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annotate python/utils.py @ 77:5e6cd36a991c

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added pretty print in empiricalDistribution
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 10 Feb 2011 22:41:38 -0500
parents 64fde2b1f96d
children 7f1e54234f96
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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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7 import moving
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8
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9 __metaclass__ = type
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11 commentChar = '#'
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13 delimiterChar = '%';
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15 #########################
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16 # simple statistics
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17 #########################
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18
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19 def computeChi2(expected, observed):
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20 '''Returns the Chi2 statistics'''
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21 result = 0.
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22 for e, o in zip(expected, observed):
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23 result += ((e-o)*(e-o))/e
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24 return result
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25
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26 class empiricalDistribution:
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27 '''Class to represent a sample of a distribution for a continuous random variable
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28 with the number of observations for each interval
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29 intervals (categories variable) are defined by their left limits, the last one being the right limit
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30 categories contain therefore one more element than the counts'''
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31 def __init__(self, categories, counts):
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32 self.categories = categories
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33 self.counts = counts
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35 def mean(self):
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36 result = 0.
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37 for i in range(len(self.counts)-1):
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38 result += self.counts[i]*(self.categories[i]+self.categories[i+1])/2
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39 return result/self.nSamples()
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40
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41 def var(self, mean = None):
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42 if not mean:
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43 m = self.mean()
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44 else:
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45 m = mean
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46 result = 0.
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47 for i in range(len(self.counts)-1):
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48 mid = (self.categories[i]+self.categories[i+1])/2
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49 result += self.counts[i]*(mid - m)*(mid - m)
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50 return result/(self.nSamples()-1)
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51
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52 def nSamples(self):
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53 return sum(self.counts)
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54
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55 def referenceCounts(self, cdf):
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56 '''cdf is a cumulative distribution function
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57 returning the probability of the variable being less that x'''
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58 # refCumulativeCounts = [0]#[cdf(self.categories[0][0])]
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59 # for inter in self.categories:
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60 # refCumulativeCounts.append(cdf(inter[1]))
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61 refCumulativeCounts = [cdf(x) for x in self.categories[1:-1]]
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62
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63 refProba = [refCumulativeCounts[0]]
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64 for i in xrange(1,len(refCumulativeCounts)):
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65 refProba.append(refCumulativeCounts[i]-refCumulativeCounts[i-1])
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66 refProba.append(1-refCumulativeCounts[-1])
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67 refCounts = [p*self.nSamples() for p in refProba]
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68
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69 return refCounts, refProba
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70
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71 def printReferenceCounts(self, refCounts=None):
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72 if refCounts:
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73 ref = refCounts
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74 else:
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75 ref = self.referenceCounts
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76 for i in xrange(len(ref[0])):
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77 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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78
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79
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80 #########################
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81 # maths section
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82 #########################
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83
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84 def ceilDecimals(v, nDecimals):
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85 '''Rounds the number at the nth decimal
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86 eg 1.23 at 0 decimal is 2, at 1 decimal is 1.3'''
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87 from math import ceil,pow
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88 tens = pow(10,nDecimals)
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89 return ceil(v*tens)/tens
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90
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91 def segmentIntersection(p1, p2, p3, p4):
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92 '''Returns the intersecting point of the segments [p1, p2] and [p3, p4], None otherwise'''
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93 from numpy import matrix
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94 from numpy.linalg import linalg, det
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95
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96 dp1 = p2-p1#[s1[0][1]-s1[0][0], s1[1][1]-s1[1][0]]
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97 dp2 = p4-p3#[s2[0][1]-s2[0][0], s2[1][1]-s2[1][0]]
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98
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99 A = matrix([[dp1.y, -dp1.x],
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100 [dp2.y, -dp2.x]])
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101 B = matrix([[dp1.y*p1.x-dp1.x*p1.y],
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102 [dp2.y*p3.x-dp2.x*p3.y]])
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103
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104 if linalg.det(A) == 0:#crossProduct(ds1, ds2) == 0:
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105 return None
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106 else:
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107 intersection = linalg.solve(A,B)
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108 if (moving.Interval(p1.x, p2.x, True).contains(intersection[0,0])
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109 and moving.Interval(p3.x, p4.x, True).contains(intersection[0,0])
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110 and moving.Interval(p1.y, p2.y, True).contains(intersection[1,0])
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111 and moving.Interval(p3.y, p4.y, True).contains(intersection[1,0])):
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112 return moving.Point(intersection[0,0], intersection[1,0])
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113 else:
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114 return None
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115
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116 def crossProduct(l1, l2):
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117 return l1[0]*l2[1]-l1[1]*l2[0]
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118
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119 def filterMovingWindow(input, halfWidth):
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120 '''Returns an array obtained after the smoothing of the input by a moving average
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121 The first and last points are copied from the original.'''
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122 width = float(halfWidth*2+1)
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123 win = ones(width,'d')
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124 result = convolve(win/width,array(inputSignal),'same')
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125 result[:halfWidth] = inputSignal[:halfWidth]
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126 result[-halfWidth:] = inputSignal[-halfWidth:]
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127 return result
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128
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129 #########################
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130 # plotting section
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131 #########################
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132
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133 class PlottingPropertyValues:
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134 def __init__(self, values):
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135 self.values = values
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136
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137 def get(self,i):
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138 return self.values[i%len(self.values)]
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139
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140 markers = PlottingPropertyValues(['+', '*', ',', '.', 'x', 'D', 's', 'o'])
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141
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142 linestyles = PlottingPropertyValues(['-', '--', '-.', ':'])
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143
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144 colors = PlottingPropertyValues('brgmyck') # 'w'
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145
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146 def plotIndicatorMap(indicatorMap, squareSize):
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147 from numpy import array, arange, ones, ma
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148 from matplotlib.pyplot import pcolor
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149 coords = array(indicatorMap.keys())
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150 minX = min(coords[:,0])
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151 minY = min(coords[:,1])
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152 X = arange(minX, max(coords[:,0])+1.1)*squareSize
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153 Y = arange(minY, max(coords[:,1])+1.1)*squareSize
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154 C = -ones((len(Y), len(X)))
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155 for k,v in indicatorMap.iteritems():
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156 C[k[1]-minY,k[0]-minX] = v
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157 masked = ma.masked_where(C<0,C)
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158 pcolor(X, Y, masked)
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159
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160 #########################
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161 # file I/O section
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162 #########################
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163
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164 def openCheck(filename, option = 'r', quit = False):
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165 '''Open file filename in read mode by default
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166 and checks it is open'''
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167 try:
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168 return open(filename, option)
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169 except IOError:
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170 print 'File %s could not be opened.' % filename
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171 if quit:
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172 from sys import exit
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173 exit()
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174 return None
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175
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176 def readline(f, commentCharacter = commentChar):
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177 '''Modified readline function to skip comments.'''
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178 s = f.readline()
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179 while (len(s) > 0) and s.startswith(commentCharacter):
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180 s = f.readline()
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181 return s.strip()
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182
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183 def getLines(f, delimiterCharacter = delimiterChar):
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184 '''Gets a complete entry (all the lines) in between delimiterChar.'''
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185 dataStrings = []
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186 s = readline(f)
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187 while (len(s) > 0) and (not s.startswith(delimiterCharacter)):
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188 dataStrings += [s.strip()]
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189 s = readline(f)
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190 return dataStrings
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191
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192 def removeExtension(filename, delimiter = '.'):
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193 '''Returns the filename minus the extension (all characters after last .)'''
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194 i = filename.rfind(delimiter)
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195 if i>0:
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196 return filename[:i]
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197 else:
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198 return filename
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199
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200 def cleanFilename(s):
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201 'cleans filenames obtained when contatenating figure characteristics'
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202 return s.replace(' ','-').replace('.','')
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203
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204 def listfiles(dirname, extension, remove = False):
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205 '''Returns the list of files with the extension in the directory dirname
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206 If remove is True, the filenames are stripped from the extension'''
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207 from os import listdir
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208 tmp = [f for f in listdir(dirname) if f.endswith(extension)]
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209 tmp.sort()
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210 if remove:
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211 return [removeExtension(f, extension) for f in tmp]
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212 else:
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213 return tmp
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214
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215 def removeFile(filename):
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216 '''Deletes the file while avoiding raising an error
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217 if the file does not exist'''
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218 if (os.path.exists(filename)):
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219 os.remove(filename)
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220
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221 def plotPolygon(poly, options = ''):
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222 from numpy.core.multiarray import array
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223 from matplotlib.pyplot import plot
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224 from shapely.geometry import Polygon
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225
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226 tmp = array(poly.exterior)
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227 plot(tmp[:,0], tmp[:,1], options)
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228
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229 def line2Floats(l, separator=' '):
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230 '''Returns the list of floats corresponding to the string'''
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231 return [float(x) for x in l.split(separator)]
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232
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233 def line2Ints(l, separator=' '):
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234 '''Returns the list of ints corresponding to the string'''
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235 return [int(x) for x in l.split(separator)]
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236
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237 #########################
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238 # running tests
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239 #########################
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240
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241 if __name__ == "__main__":
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242 import doctest
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243 import unittest
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244 suite = doctest.DocFileSuite('tests/utils.txt')
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245 #suite = doctest.DocTestSuite()
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246 unittest.TextTestRunner().run(suite)
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247 #doctest.testmod()
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248 #doctest.testfile("example.txt")