistribution(mask) self.smearer.model = self.model 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, np.ndarray) and output.shape == self.data.y.shape: unsmeared_data = np.zeros((len(self.data.x))) unsmeared_error = np.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: output[index] = self.model.evalDistribution(self.data.x[index]) x=self.data.x[index] y=output[index] sq_values = None pq_values = None if isinstance(self.model, MultiplicationModel): s_model = self.model.s_model p_model = self.model.p_model sq_values = s_model.evalDistribution(x) pq_values = p_model.evalDistribution(x) elif hasattr(self.model, "calc_composition_models"): results = self.model.calc_composition_models(x) if results is not None: pq_values, sq_values = results elapsed = time.time() - self.starttime self.complete(x=x, y=y, 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_model=unsmeared_output, unsmeared_data=unsmeared_data, unsmeared_error=unsmeared_error, pq_model=pq_values, sq_model=sq_values)
[docs] def results(self): """ Send resuts of the computation """ return [self.out, self.index]
""" Example: :: class CalcCommandline: def __init__(self, n=20000): #print(thread.get_ident()) from sasmodels.sasview_model import _make_standard_model cylinder = _make_standard_model('cylinder') model = cylinder() print(model.runXY([0.01, 0.02])) qmax = 0.01 qstep = 0.0001 self.done = False x = numpy.arange(-qmax, qmax+qstep*0.01, qstep) y = numpy.arange(-qmax, qmax+qstep*0.01, qstep) calc_thread_2D = Calc2D(x, y, None, model.clone(),None, -qmax, qmax,qstep, completefn=self.complete, updatefn=self.update , yieldtime=0.0) calc_thread_2D.queue() calc_thread_2D.ready(2.5) while not self.done: time.sleep(1) def update(self,output): print("update") def complete(self, image, data, model, elapsed, qmin, qmax,index, qstep ): print("complete") self.done = True if __name__ == "__main__": CalcCommandline() """