Changeset f3e5956 in sasview for src/sas/qtgui/Perspectives
- Timestamp:
- Sep 8, 2018 9:41:07 AM (6 years ago)
- Branches:
- ESS_GUI, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc
- Children:
- c0de493
- Parents:
- 5181e9b (diff), c8536d6c (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent. - git-author:
- Torin Cooper-Bennun <40573959+tcbennun@…> (09/08/18 09:41:07)
- git-committer:
- GitHub <noreply@…> (09/08/18 09:41:07)
- Location:
- src/sas/qtgui/Perspectives/Fitting
- Files:
-
- 7 edited
Legend:
- Unmodified
- Added
- Removed
-
src/sas/qtgui/Perspectives/Fitting/FittingUtilities.py
rb69b549 r01b4877 167 167 return rows 168 168 169 def addSimpleParametersToModel(parameters, is2D, parameters_original=None, model=None, view=None ):169 def addSimpleParametersToModel(parameters, is2D, parameters_original=None, model=None, view=None, row_num=None): 170 170 """ 171 171 Update local ModelModel with sasmodel parameters (non-dispersed, non-magnetic) … … 216 216 # Append to the model and use the combobox, if required 217 217 if None not in (model, view): 218 model.appendRow(row) 218 if row_num is None: 219 model.appendRow(row) 220 else: 221 model.insertRow(row_num, row) 222 row_num += 1 223 219 224 if cbox: 220 225 view.setIndexWidget(item2.index(), cbox) 226 221 227 rows.append(row) 222 228 -
src/sas/qtgui/Perspectives/Fitting/FittingWidget.py
r254199c rf3e5956 48 48 from sas.qtgui.Perspectives.Fitting.ReportPageLogic import ReportPageLogic 49 49 50 51 50 TAB_MAGNETISM = 4 52 51 TAB_POLY = 3 … … 188 187 189 188 # Overwrite data type descriptor 189 190 190 self.is2D = True if isinstance(self.logic.data, Data2D) else False 191 191 … … 563 563 When clicked on white space: model description 564 564 """ 565 rows = [s.row() for s in self.lstParams.selectionModel().selectedRows()] 565 rows = [s.row() for s in self.lstParams.selectionModel().selectedRows() 566 if self.isCheckable(s.row())] 566 567 menu = self.showModelDescription() if not rows else self.modelContextMenu(rows) 567 568 try: … … 799 800 def getConstraintForRow(self, row): 800 801 """ 801 For the given row, return its constraint, if any 802 """ 803 if self.isCheckable(row): 804 item = self._model_model.item(row, 1) 805 try: 806 return item.child(0).data() 807 except AttributeError: 808 # return none when no constraints 809 pass 810 return None 802 For the given row, return its constraint, if any (otherwise None) 803 """ 804 if not self.isCheckable(row): 805 return None 806 item = self._model_model.item(row, 1) 807 try: 808 return item.child(0).data() 809 except AttributeError: 810 return None 811 811 812 812 def rowHasConstraint(self, row): … … 814 814 Finds out if row of the main model has a constraint child 815 815 """ 816 if self.isCheckable(row): 817 item = self._model_model.item(row, 1) 818 if item.hasChildren(): 819 c = item.child(0).data() 820 if isinstance(c, Constraint): 821 return True 816 if not self.isCheckable(row): 817 return False 818 item = self._model_model.item(row, 1) 819 if not item.hasChildren(): 820 return False 821 c = item.child(0).data() 822 if isinstance(c, Constraint): 823 return True 822 824 return False 823 825 … … 826 828 Finds out if row of the main model has an active constraint child 827 829 """ 828 if self.isCheckable(row): 829 item = self._model_model.item(row, 1) 830 if item.hasChildren(): 831 c = item.child(0).data() 832 if isinstance(c, Constraint) and c.active: 833 return True 830 if not self.isCheckable(row): 831 return False 832 item = self._model_model.item(row, 1) 833 if not item.hasChildren(): 834 return False 835 c = item.child(0).data() 836 if isinstance(c, Constraint) and c.active: 837 return True 834 838 return False 835 839 … … 838 842 Finds out if row of the main model has an active, nontrivial constraint child 839 843 """ 840 if self.isCheckable(row): 841 item = self._model_model.item(row, 1) 842 if item.hasChildren(): 843 c = item.child(0).data() 844 if isinstance(c, Constraint) and c.func and c.active: 845 return True 844 if not self.isCheckable(row): 845 return False 846 item = self._model_model.item(row, 1) 847 if not item.hasChildren(): 848 return False 849 c = item.child(0).data() 850 if isinstance(c, Constraint) and c.func and c.active: 851 return True 846 852 return False 847 853 … … 1053 1059 # Show constraint, if present 1054 1060 row = rows[0].row() 1055 if self.rowHasConstraint(row): 1056 func = self.getConstraintForRow(row).func 1057 if func is not None: 1058 self.communicate.statusBarUpdateSignal.emit("Active constrain: "+func) 1061 if not self.rowHasConstraint(row): 1062 return 1063 func = self.getConstraintForRow(row).func 1064 if func is not None: 1065 self.communicate.statusBarUpdateSignal.emit("Active constrain: "+func) 1059 1066 1060 1067 def replaceConstraintName(self, old_name, new_name=""): … … 1807 1814 # Force data recalculation so existing charts are updated 1808 1815 self.showPlot() 1816 # This is an important processEvent. 1817 # This allows charts to be properly updated in order 1818 # of plots being applied. 1819 QtWidgets.QApplication.processEvents() 1809 1820 self.recalculatePlotData() 1810 1821 … … 2091 2102 return 2092 2103 2104 product_params = None 2105 2093 2106 if self.kernel_module is None: 2094 2107 # Structure factor is the only selected model; build it and show all its params … … 2096 2109 s_params = self.kernel_module._model_info.parameters 2097 2110 s_params_orig = s_params 2098 2099 2111 else: 2100 2112 s_kernel = self.models[structure_factor]() … … 2113 2125 if "radius_effective_mode" in all_param_names: 2114 2126 # Show all parameters 2127 # In this case, radius_effective is NOT pruned by sasmodels.product 2115 2128 s_params = modelinfo.ParameterTable(all_params[p_pars_len:p_pars_len+s_pars_len]) 2116 2129 s_params_orig = modelinfo.ParameterTable(s_kernel._model_info.parameters.kernel_parameters) 2130 product_params = modelinfo.ParameterTable( 2131 self.kernel_module._model_info.parameters.kernel_parameters[p_pars_len+s_pars_len:]) 2117 2132 else: 2118 2133 # Ensure radius_effective is not displayed 2119 2134 s_params_orig = modelinfo.ParameterTable(s_kernel._model_info.parameters.kernel_parameters[1:]) 2120 2135 if "radius_effective" in all_param_names: 2136 # In this case, radius_effective is NOT pruned by sasmodels.product 2121 2137 s_params = modelinfo.ParameterTable(all_params[p_pars_len+1:p_pars_len+s_pars_len]) 2138 product_params = modelinfo.ParameterTable( 2139 self.kernel_module._model_info.parameters.kernel_parameters[p_pars_len+s_pars_len:]) 2122 2140 else: 2141 # In this case, radius_effective is pruned by sasmodels.product 2123 2142 s_params = modelinfo.ParameterTable(all_params[p_pars_len:p_pars_len+s_pars_len-1]) 2143 product_params = modelinfo.ParameterTable( 2144 self.kernel_module._model_info.parameters.kernel_parameters[p_pars_len+s_pars_len-1:]) 2124 2145 2125 2146 # Add heading row … … 2129 2150 # Any renamed parameters are stored as data in the relevant item, for later handling 2130 2151 FittingUtilities.addSimpleParametersToModel( 2131 s_params, 2132 self.is2D, 2133 s_params_orig, 2134 self._model_model, 2135 self.lstParams) 2152 parameters=s_params, 2153 is2D=self.is2D, 2154 parameters_original=s_params_orig, 2155 model=self._model_model, 2156 view=self.lstParams) 2157 2158 # Insert product-only params into QModel 2159 if product_params: 2160 prod_rows = FittingUtilities.addSimpleParametersToModel( 2161 parameters=product_params, 2162 is2D=self.is2D, 2163 parameters_original=None, 2164 model=self._model_model, 2165 view=self.lstParams, 2166 row_num=2) 2167 2168 # Since this all happens after shells are dealt with and we've inserted rows, fix this counter 2169 self._n_shells_row += len(prod_rows) 2136 2170 2137 2171 def haveParamsToFit(self): … … 2800 2834 self.current_shell_displayed = index 2801 2835 2836 # Change 'n' in the parameter model, thereby updating the underlying model 2837 self._model_model.item(self._n_shells_row, 1).setText(str(index)) 2838 2802 2839 # Update relevant models 2803 2840 self.setPolyModel() … … 3310 3347 self._poly_model.blockSignals(False) 3311 3348 3349 3350 -
src/sas/qtgui/Perspectives/Fitting/UI/OptionsWidgetUI.ui
r79bd268 r309fa1b 32 32 <item row="0" column="1"> 33 33 <widget class="QLineEdit" name="txtMinRange"> 34 <property name="minimumSize"> 35 <size> 36 <width>80</width> 37 <height>0</height> 38 </size> 39 </property> 34 40 <property name="toolTip"> 35 41 <string><html><head/><body><p>Minimum value of Q.</p></body></html></string> … … 54 60 <item row="1" column="1"> 55 61 <widget class="QLineEdit" name="txtMaxRange"> 62 <property name="minimumSize"> 63 <size> 64 <width>80</width> 65 <height>0</height> 66 </size> 67 </property> 56 68 <property name="toolTip"> 57 69 <string><html><head/><body><p>Maximum value of Q.</p></body></html></string> -
src/sas/qtgui/Perspectives/Fitting/UnitTesting/FittingLogicTest.py
rd6c4987 rbfb5d9e 99 99 data.name = "boop" 100 100 data.id = "poop" 101 return_data = (data.x,data.y, 7, None, None, 102 0, True, 0.0, 1, data, 103 data, False, None, 104 None, None, None, 105 None) 101 # Condensed return data (new1DPlot only uses these fields) 102 return_data = dict(x = data.x, 103 y = data.y, 104 model = data, 105 data = data) 106 # return_data = (data.x,data.y, 7, None, None, 107 # 0, True, 0.0, 1, data, 108 # data, False, None, 109 # None, None, None, 110 # None, None) 106 111 107 112 new_plot = self.logic.new1DPlot(return_data=return_data, tab_id=0) … … 139 144 qmin, qmax, npts = self.logic.computeDataRange() 140 145 141 return_data = (x_0, data, 7, data, None, 142 True, 0.0, 1, 0, qmin, qmax, 143 0.1, False, None) 146 # Condensed return data (new2DPlot only uses these fields) 147 return_data = dict(image = x_0, 148 data = data, 149 page_id = 7, 150 model = data) 151 # return_data = (x_0, data, 7, data, None, 152 # True, 0.0, 1, 0, qmin, qmax, 153 # 0.1, False, None) 144 154 145 155 new_plot = self.logic.new2DPlot(return_data=return_data) -
src/sas/qtgui/Perspectives/Fitting/UnitTesting/FittingOptionsTest.py
r725d9c06 rbfb5d9e 38 38 # The combo box 39 39 self.assertIsInstance(self.widget.cbAlgorithm, QtWidgets.QComboBox) 40 self.assertEqual(self.widget.cbAlgorithm.count(), 5)40 self.assertEqual(self.widget.cbAlgorithm.count(), 6) 41 41 self.assertEqual(self.widget.cbAlgorithm.itemText(0), 'Nelder-Mead Simplex') 42 42 self.assertEqual(self.widget.cbAlgorithm.itemText(4), 'Levenberg-Marquardt') -
src/sas/qtgui/Perspectives/Fitting/FittingLogic.py
rdcabba7 r9ba91b7 161 161 Create a new 1D data instance based on fitting results 162 162 """ 163 164 163 return self._create1DPlot(tab_id, return_data['x'], return_data['y'], 165 164 return_data['model'], return_data['data']) … … 212 211 (pq_plot, sq_plot). If either are unavailable, the corresponding plot is None. 213 212 """ 214 215 pq_plot = None 216 sq_plot = None 217 218 if return_data.get('pq_values', None) is not None: 219 pq_plot = self._create1DPlot(tab_id, return_data['x'], 220 return_data['pq_values'], return_data['model'], 221 return_data['data'], component="P(Q)") 222 if return_data.get('sq_values', None) is not None: 223 sq_plot = self._create1DPlot(tab_id, return_data['x'], 224 return_data['sq_values'], return_data['model'], 225 return_data['data'], component="S(Q)") 226 227 return pq_plot, sq_plot 213 plots = [] 214 for name, result in return_data['intermediate_results'].items(): 215 plots.append(self._create1DPlot(tab_id, return_data['x'], result, 216 return_data['model'], return_data['data'], 217 component=name)) 218 return plots 228 219 229 220 def computeDataRange(self): -
src/sas/qtgui/Perspectives/Fitting/ModelThread.py
rdcabba7 r5181e9b 164 164 index = (self.qmin <= self.data.x) & (self.data.x <= self.qmax) 165 165 166 intermediate_results = None 167 166 168 # If we use a smearer, also return the unsmeared model 167 169 unsmeared_output = None … … 174 176 mask = self.data.x[first_bin:last_bin+1] 175 177 unsmeared_output = numpy.zeros((len(self.data.x))) 176 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 177 185 output = self.smearer(unsmeared_output, first_bin, last_bin) 178 186 … … 193 201 unsmeared_error=unsmeared_error 194 202 else: 195 output[index] = self.model.evalDistribution(self.data.x[index]) 196 197 sq_values = None 198 pq_values = None 199 s_model = None 200 p_model = None 201 if isinstance(self.model, MultiplicationModel): 202 s_model = self.model.s_model 203 p_model = self.model.p_model 204 elif hasattr(self.model, "calc_composition_models"): 205 results = self.model.calc_composition_models(self.data.x[index]) 206 if results is not None: 207 pq_values, sq_values = results 208 209 if pq_values is None or sq_values is None: 210 if p_model is not None and s_model is not None: 211 sq_values = numpy.zeros((len(self.data.x))) 212 pq_values = numpy.zeros((len(self.data.x))) 213 sq_values[index] = s_model.evalDistribution(self.data.x[index]) 214 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 = {} 215 242 216 243 elapsed = time.time() - self.starttime … … 223 250 source = self.source, unsmeared_output = unsmeared_output, 224 251 unsmeared_data = unsmeared_data, unsmeared_error = unsmeared_error, 225 pq_values = pq_values, sq_values = sq_values)252 intermediate_results = intermediate_results) 226 253 227 254 if LocalConfig.USING_TWISTED:
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