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annotate python/utils.py @ 301:27f06d28036d

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added simple helper for confidence intervals
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 11 Mar 2013 01:07:08 -0400
parents f6f423e25c7f
children 20f9cd972dde
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aed8eb63cdde initial commit with non-functional python code for NGSIM
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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
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9 commentChar = '#'
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11 delimiterChar = '%';
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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 confidenceInterval(mean, stdev, nSamples, percentConfidence, printLatex = False):
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62 from math import sqrt
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63 from scipy.stats.distributions import norm
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64 k = round(norm.ppf(0.5+percentConfidence/200., 0, 1)*100)/100. # 1.-(100-percentConfidence)/200.
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65 e = k*stdev/sqrt(nSamples)
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66 if printLatex:
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67 print('${0} \pm {1}\\frac{{{2}}}{{\sqrt{{{3}}}}}$'.format(mean, k, stdev, nSamples))
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68 return mean-e, mean+e
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69
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70 def computeChi2(expected, observed):
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71 '''Returns the Chi2 statistics'''
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72 result = 0.
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73 for e, o in zip(expected, observed):
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74 result += ((e-o)*(e-o))/e
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75 return result
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76
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77 class EmpiricalDistribution:
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78 def nSamples(self):
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79 return sum(self.counts)
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80
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81 def cumulativeDensityFunction(sample):
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82 '''Returns the cumulative density function of the sample of a random variable'''
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83 from numpy.core.multiarray import array
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84 from numpy.lib.function_base import unique
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85 from numpy.core.fromnumeric import sum
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86 a = array(sample)
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87 a.sort()
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88 xaxis = unique(a)
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89 counts = [sum(a <= x) for x in xaxis]
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90 return xaxis, counts
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91
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92 class EmpiricalDiscreteDistribution(EmpiricalDistribution):
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93 '''Class to represent a sample of a distribution for a discrete random variable
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94 '''
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95 from numpy.core.fromnumeric import sum
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96
85
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97 def __init__(self, categories, counts):
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98 self.categories = categories
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99 self.counts = counts
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100
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101 def mean(self):
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102 result = [float(x*y) for x,y in zip(self.categories, self.counts)]
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103 return sum(result)/self.nSamples()
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104
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105 def var(self, mean = None):
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106 if not mean:
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107 m = self.mean()
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108 else:
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109 m = mean
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110 result = 0.
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111 squares = [float((x-m)*(x-m)*y) for x,y in zip(self.categories, self.counts)]
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112 return sum(squares)/(self.nSamples()-1)
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113
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114 def referenceCounts(self, probability):
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115 '''probability is a function that returns the probability of the random variable for the category values'''
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116 refProba = [probability(c) for c in self.categories]
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117 refProba[-1] = 1-sum(refProba[:-1])
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118 refCounts = [r*self.nSamples() for r in refProba]
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119 return refCounts, refProba
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120
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121 class EmpiricalContinuousDistribution(EmpiricalDistribution):
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122 '''Class to represent a sample of a distribution for a continuous random variable
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123 with the number of observations for each interval
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124 intervals (categories variable) are defined by their left limits, the last one being the right limit
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125 categories contain therefore one more element than the counts'''
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126 def __init__(self, categories, counts):
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127 # todo add samples for initialization and everything to None? (or setSamples?)
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128 self.categories = categories
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129 self.counts = counts
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130
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131 def mean(self):
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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 result += self.counts[i]*(self.categories[i]+self.categories[i+1])/2
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135 return result/self.nSamples()
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136
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diff changeset
137 def var(self, mean = None):
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diff changeset
138 if not mean:
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diff changeset
139 m = self.mean()
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diff changeset
140 else:
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diff changeset
141 m = mean
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diff changeset
142 result = 0.
76
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diff changeset
143 for i in range(len(self.counts)-1):
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diff changeset
144 mid = (self.categories[i]+self.categories[i+1])/2
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diff changeset
145 result += self.counts[i]*(mid - m)*(mid - m)
35
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diff changeset
146 return result/(self.nSamples()-1)
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147
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diff changeset
148 def referenceCounts(self, cdf):
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diff changeset
149 '''cdf is a cumulative distribution function
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diff changeset
150 returning the probability of the variable being less that x'''
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diff changeset
151 # refCumulativeCounts = [0]#[cdf(self.categories[0][0])]
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diff changeset
152 # for inter in self.categories:
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diff changeset
153 # refCumulativeCounts.append(cdf(inter[1]))
76
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diff changeset
154 refCumulativeCounts = [cdf(x) for x in self.categories[1:-1]]
35
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diff changeset
155
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diff changeset
156 refProba = [refCumulativeCounts[0]]
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diff changeset
157 for i in xrange(1,len(refCumulativeCounts)):
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diff changeset
158 refProba.append(refCumulativeCounts[i]-refCumulativeCounts[i-1])
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diff changeset
159 refProba.append(1-refCumulativeCounts[-1])
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diff changeset
160 refCounts = [p*self.nSamples() for p in refProba]
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diff changeset
161
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diff changeset
162 return refCounts, refProba
31
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163
77
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diff changeset
164 def printReferenceCounts(self, refCounts=None):
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diff changeset
165 if refCounts:
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diff changeset
166 ref = refCounts
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diff changeset
167 else:
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diff changeset
168 ref = self.referenceCounts
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diff changeset
169 for i in xrange(len(ref[0])):
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diff changeset
170 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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diff changeset
171
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diff changeset
172
31
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diff changeset
173 #########################
27
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diff changeset
174 # maths section
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diff changeset
175 #########################
24
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176
285
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177 def LCSS(l1, l2, threshold, distance, delta = float('inf')):
284
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diff changeset
178 '''returns the longest common subsequence similarity
285
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diff changeset
179 based on the threshold on distance between two elements of lists l1, l2
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diff changeset
180 '''
284
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diff changeset
181 from numpy import zeros, int as npint
279
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diff changeset
182 m = len(l1)
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diff changeset
183 n = len(l2)
284
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diff changeset
184 similarity = zeros((m+1,n+1), dtype = npint)
279
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diff changeset
185 for i in xrange(1,m+1):
285
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parents: 284
diff changeset
186 for j in xrange(max(1,i-delta),min(n+1,i+delta)):
286
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parents: 285
diff changeset
187 if distance(l1[i-1], l2[j-1])<=threshold:
281
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parents: 279
diff changeset
188 similarity[i][j] = similarity[i-1][j-1]+1
279
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diff changeset
189 else:
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diff changeset
190 similarity[i][j] = max(similarity[i-1][j], similarity[i][j-1])
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diff changeset
191 return similarity[-1][-1]
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diff changeset
192
248
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parents: 241
diff changeset
193 def framesToTime(nFrames, frameRate, initialTime = (0.,0.,0.)):
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parents: 241
diff changeset
194 'returns hour, minutes and seconds'
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parents: 241
diff changeset
195 from math import floor
261
4aa792cb0fa9 changing framesToTime to return a datetime.time
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diff changeset
196 from datetime import time
262
a048066bd20f correcting bug in framesToTime
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diff changeset
197 seconds = int(floor(float(nFrames)/float(frameRate))+initialTime[0]*3600+initialTime[1]*60+initialTime[2])
261
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diff changeset
198 h = int(floor(seconds/3600.))
248
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parents: 241
diff changeset
199 seconds = seconds - h*3600
261
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parents: 248
diff changeset
200 m = int(floor(seconds/60))
248
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diff changeset
201 seconds = seconds - m*60
262
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parents: 261
diff changeset
202 return time(h, m, seconds)
248
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parents: 241
diff changeset
203
241
ee1caff48b03 added function to sort to list of paired data X,Y
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parents: 235
diff changeset
204 def sortXY(X,Y):
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parents: 235
diff changeset
205 'returns the sorted (x, Y(x)) sorted on X'
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parents: 235
diff changeset
206 D = {}
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diff changeset
207 for x, y in zip(X,Y):
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diff changeset
208 D[x]=y
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parents: 235
diff changeset
209 xsorted = sorted(D.keys())
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diff changeset
210 return xsorted, [D[x] for x in xsorted]
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parents: 235
diff changeset
211
32
48e56179c39e added ceil function
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parents: 31
diff changeset
212 def ceilDecimals(v, nDecimals):
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diff changeset
213 '''Rounds the number at the nth decimal
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diff changeset
214 eg 1.23 at 0 decimal is 2, at 1 decimal is 1.3'''
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diff changeset
215 from math import ceil,pow
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diff changeset
216 tens = pow(10,nDecimals)
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diff changeset
217 return ceil(v*tens)/tens
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parents: 31
diff changeset
218
152
74b1fc68d4df re-organized code to avoid cyclic python module dependencies
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parents: 116
diff changeset
219 def inBetween(bound1, bound2, x):
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parents: 116
diff changeset
220 return bound1 <= x <= bound2 or bound2 <= x<= bound1
24
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parents: 19
diff changeset
221
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diff changeset
222 def crossProduct(l1, l2):
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diff changeset
223 return l1[0]*l2[1]-l1[1]*l2[0]
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diff changeset
224
29
ca8e716cc231 added moving average filter
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parents: 27
diff changeset
225 def filterMovingWindow(input, halfWidth):
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diff changeset
226 '''Returns an array obtained after the smoothing of the input by a moving average
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diff changeset
227 The first and last points are copied from the original.'''
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parents: 27
diff changeset
228 width = float(halfWidth*2+1)
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parents: 27
diff changeset
229 win = ones(width,'d')
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parents: 27
diff changeset
230 result = convolve(win/width,array(inputSignal),'same')
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parents: 27
diff changeset
231 result[:halfWidth] = inputSignal[:halfWidth]
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parents: 27
diff changeset
232 result[-halfWidth:] = inputSignal[-halfWidth:]
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parents: 27
diff changeset
233 return result
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parents: 27
diff changeset
234
199
ca9d9104afba added utility to calibrate polynoms and plot
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parents: 197
diff changeset
235 def linearRegression(x, y, deg = 1, plotData = False):
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parents: 197
diff changeset
236 '''returns the least square estimation of the linear regression of y = ax+b
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parents: 197
diff changeset
237 as well as the plot'''
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parents: 197
diff changeset
238 from numpy.lib.polynomial import polyfit
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parents: 197
diff changeset
239 from matplotlib.pyplot import plot
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parents: 197
diff changeset
240 from numpy.core.multiarray import arange
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parents: 197
diff changeset
241 coef = polyfit(x, y, deg)
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parents: 197
diff changeset
242 if plotData:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 197
diff changeset
243 def poly(x):
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parents: 197
diff changeset
244 result = 0
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parents: 197
diff changeset
245 for i in range(len(coef)):
ca9d9104afba added utility to calibrate polynoms and plot
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parents: 197
diff changeset
246 result += coef[i]*x**(len(coef)-i-1)
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parents: 197
diff changeset
247 return result
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parents: 197
diff changeset
248 plot(x, y, 'x')
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parents: 197
diff changeset
249 xx = arange(min(x), max(x),(max(x)-min(x))/1000)
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parents: 197
diff changeset
250 plot(xx, [poly(z) for z in xx])
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parents: 197
diff changeset
251 return coef
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parents: 197
diff changeset
252
27
44689029a86f updated segmentIntersection and other
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parents: 24
diff changeset
253 #########################
45
74d2de078baf added colors, linestyles and markers to pick from
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 42
diff changeset
254 # plotting section
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 42
diff changeset
255 #########################
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parents: 42
diff changeset
256
297
f6f423e25c7f adding function to generate step plots (for cumulative number of vehicles)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 286
diff changeset
257 def stepPlot(X, firstX, lastX, initialCount = 0):
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parents: 286
diff changeset
258 '''for each value in x, increment by one the initial count
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parents: 286
diff changeset
259 returns the lists that can be plotted
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parents: 286
diff changeset
260 to obtain a step plot increasing by one for each value in x, from first to last value'''
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parents: 286
diff changeset
261
f6f423e25c7f adding function to generate step plots (for cumulative number of vehicles)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 286
diff changeset
262 sortedX = []
f6f423e25c7f adding function to generate step plots (for cumulative number of vehicles)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 286
diff changeset
263 counts = [initialCount]
f6f423e25c7f adding function to generate step plots (for cumulative number of vehicles)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 286
diff changeset
264 for x in sorted(X):
f6f423e25c7f adding function to generate step plots (for cumulative number of vehicles)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 286
diff changeset
265 sortedX += [x,x]
f6f423e25c7f adding function to generate step plots (for cumulative number of vehicles)
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parents: 286
diff changeset
266 counts.append(counts[-1])
f6f423e25c7f adding function to generate step plots (for cumulative number of vehicles)
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parents: 286
diff changeset
267 counts.append(counts[-1]+1)
f6f423e25c7f adding function to generate step plots (for cumulative number of vehicles)
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parents: 286
diff changeset
268 counts.append(counts[-1])
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parents: 286
diff changeset
269 return [firstX]+sortedX+[lastX], counts
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parents: 286
diff changeset
270
45
74d2de078baf added colors, linestyles and markers to pick from
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 42
diff changeset
271 class PlottingPropertyValues:
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272 def __init__(self, values):
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273 self.values = values
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274
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275 def __getitem__(self, i):
45
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276 return self.values[i%len(self.values)]
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277
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278 markers = PlottingPropertyValues(['+', '*', ',', '.', 'x', 'D', 's', 'o'])
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279 scatterMarkers = PlottingPropertyValues(['s','o','^','>','v','<','d','p','h','8','+','x'])
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280
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281 linestyles = PlottingPropertyValues(['-', '--', '-.', ':'])
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282
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283 colors = PlottingPropertyValues('brgmyck') # 'w'
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284
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285 def plotIndicatorMap(indicatorMap, squareSize, masked = True, defaultValue=-1):
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286 from numpy import array, arange, ones, ma
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287 from matplotlib.pyplot import pcolor
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288 coords = array(indicatorMap.keys())
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289 minX = min(coords[:,0])
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290 minY = min(coords[:,1])
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291 X = arange(minX, max(coords[:,0])+1.1)*squareSize
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292 Y = arange(minY, max(coords[:,1])+1.1)*squareSize
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293 C = defaultValue*ones((len(Y), len(X)))
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294 for k,v in indicatorMap.iteritems():
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295 C[k[1]-minY,k[0]-minX] = v
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296 if masked:
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297 pcolor(X, Y, ma.masked_where(C==defaultValue,C))
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298 else:
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299 pcolor(X, Y, C)
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300
45
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diff changeset
301 #########################
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302 # file I/O section
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303 #########################
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304
0
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305 def openCheck(filename, option = 'r', quit = False):
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306 '''Open file filename in read mode by default
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307 and checks it is open'''
0
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308 try:
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309 return open(filename, option)
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310 except IOError:
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311 print 'File %s could not be opened.' % filename
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312 if quit:
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313 from sys import exit
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314 exit()
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315 return None
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316
48
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317 def readline(f, commentCharacter = commentChar):
0
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318 '''Modified readline function to skip comments.'''
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319 s = f.readline()
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320 while (len(s) > 0) and s.startswith(commentCharacter):
0
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321 s = f.readline()
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322 return s.strip()
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323
48
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324 def getLines(f, delimiterCharacter = delimiterChar):
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325 '''Gets a complete entry (all the lines) in between delimiterChar.'''
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diff changeset
326 dataStrings = []
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327 s = readline(f)
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diff changeset
328 while (len(s) > 0) and (not s.startswith(delimiterCharacter)):
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329 dataStrings += [s.strip()]
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330 s = readline(f)
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331 return dataStrings
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332
0
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333 def removeExtension(filename, delimiter = '.'):
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334 '''Returns the filename minus the extension (all characters after last .)'''
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335 i = filename.rfind(delimiter)
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336 if i>0:
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337 return filename[:i]
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338 else:
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339 return filename
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340
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diff changeset
341 def cleanFilename(s):
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342 'cleans filenames obtained when contatenating figure characteristics'
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343 return s.replace(' ','-').replace('.','').replace('/','-')
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344
0
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345 def listfiles(dirname, extension, remove = False):
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346 '''Returns the list of files with the extension in the directory dirname
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347 If remove is True, the filenames are stripped from the extension'''
0
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348 from os import listdir
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349 tmp = [f for f in listdir(dirname) if f.endswith(extension)]
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350 tmp.sort()
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351 if remove:
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352 return [removeExtension(f, extension) for f in tmp]
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353 else:
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354 return tmp
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355
266
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diff changeset
356 def mkdir(dirname):
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357 'Creates a directory if it does not exist'
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358 import os
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359 if not os.path.exists(dirname):
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diff changeset
360 os.mkdir(dirname)
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361 else:
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diff changeset
362 print(dirname+' already exists')
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diff changeset
363
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diff changeset
364 def removeFile(filename):
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diff changeset
365 '''Deletes the file while avoiding raising an error
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366 if the file does not exist'''
266
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diff changeset
367 import os
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diff changeset
368 if (os.path.exists(filename)):
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diff changeset
369 os.remove(filename)
266
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diff changeset
370 else:
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diff changeset
371 print(filename+' does not exist')
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diff changeset
372
19
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parents: 16
diff changeset
373 def plotPolygon(poly, options = ''):
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diff changeset
374 from numpy.core.multiarray import array
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diff changeset
375 from matplotlib.pyplot import plot
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diff changeset
376 from shapely.geometry import Polygon
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diff changeset
377
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diff changeset
378 tmp = array(poly.exterior)
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diff changeset
379 plot(tmp[:,0], tmp[:,1], options)
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diff changeset
380
42
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diff changeset
381 def line2Floats(l, separator=' '):
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diff changeset
382 '''Returns the list of floats corresponding to the string'''
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diff changeset
383 return [float(x) for x in l.split(separator)]
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diff changeset
384
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diff changeset
385 def line2Ints(l, separator=' '):
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diff changeset
386 '''Returns the list of ints corresponding to the string'''
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diff changeset
387 return [int(x) for x in l.split(separator)]
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diff changeset
388
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diff changeset
389 #########################
233
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diff changeset
390 # sqlite
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diff changeset
391 #########################
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diff changeset
392
235
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parents: 233
diff changeset
393 def dropTables(connection, tableNames):
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parents: 233
diff changeset
394 'deletes the table with names in tableNames'
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diff changeset
395 cursor = connection.cursor()
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parents: 233
diff changeset
396 for tableName in tableNames:
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diff changeset
397 cursor.execute('DROP TABLE '+tableName)
233
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diff changeset
398
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diff changeset
399 #########################
31
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diff changeset
400 # running tests
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diff changeset
401 #########################
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diff changeset
402
0
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diff changeset
403 if __name__ == "__main__":
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diff changeset
404 import doctest
2
de5642925615 started implementation of TimeInterval and Spatio-temporal object
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parents: 0
diff changeset
405 import unittest
31
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diff changeset
406 suite = doctest.DocFileSuite('tests/utils.txt')
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diff changeset
407 #suite = doctest.DocTestSuite()
2
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diff changeset
408 unittest.TextTestRunner().run(suite)
de5642925615 started implementation of TimeInterval and Spatio-temporal object
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 0
diff changeset
409 #doctest.testmod()
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aed8eb63cdde initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff changeset
410 #doctest.testfile("example.txt")