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annotate python/utils.py @ 307:8e66ced156dd

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add delta to sample the time window in LCSS function
author Mohamed Gomaa
date Tue, 25 Dec 2012 02:16:10 -0500
parents f2cf16ad798f
children 6c068047edbf
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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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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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 '''
86
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86 from numpy.core.fromnumeric import sum
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87
85
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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 # todo add samples for initialization and everything to None? (or setSamples?)
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119 self.categories = categories
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120 self.counts = counts
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121
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122 def mean(self):
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123 result = 0.
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124 for i in range(len(self.counts)-1):
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125 result += self.counts[i]*(self.categories[i]+self.categories[i+1])/2
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126 return result/self.nSamples()
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127
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128 def var(self, mean = None):
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129 if not mean:
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130 m = self.mean()
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131 else:
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132 m = mean
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133 result = 0.
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134 for i in range(len(self.counts)-1):
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135 mid = (self.categories[i]+self.categories[i+1])/2
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136 result += self.counts[i]*(mid - m)*(mid - m)
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137 return result/(self.nSamples()-1)
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diff changeset
138
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diff changeset
139 def referenceCounts(self, cdf):
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140 '''cdf is a cumulative distribution function
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141 returning the probability of the variable being less that x'''
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142 # refCumulativeCounts = [0]#[cdf(self.categories[0][0])]
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diff changeset
143 # for inter in self.categories:
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144 # refCumulativeCounts.append(cdf(inter[1]))
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diff changeset
145 refCumulativeCounts = [cdf(x) for x in self.categories[1:-1]]
35
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146
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147 refProba = [refCumulativeCounts[0]]
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148 for i in xrange(1,len(refCumulativeCounts)):
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diff changeset
149 refProba.append(refCumulativeCounts[i]-refCumulativeCounts[i-1])
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diff changeset
150 refProba.append(1-refCumulativeCounts[-1])
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151 refCounts = [p*self.nSamples() for p in refProba]
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152
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153 return refCounts, refProba
31
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154
77
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155 def printReferenceCounts(self, refCounts=None):
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156 if refCounts:
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157 ref = refCounts
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158 else:
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159 ref = self.referenceCounts
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160 for i in xrange(len(ref[0])):
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161 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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162
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163
31
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164 #########################
27
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165 # maths section
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166 #########################
24
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167
307
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168 def LCSS(l1, l2, threshold, delta, distance):
284
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169 '''returns the longest common subsequence similarity
307
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170 based on the threshold on distance between two elements of lists l1, l2.
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171 looks for points that are sampled within a delta time window, can put 'inf' value to cancl this condition'''
284
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172 from numpy import zeros, int as npint
279
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173 m = len(l1)
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174 n = len(l2)
284
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diff changeset
175 similarity = zeros((m+1,n+1), dtype = npint)
279
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176 for i in xrange(1,m+1):
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177 for j in xrange(1,n+1):
307
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178 from math import fabs
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179 while fabs(i-j)<= delta:
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180 if distance(l1[i-1], l2[j-1])<threshold:
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181 similarity[i][j] = similarity[i-1][j-1]+1
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182 else:
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183 similarity[i][j] = max(similarity[i-1][j], similarity[i][j-1])
279
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184 return similarity[-1][-1]
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185
248
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diff changeset
186 def framesToTime(nFrames, frameRate, initialTime = (0.,0.,0.)):
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187 'returns hour, minutes and seconds'
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188 from math import floor
261
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189 from datetime import time
262
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190 seconds = int(floor(float(nFrames)/float(frameRate))+initialTime[0]*3600+initialTime[1]*60+initialTime[2])
261
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diff changeset
191 h = int(floor(seconds/3600.))
248
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diff changeset
192 seconds = seconds - h*3600
261
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193 m = int(floor(seconds/60))
248
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194 seconds = seconds - m*60
262
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195 return time(h, m, seconds)
248
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diff changeset
196
241
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197 def sortXY(X,Y):
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198 'returns the sorted (x, Y(x)) sorted on X'
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199 D = {}
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200 for x, y in zip(X,Y):
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201 D[x]=y
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diff changeset
202 xsorted = sorted(D.keys())
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diff changeset
203 return xsorted, [D[x] for x in xsorted]
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diff changeset
204
32
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diff changeset
205 def ceilDecimals(v, nDecimals):
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diff changeset
206 '''Rounds the number at the nth decimal
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207 eg 1.23 at 0 decimal is 2, at 1 decimal is 1.3'''
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208 from math import ceil,pow
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209 tens = pow(10,nDecimals)
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diff changeset
210 return ceil(v*tens)/tens
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diff changeset
211
152
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diff changeset
212 def inBetween(bound1, bound2, x):
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diff changeset
213 return bound1 <= x <= bound2 or bound2 <= x<= bound1
24
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214
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diff changeset
215 def crossProduct(l1, l2):
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diff changeset
216 return l1[0]*l2[1]-l1[1]*l2[0]
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217
29
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diff changeset
218 def filterMovingWindow(input, halfWidth):
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diff changeset
219 '''Returns an array obtained after the smoothing of the input by a moving average
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diff changeset
220 The first and last points are copied from the original.'''
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221 width = float(halfWidth*2+1)
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diff changeset
222 win = ones(width,'d')
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diff changeset
223 result = convolve(win/width,array(inputSignal),'same')
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diff changeset
224 result[:halfWidth] = inputSignal[:halfWidth]
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parents: 27
diff changeset
225 result[-halfWidth:] = inputSignal[-halfWidth:]
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diff changeset
226 return result
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diff changeset
227
199
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parents: 197
diff changeset
228 def linearRegression(x, y, deg = 1, plotData = False):
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parents: 197
diff changeset
229 '''returns the least square estimation of the linear regression of y = ax+b
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parents: 197
diff changeset
230 as well as the plot'''
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diff changeset
231 from numpy.lib.polynomial import polyfit
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parents: 197
diff changeset
232 from matplotlib.pyplot import plot
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parents: 197
diff changeset
233 from numpy.core.multiarray import arange
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parents: 197
diff changeset
234 coef = polyfit(x, y, deg)
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parents: 197
diff changeset
235 if plotData:
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parents: 197
diff changeset
236 def poly(x):
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parents: 197
diff changeset
237 result = 0
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parents: 197
diff changeset
238 for i in range(len(coef)):
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parents: 197
diff changeset
239 result += coef[i]*x**(len(coef)-i-1)
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diff changeset
240 return result
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parents: 197
diff changeset
241 plot(x, y, 'x')
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parents: 197
diff changeset
242 xx = arange(min(x), max(x),(max(x)-min(x))/1000)
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parents: 197
diff changeset
243 plot(xx, [poly(z) for z in xx])
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parents: 197
diff changeset
244 return coef
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parents: 197
diff changeset
245
27
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parents: 24
diff changeset
246 #########################
45
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parents: 42
diff changeset
247 # plotting section
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parents: 42
diff changeset
248 #########################
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parents: 42
diff changeset
249
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diff changeset
250 class PlottingPropertyValues:
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diff changeset
251 def __init__(self, values):
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diff changeset
252 self.values = values
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diff changeset
253
116
2bf5b76320c0 moved intersection plotting and added markers for scatter plots
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parents: 115
diff changeset
254 def __getitem__(self, i):
45
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parents: 42
diff changeset
255 return self.values[i%len(self.values)]
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parents: 42
diff changeset
256
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diff changeset
257 markers = PlottingPropertyValues(['+', '*', ',', '.', 'x', 'D', 's', 'o'])
116
2bf5b76320c0 moved intersection plotting and added markers for scatter plots
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parents: 115
diff changeset
258 scatterMarkers = PlottingPropertyValues(['s','o','^','>','v','<','d','p','h','8','+','x'])
45
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parents: 42
diff changeset
259
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parents: 42
diff changeset
260 linestyles = PlottingPropertyValues(['-', '--', '-.', ':'])
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parents: 42
diff changeset
261
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parents: 42
diff changeset
262 colors = PlottingPropertyValues('brgmyck') # 'w'
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parents: 42
diff changeset
263
115
550556378466 added functionalities to indicator maps
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parents: 86
diff changeset
264 def plotIndicatorMap(indicatorMap, squareSize, masked = True, defaultValue=-1):
65
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parents: 48
diff changeset
265 from numpy import array, arange, ones, ma
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parents: 48
diff changeset
266 from matplotlib.pyplot import pcolor
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parents: 48
diff changeset
267 coords = array(indicatorMap.keys())
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parents: 48
diff changeset
268 minX = min(coords[:,0])
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diff changeset
269 minY = min(coords[:,1])
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diff changeset
270 X = arange(minX, max(coords[:,0])+1.1)*squareSize
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parents: 48
diff changeset
271 Y = arange(minY, max(coords[:,1])+1.1)*squareSize
115
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parents: 86
diff changeset
272 C = defaultValue*ones((len(Y), len(X)))
65
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parents: 48
diff changeset
273 for k,v in indicatorMap.iteritems():
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274 C[k[1]-minY,k[0]-minX] = v
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275 if masked:
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276 pcolor(X, Y, ma.masked_where(C==defaultValue,C))
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277 else:
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278 pcolor(X, Y, C)
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279
45
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280 #########################
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281 # file I/O section
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282 #########################
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283
0
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284 def openCheck(filename, option = 'r', quit = False):
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285 '''Open file filename in read mode by default
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286 and checks it is open'''
0
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287 try:
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288 return open(filename, option)
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289 except IOError:
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290 print 'File %s could not be opened.' % filename
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291 if quit:
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292 from sys import exit
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293 exit()
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294 return None
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295
48
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296 def readline(f, commentCharacter = commentChar):
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297 '''Modified readline function to skip comments.'''
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298 s = f.readline()
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299 while (len(s) > 0) and s.startswith(commentCharacter):
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300 s = f.readline()
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301 return s.strip()
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302
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303 def getLines(f, delimiterCharacter = delimiterChar):
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304 '''Gets a complete entry (all the lines) in between delimiterChar.'''
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305 dataStrings = []
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306 s = readline(f)
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307 while (len(s) > 0) and (not s.startswith(delimiterCharacter)):
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308 dataStrings += [s.strip()]
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309 s = readline(f)
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310 return dataStrings
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311
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312 def removeExtension(filename, delimiter = '.'):
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313 '''Returns the filename minus the extension (all characters after last .)'''
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314 i = filename.rfind(delimiter)
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315 if i>0:
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316 return filename[:i]
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317 else:
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318 return filename
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319
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320 def cleanFilename(s):
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321 'cleans filenames obtained when contatenating figure characteristics'
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322 return s.replace(' ','-').replace('.','').replace('/','-')
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323
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324 def listfiles(dirname, extension, remove = False):
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325 '''Returns the list of files with the extension in the directory dirname
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326 If remove is True, the filenames are stripped from the extension'''
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327 from os import listdir
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328 tmp = [f for f in listdir(dirname) if f.endswith(extension)]
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329 tmp.sort()
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330 if remove:
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331 return [removeExtension(f, extension) for f in tmp]
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332 else:
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333 return tmp
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334
266
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335 def mkdir(dirname):
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336 'Creates a directory if it does not exist'
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337 import os
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338 if not os.path.exists(dirname):
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339 os.mkdir(dirname)
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340 else:
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341 print(dirname+' already exists')
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342
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343 def removeFile(filename):
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344 '''Deletes the file while avoiding raising an error
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345 if the file does not exist'''
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346 import os
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347 if (os.path.exists(filename)):
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348 os.remove(filename)
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349 else:
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350 print(filename+' does not exist')
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351
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352 def plotPolygon(poly, options = ''):
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353 from numpy.core.multiarray import array
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354 from matplotlib.pyplot import plot
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355 from shapely.geometry import Polygon
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356
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357 tmp = array(poly.exterior)
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358 plot(tmp[:,0], tmp[:,1], options)
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359
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360 def line2Floats(l, separator=' '):
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361 '''Returns the list of floats corresponding to the string'''
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362 return [float(x) for x in l.split(separator)]
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363
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364 def line2Ints(l, separator=' '):
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365 '''Returns the list of ints corresponding to the string'''
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366 return [int(x) for x in l.split(separator)]
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367
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368 #########################
233
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369 # sqlite
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370 #########################
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371
235
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diff changeset
372 def dropTables(connection, tableNames):
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373 'deletes the table with names in tableNames'
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374 cursor = connection.cursor()
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375 for tableName in tableNames:
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376 cursor.execute('DROP TABLE '+tableName)
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377
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378 #########################
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379 # running tests
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380 #########################
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381
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382 if __name__ == "__main__":
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383 import doctest
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384 import unittest
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385 suite = doctest.DocFileSuite('tests/utils.txt')
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386 #suite = doctest.DocTestSuite()
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387 unittest.TextTestRunner().run(suite)
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388 #doctest.testmod()
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389 #doctest.testfile("example.txt")