| 1 | import logging, sys |
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| 2 | import park,numpy,math, copy |
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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 | """ |
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| 12 | @param name: the name of the model parameter |
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| 13 | @param model: the sans model to wrap as a park model |
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| 14 | """ |
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| 15 | self._model, self._name = model,name |
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| 16 | #set the value for the parameter of the given name |
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| 17 | self.set(model.getParam(name)) |
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| 18 | |
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| 19 | def _getvalue(self): |
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| 20 | """ |
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| 21 | override the _getvalue of park parameter |
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| 22 | @return value the parameter associates with self.name |
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| 23 | """ |
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| 24 | return self._model.getParam(self.name) |
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| 25 | |
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| 26 | def _setvalue(self,value): |
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| 27 | """ |
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| 28 | override the _setvalue pf park parameter |
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| 29 | @param value: the value to set on a given parameter |
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| 30 | """ |
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| 31 | self._model.setParam(self.name, value) |
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| 32 | |
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| 33 | value = property(_getvalue,_setvalue) |
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| 34 | |
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| 35 | def _getrange(self): |
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| 36 | """ |
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| 37 | Override _getrange of park parameter |
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| 38 | return the range of parameter |
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| 39 | """ |
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| 40 | #if not self.name in self._model.getDispParamList(): |
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| 41 | lo,hi = self._model.details[self.name][1:] |
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| 42 | if lo is None: lo = -numpy.inf |
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| 43 | if hi is None: hi = numpy.inf |
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| 44 | #else: |
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| 45 | #lo,hi = self._model.details[self.name][1:] |
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| 46 | #if lo is None: lo = -numpy.inf |
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| 47 | #if hi is None: hi = numpy.inf |
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| 48 | if lo >= hi: |
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| 49 | raise ValueError,"wrong fit range for parameters" |
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| 50 | |
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| 51 | return lo,hi |
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| 52 | |
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| 53 | def _setrange(self,r): |
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| 54 | """ |
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| 55 | override _setrange of park parameter |
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| 56 | @param r: the value of the range to set |
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| 57 | """ |
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| 58 | self._model.details[self.name][1:] = r |
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| 59 | range = property(_getrange,_setrange) |
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| 60 | |
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| 61 | class Model(park.Model): |
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| 62 | """ |
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| 63 | PARK wrapper for SANS models. |
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| 64 | """ |
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| 65 | def __init__(self, sans_model, **kw): |
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| 66 | """ |
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| 67 | @param sans_model: the sans model to wrap using park interface |
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| 68 | """ |
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| 69 | park.Model.__init__(self, **kw) |
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| 70 | self.model = sans_model |
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| 71 | self.name = sans_model.name |
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| 72 | #list of parameters names |
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| 73 | self.sansp = sans_model.getParamList() |
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| 74 | #list of park parameter |
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| 75 | self.parkp = [SansParameter(p,sans_model) for p in self.sansp] |
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| 76 | #list of parameterset |
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| 77 | self.parameterset = park.ParameterSet(sans_model.name,pars=self.parkp) |
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| 78 | self.pars=[] |
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| 79 | |
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| 80 | |
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| 81 | def getParams(self,fitparams): |
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| 82 | """ |
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| 83 | return a list of value of paramter to fit |
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| 84 | @param fitparams: list of paramaters name to fit |
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| 85 | """ |
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| 86 | list=[] |
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| 87 | self.pars=[] |
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| 88 | self.pars=fitparams |
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| 89 | for item in fitparams: |
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| 90 | for element in self.parkp: |
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| 91 | if element.name ==str(item): |
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| 92 | list.append(element.value) |
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| 93 | return list |
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| 94 | |
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| 95 | |
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| 96 | def setParams(self,paramlist, params): |
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| 97 | """ |
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| 98 | Set value for parameters to fit |
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| 99 | @param params: list of value for parameters to fit |
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| 100 | """ |
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| 101 | try: |
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| 102 | for i in range(len(self.parkp)): |
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| 103 | for j in range(len(paramlist)): |
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| 104 | if self.parkp[i].name==paramlist[j]: |
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| 105 | self.parkp[i].value = params[j] |
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| 106 | self.model.setParam(self.parkp[i].name,params[j]) |
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| 107 | except: |
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| 108 | raise |
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| 109 | |
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| 110 | def eval(self,x): |
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| 111 | """ |
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| 112 | override eval method of park model. |
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| 113 | @param x: the x value used to compute a function |
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| 114 | """ |
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| 115 | try: |
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| 116 | return self.model.evalDistribution(x) |
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| 117 | except: |
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| 118 | raise |
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| 119 | |
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| 120 | |
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| 121 | class FitData1D(object): |
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| 122 | """ Wrapper class for SANS data """ |
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| 123 | def __init__(self,sans_data1d, smearer=None): |
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| 124 | """ |
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| 125 | Data can be initital with a data (sans plottable) |
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| 126 | or with vectors. |
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| 127 | |
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| 128 | self.smearer is an object of class QSmearer or SlitSmearer |
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| 129 | that will smear the theory data (slit smearing or resolution |
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| 130 | smearing) when set. |
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| 131 | |
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| 132 | The proper way to set the smearing object would be to |
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| 133 | do the following: |
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| 134 | |
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| 135 | from DataLoader.qsmearing import smear_selection |
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| 136 | fitdata1d = FitData1D(some_data) |
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| 137 | fitdata1d.smearer = smear_selection(some_data) |
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| 138 | |
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| 139 | Note that some_data _HAS_ to be of class DataLoader.data_info.Data1D |
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| 140 | |
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| 141 | Setting it back to None will turn smearing off. |
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| 142 | |
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| 143 | """ |
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| 144 | |
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| 145 | self.smearer = smearer |
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| 146 | |
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| 147 | # Initialize from Data1D object |
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| 148 | self.data=sans_data1d |
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| 149 | self.x= numpy.array(sans_data1d.x) |
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| 150 | self.y= numpy.array(sans_data1d.y) |
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| 151 | self.dx= sans_data1d.dx |
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| 152 | if sans_data1d.dy ==None or sans_data1d.dy==[]: |
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| 153 | self.dy= numpy.zeros(len(y)) |
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| 154 | else: |
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| 155 | self.dy= numpy.asarray(sans_data1d.dy) |
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| 156 | |
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| 157 | # For fitting purposes, replace zero errors by 1 |
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| 158 | #TODO: check validity for the rare case where only |
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| 159 | # a few points have zero errors |
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| 160 | self.dy[self.dy==0]=1 |
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| 161 | |
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| 162 | ## Min Q-value |
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| 163 | #Skip the Q=0 point, especially when y(q=0)=None at x[0]. |
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| 164 | if min (self.data.x) ==0.0 and self.data.x[0]==0 and not numpy.isfinite(self.data.y[0]): |
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| 165 | self.qmin = min(self.data.x[self.data.x!=0]) |
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| 166 | else: |
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| 167 | self.qmin= min (self.data.x) |
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| 168 | ## Max Q-value |
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| 169 | self.qmax= max (self.data.x) |
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| 170 | |
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| 171 | # Range used for input to smearing |
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| 172 | self._qmin_unsmeared = self.qmin |
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| 173 | self._qmax_unsmeared = self.qmax |
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| 174 | # Identify the bin range for the unsmeared and smeared spaces |
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| 175 | self.idx = (self.x>=self.qmin) & (self.x <= self.qmax) |
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| 176 | self.idx_unsmeared = (self.x>=self._qmin_unsmeared) & (self.x <= self._qmax_unsmeared) |
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| 177 | |
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| 178 | |
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| 179 | |
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| 180 | def setFitRange(self,qmin=None,qmax=None): |
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| 181 | """ to set the fit range""" |
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| 182 | # Skip Q=0 point, (especially for y(q=0)=None at x[0]). |
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| 183 | #ToDo: Fix this. |
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| 184 | if qmin==0.0 and not numpy.isfinite(self.data.y[qmin]): |
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| 185 | self.qmin = min(self.data.x[self.data.x!=0]) |
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| 186 | elif qmin!=None: |
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| 187 | self.qmin = qmin |
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| 188 | |
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| 189 | if qmax !=None: |
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| 190 | self.qmax = qmax |
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| 191 | |
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| 192 | # Determine the range needed in unsmeared-Q to cover |
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| 193 | # the smeared Q range |
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| 194 | self._qmin_unsmeared = self.qmin |
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| 195 | self._qmax_unsmeared = self.qmax |
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| 196 | |
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| 197 | self._first_unsmeared_bin = 0 |
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| 198 | self._last_unsmeared_bin = len(self.data.x)-1 |
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| 199 | |
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| 200 | if self.smearer!=None: |
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| 201 | self._first_unsmeared_bin, self._last_unsmeared_bin = self.smearer.get_bin_range(self.qmin, self.qmax) |
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| 202 | self._qmin_unsmeared = self.data.x[self._first_unsmeared_bin] |
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| 203 | self._qmax_unsmeared = self.data.x[self._last_unsmeared_bin] |
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| 204 | |
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| 205 | # Identify the bin range for the unsmeared and smeared spaces |
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| 206 | self.idx = (self.x>=self.qmin) & (self.x <= self.qmax) |
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| 207 | self.idx_unsmeared = (self.x>=self._qmin_unsmeared) & (self.x <= self._qmax_unsmeared) |
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| 208 | |
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| 209 | |
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| 210 | def getFitRange(self): |
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| 211 | """ |
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| 212 | @return the range of data.x to fit |
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| 213 | """ |
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| 214 | return self.qmin, self.qmax |
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| 215 | |
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| 216 | def residuals(self, fn): |
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| 217 | """ |
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| 218 | Compute residuals. |
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| 219 | |
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| 220 | If self.smearer has been set, use if to smear |
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| 221 | the data before computing chi squared. |
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| 222 | |
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| 223 | @param fn: function that return model value |
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| 224 | @return residuals |
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| 225 | """ |
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| 226 | # Compute theory data f(x) |
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| 227 | fx= numpy.zeros(len(self.x)) |
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| 228 | fx[self.idx_unsmeared] = fn(self.x[self.idx_unsmeared]) |
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| 229 | |
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| 230 | ## Smear theory data |
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| 231 | if self.smearer is not None: |
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| 232 | fx = self.smearer(fx, self._first_unsmeared_bin, self._last_unsmeared_bin) |
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| 233 | |
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| 234 | ## Sanity check |
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| 235 | if numpy.size(self.dy)!= numpy.size(fx): |
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| 236 | raise RuntimeError, "FitData1D: invalid error array %d <> %d" % (numpy.shape(self.dy), |
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| 237 | numpy.size(fx)) |
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| 238 | |
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| 239 | return (self.y[self.idx]-fx[self.idx])/self.dy[self.idx] |
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| 240 | |
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| 241 | |
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| 242 | |
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| 243 | def residuals_deriv(self, model, pars=[]): |
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| 244 | """ |
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| 245 | @return residuals derivatives . |
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| 246 | @note: in this case just return empty array |
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| 247 | """ |
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| 248 | return [] |
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| 249 | |
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| 250 | |
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| 251 | class FitData2D(object): |
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| 252 | """ Wrapper class for SANS data """ |
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| 253 | def __init__(self,sans_data2d): |
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| 254 | """ |
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| 255 | Data can be initital with a data (sans plottable) |
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| 256 | or with vectors. |
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| 257 | """ |
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| 258 | self.data=sans_data2d |
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| 259 | self.image = sans_data2d.data |
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| 260 | self.err_image = sans_data2d.err_data |
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| 261 | self.x_bins_array= numpy.reshape(sans_data2d.x_bins, |
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| 262 | [1,len(sans_data2d.x_bins)]) |
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| 263 | self.y_bins_array = numpy.reshape(sans_data2d.y_bins, |
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| 264 | [len(sans_data2d.y_bins),1]) |
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| 265 | |
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| 266 | x = max(self.data.xmin, self.data.xmax) |
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| 267 | y = max(self.data.ymin, self.data.ymax) |
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| 268 | |
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| 269 | ## fitting range |
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| 270 | self.qmin = 1e-16 |
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| 271 | self.qmax = math.sqrt(x*x +y*y) |
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| 272 | ## new error image for fitting purpose |
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| 273 | if self.err_image== None or self.err_image ==[]: |
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| 274 | self.res_err_image= numpy.zeros(len(self.y_bins),len(self.x_bins)) |
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| 275 | else: |
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| 276 | self.res_err_image = copy.deepcopy(self.err_image) |
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| 277 | self.res_err_image[self.err_image==0]=1 |
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| 278 | |
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| 279 | self.radius= numpy.sqrt(self.x_bins_array**2 + self.y_bins_array**2) |
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| 280 | self.index_model = (self.qmin <= self.radius)&(self.radius<= self.qmax) |
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| 281 | |
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| 282 | |
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| 283 | def setFitRange(self,qmin=None,qmax=None): |
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| 284 | """ to set the fit range""" |
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| 285 | if qmin==0.0: |
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| 286 | self.qmin = 1e-16 |
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| 287 | elif qmin!=None: |
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| 288 | self.qmin = qmin |
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| 289 | if qmax!=None: |
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| 290 | self.qmax= qmax |
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| 291 | |
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| 292 | |
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| 293 | def getFitRange(self): |
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| 294 | """ |
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| 295 | @return the range of data.x to fit |
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| 296 | """ |
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| 297 | return self.qmin, self.qmax |
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| 298 | |
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| 299 | def residuals(self, fn): |
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| 300 | |
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| 301 | res=self.index_model*(self.image - fn([self.x_bins_array, |
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| 302 | self.y_bins_array]))/self.res_err_image |
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| 303 | return res.ravel() |
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| 304 | |
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| 305 | |
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| 306 | def residuals_deriv(self, model, pars=[]): |
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| 307 | """ |
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| 308 | @return residuals derivatives . |
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| 309 | @note: in this case just return empty array |
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| 310 | """ |
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| 311 | return [] |
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| 312 | |
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| 313 | class FitAbort(Exception): |
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| 314 | """ |
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| 315 | Exception raise to stop the fit |
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| 316 | """ |
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| 317 | print"Creating fit abort Exception" |
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| 318 | |
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| 319 | |
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| 320 | class SansAssembly: |
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| 321 | """ |
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| 322 | Sans Assembly class a class wrapper to be call in optimizer.leastsq method |
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| 323 | """ |
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| 324 | def __init__(self,paramlist,Model=None , Data=None, curr_thread= None): |
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| 325 | """ |
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| 326 | @param Model: the model wrapper fro sans -model |
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| 327 | @param Data: the data wrapper for sans data |
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| 328 | """ |
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| 329 | self.model = Model |
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| 330 | self.data = Data |
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| 331 | self.paramlist=paramlist |
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| 332 | self.curr_thread= curr_thread |
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| 333 | self.res=[] |
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| 334 | self.func_name="Functor" |
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| 335 | def chisq(self, params): |
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| 336 | """ |
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| 337 | Calculates chi^2 |
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| 338 | @param params: list of parameter values |
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| 339 | @return: chi^2 |
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| 340 | """ |
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| 341 | sum = 0 |
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| 342 | for item in self.res: |
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| 343 | sum += item*item |
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| 344 | if len(self.res)==0: |
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| 345 | return None |
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| 346 | return sum/ len(self.res) |
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| 347 | |
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| 348 | def __call__(self,params): |
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| 349 | """ |
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| 350 | Compute residuals |
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| 351 | @param params: value of parameters to fit |
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| 352 | """ |
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| 353 | #import thread |
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| 354 | self.model.setParams(self.paramlist,params) |
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| 355 | self.res= self.data.residuals(self.model.eval) |
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| 356 | #if self.curr_thread != None : |
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| 357 | # try: |
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| 358 | # self.curr_thread.isquit() |
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| 359 | # except: |
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| 360 | # raise FitAbort,"stop leastsqr optimizer" |
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| 361 | return self.res |
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| 362 | |
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| 363 | class FitEngine: |
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| 364 | def __init__(self): |
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| 365 | """ |
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| 366 | Base class for scipy and park fit engine |
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| 367 | """ |
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| 368 | #List of parameter names to fit |
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| 369 | self.paramList=[] |
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| 370 | #Dictionnary of fitArrange element (fit problems) |
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| 371 | self.fitArrangeDict={} |
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| 372 | |
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| 373 | def _concatenateData(self, listdata=[]): |
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| 374 | """ |
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| 375 | _concatenateData method concatenates each fields of all data contains ins listdata. |
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| 376 | @param listdata: list of data |
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| 377 | @return Data: Data is wrapper class for sans plottable. it is created with all parameters |
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| 378 | of data concatenanted |
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| 379 | @raise: if listdata is empty will return None |
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| 380 | @raise: if data in listdata don't contain dy field ,will create an error |
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| 381 | during fitting |
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| 382 | """ |
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| 383 | #TODO: we have to refactor the way we handle data. |
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| 384 | # We should move away from plottables and move towards the Data1D objects |
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| 385 | # defined in DataLoader. Data1D allows data manipulations, which should be |
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| 386 | # used to concatenate. |
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| 387 | # In the meantime we should switch off the concatenation. |
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| 388 | #if len(listdata)>1: |
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| 389 | # raise RuntimeError, "FitEngine._concatenateData: Multiple data files is not currently supported" |
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| 390 | #return listdata[0] |
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| 391 | |
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| 392 | if listdata==[]: |
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| 393 | raise ValueError, " data list missing" |
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| 394 | else: |
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| 395 | xtemp=[] |
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| 396 | ytemp=[] |
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| 397 | dytemp=[] |
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| 398 | self.mini=None |
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| 399 | self.maxi=None |
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| 400 | |
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| 401 | for item in listdata: |
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| 402 | data=item.data |
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| 403 | mini,maxi=data.getFitRange() |
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| 404 | if self.mini==None and self.maxi==None: |
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| 405 | self.mini=mini |
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| 406 | self.maxi=maxi |
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| 407 | else: |
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| 408 | if mini < self.mini: |
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| 409 | self.mini=mini |
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| 410 | if self.maxi < maxi: |
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| 411 | self.maxi=maxi |
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| 412 | |
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| 413 | |
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| 414 | for i in range(len(data.x)): |
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| 415 | xtemp.append(data.x[i]) |
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| 416 | ytemp.append(data.y[i]) |
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| 417 | if data.dy is not None and len(data.dy)==len(data.y): |
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| 418 | dytemp.append(data.dy[i]) |
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| 419 | else: |
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| 420 | raise RuntimeError, "Fit._concatenateData: y-errors missing" |
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| 421 | data= Data(x=xtemp,y=ytemp,dy=dytemp) |
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| 422 | data.setFitRange(self.mini, self.maxi) |
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| 423 | return data |
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| 424 | |
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| 425 | |
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| 426 | def set_model(self,model,Uid,pars=[]): |
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| 427 | """ |
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| 428 | set a model on a given uid in the fit engine. |
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| 429 | @param model: the model to fit |
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| 430 | @param Uid :is the key of the fitArrange dictionnary where model is saved as a value |
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| 431 | @param pars: the list of parameters to fit |
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| 432 | @note : pars must contains only name of existing model's paramaters |
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| 433 | """ |
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| 434 | if len(pars) >0: |
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| 435 | if model==None: |
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| 436 | raise ValueError, "AbstractFitEngine: Specify parameters to fit" |
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| 437 | else: |
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| 438 | temp=[] |
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| 439 | for item in pars: |
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| 440 | if item in model.model.getParamList(): |
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| 441 | temp.append(item) |
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| 442 | self.paramList.append(item) |
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| 443 | else: |
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| 444 | raise ValueError,"wrong paramter %s used to set model %s. Choose\ |
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| 445 | parameter name within %s"%(item, model.model.name,str(model.model.getParamList())) |
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| 446 | return |
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| 447 | #A fitArrange is already created but contains dList only at Uid |
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| 448 | if self.fitArrangeDict.has_key(Uid): |
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| 449 | self.fitArrangeDict[Uid].set_model(model) |
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| 450 | self.fitArrangeDict[Uid].pars= pars |
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| 451 | else: |
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| 452 | #no fitArrange object has been create with this Uid |
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| 453 | fitproblem = FitArrange() |
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| 454 | fitproblem.set_model(model) |
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| 455 | fitproblem.pars= pars |
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| 456 | self.fitArrangeDict[Uid] = fitproblem |
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| 457 | |
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| 458 | else: |
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| 459 | raise ValueError, "park_integration:missing parameters" |
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| 460 | |
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| 461 | def set_data(self,data,Uid,smearer=None,qmin=None,qmax=None): |
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| 462 | """ Receives plottable, creates a list of data to fit,set data |
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| 463 | in a FitArrange object and adds that object in a dictionary |
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| 464 | with key Uid. |
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| 465 | @param data: data added |
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| 466 | @param Uid: unique key corresponding to a fitArrange object with data |
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| 467 | """ |
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| 468 | if data.__class__.__name__=='Data2D': |
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| 469 | fitdata=FitData2D(data) |
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| 470 | else: |
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| 471 | fitdata=FitData1D(data, smearer) |
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| 472 | |
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| 473 | fitdata.setFitRange(qmin=qmin,qmax=qmax) |
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| 474 | #A fitArrange is already created but contains model only at Uid |
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| 475 | if self.fitArrangeDict.has_key(Uid): |
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| 476 | self.fitArrangeDict[Uid].add_data(fitdata) |
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| 477 | else: |
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| 478 | #no fitArrange object has been create with this Uid |
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| 479 | fitproblem= FitArrange() |
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| 480 | fitproblem.add_data(fitdata) |
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| 481 | self.fitArrangeDict[Uid]=fitproblem |
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| 482 | |
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| 483 | def get_model(self,Uid): |
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| 484 | """ |
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| 485 | @param Uid: Uid is key in the dictionary containing the model to return |
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| 486 | @return a model at this uid or None if no FitArrange element was created |
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| 487 | with this Uid |
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| 488 | """ |
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| 489 | if self.fitArrangeDict.has_key(Uid): |
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| 490 | return self.fitArrangeDict[Uid].get_model() |
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| 491 | else: |
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| 492 | return None |
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| 493 | |
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| 494 | def remove_Fit_Problem(self,Uid): |
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| 495 | """remove fitarrange in Uid""" |
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| 496 | if self.fitArrangeDict.has_key(Uid): |
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| 497 | del self.fitArrangeDict[Uid] |
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| 498 | |
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| 499 | def select_problem_for_fit(self,Uid,value): |
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| 500 | """ |
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| 501 | select a couple of model and data at the Uid position in dictionary |
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| 502 | and set in self.selected value to value |
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| 503 | @param value: the value to allow fitting. can only have the value one or zero |
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| 504 | """ |
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| 505 | if self.fitArrangeDict.has_key(Uid): |
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| 506 | self.fitArrangeDict[Uid].set_to_fit( value) |
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| 507 | |
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| 508 | |
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| 509 | def get_problem_to_fit(self,Uid): |
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| 510 | """ |
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| 511 | return the self.selected value of the fit problem of Uid |
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| 512 | @param Uid: the Uid of the problem |
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| 513 | """ |
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| 514 | if self.fitArrangeDict.has_key(Uid): |
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| 515 | self.fitArrangeDict[Uid].get_to_fit() |
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| 516 | |
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| 517 | class FitArrange: |
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| 518 | def __init__(self): |
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| 519 | """ |
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| 520 | Class FitArrange contains a set of data for a given model |
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| 521 | to perform the Fit.FitArrange must contain exactly one model |
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| 522 | and at least one data for the fit to be performed. |
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| 523 | model: the model selected by the user |
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| 524 | Ldata: a list of data what the user wants to fit |
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| 525 | |
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| 526 | """ |
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| 527 | self.model = None |
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| 528 | self.dList =[] |
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| 529 | self.pars=[] |
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| 530 | #self.selected is zero when this fit problem is not schedule to fit |
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| 531 | #self.selected is 1 when schedule to fit |
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| 532 | self.selected = 0 |
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| 533 | |
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| 534 | def set_model(self,model): |
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| 535 | """ |
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| 536 | set_model save a copy of the model |
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| 537 | @param model: the model being set |
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| 538 | """ |
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| 539 | self.model = model |
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| 540 | |
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| 541 | def add_data(self,data): |
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| 542 | """ |
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| 543 | add_data fill a self.dList with data to fit |
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| 544 | @param data: Data to add in the list |
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| 545 | """ |
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| 546 | if not data in self.dList: |
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| 547 | self.dList.append(data) |
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| 548 | |
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| 549 | def get_model(self): |
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| 550 | """ @return: saved model """ |
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| 551 | return self.model |
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| 552 | |
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| 553 | def get_data(self): |
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| 554 | """ @return: list of data dList""" |
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| 555 | #return self.dList |
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| 556 | return self.dList[0] |
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| 557 | |
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| 558 | def remove_data(self,data): |
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| 559 | """ |
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| 560 | Remove one element from the list |
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| 561 | @param data: Data to remove from dList |
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| 562 | """ |
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| 563 | if data in self.dList: |
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| 564 | self.dList.remove(data) |
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| 565 | def set_to_fit (self, value=0): |
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| 566 | """ |
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| 567 | set self.selected to 0 or 1 for other values raise an exception |
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| 568 | @param value: integer between 0 or 1 |
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| 569 | """ |
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| 570 | self.selected= value |
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| 571 | |
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| 572 | def get_to_fit(self): |
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| 573 | """ |
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| 574 | @return self.selected value |
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| 575 | """ |
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| 576 | return self.selected |
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