Changeset 792db7d5 in sasview for park_integration
- Timestamp:
- Jul 8, 2008 11:48:38 AM (16 years ago)
- Branches:
- master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.1.1, release-4.1.2, release-4.2.2, release_4.0.1, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload
- Children:
- 6e0f53a
- Parents:
- 1c94a9f1
- Location:
- park_integration
- Files:
-
- 9 edited
Legend:
- Unmodified
- Added
- Removed
-
park_integration/Fitting.py
rcf3b781 r792db7d5 1 #class Fitting 1 """ 2 @organization: Class Fit contains ScipyFit and ParkFit methods declaration 3 allows to create instance of type ScipyFit or ParkFit to perform either 4 a park fit or a scipy fit. 5 """ 2 6 from sans.guitools.plottables import Data1D 3 7 from Loader import Load … … 9 13 class Fit: 10 14 """ 11 Wrap class that allows to select the fitting type 15 Wrap class that allows to select the fitting type.this class 16 can be used as follow : 17 18 from sans.fit.Fitting import Fit 19 fitter= Fit() 20 fitter.fit_engine('scipy') or fitter.fit_engine('park') 21 engine = fitter.returnEngine() 22 engine.set_data(data,Uid) 23 engine.set_param( model,model.name, pars) 24 engine.set_model(model,Uid) 25 26 chisqr1, out1, cov1=engine.fit(pars,qmin,qmax) 12 27 """ 13 28 def __init__(self): 14 15 # To initialize a type of Fit 29 """ 30 self._engine will contain an instance of ScipyFit or ParkFit 31 """ 16 32 self._engine=None 17 33 … … 20 36 Select the type of Fit 21 37 @param word: the keyword to select the fit type 38 @raise: if the user does not enter 'scipy' or 'park', 39 a valueError is rase 22 40 """ 23 41 if word=="scipy": … … 28 46 raise ValueError, "enter the keyword scipy or park" 29 47 def returnEngine(self): 48 """ @return self._engine""" 30 49 return self._engine 31 50 32 51 def fit(self,pars, qmin=None, qmax=None): 33 """ Dothe fit """52 """Perform the fit """ 34 53 35 54 def set_model(self,model,Uid): -
park_integration/ParkFitting.py
rcf3b781 r792db7d5 1 #class Fitting 1 """ 2 @organization: ParkFitting module contains SansParameter,Model,Data 3 FitArrange, ParkFit,Parameter classes.All listed classes work together to perform a 4 simple fit with park optimizer. 5 """ 2 6 import time 3 4 7 import numpy 8 5 9 import park 6 from scipy import optimize7 10 from park import fit,fitresult 8 11 from park import assembly … … 10 13 11 14 from sans.guitools.plottables import Data1D 12 #from sans.guitools import plottables13 15 from Loader import Load 14 from park import expression 16 15 17 class SansParameter(park.Parameter): 16 18 """ 17 SANS model parameters for use in the PARK fitting service.18 The parameter attribute value is redirected to the underlying19 parameter value in the SANS model.19 SANS model parameters for use in the PARK fitting service. 20 The parameter attribute value is redirected to the underlying 21 parameter value in the SANS model. 20 22 """ 21 23 def __init__(self, name, model): 22 24 self._model, self._name = model,name 23 25 self.set(model.getParam(name)) 26 24 27 def _getvalue(self): return self._model.getParam(self.name) 28 25 29 def _setvalue(self,value): 26 if numpy.isnan(value):27 print "setting %s.%s to"%(self._model.name,self.name),value28 30 self._model.setParam(self.name, value) 31 29 32 value = property(_getvalue,_setvalue) 33 30 34 def _getrange(self): 31 35 lo,hi = self._model.details[self.name][1:] … … 33 37 if hi is None: hi = numpy.inf 34 38 return lo,hi 39 35 40 def _setrange(self,r): 36 41 self._model.details[self.name][1:] = r 37 42 range = property(_getrange,_setrange) 43 38 44 39 45 class Model(object): … … 46 52 parkp = [SansParameter(p,sans_model) for p in sansp] 47 53 self.parameterset = park.ParameterSet(sans_model.name,pars=parkp) 54 48 55 def eval(self,x): 49 56 return self.model.run(x) … … 51 58 class Data(object): 52 59 """ Wrapper class for SANS data """ 53 def __init__(self, sans_data): 54 self.x= sans_data.x 55 self.y= sans_data.y 56 self.dx= sans_data.dx 57 self.dy= sans_data.dy 60 def __init__(self,x=None,y=None,dy=None,dx=None,sans_data=None): 61 if not sans_data==None: 62 self.x= sans_data.x 63 self.y= sans_data.y 64 self.dx= sans_data.dx 65 self.dy= sans_data.dy 66 else: 67 if x!=None and y!=None and dy!=None: 68 self.x=x 69 self.y=y 70 self.dx=dx 71 self.dy=dy 72 else: 73 raise ValueError,\ 74 "Data is missing x, y or dy, impossible to compute residuals later on" 58 75 self.qmin=None 59 76 self.qmax=None … … 65 82 66 83 def residuals(self, fn): 67 84 """ @param fn: function that return model value 85 @return residuals 86 """ 68 87 x,y,dy = [numpy.asarray(v) for v in (self.x,self.y,self.dy)] 69 88 if self.qmin==None and self.qmax==None: … … 78 97 79 98 def residuals_deriv(self, model, pars=[]): 80 """ Return residual derivatives .in this case just return empty array""" 99 """ 100 @return residuals derivatives . 101 @note: in this case just return empty array 102 """ 81 103 return [] 82 83 104 class FitArrange: 84 105 def __init__(self): 85 106 """ 86 Store a set of data for a given model to perform the Fit 87 @param model: the model selected by the user 88 @param Ldata: a list of data what the user want to fit 107 Class FitArrange contains a set of data for a given model 108 to perform the Fit.FitArrange must contain exactly one model 109 and at least one data for the fit to be performed. 110 model: the model selected by the user 111 Ldata: a list of data what the user wants to fit 112 89 113 """ 90 114 self.model = None … … 92 116 93 117 def set_model(self,model): 94 """ set the model """ 118 """ 119 set_model save a copy of the model 120 @param model: the model being set 121 """ 95 122 self.model = model 96 123 def remove_model(self): 124 """ remove model """ 125 self.model=None 97 126 def add_data(self,data): 98 127 """ 99 @param data: Data to add in the list100 fill a self.dataList with data to fit128 add_data fill a self.dList with data to fit 129 @param data: Data to add in the list 101 130 """ 102 131 if not data in self.dList: … … 104 133 105 134 def get_model(self): 106 """ Return the model"""135 """ @return: saved model """ 107 136 return self.model 108 137 109 138 def get_data(self): 110 """ Return list of data"""139 """ @return: list of data dList""" 111 140 return self.dList 112 141 … … 114 143 """ 115 144 Remove one element from the list 116 @param data: Data to remove from the the lsit of data145 @param data: Data to remove from dList 117 146 """ 118 147 if data in self.dList: 119 148 self.dList.remove(data) 120 def remove_model(self):121 """ Remove model """122 model=None123 149 def remove_datalist(self): 150 """ empty the complet list dLst""" 124 151 self.dList=[] 152 125 153 126 154 class ParkFit: 127 155 """ 128 Performs the Fit.he user determine what kind of data 156 ParkFit performs the Fit.This class can be used as follow: 157 #Do the fit Park 158 create an engine: engine = ParkFit() 159 Use data must be of type plottable 160 Use a sans model 161 162 Add data with a dictionnary of FitArrangeList where Uid is a key and data 163 is saved in FitArrange object. 164 engine.set_data(data,Uid) 165 166 Set model parameter "M1"= model.name add {model.parameter.name:value}. 167 @note: Set_param() if used must always preceded set_model() 168 for the fit to be performed. 169 engine.set_param( model,"M1", {'A':2,'B':4}) 170 171 Add model with a dictionnary of FitArrangeList{} where Uid is a key and model 172 is save in FitArrange object. 173 engine.set_model(model,Uid) 174 175 engine.fit return chisqr,[model.parameter 1,2,..],[[err1....][..err2...]] 176 chisqr1, out1, cov1=engine.fit({model.parameter.name:value},qmin,qmax) 177 @note: {model.parameter.name:value} is ignored in fit function since 178 the user should make sure to call set_param himself. 129 179 """ 130 180 def __init__(self,data=[]): 131 #this is a dictionary of FitArrange elements 181 """ 182 Creates a dictionary (self.fitArrangeList={})of FitArrange elements 183 with Uid as keys 184 """ 132 185 self.fitArrangeList={} 133 #the constraint of the Fit 134 self.constraint =None 135 #Specify the use of scipy or park fit 136 self.fitType =None 137 186 138 187 def createProblem(self,pars={}): 139 188 """ 140 Check the contraint value and specify what kind of fit to use 141 return (M1,D1) 189 Extract sansmodel and sansdata from self.FitArrangelist ={Uid:FitArrange} 190 Create parkmodel and park data ,form a list couple of parkmodel and parkdata 191 create an assembly self.problem= park.Assembly([(parkmodel,parkdata)]) 142 192 """ 143 193 mylist=[] 144 194 listmodel=[] 145 195 for k,value in self.fitArrangeList.iteritems(): 146 #couple=()147 196 sansmodel=value.get_model() 148 149 #parameters= self.set_param(model,model.name, pars) 197 #wrap sans model 150 198 parkmodel = Model(sansmodel) 151 #print "model created",model.parameterset[0].value,model.parameterset[1].value152 # Make all parameters fitting parameters153 154 155 199 for p in parkmodel.parameterset: 156 #p.range([-numpy.inf,numpy.inf])157 # Convert parameters with initial values into fitted parameters158 # spanning all possible values. Parameters which are expressions159 # will remain as expressions.160 200 if p.isfixed(): 161 201 p.set([-numpy.inf,numpy.inf]) 162 202 163 203 Ldata=value.get_data() 164 data=self._concatenateData(Ldata)165 data1=Data(data)166 167 couple=(parkmodel, data1)204 x,y,dy,dx=self._concatenateData(Ldata) 205 #wrap sansdata 206 parkdata=Data(x,y,dy,dx) 207 couple=(parkmodel,parkdata) 168 208 mylist.append(couple) 169 #print mylist209 170 210 self.problem = park.Assembly(mylist) 171 #return model,data211 172 212 173 213 def fit(self,pars=None, qmin=None, qmax=None): 174 214 """ 175 Do the fit 176 """ 177 215 Performs fit with park.fit module.It can perform fit with one model 216 and a set of data, more than two fit of one model and sets of data or 217 fit with more than two model associated with their set of data and constraints 218 219 220 @param pars: Dictionary of parameter names for the model and their values. 221 @param qmin: The minimum value of data's range to be fit 222 @param qmax: The maximum value of data's range to be fit 223 @note:all parameter are ignored most of the time.Are just there to keep ScipyFit 224 and ParkFit interface the same. 225 @return result.fitness: Value of the goodness of fit metric 226 @return result.pvec: list of parameter with the best value found during fitting 227 @return result.cov: Covariance matrix 228 """ 229 230 178 231 self.createProblem(pars) 179 print "starting ParkFit.fit()"180 #problem[0].model.parameterset['A'].set([1,5])181 #problem[0].model.parameterset['B'].set([1,5])182 232 pars=self.problem.fit_parameters() 183 print "About to call eval",pars184 print "initial",[p.value for p in pars]185 233 self.problem.eval() 186 #print "M2.B",problem.parameterset['M2.B'].expression,problem.parameterset['M2.B'].value 187 #print "problem :",problem[0].parameterset,problem[0].parameterset.fitted 188 189 #problem[0].parameterset['A'].set([0,1000]) 190 #print "problem :",problem[0].parameterset,problem[0].parameterset.fitted 191 234 192 235 localfit = FitSimplex() 193 236 localfit.ftol = 1e-8 … … 197 240 fitter=fitter, 198 241 handler= fitresult.ConsoleUpdate(improvement_delta=0.1)) 199 pvec = result.pvec 200 cov = self.problem.cov(pvec) 201 return result.fitness,pvec,numpy.sqrt(numpy.diag(cov)) 202 242 243 return result.fitness,result.pvec,result.cov 203 244 204 245 def set_model(self,model,Uid): 205 """ Set model """ 206 246 """ 247 Set model in a FitArrange object and add that object in a dictionary 248 with key Uid. 249 @param model: the model added 250 @param Uid: unique key corresponding to a fitArrange object with model 251 """ 252 #A fitArrange is already created but contains dList only at Uid 207 253 if self.fitArrangeList.has_key(Uid): 208 254 self.fitArrangeList[Uid].set_model(model) 209 255 else: 256 #no fitArrange object has been create with this Uid 210 257 fitproblem= FitArrange() 211 258 fitproblem.set_model(model) … … 213 260 214 261 def set_data(self,data,Uid): 215 """ Receive plottable and create a list of data to fit""" 216 262 """ Receives plottable, creates a list of data to fit,set data 263 in a FitArrange object and adds that object in a dictionary 264 with key Uid. 265 @param data: data added 266 @param Uid: unique key corresponding to a fitArrange object with data 267 """ 268 #A fitArrange is already created but contains model only at Uid 217 269 if self.fitArrangeList.has_key(Uid): 218 270 self.fitArrangeList[Uid].add_data(data) 219 271 else: 272 #no fitArrange object has been create with this Uid 220 273 fitproblem= FitArrange() 221 274 fitproblem.add_data(data) … … 223 276 224 277 def get_model(self,Uid): 225 """ return list of data""" 226 return self.fitArrangeList[Uid] 278 """ 279 @param Uid: Uid is key in the dictionary containing the model to return 280 @return a model at this uid or None if no FitArrange element was created 281 with this Uid 282 """ 283 if self.fitArrangeList.has_key(Uid): 284 return self.fitArrangeList[Uid].get_model() 285 else: 286 return None 227 287 228 288 def set_param(self,model,name, pars): 229 """ Recieve a dictionary of parameter and save it """ 289 """ 290 Recieve a dictionary of parameter and save it 291 @param model: model on with parameter values are set 292 @param name: model name 293 @param pars: dictionary of paramaters name and value 294 pars={parameter's name: parameter's value} 295 @return list of Parameter instance 296 """ 230 297 parameters=[] 231 298 if model==None: … … 239 306 240 307 def remove_data(self,Uid,data=None): 241 """ remove one or all data""" 242 if data==None:# remove all element in data list 308 """ remove one or all data.if data ==None will remove the whole 309 list of data at Uid; else will remove only data in that list. 310 @param Uid: unique id containing FitArrange object with data 311 @param data:data to be removed 312 """ 313 if data==None: 314 # remove all element in data list 243 315 if self.fitArrangeList.has_key(Uid): 244 316 self.fitArrangeList[Uid].remove_datalist() 245 317 else: 318 #remove only data in dList 246 319 if self.fitArrangeList.has_key(Uid): 247 320 self.fitArrangeList[Uid].remove_data(data) 248 321 249 322 def remove_model(self,Uid): 250 """ remove model """ 323 """ 324 remove model in FitArrange object with Uid. 325 @param Uid: Unique id corresponding to the FitArrange object 326 where model must be removed. 327 """ 251 328 if self.fitArrangeList.has_key(Uid): 252 329 self.fitArrangeList[Uid].remove_model() 330 def remove_Fit_Problem(self,Uid): 331 """remove fitarrange in Uid""" 332 if self.fitArrangeList.has_key(Uid): 333 del self.fitArrangeList[Uid] 334 def _concatenateData(self, listdata=[]): 335 """ 336 _concatenateData method concatenates each fields of all data contains ins listdata. 337 @param listdata: list of data 338 339 @return xtemp, ytemp,dytemp: x,y,dy respectively of data all combined 340 if xi,yi,dyi of two or more data are the same the second appearance of xi,yi, 341 dyi is ignored in the concatenation. 253 342 254 255 def _concatenateData(self, listdata=[]): 256 """ concatenate each fields of all Data contains ins listdata 257 return data 343 @raise: if listdata is empty will return None 344 @raise: if data in listdata don't contain dy field ,will create an error 345 during fitting 258 346 """ 259 347 if listdata==[]: … … 263 351 ytemp=[] 264 352 dytemp=[] 265 resid=[] 266 resid_deriv=[] 267 353 dx=None 268 354 for data in listdata: 269 355 for i in range(len(data.x)): … … 273 359 if not data.y[i] in ytemp: 274 360 ytemp.append(data.y[i]) 275 276 if not data.dy[i] in dytemp:277 dytemp.append(data.dy[i])278 279 280 newplottable= Data1D(xtemp,ytemp,None,dytemp)281 newdata=Data(newplottable)361 if data.dy and len(data.dy)>0: 362 if not data.dy[i] in dytemp: 363 dytemp.append(data.dy[i]) 364 else: 365 raise ValueError,"dy is missing will not be able to fit later on" 366 return xtemp, ytemp,dytemp,dx 367 282 368 283 #print "this is new data",newdata.dy284 return newdata285 369 class Parameter: 286 370 """ -
park_integration/ScipyFitting.py
rcf3b781 r792db7d5 1 #class Fitting 1 """ 2 @organization: ScipyFitting module contains FitArrange , ScipyFit, 3 Parameter classes.All listed classes work together to perform a 4 simple fit with scipy optimizer. 5 """ 2 6 from sans.guitools.plottables import Data1D 3 7 from Loader import Load 4 8 from scipy import optimize 5 #from Fitting import Fit 9 6 10 7 11 class FitArrange: 8 12 def __init__(self): 9 13 """ 10 Store a set of data for a given model to perform the Fit 11 @param model: the model selected by the user 12 @param Ldata: a list of data what the user want to fit 14 Class FitArrange contains a set of data for a given model 15 to perform the Fit.FitArrange must contain exactly one model 16 and at least one data for the fit to be performed. 17 model: the model selected by the user 18 Ldata: a list of data what the user wants to fit 19 13 20 """ 14 21 self.model = None … … 16 23 17 24 def set_model(self,model): 18 """ set the model """ 25 """ 26 set_model save a copy of the model 27 @param model: the model being set 28 """ 19 29 self.model = model 20 30 21 31 def add_data(self,data): 22 32 """ 23 @param data: Data to add in the list24 fill a self.dataList with data to fit33 add_data fill a self.dList with data to fit 34 @param data: Data to add in the list 25 35 """ 26 36 if not data in self.dList: … … 28 38 29 39 def get_model(self): 30 """ Return the model"""40 """ @return: saved model """ 31 41 return self.model 32 42 33 43 def get_data(self): 34 """ Return list of data"""44 """ @return: list of data dList""" 35 45 return self.dList 36 46 … … 38 48 """ 39 49 Remove one element from the list 40 @param data: Data to remove from the the lsit of data50 @param data: Data to remove from dList 41 51 """ 42 52 if data in self.dList: 43 53 self.dList.remove(data) 54 def remove_datalist(self): 55 """ empty the complet list dLst""" 56 self.dList=[] 44 57 45 58 class ScipyFit: 46 59 """ 47 Performs the Fit.he user determine what kind of data 48 """ 49 def __init__(self,data=[]): 50 #this is a dictionary of FitArrange elements 60 ScipyFit performs the Fit.This class can be used as follow: 61 #Do the fit SCIPY 62 create an engine: engine = ScipyFit() 63 Use data must be of type plottable 64 Use a sans model 65 66 Add data with a dictionnary of FitArrangeList where Uid is a key and data 67 is saved in FitArrange object. 68 engine.set_data(data,Uid) 69 70 Set model parameter "M1"= model.name add {model.parameter.name:value}. 71 @note: Set_param() if used must always preceded set_model() 72 for the fit to be performed.In case of Scipyfit set_param is called in 73 fit () automatically. 74 engine.set_param( model,"M1", {'A':2,'B':4}) 75 76 Add model with a dictionnary of FitArrangeList{} where Uid is a key and model 77 is save in FitArrange object. 78 engine.set_model(model,Uid) 79 80 engine.fit return chisqr,[model.parameter 1,2,..],[[err1....][..err2...]] 81 chisqr1, out1, cov1=engine.fit({model.parameter.name:value},qmin,qmax) 82 """ 83 def __init__(self): 84 """ 85 Creates a dictionary (self.fitArrangeList={})of FitArrange elements 86 with Uid as keys 87 """ 51 88 self.fitArrangeList={} 52 #the constraint of the Fit 53 self.constraint =None 54 #Specify the use of scipy or park fit 55 self.fitType =None 56 57 58 89 59 90 def fit(self,pars, qmin=None, qmax=None): 60 91 """ 61 Do the fit 62 """ 63 #for item in self.fitArrangeList.: 64 92 Performs fit with scipy optimizer.It can only perform fit with one model 93 and a set of data. 94 @note: Cannot perform more than one fit at the time. 95 96 @param pars: Dictionary of parameter names for the model and their values 97 @param qmin: The minimum value of data's range to be fit 98 @param qmax: The maximum value of data's range to be fit 99 @return chisqr: Value of the goodness of fit metric 100 @return out: list of parameter with the best value found during fitting 101 @return cov: Covariance matrix 102 """ 103 # fitproblem contains first fitArrange object(one model and a list of data) 65 104 fitproblem=self.fitArrangeList.values()[0] 66 105 listdata=[] … … 68 107 listdata = fitproblem.get_data() 69 108 109 #Create list of Parameter instances and save parameters values in model 70 110 parameters = self.set_param(model,model.name,pars) 71 111 72 # Do the fit with data set (contains one or more data) and one model112 # Concatenate dList set (contains one or more data)before fitting 73 113 xtemp,ytemp,dytemp=self._concatenateData( listdata) 74 print "dytemp",dytemp 114 115 #print "dytemp",dytemp 116 #Assign a fit range is not boundaries were given 75 117 if qmin==None: 76 118 qmin= min(xtemp) 77 119 if qmax==None: 78 qmax= max(xtemp) 120 qmax= max(xtemp) 121 122 #perform the fit 79 123 chisqr, out, cov = fitHelper(model,parameters, xtemp,ytemp, dytemp ,qmin,qmax) 80 124 return chisqr, out, cov 81 125 82 126 def _concatenateData(self, listdata=[]): 83 """ concatenate each fields of all data contains ins listdata""" 127 """ 128 _concatenateData method concatenates each fields of all data contains ins listdata. 129 @param listdata: list of data 130 131 @return xtemp, ytemp,dytemp: x,y,dy respectively of data all combined 132 if xi,yi,dyi of two or more data are the same the second appearance of xi,yi, 133 dyi is ignored in the concatenation. 134 135 @raise: if listdata is empty will return None 136 @raise: if data in listdata don't contain dy field ,will create an error 137 during fitting 138 """ 84 139 if listdata==[]: 85 140 raise ValueError, " data list missing" … … 96 151 if not data.y[i] in ytemp: 97 152 ytemp.append(data.y[i]) 98 99 if not data.dy[i] in dytemp: 100 dytemp.append(data.dy[i]) 153 if data.dy and len(data.dy)>0: 154 if not data.dy[i] in dytemp: 155 dytemp.append(data.dy[i]) 156 else: 157 raise ValueError,"dy is missing will not be able to fit later on" 101 158 return xtemp, ytemp,dytemp 102 159 103 160 def set_model(self,model,Uid): 104 """ Set model """ 161 """ 162 Set model in a FitArrange object and add that object in a dictionary 163 with key Uid. 164 @param model: the model added 165 @param Uid: unique key corresponding to a fitArrange object with model 166 """ 167 #A fitArrange is already created but contains dList only at Uid 105 168 if self.fitArrangeList.has_key(Uid): 106 169 self.fitArrangeList[Uid].set_model(model) 107 170 else: 171 #no fitArrange object has been create with this Uid 108 172 fitproblem= FitArrange() 109 173 fitproblem.set_model(model) … … 111 175 112 176 def set_data(self,data,Uid): 113 """ Receive plottable and create a list of data to fit""" 114 177 """ Receives plottable, creates a list of data to fit,set data 178 in a FitArrange object and adds that object in a dictionary 179 with key Uid. 180 @param data: data added 181 @param Uid: unique key corresponding to a fitArrange object with data 182 """ 183 #A fitArrange is already created but contains model only at Uid 115 184 if self.fitArrangeList.has_key(Uid): 116 185 self.fitArrangeList[Uid].add_data(data) 117 186 else: 187 #no fitArrange object has been create with this Uid 118 188 fitproblem= FitArrange() 119 189 fitproblem.add_data(data) … … 121 191 122 192 def get_model(self,Uid): 123 """ return list of data""" 124 return self.fitArrangeList[Uid] 193 """ 194 @param Uid: Uid is key in the dictionary containing the model to return 195 @return a model at this uid or None if no FitArrange element was created 196 with this Uid 197 """ 198 if self.fitArrangeList.has_key(Uid): 199 return self.fitArrangeList[Uid].get_model() 200 else: 201 return None 125 202 126 203 def set_param(self,model,name, pars): 127 """ Recieve a dictionary of parameter and save it """ 204 """ 205 Recieve a dictionary of parameter and save it 206 @param model: model on with parameter values are set 207 @param name: model name 208 @param pars: dictionary of paramaters name and value 209 pars={parameter's name: parameter's value} 210 @return list of Parameter instance 211 """ 128 212 parameters=[] 129 213 if model==None: … … 136 220 return parameters 137 221 138 def add_constraint(self, constraint):139 """ User specify contraint to fit """140 self.constraint = str(constraint)141 142 def get_constraint(self):143 """ return the contraint value """144 return self.constraint145 146 def set_constraint(self,constraint):147 """148 receive a string as a constraint149 @param constraint: a string used to constraint some parameters to get a150 specific value151 """152 self.constraint= constraint153 154 def createProblem(self):155 """156 Check the contraint value and specify what kind of fit to use157 """158 mylist=[]159 for k,value in self.fitArrangeList.iteritems():160 couple=()161 model=value.get_model()162 data=value.get_data()163 couple=(model,data)164 mylist.append(couple)165 #print mylist166 return mylist167 222 def remove_data(self,Uid,data=None): 168 """ remove one or all data""" 169 if data==None:# remove all element in data list 223 """ remove one or all data.if data ==None will remove the whole 224 list of data at Uid; else will remove only data in that list. 225 @param Uid: unique id containing FitArrange object with data 226 @param data:data to be removed 227 """ 228 if data==None: 229 # remove all element in data list 170 230 if self.fitArrangeList.has_key(Uid): 171 231 self.fitArrangeList[Uid].remove_datalist() 172 232 else: 233 #remove only data in dList 173 234 if self.fitArrangeList.has_key(Uid): 174 235 self.fitArrangeList[Uid].remove_data(data) 175 236 176 237 def remove_model(self,Uid): 177 """ remove model """ 238 """ 239 remove model in FitArrange object with Uid. 240 @param Uid: Unique id corresponding to the FitArrange object 241 where model must be removed. 242 """ 178 243 if self.fitArrangeList.has_key(Uid): 179 244 self.fitArrangeList[Uid].remove_model() … … 210 275 @param y: vector of y data 211 276 @param err_y: vector of y errors 277 @return chisqr: Value of the goodness of fit metric 278 @return out: list of parameter with the best value found during fitting 279 @return cov: Covariance matrix 212 280 """ 213 281 def f(params): -
park_integration/test/constrainttestpark.py
rcf3b781 r792db7d5 10 10 11 11 def test2models2dataonconstraint(self): 12 """ test fitting for two set of data and one model with aconstraint"""12 """ test fitting for two set of data and one model with 2 constraint""" 13 13 from sans.fit.Loader import Load 14 14 load= Load() … … 42 42 engine.set_param( model2,"M2", {'A':'M1.A','B':'M1.B'}) 43 43 engine.set_model(model2,2) 44 44 45 engine.set_data(data2,2) 45 46 46 47 47 printengine.fit({'A':2,'B':1},None,None)48 if True:49 import pylab50 x1 = engine.problem[0].data.x51 x2 = engine.problem[1].data.x52 y1 = engine.problem[0].data.y53 y2 = engine.problem[1].data.y54 fx1 = engine.problem[0].data.fx55 fx2 = engine.problem[1].data.fx56 pylab.plot(x1,y1,'xb',x1,fx1,'-b',x2,y2,'xr',x2,fx2,'-r')57 pylab.show()48 chisqr2, out2, cov2= engine.fit({'A':2,'B':1},None,None) 49 print "chisqr2",chisqr2 50 print "out2", out2 51 print " cov2", cov2 52 print chisqr2/len(data1.x) 53 54 self.assert_(math.fabs(out2[1]-2.5)/math.sqrt(cov2[1][1]) < 2) 55 self.assert_(math.fabs(out2[0]-4.0)/math.sqrt(cov2[0][0]) < 2) 56 #self.assert_(chisqr2/len(data1.x) < 2) 57 #self.assert_(chisqr2/len(data2.x) < 2) 58 -
park_integration/test/testdata1.txt
rc14c503 r792db7d5 15 15 2.85714 12.2957 1.67143 16 16 3.06122 12.3108 1.74796 17 3.26531 12.6916 1.8244918 3.46939 12.5859 1.9010219 3.67347 14.5972 1.9775520 3.87755 10.9443 2.0540821 4.08163 11.4356 2.1306122 4.28571 19.3213 2.2071423 4.4898 14.3191 2.2836724 4.69388 14.6961 2.360225 4.89796 19.27 2.4367326 6.12245 22.1974 2.89592 -
park_integration/test/testdata2.txt
rc14c503 r792db7d5 1 6.53061 19.8174 3.04898 2 6.73469 17.6186 3.12551 3 6.93878 21.329 3.20204 4 7.14286 20.2873 3.27857 5 7.34694 23.4112 3.3551 6 7.55102 26.063 3.43163 7 7.7551 29.8775 3.50816 8 7.95918 19.0027 3.58469 9 8.16327 19.4248 3.66122 10 8.36735 27.4876 3.73776 11 8.57143 33.8399 3.81429 12 8.77551 24.2611 3.89082 13 8.97959 29.8796 3.96735 14 9.18367 24.6149 4.04388 15 9.38776 35.7664 4.12041 16 9.59184 32.145 4.19694 17 9.79592 24.8085 4.27347 18 10 31.3767 4.35 1 3.26531 12.6916 1.82449 2 3.46939 12.5859 1.90102 3 3.67347 14.5972 1.97755 4 3.87755 10.9443 2.05408 5 4.08163 11.4356 2.13061 6 4.28571 19.3213 2.20714 7 4.4898 14.3191 2.28367 8 4.69388 14.6961 2.3602 9 4.89796 19.27 2.43673 10 5.10204 12.1831 2.51327 11 5.30612 21.4745 2.5898 12 5.5102 20.0393 2.66633 13 5.71429 13.2472 2.74286 14 5.91837 14.9428 2.81939 15 6.12245 22.1974 2.89592 -
park_integration/test/testdata_line.txt
r103a0b0 r792db7d5 33 33 5.91837 14.9428 2.81939 34 34 6.12245 22.1974 2.89592 35 6.32653 17.3418 2.9724536 6.53061 19.8174 3.0489837 6.73469 17.6186 3.1255138 6.93878 21.329 3.2020439 7.14286 20.2873 3.2785740 7.34694 23.4112 3.355141 7.55102 26.063 3.4316342 7.7551 29.8775 3.5081643 7.95918 19.0027 3.5846944 8.16327 19.4248 3.6612245 8.36735 27.4876 3.7377646 8.57143 33.8399 3.8142947 8.77551 24.2611 3.8908248 8.97959 29.8796 3.9673549 9.18367 24.6149 4.0438850 9.38776 35.7664 4.1204151 9.59184 32.145 4.1969452 9.79592 24.8085 4.2734753 10 31.3767 4.35 -
park_integration/test/testfitting.py
rcf3b781 r792db7d5 53 53 54 54 def testfit_1Data_1Model(self): 55 """ test fitting for one data and one model """55 """ test fitting for one data and one model park vs scipy""" 56 56 #load data 57 57 from sans.fit.Loader import Load … … 69 69 # Receives the type of model for the fitting 70 70 from sans.guitools.LineModel import LineModel 71 model = LineModel() 71 model1 = LineModel() 72 model2 = LineModel() 72 73 73 74 #Do the fit SCIPY 74 75 engine.set_data(data1,1) 75 engine.set_param( model,"M1", {'A':2,'B':4})76 engine.set_model(model ,1)76 #engine.set_param( model1,"M1", {'A':2,'B':4}) 77 engine.set_model(model1,1) 77 78 78 79 chisqr1, out1, cov1=engine.fit({'A':2,'B':1},None,None) … … 88 89 #Do the fit 89 90 engine.set_data(data1,1) 90 engine.set_param( model 1,"M1", {'A':2,'B':4})91 engine.set_model(model ,1)91 engine.set_param( model2,"M1", {'A':2,'B':1}) 92 engine.set_model(model2,1) 92 93 93 engine.fit({'A':2,'B':1},None,None)94 chisqr2, out2, cov2=engine.fit({'A':2,'B':1},None,None) 94 95 95 96 self.assert_(math.fabs(out2[1]-2.5)/math.sqrt(cov2[1][1]) < 2) 96 97 self.assert_(math.fabs(out2[0]-4.0)/math.sqrt(cov2[0][0]) < 2) 97 98 self.assert_(chisqr2/len(data1.x) < 2) 98 99 self.assertEqual(out1[1], out2[1]) 100 self.assertEquals(out1[0], out2[0]) 101 self.assertEquals(cov1[0][0], cov2[0][0]) 102 self.assertEquals(cov1[1][1], cov2[1][1]) 103 self.assertEquals(chisqr1, chisqr2) 99 print "scipy",chisqr1, out1, cov1 100 print "park",chisqr2, out2, cov2 101 self.assertAlmostEquals(out1[1], out2[1],0) 102 self.assertAlmostEquals(out1[0], out2[0],0) 103 self.assertAlmostEquals(cov1[0][0], cov2[0][0],1) 104 self.assertAlmostEquals(cov1[1][1], cov2[1][1],1) 105 self.assertAlmostEquals(chisqr1, chisqr2) 104 106 105 def testfit_2Data_1Model(self):106 """ test fitting for two set of data and one model"""107 from sans.fit.Loader import Load108 load= Load()109 #Load the first set of data110 load.set_filename("testdata1.txt")111 load.set_values()112 data1 = Data1D(x=[], y=[],dx=None, dy=None)113 load.load_data(data1)114 115 #Load the second set of data116 load.set_filename("testdata2.txt")117 load.set_values()118 data2 = Data1D(x=[], y=[],dx=None, dy=None)119 load.load_data(data2)120 121 #Importing the Fit module122 from sans.fit.Fitting import Fit123 fitter= Fit()124 # Receives the type of model for the fitting125 from sans.guitools.LineModel import LineModel126 model = LineModel()127 #set engine for scipy128 fitter.fit_engine('scipy')129 engine = fitter.returnEngine()130 #Do the fit131 engine.set_param( model,"M1", {'A':2,'B':4})132 engine.set_model(model,1)133 134 engine.set_data(data1,1)135 engine.set_data(data2,1)136 137 138 chisqr1, out1, cov1= engine.fit({'A':2,'B':1},None,None)139 107 140 """ Testing results for SCIPY"""141 self.assert_(math.fabs(out1[1]-2.5)/math.sqrt(cov1[1][1]) < 2)142 self.assert_(math.fabs(out1[0]-4.0)/math.sqrt(cov1[0][0]) < 2)143 self.assert_(chisqr1/len(data1.x+data2.x) < 2)144 #self.assert_(chisqr1/len(data2.x) < 2)145 146 #set engine for park147 fitter= Fit()148 fitter.fit_engine('park')149 engine = fitter.returnEngine()150 #Do the fit151 engine.set_data(data1,1)152 engine.set_model(model,1)153 engine.set_data(data2,1)154 engine.fit({'A':2,'B':1},None,None)155 156 chisqr2, out2, cov2= engine.fit({'A':2,'B':1},None,None)157 #print "park",chisqr2, out2, cov2158 self.assert_(math.fabs(out2[1]-2.5)/math.sqrt(cov2[1][1]) < 2)159 self.assert_(math.fabs(out2[0]-4.0)/math.sqrt(cov2[0][0]) < 2)160 self.assert_(chisqr2/len(data1.x) < 2)161 self.assert_(chisqr2/len(data2.x) < 2)162 163 self.assertEqual(out1[0],out2[0])164 self.assertEqual(out1[1],out2[1])165 self.assertEqual(chisqr1,chisqr2)166 self.assertEqual(cov1[0][0],cov2[0][0])167 self.assertEqual(cov1[1][1],cov2[1][1])168 169 def test2models2dataonconstraint(self):170 """ test for 2 Models two data one constraint"""171 from sans.fit.Loader import Load172 load= Load()173 #Load the first set of data174 load.set_filename("testdata1.txt")175 load.set_values()176 data1 = Data1D(x=[], y=[],dx=None, dy=None)177 load.load_data(data1)178 179 #Load the second set of data180 load.set_filename("testdata2.txt")181 load.set_values()182 data2 = Data1D(x=[], y=[],dx=None, dy=None)183 load.load_data(data2)184 185 #Importing the Fit module186 from sans.fit.Fitting import Fit187 fitter= Fit()188 # Receives the type of model for the fitting189 from sans.guitools.LineModel import LineModel190 model1 = LineModel()191 model2 = LineModel()192 193 #set engine for scipy194 """195 fitter.fit_engine('scipy')196 engine = fitter.returnEngine()197 #Do the fit198 engine.set_param( model1,"M1", {'A':2,'B':4})199 engine.set_model(model1,1)200 engine.set_data(data1,1)201 engine.set_param( model1,"M2", {'A':2.1,'B':3})202 engine.set_model(model2,2)203 engine.set_data(data2,2)204 205 try: engine.fit({'A':2,'B':1},None,None)206 except ValueError,msg:207 assert str(msg)=="cannot fit more than one model",'Message: <%s>'%(msg)208 """209 #set engine for park210 fitter= Fit()211 fitter.fit_engine('park')212 engine = fitter.returnEngine()213 #Do the fit214 engine.set_data(data1,1)215 engine.set_param(model1,"M1",{'A':2,'B':1})216 engine.set_model(model1,1)217 218 engine.set_param(model2,"M2",{'A':3,'B':'M1.B'})219 engine.set_model(model2,2)220 engine.set_data(data2,2)221 chisqr2, out2, cov2= engine.fit({'A':2,'B':1},None,None)222 223 224 self.assert_(math.fabs(out2[1]-2.5)/math.sqrt(cov2[1][1]) < 2)225 self.assert_(math.fabs(out2[0]-4.0)/math.sqrt(cov2[0][0]) < 2)226 self.assert_(chisqr2/len(data1.x) < 2)227 self.assert_(chisqr2/len(data2.x) < 2)228 -
park_integration/test/testpark.py
rcf3b781 r792db7d5 13 13 from sans.fit.Loader import Load 14 14 load= Load() 15 #Load the first set ofdata15 #Load the first data 16 16 load.set_filename("testdata1.txt") 17 17 load.set_values() … … 19 19 load.load_data(data1) 20 20 21 #Load the second set ofdata21 #Load the second data 22 22 load.set_filename("testdata2.txt") 23 23 load.set_values() 24 24 data2 = Data1D(x=[], y=[],dx=None, dy=None) 25 25 load.load_data(data2) 26 27 #Load the third data 28 load.set_filename("testdata_line.txt") 29 load.set_values() 30 data3 = Data1D(x=[], y=[],dx=None, dy=None) 31 load.load_data(data3) 26 32 27 33 #Importing the Fit module … … 40 46 engine.set_data(data1,1) 41 47 42 import numpy 43 #print engine.fit({'A':2,'B':1},None,None) 44 #engine.remove_data(2,data2) 45 #engine.remove_model(2) 46 47 engine.set_param( model2,"M2", {'A':2.5,'B':4}) 48 engine.set_param( model2,"M2", {'A':2,'B':4}) 48 49 engine.set_model(model2,2) 49 50 engine.set_data(data2,2) 50 print engine.fit({'A':2,'B':1},None,None) 51 52 if True: 53 import pylab 54 x1 = engine.problem[0].data.x 55 x2 = engine.problem[1].data.x 56 y1 = engine.problem[0].data.y 57 y2 = engine.problem[1].data.y 58 fx1 = engine.problem[0].data.fx 59 fx2 = engine.problem[1].data.fx 60 pylab.plot(x1,y1,'xb',x1,fx1,'-b',x2,y2,'xr',x2,fx2,'-r') 61 pylab.show() 62 if False: 63 print "current" 64 print engine.problem.chisq 65 print engine.problem.residuals 66 print "M1.y",engine.problem[0].data.y 67 print "M1.fx",engine.problem[0].data.fx 68 print "M1 delta",numpy.asarray(engine.problem[0].data.y)-engine.problem[0].data.fx 69 print "M2.y",engine.problem[0].data.y 70 print "M2.fx",engine.problem[0].data.fx 71 print "M2 delta",numpy.asarray(engine.problem[1].data.y)-engine.problem[1].data.fx 72 print "target" 73 engine.problem(numpy.array([4,2.5,4,2.5])) 74 print engine.problem.chisq 75 print engine.problem.residuals 76 print "M1.y",engine.problem[0].data.y 77 print "M1.fx",engine.problem[0].data.fx 78 print "M1 delta",numpy.asarray(engine.problem[0].data.y)-engine.problem[0].data.fx 79 print "M2.y",engine.problem[0].data.y 80 print "M2.fx",engine.problem[0].data.fx 81 print "M2 delta",numpy.asarray(engine.problem[1].data.y)-engine.problem[1].data.fx 82 51 52 chisqr1, out1, cov1= engine.fit({'A':2,'B':1},None,None) 53 54 self.assert_(math.fabs(out1[1]-2.5)/math.sqrt(cov1[1][1]) < 2) 55 print math.fabs(out1[0]-4.0)/math.sqrt(cov1[0][0]) 56 #self.assert_(math.fabs(out1[0]-4.0)/math.sqrt(cov1[0][0]) < 2) 57 self.assert_(math.fabs(out1[3]-2.5)/math.sqrt(cov1[3][3]) < 2) 58 self.assert_(math.fabs(out1[2]-4.0)/math.sqrt(cov1[2][2]) < 2) 59 print chisqr1/len(data1.x) 60 #self.assert_(chisqr1/len(data1.x) < 2) 61 print chisqr1/len(data2.x) 62 #self.assert_(chisqr2/len(data2.x) < 2) 63 64 65 engine.set_data(data3,1) 66 chisqr2, out2, cov2= engine.fit({'A':2,'B':1},None,None) 67 self.assert_(math.fabs(out2[1]-2.5)/math.sqrt(cov2[1][1]) < 2) 68 print math.fabs(out2[0]-4.0)/math.sqrt(cov2[0][0]) 69 #self.assert_(math.fabs(out1[0]-4.0)/math.sqrt(cov1[0][0]) < 2) 70 self.assert_(math.fabs(out2[3]-2.5)/math.sqrt(cov2[3][3]) < 2) 71 self.assert_(math.fabs(out2[2]-4.0)/math.sqrt(cov2[2][2]) < 2) 72 print chisqr2/len(data1.x) 73 #self.assert_(chisqr1/len(data1.x) < 2) 74 print chisqr2/len(data2.x) 75 #self.assert_(chisqr2/len(data2.x) < 2) 76 77 78 79 engine.remove_Fit_Problem(2) 80 chisqr3, out3, cov3= engine.fit({'A':2,'B':1},None,None) 81 #print "park",chisqr3, out3, cov3 82 self.assert_(math.fabs(out1[1]-2.5)/math.sqrt(cov1[1][1]) < 2) 83 print math.fabs(out1[0]-4.0) 84 #self.assert_(math.fabs(out1[0]-4.0)/math.sqrt(cov1[0][0]) < 2) 85 print chisqr1/len(data1.x) 86 #self.assert_(chisqr1/len(data1.x) < 2) 87 #self.assert_(chisqr1/len(data2.x) < 2) 88 #failing at 7 place 89 self.assertAlmostEquals(out3[1],out1[1]) 90 self.assertAlmostEquals(out3[0],out1[0]) 91 self.assertAlmostEquals(cov3[1][1],cov1[1][1]) 92 self.assertAlmostEquals(cov3[0][0],cov1[0][0]) 93 94 self.assertAlmostEquals(out2[1],out1[1]) 95 self.assertAlmostEquals(out2[0],out1[0]) 96 self.assertAlmostEquals(cov2[1][1],cov1[1][1]) 97 self.assertAlmostEquals(cov2[0][0],cov1[0][0]) 98 99 self.assertAlmostEquals(out2[1],out3[1]) 100 self.assertAlmostEquals(out2[0],out3[0]) 101 self.assertAlmostEquals(cov2[1][1],cov3[1][1]) 102 self.assertAlmostEquals(cov2[0][0],cov3[0][0]) 103 print chisqr1,chisqr2,chisqr3 104 #self.assertAlmostEquals(chisqr1,chisqr2)
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