1 | try: |
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2 | from sans.models.prototypes.SimCylinder import SimCylinder |
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3 | except: |
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4 | print "This test uses the prototypes module." |
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5 | |
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6 | from sans.models.CylinderModel import CylinderModel |
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7 | |
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8 | import os, sys |
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9 | |
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10 | def simulate(filename="simul.txt", npts=500): |
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11 | output_file = open(filename, 'w') |
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12 | #output_file = open("simul_phi=0_theta=1_57_l=400_r=20_9.txt", 'w') |
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13 | |
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14 | sim = SimCylinder() |
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15 | sim.setParam('scale', 1.0) |
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16 | #sim.setParam('length', 100.0) |
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17 | #sim.setParam('radius', 40.0) |
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18 | sim.setParam('length', 400.0) |
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19 | sim.setParam('radius', 20.0) |
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20 | sim.setParam('theta', 1.57) |
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21 | sim.setParam('phi', 0.0) |
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22 | sim.setParam('qmax', 0.2) |
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23 | |
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24 | output_file.write("<q_value> <ana_value> <sim_value>\n") |
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25 | norma = 0 |
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26 | for i in range(npts): |
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27 | q = 1.0/npts*(i+1) |
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28 | |
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29 | cyl_val = sim.run(q) |
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30 | sim_val = sim.run([q, 0.0]) |
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31 | if i==0: |
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32 | norma = cyl_val/sim_val |
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33 | |
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34 | #print q, cyl_val, sim_val, sim_val/cyl_val |
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35 | output_file.write("%10g %10g %10g\n" % (q, cyl_val, sim_val*norma)) |
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36 | |
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37 | output_file.close() |
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38 | |
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39 | def create_model(): |
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40 | sim = SimCylinder() |
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41 | sim.setParam('scale', 1.0) |
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42 | sim.setParam('length', 100.0) |
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43 | sim.setParam('radius', 40.0) |
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44 | # simul and simul_2 |
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45 | #sim.setParam('theta', 1.57) |
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46 | #sim.setParam('phi', 0.0) |
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47 | sim.setParam('theta', 0.0) |
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48 | sim.setParam('phi', 1.0) |
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49 | sim.setParam('qmax', 0.2) |
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50 | return sim |
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51 | |
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52 | def comp(): |
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53 | """ |
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54 | Simple test that should give 1 for the ratio |
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55 | of simulated and analytical |
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56 | """ |
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57 | sim = create_model() |
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58 | |
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59 | ana_val = sim.run(.05) |
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60 | sim_val = sim.run([0.05, 0]) |
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61 | print ana_val, sim_val, ana_val/sim_val |
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62 | |
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63 | |
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64 | # ana_val = sim.run(.05) |
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65 | # sim_val = sim.run([0.05, 1]) |
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66 | # print ana_val, sim_val, ana_val/sim_val |
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67 | |
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68 | # sim = create_model() |
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69 | # sim.setParam('theta', 1.0) |
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70 | # sim.setParam('phi', 1.0) |
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71 | # ana_val = sim.run(.05) |
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72 | # sim_val = sim.run([0.05, 1]) |
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73 | # print ana_val, sim_val, ana_val/sim_val |
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74 | |
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75 | # sim = create_model() |
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76 | # sim.setParam('theta', 1.0) |
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77 | # sim.setParam('phi', 2.0) |
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78 | # ana_val = sim.run(.05) |
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79 | # sim_val = sim.run([0.05, 1]) |
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80 | # print ana_val, sim_val, ana_val/sim_val |
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81 | |
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82 | sim = create_model() |
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83 | sim.setParam('theta', 1.0) |
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84 | sim.setParam('phi', -2.0) |
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85 | ana_val = sim.run(.05) |
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86 | sim_val = sim.run([0.15, 1]) |
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87 | print ana_val, sim_val, ana_val/sim_val |
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88 | |
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89 | def add_error_to_file(prefix = "", dir = "output"): |
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90 | import os, math |
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91 | |
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92 | # Open standard deviation file |
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93 | std_file = open("%s/std_simul.txt" % dir, 'r') |
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94 | std_content = std_file.read() |
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95 | std_lines = std_content.split('\n') |
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96 | |
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97 | std_values = {} |
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98 | for line in std_lines: |
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99 | if line == std_lines[0]: |
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100 | continue |
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101 | |
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102 | toks = line.split() |
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103 | if not len(toks) > 3: |
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104 | continue |
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105 | |
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106 | std_values[toks[0]] = float(toks[2]) |
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107 | |
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108 | std_file.close() |
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109 | |
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110 | # Read the files |
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111 | |
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112 | file_list = os.listdir(dir) |
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113 | |
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114 | for file in file_list: |
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115 | if file.count(prefix)>0: |
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116 | print "Reading", file |
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117 | |
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118 | file_obj = open("%s/%s" % (dir, file), 'r') |
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119 | |
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120 | # open new file |
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121 | file_new = open("%s/error-%s" % (dir, file), 'w') |
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122 | file_content = file_obj.read() |
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123 | lines = file_content.split('\n') |
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124 | for line in lines: |
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125 | if line == lines[0]: |
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126 | file_new.write(line+'\n') |
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127 | continue |
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128 | |
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129 | toks = line.split() |
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130 | |
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131 | if not len(toks) == 3: |
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132 | file_new.write(line+'\n') |
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133 | continue |
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134 | |
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135 | error = 0 |
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136 | if toks[0].lower() in std_values: |
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137 | error = float(toks[2]) * std_values[toks[0].lower()] |
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138 | |
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139 | file_new.write(line+" %10g\n" % error) |
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140 | |
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141 | file_obj.close() |
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142 | file_new.close() |
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143 | |
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144 | |
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145 | |
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146 | def std_estimate(prefix = "", dir="output"): |
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147 | import os, math |
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148 | |
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149 | file_list = os.listdir(dir) |
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150 | output_file = open("%s/std_simul.txt" % dir, 'w') |
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151 | output_file.write("<q_value> <std> <frac>\n") |
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152 | values = {} |
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153 | |
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154 | for file in file_list: |
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155 | if file.count(prefix)>0: |
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156 | print "Reading", file |
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157 | file_obj = open("%s/%s" % (dir, file), 'r') |
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158 | file_content = file_obj.read() |
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159 | lines = file_content.split('\n') |
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160 | for line in lines: |
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161 | if line == lines[0]: |
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162 | continue |
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163 | toks = line.split() |
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164 | |
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165 | if not len(toks) == 3: |
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166 | continue |
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167 | |
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168 | if toks[0] not in values: |
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169 | values[toks[0]] = [] |
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170 | |
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171 | values[toks[0]].append(float(toks[2])) |
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172 | |
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173 | q_list = values.keys() |
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174 | q_list.sort() |
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175 | for q in q_list: |
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176 | num = len(values[q]) |
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177 | sum = 0 |
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178 | mean = 0 |
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179 | for val in range(num): |
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180 | mean += values[q][val] |
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181 | mean /= num |
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182 | |
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183 | for val in range(num): |
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184 | diff = values[q][val] - mean |
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185 | sum += diff * diff |
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186 | sum /= (num-1) |
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187 | #print q, math.sqrt(sum) |
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188 | |
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189 | output_file.write("%10s %10g %10g %10g\n" % (q, math.sqrt(sum), math.sqrt(sum)/mean, math.sqrt(sum)/mean*100.0)) |
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190 | |
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191 | output_file.close() |
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192 | |
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193 | |
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194 | # sqrt( 1/(N-1) sum[ (x-mean)**2 ] ) |
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195 | |
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196 | def test_volume_pts(): |
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197 | import random, math, time |
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198 | |
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199 | print "Generating points" |
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200 | pt_list = [] |
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201 | for i in range(50000): |
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202 | x = random.random() |
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203 | y = random.random() |
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204 | z = random.random() |
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205 | pt_list.append([x, y, z]) |
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206 | |
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207 | # Compute list of closest distance for each point |
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208 | print "Computing distances" |
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209 | closest_list = [] |
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210 | t_init = time.time() |
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211 | |
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212 | for i in range(len(pt_list)): |
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213 | if math.fmod(i,100)==0: |
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214 | print i, time.time()-t_init |
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215 | t_init = time.time() |
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216 | pt = pt_list[i] |
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217 | |
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218 | min_dist = -1.0 |
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219 | for j in range(i+1, len(pt_list)): |
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220 | pt_2 = pt_list[j] |
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221 | #print pt_2 |
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222 | dx = pt[0]-pt_2[0] |
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223 | dy = pt[1]-pt_2[1] |
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224 | dz = pt[2]-pt_2[2] |
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225 | dist = math.sqrt( dx*dx + dy*dy + dz*dz ) |
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226 | if min_dist<0 or dist < min_dist: |
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227 | min_dist = dist |
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228 | |
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229 | closest_list.append(min_dist) |
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230 | |
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231 | # Analyze list of distances |
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232 | print "Analyzing" |
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233 | sum = 0 |
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234 | for d in closest_list: |
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235 | sum += d |
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236 | average = sum/len(closest_list) |
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237 | |
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238 | sum = 0 |
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239 | for d in closest_list: |
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240 | diff = d - average |
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241 | sum += diff*diff |
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242 | sum /= len(closest_list) |
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243 | |
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244 | print "%5g +/- %5g)" % (average, math.sqrt(sum)) |
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245 | |
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246 | |
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247 | |
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248 | |
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249 | if __name__ == '__main__': |
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250 | #test_volume_pts() |
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251 | #simulate("simul_phi=0_theta=1_57_l=400_r=20_test.txt") |
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252 | #std_estimate("simul_phi=0_theta=1_57_l=400_r=20") |
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253 | #add_error_to_file("l=400_r=20.txt") |
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254 | |
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255 | option = "error" |
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256 | |
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257 | #if len(sys.argv)>1: |
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258 | if len(option)>0: |
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259 | if option=="gen": |
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260 | ifile = 0 |
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261 | for i in range(100): |
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262 | ifile += 1 |
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263 | #print "simul_qmax=1_phi=0_theta=1_57_l=400_r=20_%g.txt" % ifile |
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264 | #simulate("simul_phi=0_theta=1_57_l=400_r=20_%g.txt" % ifile) |
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265 | print "simul_qmax=1_phi=0_theta=1_57_l=400_r=20_%g.txt" % ifile |
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266 | simulate("output/pt5000/simul_phi=0_theta=1_57_l=400_r=20_%g.txt" % ifile) |
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267 | elif option=="comp": |
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268 | comp() |
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269 | elif option=="std": |
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270 | #std_estimate("simul_phi=0_theta=1_57_l=400_r=20") |
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271 | std_estimate("simul_phi=0_theta=1_57_l=400_r=20", "output/pt5000") |
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272 | elif option=="error": |
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273 | #add_error_to_file("l=400_r=20.txt") |
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274 | add_error_to_file("l=400_r=20_1.txt", "output/pt5000") |
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275 | |
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