[4c718654] | 1 | |
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[48882d1] | 2 | import park,numpy |
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| 3 | |
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| 4 | class SansParameter(park.Parameter): |
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| 5 | """ |
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| 6 | SANS model parameters for use in the PARK fitting service. |
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| 7 | The parameter attribute value is redirected to the underlying |
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| 8 | parameter value in the SANS model. |
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| 9 | """ |
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| 10 | def __init__(self, name, model): |
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| 11 | self._model, self._name = model,name |
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| 12 | self.set(model.getParam(name)) |
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| 13 | |
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| 14 | def _getvalue(self): return self._model.getParam(self.name) |
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| 15 | |
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| 16 | def _setvalue(self,value): |
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| 17 | self._model.setParam(self.name, value) |
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| 18 | |
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| 19 | value = property(_getvalue,_setvalue) |
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| 20 | |
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| 21 | def _getrange(self): |
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| 22 | lo,hi = self._model.details[self.name][1:] |
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| 23 | if lo is None: lo = -numpy.inf |
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| 24 | if hi is None: hi = numpy.inf |
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| 25 | return lo,hi |
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| 26 | |
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| 27 | def _setrange(self,r): |
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| 28 | self._model.details[self.name][1:] = r |
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| 29 | range = property(_getrange,_setrange) |
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| 30 | |
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| 31 | |
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| 32 | class Model(object): |
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| 33 | """ |
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| 34 | PARK wrapper for SANS models. |
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| 35 | """ |
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| 36 | def __init__(self, sans_model): |
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| 37 | self.model = sans_model |
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| 38 | #print "ParkFitting:sans model",self.model |
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| 39 | self.sansp = sans_model.getParamList() |
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| 40 | #print "ParkFitting: sans model parameter list",sansp |
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| 41 | self.parkp = [SansParameter(p,sans_model) for p in self.sansp] |
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| 42 | #print "ParkFitting: park model parameter ",self.parkp |
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| 43 | self.parameterset = park.ParameterSet(sans_model.name,pars=self.parkp) |
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| 44 | self.pars=[] |
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| 45 | |
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| 46 | def getParams(self,fitparams): |
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| 47 | list=[] |
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| 48 | self.pars=[] |
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| 49 | self.pars=fitparams |
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| 50 | for item in fitparams: |
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| 51 | for element in self.parkp: |
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| 52 | if element.name ==str(item): |
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| 53 | list.append(element.value) |
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| 54 | #print "abstractfitengine: getparams",list |
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| 55 | return list |
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| 56 | |
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| 57 | def setParams(self, params): |
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| 58 | list=[] |
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| 59 | for item in self.parkp: |
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| 60 | list.append(item.name) |
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| 61 | list.sort() |
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| 62 | for i in range(len(params)): |
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| 63 | #self.parkp[i].value = params[i] |
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| 64 | #print "abstractfitengine: set-params",list[i],params[i] |
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| 65 | |
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| 66 | self.model.setParam(list[i],params[i]) |
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| 67 | |
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| 68 | def eval(self,x): |
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| 69 | #print "eval",self.parameterset[0].value,self.parameterset[1].value |
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| 70 | return self.model.runXY(x) |
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| 71 | |
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| 72 | |
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| 73 | class Data(object): |
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| 74 | """ Wrapper class for SANS data """ |
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| 75 | def __init__(self,x=None,y=None,dy=None,dx=None,sans_data=None): |
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| 76 | |
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| 77 | if sans_data !=None: |
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| 78 | self.x= sans_data.x |
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| 79 | self.y= sans_data.y |
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| 80 | self.dx= sans_data.dx |
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| 81 | self.dy= sans_data.dy |
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| 82 | |
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| 83 | elif (x!=None and y!=None and dy!=None): |
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| 84 | self.x=x |
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| 85 | self.y=y |
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| 86 | self.dx=dx |
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| 87 | self.dy=dy |
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| 88 | else: |
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| 89 | raise ValueError,\ |
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| 90 | "Data is missing x, y or dy, impossible to compute residuals later on" |
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| 91 | self.qmin=None |
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| 92 | self.qmax=None |
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| 93 | |
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| 94 | def setFitRange(self,mini=None,maxi=None): |
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| 95 | """ to set the fit range""" |
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| 96 | self.qmin=mini |
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| 97 | self.qmax=maxi |
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| 98 | def getFitRange(self): |
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| 99 | return self.qmin, self.qmax |
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| 100 | def residuals(self, fn): |
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| 101 | """ @param fn: function that return model value |
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| 102 | @return residuals |
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| 103 | """ |
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| 104 | x,y,dy = [numpy.asarray(v) for v in (self.x,self.y,self.dy)] |
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| 105 | if self.qmin==None and self.qmax==None: |
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| 106 | fx =[fn(v) for v in x] |
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| 107 | return (y - fx)/dy |
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| 108 | else: |
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| 109 | idx = (x>=self.qmin) & (x <= self.qmax) |
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| 110 | fx = [fn(item)for item in x[idx ]] |
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| 111 | return (y[idx] - fx)/dy[idx] |
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| 112 | |
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| 113 | |
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| 114 | |
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| 115 | def residuals_deriv(self, model, pars=[]): |
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| 116 | """ |
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| 117 | @return residuals derivatives . |
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| 118 | @note: in this case just return empty array |
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| 119 | """ |
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| 120 | return [] |
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| 121 | |
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| 122 | class sansAssembly: |
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| 123 | def __init__(self,Model=None , Data=None): |
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| 124 | self.model = Model |
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| 125 | self.data = Data |
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| 126 | self.res=[] |
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| 127 | def chisq(self, params): |
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| 128 | """ |
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| 129 | Calculates chi^2 |
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| 130 | @param params: list of parameter values |
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| 131 | @return: chi^2 |
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| 132 | """ |
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| 133 | sum = 0 |
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| 134 | for item in self.res: |
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| 135 | sum += item*item |
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| 136 | return sum |
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| 137 | def __call__(self,params): |
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| 138 | self.model.setParams(params) |
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| 139 | self.res= self.data.residuals(self.model.eval) |
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| 140 | return self.res |
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| 141 | |
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[4c718654] | 142 | class FitEngine: |
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[ee5b04c] | 143 | def __init__(self): |
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| 144 | self.paramList=[] |
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[4c718654] | 145 | def _concatenateData(self, listdata=[]): |
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| 146 | """ |
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| 147 | _concatenateData method concatenates each fields of all data contains ins listdata. |
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| 148 | @param listdata: list of data |
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| 149 | |
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[48882d1] | 150 | @return Data: |
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[4c718654] | 151 | |
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| 152 | @raise: if listdata is empty will return None |
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| 153 | @raise: if data in listdata don't contain dy field ,will create an error |
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| 154 | during fitting |
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| 155 | """ |
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| 156 | if listdata==[]: |
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| 157 | raise ValueError, " data list missing" |
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| 158 | else: |
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| 159 | xtemp=[] |
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| 160 | ytemp=[] |
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| 161 | dytemp=[] |
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[48882d1] | 162 | self.mini=None |
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| 163 | self.maxi=None |
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[4c718654] | 164 | |
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| 165 | for data in listdata: |
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[48882d1] | 166 | mini,maxi=data.getFitRange() |
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| 167 | if self.mini==None and self.maxi==None: |
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| 168 | self.mini=mini |
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| 169 | self.maxi=maxi |
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| 170 | else: |
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| 171 | if mini < self.mini: |
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| 172 | self.mini=mini |
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| 173 | if self.maxi < maxi: |
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| 174 | self.maxi=maxi |
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| 175 | |
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| 176 | |
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[4c718654] | 177 | for i in range(len(data.x)): |
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| 178 | xtemp.append(data.x[i]) |
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| 179 | ytemp.append(data.y[i]) |
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| 180 | if data.dy is not None and len(data.dy)==len(data.y): |
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| 181 | dytemp.append(data.dy[i]) |
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| 182 | else: |
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[ee5b04c] | 183 | raise RuntimeError, "Fit._concatenateData: y-errors missing" |
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[48882d1] | 184 | #return xtemp, ytemp,dytemp |
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| 185 | data= Data(x=xtemp,y=ytemp,dy=dytemp) |
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| 186 | data.setFitRange(self.mini, self.maxi) |
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| 187 | return data |
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[f44dbc7] | 188 | def set_model(self,model,name,Uid,pars=[]): |
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| 189 | if len(pars) >0: |
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[48882d1] | 190 | self.paramList = [] |
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[6831a99] | 191 | if model==None: |
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[f44dbc7] | 192 | raise ValueError, "AbstractFitEngine: Specify parameters to fit" |
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[6831a99] | 193 | else: |
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[3c404d3] | 194 | model.model.name = name |
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[48882d1] | 195 | self.paramList=pars |
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[6831a99] | 196 | #A fitArrange is already created but contains dList only at Uid |
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| 197 | if self.fitArrangeList.has_key(Uid): |
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| 198 | self.fitArrangeList[Uid].set_model(model) |
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| 199 | else: |
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| 200 | #no fitArrange object has been create with this Uid |
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[48882d1] | 201 | fitproblem = FitArrange() |
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[6831a99] | 202 | fitproblem.set_model(model) |
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[48882d1] | 203 | self.fitArrangeList[Uid] = fitproblem |
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[d4b0687] | 204 | else: |
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[6831a99] | 205 | raise ValueError, "park_integration:missing parameters" |
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[48882d1] | 206 | |
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| 207 | def set_data(self,data,Uid,qmin=None,qmax=None): |
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[d4b0687] | 208 | """ Receives plottable, creates a list of data to fit,set data |
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| 209 | in a FitArrange object and adds that object in a dictionary |
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| 210 | with key Uid. |
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| 211 | @param data: data added |
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| 212 | @param Uid: unique key corresponding to a fitArrange object with data |
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| 213 | """ |
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[48882d1] | 214 | if qmin !=None and qmax !=None: |
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| 215 | data.setFitRange(mini=qmin,maxi=qmax) |
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[d4b0687] | 216 | #A fitArrange is already created but contains model only at Uid |
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| 217 | if self.fitArrangeList.has_key(Uid): |
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| 218 | self.fitArrangeList[Uid].add_data(data) |
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| 219 | else: |
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| 220 | #no fitArrange object has been create with this Uid |
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| 221 | fitproblem= FitArrange() |
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| 222 | fitproblem.add_data(data) |
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[48882d1] | 223 | self.fitArrangeList[Uid]=fitproblem |
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| 224 | |
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[d4b0687] | 225 | def get_model(self,Uid): |
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| 226 | """ |
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| 227 | @param Uid: Uid is key in the dictionary containing the model to return |
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| 228 | @return a model at this uid or None if no FitArrange element was created |
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| 229 | with this Uid |
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| 230 | """ |
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| 231 | if self.fitArrangeList.has_key(Uid): |
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| 232 | return self.fitArrangeList[Uid].get_model() |
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| 233 | else: |
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| 234 | return None |
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| 235 | |
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| 236 | def remove_Fit_Problem(self,Uid): |
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| 237 | """remove fitarrange in Uid""" |
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| 238 | if self.fitArrangeList.has_key(Uid): |
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| 239 | del self.fitArrangeList[Uid] |
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[4c718654] | 240 | |
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| 241 | |
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[d4b0687] | 242 | class FitArrange: |
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| 243 | def __init__(self): |
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| 244 | """ |
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| 245 | Class FitArrange contains a set of data for a given model |
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| 246 | to perform the Fit.FitArrange must contain exactly one model |
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| 247 | and at least one data for the fit to be performed. |
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| 248 | model: the model selected by the user |
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| 249 | Ldata: a list of data what the user wants to fit |
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| 250 | |
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| 251 | """ |
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| 252 | self.model = None |
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| 253 | self.dList =[] |
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| 254 | |
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| 255 | def set_model(self,model): |
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| 256 | """ |
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| 257 | set_model save a copy of the model |
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| 258 | @param model: the model being set |
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| 259 | """ |
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| 260 | self.model = model |
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| 261 | |
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| 262 | def add_data(self,data): |
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| 263 | """ |
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| 264 | add_data fill a self.dList with data to fit |
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| 265 | @param data: Data to add in the list |
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| 266 | """ |
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| 267 | if not data in self.dList: |
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| 268 | self.dList.append(data) |
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| 269 | |
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| 270 | def get_model(self): |
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| 271 | """ @return: saved model """ |
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| 272 | return self.model |
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| 273 | |
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| 274 | def get_data(self): |
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| 275 | """ @return: list of data dList""" |
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| 276 | return self.dList |
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| 277 | |
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| 278 | def remove_data(self,data): |
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| 279 | """ |
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| 280 | Remove one element from the list |
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| 281 | @param data: Data to remove from dList |
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| 282 | """ |
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| 283 | if data in self.dList: |
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| 284 | self.dList.remove(data) |
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[94b44293] | 285 | |
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[4c718654] | 286 | |
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| 287 | |
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| 288 | |
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