Mercurial Hosting > traffic-intelligence
annotate python/utils.py @ 231:249d65ff6c35
merged modifications for windows
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 02 Jul 2012 23:49:39 -0400 |
parents | ca9d9104afba |
children | ab1a11176d7b |
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1 #! /usr/bin/env python |
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2 ''' Generic utilities.''' |
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3 |
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4 #from numpy import * |
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5 #from pylab import * |
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6 |
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7 __metaclass__ = type |
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8 |
45
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9 commentChar = '#' |
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10 |
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11 delimiterChar = '%'; |
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12 |
185
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13 ######################### |
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14 # Enumerations |
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15 ######################### |
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16 |
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17 def inverseEnumeration(l): |
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18 'Returns the dictionary that provides for each element in the input list its index in the input list' |
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19 result = {} |
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20 for i,x in enumerate(l): |
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21 result[x] = i |
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22 return result |
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23 |
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24 ######################### |
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25 # CLI utils |
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26 ######################### |
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27 |
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28 def parseCLIOptions(helpMessage, options, cliArgs, optionalOptions=[]): |
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29 ''' Simple function to handle similar argument parsing |
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30 Returns the dictionary of options and their values |
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31 |
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32 * cliArgs are most likely directly sys.argv |
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33 (only the elements after the first one are considered) |
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34 |
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35 * options should be a list of strings for getopt options, |
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36 eg ['frame=','correspondences=','video='] |
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37 A value must be provided for each option, or the program quits''' |
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38 import sys, getopt |
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39 from numpy.core.fromnumeric import all |
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40 optionValues, args = getopt.getopt(cliArgs[1:], 'h', ['help']+options+optionalOptions) |
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41 optionValues = dict(optionValues) |
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42 |
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43 if '--help' in optionValues.keys() or '-h' in optionValues.keys(): |
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44 print(helpMessage+ |
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45 '\n - Compulsory options: '+' '.join([opt.replace('=','') for opt in options])+ |
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46 '\n - Non-compulsory options: '+' '.join([opt.replace('=','') for opt in optionalOptions])) |
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47 sys.exit() |
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48 |
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49 missingArgument = [('--'+opt.replace('=','') in optionValues.keys()) for opt in options] |
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50 if not all(missingArgument): |
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51 print('Missing argument') |
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52 print(optionValues) |
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53 sys.exit() |
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54 |
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55 return optionValues |
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56 |
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57 ######################### |
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58 # simple statistics |
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59 ######################### |
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60 |
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61 def computeChi2(expected, observed): |
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62 '''Returns the Chi2 statistics''' |
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63 result = 0. |
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64 for e, o in zip(expected, observed): |
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65 result += ((e-o)*(e-o))/e |
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66 return result |
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67 |
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68 class EmpiricalDistribution: |
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69 def nSamples(self): |
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70 return sum(self.counts) |
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71 |
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72 def cumulativeDensityFunction(sample): |
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73 'Returns the cumulative density function of the sample of a random variable' |
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74 from numpy.core.multiarray import array |
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75 from numpy.lib.function_base import unique |
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76 from numpy.core.fromnumeric import sum |
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77 a = array(sample) |
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78 a.sort() |
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79 xaxis = unique(a) |
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80 counts = [sum(a <= x) for x in xaxis] |
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81 return xaxis, counts |
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82 |
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83 class EmpiricalDiscreteDistribution(EmpiricalDistribution): |
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84 '''Class to represent a sample of a distribution for a discrete random variable |
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85 ''' |
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86 from numpy.core.fromnumeric import sum |
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87 |
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88 def __init__(self, categories, counts): |
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89 self.categories = categories |
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90 self.counts = counts |
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91 |
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92 def mean(self): |
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93 result = [float(x*y) for x,y in zip(self.categories, self.counts)] |
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94 return sum(result)/self.nSamples() |
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95 |
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96 def var(self, mean = None): |
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97 if not mean: |
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98 m = self.mean() |
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99 else: |
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100 m = mean |
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101 result = 0. |
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102 squares = [float((x-m)*(x-m)*y) for x,y in zip(self.categories, self.counts)] |
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103 return sum(squares)/(self.nSamples()-1) |
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104 |
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105 def referenceCounts(self, probability): |
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106 '''probability is a function that returns the probability of the random variable for the category values''' |
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107 refProba = [probability(c) for c in self.categories] |
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108 refProba[-1] = 1-sum(refProba[:-1]) |
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109 refCounts = [r*self.nSamples() for r in refProba] |
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110 return refCounts, refProba |
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111 |
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112 class EmpiricalContinuousDistribution(EmpiricalDistribution): |
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113 '''Class to represent a sample of a distribution for a continuous random variable |
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114 with the number of observations for each interval |
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115 intervals (categories variable) are defined by their left limits, the last one being the right limit |
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116 categories contain therefore one more element than the counts''' |
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117 def __init__(self, categories, counts): |
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118 self.categories = categories |
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119 self.counts = counts |
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120 |
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121 def mean(self): |
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122 result = 0. |
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123 for i in range(len(self.counts)-1): |
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124 result += self.counts[i]*(self.categories[i]+self.categories[i+1])/2 |
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125 return result/self.nSamples() |
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126 |
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127 def var(self, mean = None): |
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128 if not mean: |
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129 m = self.mean() |
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130 else: |
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131 m = mean |
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132 result = 0. |
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133 for i in range(len(self.counts)-1): |
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134 mid = (self.categories[i]+self.categories[i+1])/2 |
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135 result += self.counts[i]*(mid - m)*(mid - m) |
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136 return result/(self.nSamples()-1) |
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137 |
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138 def referenceCounts(self, cdf): |
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139 '''cdf is a cumulative distribution function |
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140 returning the probability of the variable being less that x''' |
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141 # refCumulativeCounts = [0]#[cdf(self.categories[0][0])] |
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142 # for inter in self.categories: |
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143 # refCumulativeCounts.append(cdf(inter[1])) |
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144 refCumulativeCounts = [cdf(x) for x in self.categories[1:-1]] |
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145 |
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146 refProba = [refCumulativeCounts[0]] |
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147 for i in xrange(1,len(refCumulativeCounts)): |
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148 refProba.append(refCumulativeCounts[i]-refCumulativeCounts[i-1]) |
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149 refProba.append(1-refCumulativeCounts[-1]) |
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150 refCounts = [p*self.nSamples() for p in refProba] |
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151 |
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152 return refCounts, refProba |
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153 |
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154 def printReferenceCounts(self, refCounts=None): |
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155 if refCounts: |
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156 ref = refCounts |
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157 else: |
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158 ref = self.referenceCounts |
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159 for i in xrange(len(ref[0])): |
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160 print('{0}-{1} & {2:0.3} & {3:0.3} \\\\'.format(self.categories[i],self.categories[i+1],ref[1][i], ref[0][i])) |
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161 |
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162 |
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163 ######################### |
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164 # maths section |
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165 ######################### |
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166 |
32 | 167 def ceilDecimals(v, nDecimals): |
168 '''Rounds the number at the nth decimal | |
169 eg 1.23 at 0 decimal is 2, at 1 decimal is 1.3''' | |
170 from math import ceil,pow | |
171 tens = pow(10,nDecimals) | |
172 return ceil(v*tens)/tens | |
173 | |
152
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174 def inBetween(bound1, bound2, x): |
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175 return bound1 <= x <= bound2 or bound2 <= x<= bound1 |
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176 |
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177 def crossProduct(l1, l2): |
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178 return l1[0]*l2[1]-l1[1]*l2[0] |
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179 |
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180 def filterMovingWindow(input, halfWidth): |
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181 '''Returns an array obtained after the smoothing of the input by a moving average |
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182 The first and last points are copied from the original.''' |
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183 width = float(halfWidth*2+1) |
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184 win = ones(width,'d') |
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185 result = convolve(win/width,array(inputSignal),'same') |
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186 result[:halfWidth] = inputSignal[:halfWidth] |
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187 result[-halfWidth:] = inputSignal[-halfWidth:] |
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188 return result |
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189 |
199
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190 def linearRegression(x, y, deg = 1, plotData = False): |
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191 '''returns the least square estimation of the linear regression of y = ax+b |
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192 as well as the plot''' |
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193 from numpy.lib.polynomial import polyfit |
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194 from matplotlib.pyplot import plot |
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195 from numpy.core.multiarray import arange |
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196 coef = polyfit(x, y, deg) |
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197 if plotData: |
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198 def poly(x): |
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199 result = 0 |
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200 for i in range(len(coef)): |
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201 result += coef[i]*x**(len(coef)-i-1) |
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202 return result |
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203 plot(x, y, 'x') |
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204 xx = arange(min(x), max(x),(max(x)-min(x))/1000) |
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205 plot(xx, [poly(z) for z in xx]) |
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206 return coef |
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207 |
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208 ######################### |
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209 # plotting section |
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210 ######################### |
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211 |
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212 class PlottingPropertyValues: |
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213 def __init__(self, values): |
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214 self.values = values |
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215 |
116
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216 def __getitem__(self, i): |
45
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217 return self.values[i%len(self.values)] |
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218 |
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219 markers = PlottingPropertyValues(['+', '*', ',', '.', 'x', 'D', 's', 'o']) |
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220 scatterMarkers = PlottingPropertyValues(['s','o','^','>','v','<','d','p','h','8','+','x']) |
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221 |
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222 linestyles = PlottingPropertyValues(['-', '--', '-.', ':']) |
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223 |
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224 colors = PlottingPropertyValues('brgmyck') # 'w' |
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225 |
115
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226 def plotIndicatorMap(indicatorMap, squareSize, masked = True, defaultValue=-1): |
65
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227 from numpy import array, arange, ones, ma |
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228 from matplotlib.pyplot import pcolor |
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229 coords = array(indicatorMap.keys()) |
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230 minX = min(coords[:,0]) |
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231 minY = min(coords[:,1]) |
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232 X = arange(minX, max(coords[:,0])+1.1)*squareSize |
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233 Y = arange(minY, max(coords[:,1])+1.1)*squareSize |
115
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234 C = defaultValue*ones((len(Y), len(X))) |
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235 for k,v in indicatorMap.iteritems(): |
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236 C[k[1]-minY,k[0]-minX] = v |
115
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237 if masked: |
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238 pcolor(X, Y, ma.masked_where(C==defaultValue,C)) |
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239 else: |
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240 pcolor(X, Y, C) |
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241 |
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242 ######################### |
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243 # file I/O section |
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244 ######################### |
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245 |
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246 def openCheck(filename, option = 'r', quit = False): |
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247 '''Open file filename in read mode by default |
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248 and checks it is open''' |
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249 try: |
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250 return open(filename, option) |
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251 except IOError: |
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252 print 'File %s could not be opened.' % filename |
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253 if quit: |
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254 from sys import exit |
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255 exit() |
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256 return None |
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257 |
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258 def readline(f, commentCharacter = commentChar): |
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259 '''Modified readline function to skip comments.''' |
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260 s = f.readline() |
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261 while (len(s) > 0) and s.startswith(commentCharacter): |
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262 s = f.readline() |
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263 return s.strip() |
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264 |
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265 def getLines(f, delimiterCharacter = delimiterChar): |
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266 '''Gets a complete entry (all the lines) in between delimiterChar.''' |
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267 dataStrings = [] |
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268 s = readline(f) |
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269 while (len(s) > 0) and (not s.startswith(delimiterCharacter)): |
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270 dataStrings += [s.strip()] |
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271 s = readline(f) |
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272 return dataStrings |
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273 |
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274 def removeExtension(filename, delimiter = '.'): |
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275 '''Returns the filename minus the extension (all characters after last .)''' |
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276 i = filename.rfind(delimiter) |
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277 if i>0: |
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278 return filename[:i] |
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279 else: |
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280 return filename |
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281 |
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282 def cleanFilename(s): |
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283 'cleans filenames obtained when contatenating figure characteristics' |
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284 return s.replace(' ','-').replace('.','') |
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285 |
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286 def listfiles(dirname, extension, remove = False): |
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287 '''Returns the list of files with the extension in the directory dirname |
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288 If remove is True, the filenames are stripped from the extension''' |
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289 from os import listdir |
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290 tmp = [f for f in listdir(dirname) if f.endswith(extension)] |
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291 tmp.sort() |
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292 if remove: |
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293 return [removeExtension(f, extension) for f in tmp] |
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294 else: |
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295 return tmp |
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296 |
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297 def removeFile(filename): |
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298 '''Deletes the file while avoiding raising an error |
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299 if the file does not exist''' |
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300 if (os.path.exists(filename)): |
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301 os.remove(filename) |
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302 |
19 | 303 def plotPolygon(poly, options = ''): |
304 from numpy.core.multiarray import array | |
305 from matplotlib.pyplot import plot | |
306 from shapely.geometry import Polygon | |
307 | |
308 tmp = array(poly.exterior) | |
309 plot(tmp[:,0], tmp[:,1], options) | |
310 | |
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311 def line2Floats(l, separator=' '): |
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312 '''Returns the list of floats corresponding to the string''' |
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313 return [float(x) for x in l.split(separator)] |
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314 |
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315 def line2Ints(l, separator=' '): |
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316 '''Returns the list of ints corresponding to the string''' |
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317 return [int(x) for x in l.split(separator)] |
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318 |
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319 ######################### |
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320 # running tests |
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321 ######################### |
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322 |
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323 if __name__ == "__main__": |
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324 import doctest |
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325 import unittest |
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326 suite = doctest.DocFileSuite('tests/utils.txt') |
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327 #suite = doctest.DocTestSuite() |
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328 unittest.TextTestRunner().run(suite) |
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329 #doctest.testmod() |
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330 #doctest.testfile("example.txt") |