| 1 | """ |
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| 2 | Calculation thread for modeling |
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| 3 | """ |
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| 4 | |
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| 5 | import time |
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| 6 | import numpy as np |
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| 7 | import math |
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| 8 | from sas.sascalc.data_util.calcthread import CalcThread |
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| 9 | from sas.sascalc.fit.MultiplicationModel import MultiplicationModel |
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| 10 | |
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| 11 | class Calc2D(CalcThread): |
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| 12 | """ |
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| 13 | Compute 2D model |
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| 14 | This calculation assumes a 2-fold symmetry of the model |
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| 15 | where points are computed for one half of the detector |
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| 16 | and I(qx, qy) = I(-qx, -qy) is assumed. |
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| 17 | """ |
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| 18 | def __init__(self, data, model, smearer, qmin, qmax, page_id, |
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| 19 | state=None, |
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| 20 | weight=None, |
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| 21 | fid=None, |
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| 22 | toggle_mode_on=False, |
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| 23 | completefn=None, |
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| 24 | updatefn=None, |
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| 25 | update_chisqr=True, |
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| 26 | source='model', |
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| 27 | yieldtime=0.04, |
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| 28 | worktime=0.04, |
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| 29 | exception_handler=None, |
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| 30 | ): |
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| 31 | CalcThread.__init__(self, completefn, updatefn, yieldtime, worktime, |
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| 32 | exception_handler=exception_handler) |
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| 33 | self.qmin = qmin |
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| 34 | self.qmax = qmax |
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| 35 | self.weight = weight |
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| 36 | self.fid = fid |
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| 37 | #self.qstep = qstep |
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| 38 | self.toggle_mode_on = toggle_mode_on |
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| 39 | self.data = data |
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| 40 | self.page_id = page_id |
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| 41 | self.state = None |
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| 42 | # the model on to calculate |
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| 43 | self.model = model |
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| 44 | self.smearer = smearer |
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| 45 | self.starttime = 0 |
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| 46 | self.update_chisqr = update_chisqr |
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| 47 | self.source = source |
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| 48 | |
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| 49 | def compute(self): |
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| 50 | """ |
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| 51 | Compute the data given a model function |
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| 52 | """ |
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| 53 | self.starttime = time.time() |
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| 54 | # Determine appropriate q range |
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| 55 | if self.qmin is None: |
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| 56 | self.qmin = 0 |
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| 57 | if self.qmax is None: |
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| 58 | if self.data is not None: |
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| 59 | newx = math.pow(max(math.fabs(self.data.xmax), |
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| 60 | math.fabs(self.data.xmin)), 2) |
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| 61 | newy = math.pow(max(math.fabs(self.data.ymax), |
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| 62 | math.fabs(self.data.ymin)), 2) |
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| 63 | self.qmax = math.sqrt(newx + newy) |
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| 64 | |
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| 65 | if self.data is None: |
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| 66 | msg = "Compute Calc2D receive data = %s.\n" % str(self.data) |
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| 67 | raise ValueError(msg) |
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| 68 | |
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| 69 | # Define matrix where data will be plotted |
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| 70 | radius = np.sqrt((self.data.qx_data * self.data.qx_data) + \ |
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| 71 | (self.data.qy_data * self.data.qy_data)) |
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| 72 | |
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| 73 | # For theory, qmax is based on 1d qmax |
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| 74 | # so that must be mulitified by sqrt(2) to get actual max for 2d |
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| 75 | index_model = (self.qmin <= radius) & (radius <= self.qmax) |
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| 76 | index_model = index_model & self.data.mask |
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| 77 | index_model = index_model & np.isfinite(self.data.data) |
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| 78 | |
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| 79 | if self.smearer is not None: |
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| 80 | # Set smearer w/ data, model and index. |
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| 81 | fn = self.smearer |
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| 82 | fn.set_model(self.model) |
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| 83 | fn.set_index(index_model) |
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| 84 | # Calculate smeared Intensity |
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| 85 | #(by Gaussian averaging): DataLoader/smearing2d/Smearer2D() |
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| 86 | value = fn.get_value() |
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| 87 | else: |
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| 88 | # calculation w/o smearing |
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| 89 | value = self.model.evalDistribution([ |
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| 90 | self.data.qx_data[index_model], |
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| 91 | self.data.qy_data[index_model] |
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| 92 | ]) |
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| 93 | output = np.zeros(len(self.data.qx_data)) |
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| 94 | # output default is None |
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| 95 | # This method is to distinguish between masked |
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| 96 | #point(nan) and data point = 0. |
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| 97 | output = output / output |
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| 98 | # set value for self.mask==True, else still None to Plottools |
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| 99 | output[index_model] = value |
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| 100 | elapsed = time.time() - self.starttime |
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| 101 | self.complete(image=output, |
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| 102 | data=self.data, |
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| 103 | page_id=self.page_id, |
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| 104 | model=self.model, |
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| 105 | state=self.state, |
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| 106 | toggle_mode_on=self.toggle_mode_on, |
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| 107 | elapsed=elapsed, |
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| 108 | index=index_model, |
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| 109 | fid=self.fid, |
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| 110 | qmin=self.qmin, |
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| 111 | qmax=self.qmax, |
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| 112 | weight=self.weight, |
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| 113 | #qstep=self.qstep, |
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| 114 | update_chisqr=self.update_chisqr, |
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| 115 | source=self.source) |
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| 116 | |
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| 117 | |
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| 118 | class Calc1D(CalcThread): |
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| 119 | """ |
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| 120 | Compute 1D data |
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| 121 | """ |
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| 122 | def __init__(self, model, |
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| 123 | page_id, |
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| 124 | data, |
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| 125 | fid=None, |
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| 126 | qmin=None, |
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| 127 | qmax=None, |
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| 128 | weight=None, |
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| 129 | smearer=None, |
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| 130 | toggle_mode_on=False, |
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| 131 | state=None, |
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| 132 | completefn=None, |
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| 133 | update_chisqr=True, |
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| 134 | source='model', |
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| 135 | updatefn=None, |
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| 136 | yieldtime=0.01, |
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| 137 | worktime=0.01, |
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| 138 | exception_handler=None, |
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| 139 | ): |
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| 140 | """ |
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| 141 | """ |
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| 142 | CalcThread.__init__(self, completefn, updatefn, yieldtime, worktime, |
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| 143 | exception_handler=exception_handler) |
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| 144 | self.fid = fid |
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| 145 | self.data = data |
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| 146 | self.qmin = qmin |
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| 147 | self.qmax = qmax |
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| 148 | self.model = model |
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| 149 | self.weight = weight |
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| 150 | self.toggle_mode_on = toggle_mode_on |
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| 151 | self.state = state |
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| 152 | self.page_id = page_id |
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| 153 | self.smearer = smearer |
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| 154 | self.starttime = 0 |
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| 155 | self.update_chisqr = update_chisqr |
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| 156 | self.source = source |
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| 157 | self.out = None |
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| 158 | self.index = None |
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| 159 | |
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| 160 | def compute(self): |
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| 161 | """ |
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| 162 | Compute model 1d value given qmin , qmax , x value |
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| 163 | """ |
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| 164 | self.starttime = time.time() |
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| 165 | output = np.zeros((len(self.data.x))) |
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| 166 | index = (self.qmin <= self.data.x) & (self.data.x <= self.qmax) |
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| 167 | |
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| 168 | # If we use a smearer, also return the unsmeared model |
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| 169 | unsmeared_output = None |
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| 170 | unsmeared_data = None |
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| 171 | unsmeared_error = None |
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| 172 | ##smearer the ouput of the plot |
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| 173 | if self.smearer is not None: |
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| 174 | first_bin, last_bin = self.smearer.get_bin_range(self.qmin, |
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| 175 | self.qmax) |
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| 176 | mask = self.data.x[first_bin:last_bin+1] |
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| 177 | unsmeared_output = np.zeros((len(self.data.x))) |
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| 178 | unsmeared_output[first_bin:last_bin+1] = self.model.evalDistribution(mask) |
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| 179 | self.smearer.model = self.model |
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| 180 | output = self.smearer(unsmeared_output, first_bin, last_bin) |
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| 181 | |
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| 182 | # Rescale data to unsmeared model |
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| 183 | # Check that the arrays are compatible. If we only have a model but no data, |
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| 184 | # the length of data.y will be zero. |
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| 185 | if isinstance(self.data.y, np.ndarray) and output.shape == self.data.y.shape: |
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| 186 | unsmeared_data = np.zeros((len(self.data.x))) |
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| 187 | unsmeared_error = np.zeros((len(self.data.x))) |
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| 188 | unsmeared_data[first_bin:last_bin+1] = self.data.y[first_bin:last_bin+1]\ |
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| 189 | * unsmeared_output[first_bin:last_bin+1]\ |
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| 190 | / output[first_bin:last_bin+1] |
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| 191 | unsmeared_error[first_bin:last_bin+1] = self.data.dy[first_bin:last_bin+1]\ |
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| 192 | * unsmeared_output[first_bin:last_bin+1]\ |
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| 193 | / output[first_bin:last_bin+1] |
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| 194 | unsmeared_output=unsmeared_output[index] |
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| 195 | unsmeared_data=unsmeared_data[index] |
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| 196 | unsmeared_error=unsmeared_error |
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| 197 | else: |
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| 198 | output[index] = self.model.evalDistribution(self.data.x[index]) |
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| 199 | |
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| 200 | sq_values = None |
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| 201 | pq_values = None |
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| 202 | s_model = None |
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| 203 | p_model = None |
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| 204 | if isinstance(self.model, MultiplicationModel): |
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| 205 | s_model = self.model.s_model |
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| 206 | p_model = self.model.p_model |
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| 207 | elif hasattr(self.model, "get_composition_models"): |
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| 208 | p_model, s_model = self.model.get_composition_models() |
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| 209 | |
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| 210 | if p_model is not None and s_model is not None: |
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| 211 | sq_values = np.zeros((len(self.data.x))) |
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| 212 | pq_values = np.zeros((len(self.data.x))) |
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| 213 | sq_values[index] = s_model.evalDistribution(self.data.x[index]) |
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| 214 | pq_values[index] = p_model.evalDistribution(self.data.x[index]) |
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| 215 | |
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| 216 | elapsed = time.time() - self.starttime |
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| 217 | |
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| 218 | self.complete(x=self.data.x[index], y=output[index], |
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| 219 | page_id=self.page_id, |
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| 220 | state=self.state, |
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| 221 | weight=self.weight, |
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| 222 | fid=self.fid, |
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| 223 | toggle_mode_on=self.toggle_mode_on, |
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| 224 | elapsed=elapsed, index=index, model=self.model, |
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| 225 | data=self.data, |
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| 226 | update_chisqr=self.update_chisqr, |
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| 227 | source=self.source, |
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| 228 | unsmeared_model=unsmeared_output, |
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| 229 | unsmeared_data=unsmeared_data, |
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| 230 | unsmeared_error=unsmeared_error, |
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| 231 | pq_model=pq_values, |
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| 232 | sq_model=sq_values) |
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| 233 | |
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| 234 | def results(self): |
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| 235 | """ |
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| 236 | Send resuts of the computation |
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| 237 | """ |
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| 238 | return [self.out, self.index] |
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| 239 | |
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| 240 | """ |
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| 241 | Example: :: |
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| 242 | |
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| 243 | class CalcCommandline: |
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| 244 | def __init__(self, n=20000): |
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| 245 | #print thread.get_ident() |
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| 246 | from sas.models.CylinderModel import CylinderModel |
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| 247 | |
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| 248 | model = CylinderModel() |
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| 249 | |
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| 250 | |
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| 251 | print model.runXY([0.01, 0.02]) |
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| 252 | |
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| 253 | qmax = 0.01 |
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| 254 | qstep = 0.0001 |
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| 255 | self.done = False |
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| 256 | |
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| 257 | x = numpy.arange(-qmax, qmax+qstep*0.01, qstep) |
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| 258 | y = numpy.arange(-qmax, qmax+qstep*0.01, qstep) |
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| 259 | |
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| 260 | |
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| 261 | calc_thread_2D = Calc2D(x, y, None, model.clone(),None, |
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| 262 | -qmax, qmax,qstep, |
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| 263 | completefn=self.complete, |
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| 264 | updatefn=self.update , |
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| 265 | yieldtime=0.0) |
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| 266 | |
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| 267 | calc_thread_2D.queue() |
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| 268 | calc_thread_2D.ready(2.5) |
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| 269 | |
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| 270 | while not self.done: |
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| 271 | time.sleep(1) |
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| 272 | |
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| 273 | def update(self,output): |
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| 274 | print "update" |
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| 275 | |
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| 276 | def complete(self, image, data, model, elapsed, qmin, qmax,index, qstep ): |
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| 277 | print "complete" |
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| 278 | self.done = True |
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| 279 | |
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| 280 | if __name__ == "__main__": |
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| 281 | CalcCommandline() |
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| 282 | """ |
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