Changes in src/sas/qtgui/Perspectives/Fitting/ModelThread.py [2df558e:5181e9b] in sasview
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src/sas/qtgui/Perspectives/Fitting/ModelThread.py (modified) (4 diffs)
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src/sas/qtgui/Perspectives/Fitting/ModelThread.py
r2df558e r5181e9b 101 101 elapsed = time.time() - self.starttime 102 102 103 res = dict(image = output, data = self.data, page_id = self.page_id, 104 model = self.model, state = self.state, 105 toggle_mode_on = self.toggle_mode_on, elapsed = elapsed, 106 index = index_model, fid = self.fid, 107 qmin = self.qmin, qmax = self.qmax, 108 weight = self.weight, update_chisqr = self.update_chisqr, 109 source = self.source) 110 103 111 if LocalConfig.USING_TWISTED: 104 return (output, 105 self.data, 106 self.page_id, 107 self.model, 108 self.state, 109 self.toggle_mode_on, 110 elapsed, 111 index_model, 112 self.fid, 113 self.qmin, 114 self.qmax, 115 self.weight, 116 self.update_chisqr, 117 self.source) 118 else: 119 self.completefn((output, 120 self.data, 121 self.page_id, 122 self.model, 123 self.state, 124 self.toggle_mode_on, 125 elapsed, 126 index_model, 127 self.fid, 128 self.qmin, 129 self.qmax, 130 self.weight, 131 #qstep=self.qstep, 132 self.update_chisqr, 133 self.source)) 134 112 return res 113 else: 114 self.completefn(res) 135 115 136 116 class Calc1D(CalcThread): … … 184 164 index = (self.qmin <= self.data.x) & (self.data.x <= self.qmax) 185 165 166 intermediate_results = None 167 186 168 # If we use a smearer, also return the unsmeared model 187 169 unsmeared_output = None … … 194 176 mask = self.data.x[first_bin:last_bin+1] 195 177 unsmeared_output = numpy.zeros((len(self.data.x))) 196 unsmeared_output[first_bin:last_bin+1] = self.model.evalDistribution(mask) 178 179 return_data = self.model.calculate_Iq(mask) 180 if isinstance(return_data, tuple): 181 # see sasmodels beta_approx: SasviewModel.calculate_Iq 182 # TODO: implement intermediate results in smearers 183 return_data, _ = return_data 184 unsmeared_output[first_bin:last_bin+1] = return_data 197 185 output = self.smearer(unsmeared_output, first_bin, last_bin) 198 186 … … 213 201 unsmeared_error=unsmeared_error 214 202 else: 215 output[index] = self.model.evalDistribution(self.data.x[index]) 216 217 sq_values = None 218 pq_values = None 219 s_model = None 220 p_model = None 221 if isinstance(self.model, MultiplicationModel): 222 s_model = self.model.s_model 223 p_model = self.model.p_model 224 elif hasattr(self.model, "calc_composition_models"): 225 results = self.model.calc_composition_models(self.data.x[index]) 226 if results is not None: 227 pq_values, sq_values = results 228 229 if pq_values is None or sq_values is None: 230 if p_model is not None and s_model is not None: 231 sq_values = numpy.zeros((len(self.data.x))) 232 pq_values = numpy.zeros((len(self.data.x))) 233 sq_values[index] = s_model.evalDistribution(self.data.x[index]) 234 pq_values[index] = p_model.evalDistribution(self.data.x[index]) 203 return_data = self.model.calculate_Iq(self.data.x[index]) 204 if isinstance(return_data, tuple): 205 # see sasmodels beta_approx: SasviewModel.calculate_Iq 206 return_data, intermediate_results = return_data 207 output[index] = return_data 208 209 if intermediate_results: 210 # the model returns a callable which is then used to retrieve the data 211 intermediate_results = intermediate_results() 212 else: 213 # TODO: this conditional branch needs refactoring 214 sq_values = None 215 pq_values = None 216 s_model = None 217 p_model = None 218 219 if isinstance(self.model, MultiplicationModel): 220 s_model = self.model.s_model 221 p_model = self.model.p_model 222 223 elif hasattr(self.model, "calc_composition_models"): 224 results = self.model.calc_composition_models(self.data.x[index]) 225 if results is not None: 226 pq_values, sq_values = results 227 228 if pq_values is None or sq_values is None: 229 if p_model is not None and s_model is not None: 230 sq_values = numpy.zeros((len(self.data.x))) 231 pq_values = numpy.zeros((len(self.data.x))) 232 sq_values[index] = s_model.evalDistribution(self.data.x[index]) 233 pq_values[index] = p_model.evalDistribution(self.data.x[index]) 234 235 if pq_values is not None and sq_values is not None: 236 intermediate_results = { 237 "P(Q)": pq_values, 238 "S(Q)": sq_values 239 } 240 else: 241 intermediate_results = {} 235 242 236 243 elapsed = time.time() - self.starttime 237 244 245 res = dict(x = self.data.x[index], y = output[index], 246 page_id = self.page_id, state = self.state, weight = self.weight, 247 fid = self.fid, toggle_mode_on = self.toggle_mode_on, 248 elapsed = elapsed, index = index, model = self.model, 249 data = self.data, update_chisqr = self.update_chisqr, 250 source = self.source, unsmeared_output = unsmeared_output, 251 unsmeared_data = unsmeared_data, unsmeared_error = unsmeared_error, 252 intermediate_results = intermediate_results) 253 238 254 if LocalConfig.USING_TWISTED: 239 return (self.data.x[index], output[index], 240 self.page_id, 241 self.state, 242 self.weight, 243 self.fid, 244 self.toggle_mode_on, 245 elapsed, index, self.model, 246 self.data, 247 self.update_chisqr, 248 self.source, 249 unsmeared_output, unsmeared_data, unsmeared_error, 250 pq_values, sq_values) 251 else: 252 self.completefn((self.data.x[index], output[index], 253 self.page_id, 254 self.state, 255 self.weight, 256 self.fid, 257 self.toggle_mode_on, 258 elapsed, index, self.model, 259 self.data, 260 self.update_chisqr, 261 self.source, 262 unsmeared_output, unsmeared_data, unsmeared_error, 263 pq_values, sq_values)) 255 return res 256 else: 257 self.completefn(res) 264 258 265 259 def results(self):
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