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annotate python/utils.py @ 115:550556378466

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added functionalities to indicator maps
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Sat, 23 Jul 2011 02:18:16 -0400
parents f03ec4697a09
children 2bf5b76320c0
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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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10
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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 def nSamples(self):
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28 return sum(self.counts)
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29
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31 class EmpiricalDiscreteDistribution(EmpiricalDistribution):
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32 '''Class to represent a sample of a distribution for a discrete random variable
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33 '''
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34 from numpy.core.fromnumeric import sum
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35
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36 def __init__(self, categories, counts):
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37 self.categories = categories
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38 self.counts = counts
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39
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40 def mean(self):
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41 result = [float(x*y) for x,y in zip(self.categories, self.counts)]
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42 return sum(result)/self.nSamples()
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43
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44 def var(self, mean = None):
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45 if not mean:
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46 m = self.mean()
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47 else:
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48 m = mean
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49 result = 0.
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50 squares = [float((x-m)*(x-m)*y) for x,y in zip(self.categories, self.counts)]
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51 return sum(squares)/(self.nSamples()-1)
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52
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53 def referenceCounts(self, probability):
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54 '''probability is a function that returns the probability of the random variable for the category values'''
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55 refProba = [probability(c) for c in self.categories]
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56 refProba[-1] = 1-sum(refProba[:-1])
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57 refCounts = [r*self.nSamples() for r in refProba]
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58 return refCounts, refProba
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59
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60 class EmpiricalContinuousDistribution(EmpiricalDistribution):
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61 '''Class to represent a sample of a distribution for a continuous random variable
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62 with the number of observations for each interval
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63 intervals (categories variable) are defined by their left limits, the last one being the right limit
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64 categories contain therefore one more element than the counts'''
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65 def __init__(self, categories, counts):
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66 self.categories = categories
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67 self.counts = counts
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68
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69 def mean(self):
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70 result = 0.
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71 for i in range(len(self.counts)-1):
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72 result += self.counts[i]*(self.categories[i]+self.categories[i+1])/2
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73 return result/self.nSamples()
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74
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75 def var(self, mean = None):
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76 if not mean:
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77 m = self.mean()
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78 else:
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79 m = mean
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80 result = 0.
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81 for i in range(len(self.counts)-1):
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82 mid = (self.categories[i]+self.categories[i+1])/2
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83 result += self.counts[i]*(mid - m)*(mid - m)
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84 return result/(self.nSamples()-1)
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85
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86 def referenceCounts(self, cdf):
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87 '''cdf is a cumulative distribution function
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88 returning the probability of the variable being less that x'''
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89 # refCumulativeCounts = [0]#[cdf(self.categories[0][0])]
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90 # for inter in self.categories:
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91 # refCumulativeCounts.append(cdf(inter[1]))
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92 refCumulativeCounts = [cdf(x) for x in self.categories[1:-1]]
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93
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94 refProba = [refCumulativeCounts[0]]
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95 for i in xrange(1,len(refCumulativeCounts)):
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96 refProba.append(refCumulativeCounts[i]-refCumulativeCounts[i-1])
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97 refProba.append(1-refCumulativeCounts[-1])
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98 refCounts = [p*self.nSamples() for p in refProba]
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99
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100 return refCounts, refProba
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101
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102 def printReferenceCounts(self, refCounts=None):
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103 if refCounts:
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104 ref = refCounts
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105 else:
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106 ref = self.referenceCounts
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107 for i in xrange(len(ref[0])):
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108 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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109
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110
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111 #########################
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112 # maths section
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113 #########################
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114
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115 def ceilDecimals(v, nDecimals):
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116 '''Rounds the number at the nth decimal
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117 eg 1.23 at 0 decimal is 2, at 1 decimal is 1.3'''
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118 from math import ceil,pow
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119 tens = pow(10,nDecimals)
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120 return ceil(v*tens)/tens
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121
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122 def segmentIntersection(p1, p2, p3, p4):
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123 '''Returns the intersecting point of the segments [p1, p2] and [p3, p4], None otherwise'''
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124 from numpy import matrix
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125 from numpy.linalg import linalg, det
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126
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127 dp1 = p2-p1#[s1[0][1]-s1[0][0], s1[1][1]-s1[1][0]]
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128 dp2 = p4-p3#[s2[0][1]-s2[0][0], s2[1][1]-s2[1][0]]
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129
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130 A = matrix([[dp1.y, -dp1.x],
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131 [dp2.y, -dp2.x]])
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132 B = matrix([[dp1.y*p1.x-dp1.x*p1.y],
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133 [dp2.y*p3.x-dp2.x*p3.y]])
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134
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135 if linalg.det(A) == 0:#crossProduct(ds1, ds2) == 0:
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136 return None
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137 else:
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138 intersection = linalg.solve(A,B)
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139 if (moving.Interval(p1.x, p2.x, True).contains(intersection[0,0])
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140 and moving.Interval(p3.x, p4.x, True).contains(intersection[0,0])
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141 and moving.Interval(p1.y, p2.y, True).contains(intersection[1,0])
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142 and moving.Interval(p3.y, p4.y, True).contains(intersection[1,0])):
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143 return moving.Point(intersection[0,0], intersection[1,0])
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144 else:
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145 return None
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146
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147 def crossProduct(l1, l2):
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148 return l1[0]*l2[1]-l1[1]*l2[0]
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149
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150 def filterMovingWindow(input, halfWidth):
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151 '''Returns an array obtained after the smoothing of the input by a moving average
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152 The first and last points are copied from the original.'''
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153 width = float(halfWidth*2+1)
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154 win = ones(width,'d')
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155 result = convolve(win/width,array(inputSignal),'same')
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156 result[:halfWidth] = inputSignal[:halfWidth]
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157 result[-halfWidth:] = inputSignal[-halfWidth:]
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158 return result
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159
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160 #########################
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161 # plotting section
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162 #########################
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163
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164 class PlottingPropertyValues:
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165 def __init__(self, values):
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166 self.values = values
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167
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168 def get(self,i):
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169 return self.values[i%len(self.values)]
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170
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171 markers = PlottingPropertyValues(['+', '*', ',', '.', 'x', 'D', 's', 'o'])
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172
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173 linestyles = PlottingPropertyValues(['-', '--', '-.', ':'])
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174
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175 colors = PlottingPropertyValues('brgmyck') # 'w'
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176
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177 def plotIndicatorMap(indicatorMap, squareSize, masked = True, defaultValue=-1):
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178 from numpy import array, arange, ones, ma
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179 from matplotlib.pyplot import pcolor
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180 coords = array(indicatorMap.keys())
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181 minX = min(coords[:,0])
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182 minY = min(coords[:,1])
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183 X = arange(minX, max(coords[:,0])+1.1)*squareSize
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184 Y = arange(minY, max(coords[:,1])+1.1)*squareSize
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185 C = defaultValue*ones((len(Y), len(X)))
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186 for k,v in indicatorMap.iteritems():
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187 C[k[1]-minY,k[0]-minX] = v
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188 if masked:
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189 pcolor(X, Y, ma.masked_where(C==defaultValue,C))
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190 else:
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191 pcolor(X, Y, C)
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192
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193 #########################
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194 # file I/O section
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195 #########################
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196
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197 def openCheck(filename, option = 'r', quit = False):
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198 '''Open file filename in read mode by default
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199 and checks it is open'''
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200 try:
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201 return open(filename, option)
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202 except IOError:
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203 print 'File %s could not be opened.' % filename
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204 if quit:
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205 from sys import exit
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206 exit()
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207 return None
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208
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209 def readline(f, commentCharacter = commentChar):
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210 '''Modified readline function to skip comments.'''
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211 s = f.readline()
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212 while (len(s) > 0) and s.startswith(commentCharacter):
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213 s = f.readline()
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214 return s.strip()
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215
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216 def getLines(f, delimiterCharacter = delimiterChar):
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217 '''Gets a complete entry (all the lines) in between delimiterChar.'''
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218 dataStrings = []
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219 s = readline(f)
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220 while (len(s) > 0) and (not s.startswith(delimiterCharacter)):
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221 dataStrings += [s.strip()]
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222 s = readline(f)
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223 return dataStrings
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224
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225 def removeExtension(filename, delimiter = '.'):
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226 '''Returns the filename minus the extension (all characters after last .)'''
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227 i = filename.rfind(delimiter)
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228 if i>0:
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229 return filename[:i]
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230 else:
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231 return filename
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232
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233 def cleanFilename(s):
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234 'cleans filenames obtained when contatenating figure characteristics'
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235 return s.replace(' ','-').replace('.','')
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236
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237 def listfiles(dirname, extension, remove = False):
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238 '''Returns the list of files with the extension in the directory dirname
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239 If remove is True, the filenames are stripped from the extension'''
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240 from os import listdir
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241 tmp = [f for f in listdir(dirname) if f.endswith(extension)]
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242 tmp.sort()
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243 if remove:
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244 return [removeExtension(f, extension) for f in tmp]
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245 else:
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246 return tmp
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247
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248 def removeFile(filename):
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249 '''Deletes the file while avoiding raising an error
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250 if the file does not exist'''
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251 if (os.path.exists(filename)):
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252 os.remove(filename)
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253
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254 def plotPolygon(poly, options = ''):
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255 from numpy.core.multiarray import array
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256 from matplotlib.pyplot import plot
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257 from shapely.geometry import Polygon
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258
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259 tmp = array(poly.exterior)
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260 plot(tmp[:,0], tmp[:,1], options)
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261
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262 def line2Floats(l, separator=' '):
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263 '''Returns the list of floats corresponding to the string'''
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264 return [float(x) for x in l.split(separator)]
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265
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266 def line2Ints(l, separator=' '):
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267 '''Returns the list of ints corresponding to the string'''
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268 return [int(x) for x in l.split(separator)]
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269
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270 #########################
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271 # running tests
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272 #########################
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273
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274 if __name__ == "__main__":
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275 import doctest
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276 import unittest
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c000f37c316d moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents: 29
diff changeset
277 suite = doctest.DocFileSuite('tests/utils.txt')
c000f37c316d moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents: 29
diff changeset
278 #suite = doctest.DocTestSuite()
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de5642925615 started implementation of TimeInterval and Spatio-temporal object
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 0
diff changeset
279 unittest.TextTestRunner().run(suite)
de5642925615 started implementation of TimeInterval and Spatio-temporal object
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 0
diff changeset
280 #doctest.testmod()
0
aed8eb63cdde initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff changeset
281 #doctest.testfile("example.txt")