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| 2 | |
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| 3 | import time |
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| 4 | from data_util.calcthread import CalcThread |
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| 5 | import sys |
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| 6 | import numpy,math |
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| 7 | from DataLoader.smearing_2d import Smearer2D |
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| 8 | |
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| 9 | class Calc2D(CalcThread): |
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| 10 | """ |
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| 11 | Compute 2D model |
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| 12 | This calculation assumes a 2-fold symmetry of the model |
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| 13 | where points are computed for one half of the detector |
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| 14 | and I(qx, qy) = I(-qx, -qy) is assumed. |
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| 15 | """ |
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| 16 | def __init__(self, x, y, data,model,smearer,qmin, qmax,qstep, |
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| 17 | id , |
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| 18 | state=None, |
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| 19 | toggle_mode_on=False, |
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| 20 | completefn = None, |
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| 21 | updatefn = None, |
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| 22 | yieldtime = 0.01, |
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| 23 | worktime = 0.01 |
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| 24 | ): |
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| 25 | CalcThread.__init__(self,completefn, |
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| 26 | updatefn, |
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| 27 | yieldtime, |
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| 28 | worktime) |
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| 29 | self.qmin= qmin |
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| 30 | self.qmax= qmax |
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| 31 | self.qstep= qstep |
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| 32 | self.toggle_mode_on = toggle_mode_on |
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| 33 | self.x = x |
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| 34 | self.y = y |
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| 35 | self.data= data |
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| 36 | self.page_id = id |
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| 37 | self.state = None |
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| 38 | # the model on to calculate |
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| 39 | self.model = model |
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| 40 | self.smearer = smearer#(data=self.data,model=self.model) |
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| 41 | self.starttime = 0 |
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| 42 | |
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| 43 | def compute(self): |
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| 44 | """ |
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| 45 | Compute the data given a model function |
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| 46 | """ |
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| 47 | self.starttime = time.time() |
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| 48 | # Determine appropriate q range |
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| 49 | if self.qmin==None: |
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| 50 | self.qmin = 0 |
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| 51 | if self.qmax== None: |
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| 52 | if self.data !=None: |
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| 53 | newx= math.pow(max(math.fabs(self.data.xmax),math.fabs(self.data.xmin)),2) |
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| 54 | newy= math.pow(max(math.fabs(self.data.ymax),math.fabs(self.data.ymin)),2) |
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| 55 | self.qmax=math.sqrt( newx + newy ) |
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| 56 | |
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| 57 | if self.data != None: |
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| 58 | self.I_data = self.data.data |
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| 59 | self.qx_data = self.data.qx_data |
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| 60 | self.qy_data = self.data.qy_data |
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| 61 | self.dqx_data = self.data.dqx_data |
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| 62 | self.dqy_data = self.data.dqy_data |
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| 63 | self.mask = self.data.mask |
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| 64 | else: |
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| 65 | xbin = numpy.linspace(start= -1*self.qmax, |
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| 66 | stop= self.qmax, |
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| 67 | num= self.qstep, |
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| 68 | endpoint=True ) |
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| 69 | ybin = numpy.linspace(start= -1*self.qmax, |
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| 70 | stop= self.qmax, |
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| 71 | num= self.qstep, |
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| 72 | endpoint=True ) |
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| 73 | |
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| 74 | new_xbin = numpy.tile(xbin, (len(ybin),1)) |
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| 75 | new_ybin = numpy.tile(ybin, (len(xbin),1)) |
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| 76 | new_ybin = new_ybin.swapaxes(0,1) |
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| 77 | new_xbin = new_xbin.flatten() |
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| 78 | new_ybin = new_ybin.flatten() |
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| 79 | self.qy_data = new_ybin |
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| 80 | self.qx_data = new_xbin |
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| 81 | # fake data |
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| 82 | self.I_data = numpy.ones(len(self.qx_data)) |
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| 83 | |
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| 84 | self.mask = numpy.ones(len(self.qx_data),dtype=bool) |
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| 85 | |
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| 86 | # Define matrix where data will be plotted |
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| 87 | radius= numpy.sqrt( self.qx_data*self.qx_data + self.qy_data*self.qy_data ) |
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| 88 | index_data= (self.qmin<= radius)&(self.mask) |
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| 89 | |
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| 90 | # For theory, qmax is based on 1d qmax |
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| 91 | # so that must be mulitified by sqrt(2) to get actual max for 2d |
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| 92 | index_model = ((self.qmin <= radius)&(radius<= self.qmax)) |
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| 93 | index_model = (index_model)&(self.mask) |
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| 94 | index_model = (index_model)&(numpy.isfinite(self.I_data)) |
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| 95 | if self.data ==None: |
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| 96 | # Only qmin value will be consider for the detector |
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| 97 | index_model = index_data |
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| 98 | |
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| 99 | if self.smearer != None: |
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| 100 | # Set smearer w/ data, model and index. |
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| 101 | fn = self.smearer |
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| 102 | fn.set_model(self.model) |
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| 103 | fn.set_index(index_model) |
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| 104 | # Get necessary data from self.data and set the data for smearing |
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| 105 | fn.get_data() |
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| 106 | # Calculate smeared Intensity (by Gaussian averaging): DataLoader/smearing2d/Smearer2D() |
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| 107 | value = fn.get_value() |
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| 108 | |
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| 109 | else: |
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| 110 | # calculation w/o smearing |
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| 111 | value = self.model.evalDistribution([self.qx_data[index_model],self.qy_data[index_model]]) |
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| 112 | |
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| 113 | output = numpy.zeros(len(self.qx_data)) |
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| 114 | |
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| 115 | # output default is None |
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| 116 | # This method is to distinguish between masked point(nan) and data point = 0. |
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| 117 | output = output/output |
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| 118 | # set value for self.mask==True, else still None to Plottools |
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| 119 | output[index_model] = value |
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| 120 | |
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| 121 | elapsed = time.time()-self.starttime |
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| 122 | self.complete(image=output, |
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| 123 | data=self.data, |
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| 124 | id=self.page_id, |
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| 125 | model=self.model, |
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| 126 | state=self.state, |
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| 127 | toggle_mode_on=self.toggle_mode_on, |
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| 128 | elapsed=elapsed, |
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| 129 | index=index_model, |
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| 130 | qmin=self.qmin, |
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| 131 | qmax=self.qmax, |
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| 132 | qstep=self.qstep) |
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| 133 | |
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| 134 | |
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| 135 | class Calc1D(CalcThread): |
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| 136 | """ |
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| 137 | Compute 1D data |
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| 138 | """ |
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| 139 | def __init__(self, x, model, |
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| 140 | id, |
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| 141 | data=None, |
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| 142 | qmin=None, |
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| 143 | qmax=None, |
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| 144 | smearer=None, |
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| 145 | toggle_mode_on=False, |
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| 146 | state=None, |
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| 147 | completefn = None, |
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| 148 | updatefn = None, |
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| 149 | yieldtime = 0.01, |
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| 150 | worktime = 0.01 |
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| 151 | ): |
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| 152 | """ |
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| 153 | """ |
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| 154 | CalcThread.__init__(self,completefn, |
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| 155 | updatefn, |
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| 156 | yieldtime, |
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| 157 | worktime) |
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| 158 | self.x = numpy.array(x) |
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| 159 | self.data = data |
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| 160 | self.qmin = qmin |
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| 161 | self.qmax = qmax |
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| 162 | self.model = model |
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| 163 | self.toggle_mode_on = toggle_mode_on |
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| 164 | self.state = state |
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| 165 | self.page_id = id |
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| 166 | self.smearer = smearer |
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| 167 | self.starttime = 0 |
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| 168 | |
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| 169 | def compute(self): |
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| 170 | """ |
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| 171 | Compute model 1d value given qmin , qmax , x value |
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| 172 | """ |
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| 173 | self.starttime = time.time() |
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| 174 | output = numpy.zeros((len(self.x))) |
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| 175 | index= (self.qmin <= self.x)& (self.x <= self.qmax) |
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| 176 | |
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| 177 | ##smearer the ouput of the plot |
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| 178 | if self.smearer!=None: |
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| 179 | first_bin, last_bin = self.smearer.get_bin_range(self.qmin, self.qmax) |
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| 180 | output[first_bin:last_bin] = self.model.evalDistribution(self.x[first_bin:last_bin]) |
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| 181 | output = self.smearer(output, first_bin, last_bin) |
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| 182 | else: |
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| 183 | output[index] = self.model.evalDistribution(self.x[index]) |
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| 184 | |
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| 185 | elapsed = time.time() - self.starttime |
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| 186 | |
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| 187 | self.complete(x=self.x[index], y=output[index], |
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| 188 | id=self.page_id, |
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| 189 | state=self.state, |
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| 190 | toggle_mode_on=self.toggle_mode_on, |
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| 191 | elapsed=elapsed,index=index, model=self.model, |
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| 192 | data=self.data) |
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| 193 | |
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| 194 | def results(self): |
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| 195 | """ |
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| 196 | Send resuts of the computation |
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| 197 | """ |
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| 198 | return [self.out, self.index] |
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| 199 | |
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| 200 | """ |
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| 201 | Example: :: |
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| 202 | |
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| 203 | class CalcCommandline: |
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| 204 | def __init__(self, n=20000): |
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| 205 | #print thread.get_ident() |
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| 206 | from sans.models.CylinderModel import CylinderModel |
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| 207 | |
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| 208 | model = CylinderModel() |
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| 209 | |
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| 210 | |
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| 211 | print model.runXY([0.01, 0.02]) |
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| 212 | |
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| 213 | qmax = 0.01 |
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| 214 | qstep = 0.0001 |
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| 215 | self.done = False |
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| 216 | |
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| 217 | x = numpy.arange(-qmax, qmax+qstep*0.01, qstep) |
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| 218 | y = numpy.arange(-qmax, qmax+qstep*0.01, qstep) |
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| 219 | |
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| 220 | |
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| 221 | calc_thread_2D = Calc2D(x, y, None, model.clone(),None, |
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| 222 | -qmax, qmax,qstep, |
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| 223 | completefn=self.complete, |
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| 224 | updatefn=self.update , |
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| 225 | yieldtime=0.0) |
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| 226 | |
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| 227 | calc_thread_2D.queue() |
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| 228 | calc_thread_2D.ready(2.5) |
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| 229 | |
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| 230 | while not self.done: |
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| 231 | time.sleep(1) |
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| 232 | |
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| 233 | def update(self,output): |
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| 234 | print "update" |
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| 235 | |
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| 236 | def complete(self, image, data, model, elapsed, qmin, qmax,index, qstep ): |
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| 237 | print "complete" |
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| 238 | self.done = True |
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| 239 | |
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| 240 | if __name__ == "__main__": |
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| 241 | CalcCommandline() |
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| 242 | """ |
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