1 | |
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2 | import math |
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3 | import os |
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4 | import unittest |
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5 | from scipy.stats import binned_statistic_2d |
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6 | import numpy as np |
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7 | |
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8 | import sas.sascalc.dataloader.data_info as data_info |
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9 | from sas.sascalc.dataloader.loader import Loader |
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10 | from sas.sascalc.dataloader.manipulations import (Boxavg, Boxsum, |
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11 | CircularAverage, Ring, |
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12 | SectorPhi, SectorQ, SlabX, |
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13 | SlabY, get_q, |
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14 | reader2D_converter) |
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15 | |
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16 | |
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17 | def find(filename): |
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18 | return os.path.join(os.path.dirname(__file__), filename) |
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19 | |
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20 | |
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21 | class Averaging(unittest.TestCase): |
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22 | """ |
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23 | Test averaging manipulations on a flat distribution |
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24 | """ |
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25 | |
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26 | def setUp(self): |
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27 | """ |
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28 | Create a flat 2D distribution. All averaging results |
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29 | should return the predefined height of the distribution (1.0). |
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30 | """ |
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31 | x_0 = np.ones([100, 100]) |
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32 | dx_0 = np.ones([100, 100]) |
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33 | |
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34 | self.data = data_info.Data2D(data=x_0, err_data=dx_0) |
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35 | detector = data_info.Detector() |
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36 | detector.distance = 1000.0 # mm |
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37 | detector.pixel_size.x = 1.0 # mm |
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38 | detector.pixel_size.y = 1.0 # mm |
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39 | |
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40 | # center in pixel position = (len(x_0)-1)/2 |
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41 | detector.beam_center.x = (len(x_0) - 1) / 2 # pixel number |
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42 | detector.beam_center.y = (len(x_0) - 1) / 2 # pixel number |
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43 | self.data.detector.append(detector) |
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44 | |
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45 | source = data_info.Source() |
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46 | source.wavelength = 10.0 # A |
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47 | self.data.source = source |
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48 | |
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49 | # get_q(dx, dy, det_dist, wavelength) where units are mm,mm,mm,and A |
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50 | # respectively. |
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51 | self.qmin = get_q(1.0, 1.0, detector.distance, source.wavelength) |
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52 | self.qmax = get_q(49.5, 49.5, detector.distance, source.wavelength) |
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53 | |
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54 | self.qstep = len(x_0) |
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55 | x = np.linspace(start=-1 * self.qmax, |
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56 | stop=self.qmax, |
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57 | num=self.qstep, |
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58 | endpoint=True) |
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59 | y = np.linspace(start=-1 * self.qmax, |
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60 | stop=self.qmax, |
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61 | num=self.qstep, |
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62 | endpoint=True) |
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63 | self.data.x_bins = x |
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64 | self.data.y_bins = y |
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65 | self.data = reader2D_converter(self.data) |
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66 | |
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67 | def test_ring_flat_distribution(self): |
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68 | """ |
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69 | Test ring averaging |
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70 | """ |
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71 | r = Ring(r_min=2 * self.qmin, r_max=5 * self.qmin, |
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72 | center_x=self.data.detector[0].beam_center.x, |
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73 | center_y=self.data.detector[0].beam_center.y) |
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74 | r.nbins_phi = 20 |
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75 | |
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76 | o = r(self.data) |
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77 | for i in range(20): |
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78 | self.assertEqual(o.y[i], 1.0) |
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79 | |
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80 | def test_sectorphi_full(self): |
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81 | """ |
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82 | Test sector averaging |
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83 | """ |
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84 | r = SectorPhi(r_min=self.qmin, r_max=3 * self.qmin, |
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85 | phi_min=0, phi_max=math.pi * 2.0) |
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86 | r.nbins_phi = 20 |
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87 | o = r(self.data) |
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88 | for i in range(7): |
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89 | self.assertEqual(o.y[i], 1.0) |
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90 | |
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91 | def test_sectorphi_partial(self): |
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92 | """ |
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93 | """ |
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94 | phi_max = math.pi * 1.5 |
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95 | r = SectorPhi(r_min=self.qmin, r_max=3 * self.qmin, |
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96 | phi_min=0, phi_max=phi_max) |
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97 | self.assertEqual(r.phi_max, phi_max) |
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98 | r.nbins_phi = 20 |
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99 | o = r(self.data) |
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100 | self.assertEqual(r.phi_max, phi_max) |
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101 | for i in range(17): |
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102 | self.assertEqual(o.y[i], 1.0) |
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103 | |
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104 | |
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105 | class DataInfoTests(unittest.TestCase): |
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106 | |
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107 | def setUp(self): |
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108 | filepath = find('MAR07232_rest.h5') |
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109 | self.data_list = Loader().load(filepath) |
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110 | self.data = self.data_list[0] |
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111 | |
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112 | def test_ring(self): |
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113 | """ |
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114 | Test ring averaging |
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115 | """ |
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116 | r = Ring(r_min=.005, r_max=.01, |
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117 | center_x=self.data.detector[0].beam_center.x, |
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118 | center_y=self.data.detector[0].beam_center.y, |
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119 | nbins=20) |
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120 | ##r.nbins_phi = 20 |
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121 | |
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122 | o = r(self.data) |
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123 | filepath = find('ring_testdata.txt') |
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124 | answer_list = Loader().load(filepath) |
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125 | answer = answer_list[0] |
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126 | |
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127 | self.assertEqual(len(answer_list), 1) |
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128 | for i in range(r.nbins_phi - 1): |
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129 | self.assertAlmostEqual(o.x[i + 1], answer.x[i], 4) |
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130 | self.assertAlmostEqual(o.y[i + 1], answer.y[i], 4) |
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131 | self.assertAlmostEqual(o.dy[i + 1], answer.dy[i], 4) |
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132 | |
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133 | def test_circularavg(self): |
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134 | """ |
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135 | Test circular averaging |
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136 | The test data was not generated by IGOR. |
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137 | """ |
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138 | r = CircularAverage(r_min=.00, r_max=.025, |
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139 | bin_width=0.0003) |
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140 | r.nbins_phi = 20 |
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141 | |
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142 | o = r(self.data) |
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143 | |
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144 | filepath = find('avg_testdata.txt') |
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145 | answer = Loader().load(filepath)[0] |
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146 | for i in range(r.nbins_phi): |
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147 | self.assertAlmostEqual(o.x[i], answer.x[i], 4) |
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148 | self.assertAlmostEqual(o.y[i], answer.y[i], 4) |
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149 | self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) |
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150 | |
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151 | def test_box(self): |
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152 | """ |
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153 | Test circular averaging |
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154 | The test data was not generated by IGOR. |
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155 | """ |
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156 | |
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157 | r = Boxsum(x_min=.01, x_max=.015, y_min=0.01, y_max=0.015) |
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158 | s, ds, npoints = r(self.data) |
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159 | self.assertAlmostEqual(s, 34.278990899999997, 4) |
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160 | self.assertAlmostEqual(ds, 7.8007981835194293, 4) |
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161 | self.assertAlmostEqual(npoints, 324.0000, 4) |
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162 | |
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163 | r = Boxavg(x_min=.01, x_max=.015, y_min=0.01, y_max=0.015) |
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164 | s, ds = r(self.data) |
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165 | self.assertAlmostEqual(s, 0.10579935462962962, 4) |
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166 | self.assertAlmostEqual(ds, 0.024076537603455028, 4) |
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167 | |
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168 | def test_slabX(self): |
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169 | """ |
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170 | Test slab in X |
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171 | The test data was not generated by IGOR. |
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172 | """ |
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173 | |
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174 | r = SlabX(x_min=-.01, x_max=.01, y_min=-0.0002, |
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175 | y_max=0.0002, bin_width=0.0004) |
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176 | r.fold = False |
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177 | o = r(self.data) |
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178 | |
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179 | filepath = find('slabx_testdata.txt') |
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180 | answer = Loader().load(filepath)[0] |
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181 | for i in range(len(o.x)): |
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182 | self.assertAlmostEqual(o.x[i], answer.x[i], 4) |
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183 | self.assertAlmostEqual(o.y[i], answer.y[i], 4) |
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184 | self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) |
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185 | |
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186 | def test_slabY(self): |
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187 | """ |
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188 | Test slab in Y |
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189 | The test data was not generated by IGOR. |
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190 | """ |
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191 | |
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192 | r = SlabY(x_min=.005, x_max=.01, y_min=- |
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193 | 0.01, y_max=0.01, bin_width=0.0004) |
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194 | r.fold = False |
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195 | o = r(self.data) |
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196 | |
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197 | filepath = find('slaby_testdata.txt') |
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198 | answer = Loader().load(filepath)[0] |
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199 | for i in range(len(o.x)): |
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200 | self.assertAlmostEqual(o.x[i], answer.x[i], 4) |
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201 | self.assertAlmostEqual(o.y[i], answer.y[i], 4) |
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202 | self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) |
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203 | |
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204 | def test_sectorphi_full(self): |
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205 | """ |
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206 | Test sector averaging I(phi) |
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207 | When considering the whole azimuthal range (2pi), |
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208 | the answer should be the same as ring averaging. |
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209 | The test data was not generated by IGOR. |
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210 | """ |
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211 | |
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212 | nbins = 19 |
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213 | phi_min = math.pi / (nbins + 1) |
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214 | phi_max = math.pi * 2 - phi_min |
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215 | |
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216 | r = SectorPhi(r_min=.005, |
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217 | r_max=.01, |
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218 | phi_min=phi_min, |
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219 | phi_max=phi_max, |
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220 | nbins=nbins) |
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221 | o = r(self.data) |
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222 | |
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223 | filepath = find('ring_testdata.txt') |
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224 | answer = Loader().load(filepath)[0] |
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225 | for i in range(len(o.x)): |
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226 | self.assertAlmostEqual(o.x[i], answer.x[i], 4) |
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227 | self.assertAlmostEqual(o.y[i], answer.y[i], 4) |
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228 | self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) |
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229 | |
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230 | def test_sectorphi_quarter(self): |
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231 | """ |
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232 | Test sector averaging I(phi) |
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233 | The test data was not generated by IGOR. |
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234 | """ |
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235 | |
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236 | r = SectorPhi(r_min=.005, r_max=.01, phi_min=0, phi_max=math.pi / 2.0) |
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237 | r.nbins_phi = 20 |
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238 | o = r(self.data) |
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239 | |
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240 | filepath = find('sectorphi_testdata.txt') |
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241 | answer = Loader().load(filepath)[0] |
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242 | for i in range(len(o.x)): |
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243 | self.assertAlmostEqual(o.x[i], answer.x[i], 4) |
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244 | self.assertAlmostEqual(o.y[i], answer.y[i], 4) |
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245 | self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) |
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246 | |
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247 | def test_sectorq_full(self): |
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248 | """ |
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249 | Test sector averaging I(q) |
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250 | The test data was not generated by IGOR. |
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251 | """ |
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252 | |
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253 | r = SectorQ(r_min=.005, r_max=.01, phi_min=0, phi_max=math.pi / 2.0) |
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254 | r.nbins_phi = 20 |
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255 | o = r(self.data) |
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256 | |
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257 | filepath = find('sectorq_testdata.txt') |
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258 | answer = Loader().load(filepath)[0] |
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259 | for i in range(len(o.x)): |
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260 | self.assertAlmostEqual(o.x[i], answer.x[i], 4) |
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261 | self.assertAlmostEqual(o.y[i], answer.y[i], 4) |
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262 | self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) |
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263 | |
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264 | def test_sectorq_log(self): |
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265 | """ |
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266 | Test sector averaging I(q) |
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267 | The test data was not generated by IGOR. |
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268 | """ |
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269 | |
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270 | r = SectorQ(r_min=.005, r_max=.01, phi_min=0, phi_max=math.pi / 2.0, base=10) |
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271 | r.nbins_phi = 20 |
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272 | o = r(self.data) |
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273 | |
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274 | expected_binning = np.logspace(np.log10(0.005), np.log10(0.01), 20, base=10) |
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275 | for i in range(len(o.x)): |
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276 | self.assertAlmostEqual(o.x[i], expected_binning[i], 3) |
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277 | |
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278 | # TODO: Test for Y values (o.y) |
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279 | # print len(self.data.x_bins) |
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280 | # print len(self.data.y_bins) |
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281 | # print self.data.q_data.shape |
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282 | # data_to_bin = np.array(self.data.q_data) |
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283 | # data_to_bin = data_to_bin.reshape(len(self.data.x_bins), len(self.data.y_bins)) |
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284 | # H, xedges, yedges, binnumber = binned_statistic_2d(self.data.x_bins, self.data.y_bins, data_to_bin, |
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285 | # bins=[[0, math.pi / 2.0], expected_binning], statistic='mean') |
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286 | # xedges_width = (xedges[1] - xedges[0]) |
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287 | # xedges_center = xedges[1:] - xedges_width / 2 |
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288 | |
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289 | # yedges_width = (yedges[1] - yedges[0]) |
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290 | # yedges_center = yedges[1:] - yedges_width / 2 |
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291 | |
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292 | # print H.flatten().shape |
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293 | # print o.y.shape |
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294 | |
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295 | |
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296 | if __name__ == '__main__': |
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297 | unittest.main() |
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