Mercurial Hosting > traffic-intelligence
annotate python/utils.py @ 1009:0d29b75f74ea
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author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 04 Jun 2018 10:25:30 -0400 |
parents | 4f3387a242a1 |
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1 #! /usr/bin/env python |
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2 # -*- coding: utf-8 -*- |
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3 ''' Generic utilities.''' |
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4 |
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5 import matplotlib.pyplot as plt |
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6 from datetime import time, datetime |
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7 from argparse import ArgumentTypeError |
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8 from math import sqrt, ceil, floor |
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9 from scipy.stats import kruskal, shapiro, lognorm |
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10 from scipy.spatial import distance |
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11 from scipy.sparse import dok_matrix |
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12 from numpy import zeros, array, exp, sum as npsum, int as npint, arange, cumsum, median, isnan, ones, convolve, dtype, isnan, NaN, mean, ma, isinf, savez, load as npload, log |
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13 |
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14 |
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15 datetimeFormat = "%Y-%m-%d %H:%M:%S" |
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16 |
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17 sjcamDatetimeFormat = "%Y_%m%d_%H%M%S"#2017_0626_143720 |
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18 |
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19 ######################### |
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20 # Strings |
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21 ######################### |
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22 |
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23 def upperCaseFirstLetter(s): |
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24 words = s.split(' ') |
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25 lowerWords = [w[0].upper()+w[1:].lower() for w in words] |
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26 return ' '.join(lowerWords) |
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27 |
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28 class TimeConverter: |
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29 def __init__(self, datetimeFormat = datetimeFormat): |
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30 self.datetimeFormat = datetimeFormat |
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31 |
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32 def convert(self, s): |
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33 try: |
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34 return datetime.strptime(s, self.datetimeFormat) |
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35 except ValueError: |
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36 msg = "Not a valid date: '{0}'.".format(s) |
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37 raise ArgumentTypeError(msg) |
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38 |
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39 ######################### |
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40 # Enumerations |
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41 ######################### |
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42 |
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43 def inverseEnumeration(l): |
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44 'Returns the dictionary that provides for each element in the input list its index in the input list' |
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45 result = {} |
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46 for i,x in enumerate(l): |
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47 result[x] = i |
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48 return result |
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49 |
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50 ######################### |
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51 # Simple statistics |
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52 ######################### |
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53 |
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54 def logNormalMeanVar(loc, scale): |
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55 '''location and scale are respectively the mean and standard deviation of the normal in the log-normal distribution |
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56 https://en.wikipedia.org/wiki/Log-normal_distribution |
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57 |
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58 same as lognorm.stats(scale, 0, exp(loc))''' |
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59 mean = exp(loc+(scale**2)/2) |
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60 var = (exp(scale**2)-1)*exp(2*loc+scale**2) |
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61 return mean, var |
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62 |
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63 def fitLogNormal(x): |
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64 'returns the fitted location and scale of the lognormal (general definition)' |
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65 shape, loc, scale = lognorm.fit(x, floc=0.) |
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66 return log(scale), shape |
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67 |
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68 def sampleSize(stdev, tolerance, percentConfidence, nRoundingDigits = None, printLatex = False): |
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69 from scipy.stats.distributions import norm |
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70 if nRoundingDigits is None: |
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71 k = round(norm.ppf(0.5+percentConfidence/200., 0, 1), 2) # 1.-(100-percentConfidence)/200. |
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72 else: |
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73 k = round(norm.ppf(0.5+percentConfidence/200., 0, 1), nRoundingDigits) |
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74 stdev = round(stdev, nRoundingDigits) |
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75 tolerance = round(tolerance, nRoundingDigits) |
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76 if printLatex: |
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77 print('$z_{{{}}}^2\\frac{{s^2}}{{e^2}}={}^2\\frac{{{}^2}}{{{}^2}}$'.format(0.5+percentConfidence/200.,k, stdev, tolerance)) |
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78 return (k*stdev/tolerance)**2 |
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79 |
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80 def confidenceInterval(mean, stdev, nSamples, percentConfidence, trueStd = True, printLatex = False): |
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81 '''if trueStd, use normal distribution, otherwise, Student |
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82 |
973 | 83 Use otherwise t.interval or norm.interval for the boundaries |
84 ex: norm.interval(0.95) | |
85 t.interval(0.95, nSamples-1)''' | |
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86 from scipy.stats.distributions import norm, t |
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87 if trueStd: |
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88 k = round(norm.ppf(0.5+percentConfidence/200., 0, 1), 2) |
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89 else: # use Student |
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90 k = round(t.ppf(0.5+percentConfidence/200., nSamples-1), 2) |
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91 e = k*stdev/sqrt(nSamples) |
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92 if printLatex: |
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93 print('${0} \pm {1}\\frac{{{2}}}{{\sqrt{{{3}}}}}$'.format(mean, k, stdev, nSamples)) |
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94 return mean-e, mean+e |
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95 |
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96 def computeChi2(expected, observed): |
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97 '''Returns the Chi2 statistics''' |
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98 return sum([((e-o)*(e-o))/float(e) for e, o in zip(expected, observed)]) |
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99 |
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100 class DistributionSample(object): |
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101 def nSamples(self): |
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102 return sum(self.counts) |
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103 |
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104 def cumulativeDensityFunction(sample, normalized = False): |
276 | 105 '''Returns the cumulative density function of the sample of a random variable''' |
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106 xaxis = sorted(sample) |
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107 counts = arange(1,len(sample)+1) # dtype = float |
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108 if normalized: |
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109 counts /= float(len(sample)) |
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110 return xaxis, counts |
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111 |
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112 class DiscreteDistributionSample(DistributionSample): |
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113 '''Class to represent a sample of a distribution for a discrete random variable''' |
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114 def __init__(self, categories, counts): |
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115 self.categories = categories |
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116 self.counts = counts |
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117 |
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118 def mean(self): |
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119 result = [float(x*y) for x,y in zip(self.categories, self.counts)] |
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120 return npsum(result)/self.nSamples() |
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121 |
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122 def var(self, mean = None): |
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123 if not mean: |
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124 m = self.mean() |
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125 else: |
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126 m = mean |
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127 result = 0. |
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128 squares = [float((x-m)*(x-m)*y) for x,y in zip(self.categories, self.counts)] |
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129 return npsum(squares)/(self.nSamples()-1) |
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130 |
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131 def referenceCounts(self, probability): |
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132 '''probability is a function that returns the probability of the random variable for the category values''' |
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133 refProba = [probability(c) for c in self.categories] |
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134 refProba[-1] = 1-npsum(refProba[:-1]) |
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135 refCounts = [r*self.nSamples() for r in refProba] |
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136 return refCounts, refProba |
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137 |
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138 class ContinuousDistributionSample(DistributionSample): |
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139 '''Class to represent a sample of a distribution for a continuous random variable |
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140 with the number of observations for each interval |
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141 intervals (categories variable) are defined by their left limits, the last one being the right limit |
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142 categories contain therefore one more element than the counts''' |
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143 def __init__(self, categories, counts): |
276 | 144 # todo add samples for initialization and everything to None? (or setSamples?) |
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145 self.categories = categories |
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146 self.counts = counts |
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147 |
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148 @staticmethod |
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149 def generate(sample, categories): |
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150 if min(sample) < min(categories): |
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151 print('Sample has lower min than proposed categories ({}, {})'.format(min(sample), min(categories))) |
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152 if max(sample) > max(categories): |
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153 print('Sample has higher max than proposed categories ({}, {})'.format(max(sample), max(categories))) |
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154 dist = ContinuousDistributionSample(sorted(categories), [0]*(len(categories)-1)) |
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155 for s in sample: |
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156 i = 0 |
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157 while i<len(dist.categories) and dist.categories[i] <= s: |
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158 i += 1 |
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159 if i <= len(dist.counts): |
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160 dist.counts[i-1] += 1 |
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161 #print('{} in {} {}'.format(s, dist.categories[i-1], dist.categories[i])) |
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162 else: |
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163 print('Element {} is not in the categories'.format(s)) |
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164 return dist |
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165 |
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166 def mean(self): |
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167 result = 0. |
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168 for i in range(len(self.counts)-1): |
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169 result += self.counts[i]*(self.categories[i]+self.categories[i+1])/2 |
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170 return result/self.nSamples() |
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171 |
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172 def var(self, mean = None): |
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173 if not mean: |
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174 m = self.mean() |
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175 else: |
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176 m = mean |
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177 result = 0. |
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178 for i in range(len(self.counts)-1): |
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179 mid = (self.categories[i]+self.categories[i+1])/2 |
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180 result += self.counts[i]*(mid - m)*(mid - m) |
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181 return result/(self.nSamples()-1) |
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182 |
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183 def referenceCounts(self, cdf): |
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184 '''cdf is a cumulative distribution function |
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185 returning the probability of the variable being less that x''' |
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186 # refCumulativeCounts = [0]#[cdf(self.categories[0][0])] |
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187 # for inter in self.categories: |
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188 # refCumulativeCounts.append(cdf(inter[1])) |
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189 refCumulativeCounts = [cdf(x) for x in self.categories[1:-1]] |
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190 |
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191 refProba = [refCumulativeCounts[0]] |
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192 for i in xrange(1,len(refCumulativeCounts)): |
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193 refProba.append(refCumulativeCounts[i]-refCumulativeCounts[i-1]) |
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194 refProba.append(1-refCumulativeCounts[-1]) |
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195 refCounts = [p*self.nSamples() for p in refProba] |
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196 |
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197 return refCounts, refProba |
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198 |
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199 def printReferenceCounts(self, refCounts=None): |
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200 if refCounts: |
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201 ref = refCounts |
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202 else: |
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203 ref = self.referenceCounts |
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204 for i in xrange(len(ref[0])): |
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205 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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206 |
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207 |
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208 ######################### |
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209 # maths section |
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210 ######################### |
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211 |
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212 # def kernelSmoothing(sampleX, X, Y, weightFunc, halfwidth): |
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213 # '''Returns a smoothed weighted version of Y at the predefined values of sampleX |
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214 # Sum_x weight(sample_x,x) * y(x)''' |
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215 # from numpy import zeros, array |
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216 # smoothed = zeros(len(sampleX)) |
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217 # for i,x in enumerate(sampleX): |
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218 # weights = array([weightFunc(x,xx, halfwidth) for xx in X]) |
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219 # if sum(weights)>0: |
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220 # smoothed[i] = sum(weights*Y)/sum(weights) |
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221 # else: |
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222 # smoothed[i] = 0 |
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223 # return smoothed |
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224 |
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225 def kernelSmoothing(x, X, Y, weightFunc, halfwidth): |
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226 '''Returns the smoothed estimate of (X,Y) at x |
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227 Sum_x weight(sample_x,x) * y(x)''' |
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228 weights = array([weightFunc(x,observedx, halfwidth) for observedx in X]) |
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229 if sum(weights)>0: |
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230 return sum(weights*Y)/sum(weights) |
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231 else: |
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232 return 0 |
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233 |
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234 def uniform(center, x, halfwidth): |
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235 if abs(center-x)<halfwidth: |
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236 return 1. |
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237 else: |
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238 return 0. |
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239 |
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240 def gaussian(center, x, halfwidth): |
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241 return exp(-((center-x)/halfwidth)**2/2) |
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242 |
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243 def epanechnikov(center, x, halfwidth): |
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244 diff = abs(center-x) |
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245 if diff<halfwidth: |
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246 return 1.-(diff/halfwidth)**2 |
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247 else: |
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248 return 0. |
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249 |
434 | 250 def triangular(center, x, halfwidth): |
251 diff = abs(center-x) | |
252 if diff<halfwidth: | |
253 return 1.-abs(diff/halfwidth) | |
254 else: | |
255 return 0. | |
433
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256 |
518
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257 def medianSmoothing(x, X, Y, halfwidth): |
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258 '''Returns the media of Y's corresponding to X's in the interval [x-halfwidth, x+halfwidth]''' |
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259 return median([y for observedx, y in zip(X,Y) if abs(x-observedx)<halfwidth]) |
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260 |
521
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changed argMaxDict name to argmaxDict
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261 def argmaxDict(d): |
561
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262 return max(d, key=d.get) |
279
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263 |
837
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264 def deltaFrames(t1, t2, frameRate): |
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265 '''Returns the number of frames between t1 and t2 |
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266 positive if t1<=t2, negative otherwise''' |
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267 if t1 > t2: |
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268 return -(t1-t2).seconds*frameRate |
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269 else: |
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270 return (t2-t1).seconds*frameRate |
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271 |
395
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272 def framesToTime(nFrames, frameRate, initialTime = time()): |
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273 '''returns a datetime.time for the time in hour, minutes and seconds |
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274 initialTime is a datetime.time''' |
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275 seconds = int(floor(float(nFrames)/float(frameRate))+initialTime.hour*3600+initialTime.minute*60+initialTime.second) |
261
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276 h = int(floor(seconds/3600.)) |
248
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277 seconds = seconds - h*3600 |
261
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diff
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278 m = int(floor(seconds/60)) |
248
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241
diff
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279 seconds = seconds - m*60 |
262
a048066bd20f
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261
diff
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280 return time(h, m, seconds) |
248
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diff
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281 |
381
387cc0142211
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diff
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282 def timeToFrames(t, frameRate): |
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diff
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283 return frameRate*(t.hour*3600+t.minute*60+t.second) |
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284 |
241
ee1caff48b03
added function to sort to list of paired data X,Y
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235
diff
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285 def sortXY(X,Y): |
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diff
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286 'returns the sorted (x, Y(x)) sorted on X' |
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diff
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287 D = {} |
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diff
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288 for x, y in zip(X,Y): |
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diff
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289 D[x]=y |
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diff
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290 xsorted = sorted(D.keys()) |
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diff
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291 return xsorted, [D[x] for x in xsorted] |
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diff
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|
292 |
733 | 293 def compareLengthForSort(i, j): |
294 if len(i) < len(j): | |
295 return -1 | |
296 elif len(i) == len(j): | |
297 return 0 | |
298 else: | |
299 return 1 | |
300 | |
301 def sortByLength(instances, reverse = False): | |
302 '''Returns a new list with the instances sorted by length (method __len__) | |
303 reverse is passed to sorted''' | |
997
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updated utils to python 3
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parents:
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diff
changeset
|
304 return sorted(instances, key = len, reverse = reverse) |
733 | 305 |
32 | 306 def ceilDecimals(v, nDecimals): |
307 '''Rounds the number at the nth decimal | |
308 eg 1.23 at 0 decimal is 2, at 1 decimal is 1.3''' | |
670
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corrected other missing imports
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|
309 tens = 10**nDecimals |
32 | 310 return ceil(v*tens)/tens |
311 | |
152
74b1fc68d4df
re-organized code to avoid cyclic python module dependencies
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116
diff
changeset
|
312 def inBetween(bound1, bound2, x): |
698
8d99a9e16644
added clarification comments
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689
diff
changeset
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313 'useful if one does not know the order of bound1/bound2' |
569
0057c04f94d5
work in progress on intersections (for PET)
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diff
changeset
|
314 return bound1 <= x <= bound2 or bound2 <= x <= bound1 |
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diff
changeset
|
315 |
0057c04f94d5
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diff
changeset
|
316 def pointDistanceL2(x1,y1,x2,y2): |
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diff
changeset
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317 ''' Compute point-to-point distance (L2 norm, ie Euclidean distance)''' |
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diff
changeset
|
318 return sqrt((x2-x1)**2+(y2-y1)**2) |
24
6fb59cfb201e
first version of segmentIntersection
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19
diff
changeset
|
319 |
6fb59cfb201e
first version of segmentIntersection
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19
diff
changeset
|
320 def crossProduct(l1, l2): |
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19
diff
changeset
|
321 return l1[0]*l2[1]-l1[1]*l2[0] |
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diff
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322 |
574
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diff
changeset
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323 def cat_mvgavg(cat_list, halfWidth): |
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diff
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324 ''' Return a list of categories/values smoothed according to a window. |
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diff
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325 halfWidth is the search radius on either side''' |
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diff
changeset
|
326 from copy import deepcopy |
659
784298512b60
minor modifications
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637
diff
changeset
|
327 smoothed = deepcopy(cat_list) |
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637
diff
changeset
|
328 for point in range(len(cat_list)): |
574
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diff
changeset
|
329 lower_bound_check = max(0,point-halfWidth) |
659
784298512b60
minor modifications
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parents:
637
diff
changeset
|
330 upper_bound_check = min(len(cat_list)-1,point+halfWidth+1) |
784298512b60
minor modifications
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parents:
637
diff
changeset
|
331 window_values = cat_list[lower_bound_check:upper_bound_check] |
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diff
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332 smoothed[point] = max(set(window_values), key=window_values.count) |
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diff
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|
333 return smoothed |
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diff
changeset
|
334 |
547 | 335 def filterMovingWindow(inputSignal, halfWidth): |
29
ca8e716cc231
added moving average filter
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parents:
27
diff
changeset
|
336 '''Returns an array obtained after the smoothing of the input by a moving average |
ca8e716cc231
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parents:
27
diff
changeset
|
337 The first and last points are copied from the original.''' |
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parents:
27
diff
changeset
|
338 width = float(halfWidth*2+1) |
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parents:
27
diff
changeset
|
339 win = ones(width,'d') |
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27
diff
changeset
|
340 result = convolve(win/width,array(inputSignal),'same') |
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diff
changeset
|
341 result[:halfWidth] = inputSignal[:halfWidth] |
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27
diff
changeset
|
342 result[-halfWidth:] = inputSignal[-halfWidth:] |
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parents:
27
diff
changeset
|
343 return result |
ca8e716cc231
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27
diff
changeset
|
344 |
199
ca9d9104afba
added utility to calibrate polynoms and plot
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197
diff
changeset
|
345 def linearRegression(x, y, deg = 1, plotData = False): |
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parents:
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diff
changeset
|
346 '''returns the least square estimation of the linear regression of y = ax+b |
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diff
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347 as well as the plot''' |
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diff
changeset
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348 from numpy.lib.polynomial import polyfit |
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diff
changeset
|
349 from numpy.core.multiarray import arange |
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197
diff
changeset
|
350 coef = polyfit(x, y, deg) |
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diff
changeset
|
351 if plotData: |
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diff
changeset
|
352 def poly(x): |
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diff
changeset
|
353 result = 0 |
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parents:
197
diff
changeset
|
354 for i in range(len(coef)): |
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parents:
197
diff
changeset
|
355 result += coef[i]*x**(len(coef)-i-1) |
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197
diff
changeset
|
356 return result |
668
f8dcf483b296
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parents:
667
diff
changeset
|
357 plt.plot(x, y, 'x') |
199
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parents:
197
diff
changeset
|
358 xx = arange(min(x), max(x),(max(x)-min(x))/1000) |
668
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parents:
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diff
changeset
|
359 plt.plot(xx, [poly(z) for z in xx]) |
199
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parents:
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diff
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|
360 return coef |
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diff
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361 |
668
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diff
changeset
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362 def correlation(data, correlationMethod = 'pearson', plotFigure = False, displayNames = None, figureFilename = None): |
f8dcf483b296
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parents:
667
diff
changeset
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363 '''Computes (and displays) the correlation matrix for a pandas DataFrame''' |
677
ae07c7b4cf87
update to utils for pavement results
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parents:
676
diff
changeset
|
364 columns = data.columns.tolist() |
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parents:
676
diff
changeset
|
365 for var in data.columns: |
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parents:
676
diff
changeset
|
366 uniqueValues = data[var].unique() |
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parents:
676
diff
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367 if len(uniqueValues) == 1 or data.dtypes[var] == dtype('O') or (len(uniqueValues) == 2 and len(data.loc[~isnan(data[var]), var].unique()) == 1): # last condition: only one other value than nan |
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368 columns.remove(var) |
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369 c=data[columns].corr(correlationMethod) |
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370 if plotFigure: |
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371 fig = plt.figure(figsize=(4+0.4*c.shape[0], 0.4*c.shape[0])) |
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372 fig.add_subplot(1,1,1) |
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373 #plt.imshow(np.fabs(c), interpolation='none') |
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374 plt.imshow(c, vmin=-1., vmax = 1., interpolation='none', cmap = 'RdYlBu_r') # coolwarm |
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375 if displayNames is not None: |
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376 colnames = [displayNames.get(s.strip(), s.strip()) for s in columns] |
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377 else: |
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378 colnames = columns |
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379 #correlation.plot_corr(c, xnames = colnames, normcolor=True, title = filename) |
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380 plt.xticks(range(len(colnames)), colnames, rotation=90) |
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381 plt.yticks(range(len(colnames)), colnames) |
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382 plt.tick_params('both', length=0) |
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383 plt.subplots_adjust(bottom = 0.29) |
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384 plt.colorbar() |
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385 plt.title('Correlation ({})'.format(correlationMethod)) |
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386 plt.tight_layout() |
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387 if len(colnames) > 50: |
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388 plt.subplots_adjust(left=.06) |
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389 if figureFilename is not None: |
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390 plt.savefig(figureFilename, dpi = 150, transparent = True) |
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391 return c |
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392 |
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393 def addDummies(data, variables, allVariables = True): |
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394 '''Add binary dummy variables for each value of a nominal variable |
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395 in a pandas DataFrame''' |
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396 newVariables = [] |
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397 for var in variables: |
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398 if var in data.columns and data.dtypes[var] == dtype('O') and len(data[var].unique()) > 2: |
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399 values = data[var].unique() |
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400 if not allVariables: |
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401 values = values[:-1] |
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402 for val in values: |
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403 if val is not NaN: |
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404 newVariable = (var+'_{}'.format(val)).replace('.','').replace(' ','').replace('-','') |
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405 data[newVariable] = (data[var] == val) |
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406 newVariables.append(newVariable) |
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407 return newVariables |
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408 |
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409 def kruskalWallis(data, dependentVariable, independentVariable, plotFigure = False, filenamePrefix = None, figureFileType = 'pdf', saveLatex = False, renameVariables = lambda s: s, kwCaption = ''): |
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410 '''Studies the influence of (nominal) independent variable over the dependent variable |
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411 |
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412 Makes tests if the conditional distributions are normal |
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413 using the Shapiro-Wilk test (in which case ANOVA could be used) |
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414 Implements uses the non-parametric Kruskal Wallis test''' |
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415 tmp = data[data[independentVariable].notnull()] |
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416 independentVariableValues = sorted(tmp[independentVariable].unique().tolist()) |
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417 if len(independentVariableValues) >= 2: |
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418 if saveLatex: |
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419 from storage import openCheck |
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420 out = openCheck(filenamePrefix+'-{}-{}.tex'.format(dependentVariable, independentVariable), 'w') |
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421 for x in independentVariableValues: |
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422 print('Shapiro-Wilk normality test for {} when {}={}: {} obs'.format(dependentVariable,independentVariable, x, len(tmp.loc[tmp[independentVariable] == x, dependentVariable]))) |
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423 if len(tmp.loc[tmp[independentVariable] == x, dependentVariable]) >= 3: |
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424 print(shapiro(tmp.loc[tmp[independentVariable] == x, dependentVariable])) |
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425 if plotFigure: |
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426 plt.figure() |
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427 plt.boxplot([tmp.loc[tmp[independentVariable] == x, dependentVariable] for x in independentVariableValues]) |
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428 #q25, q75 = tmp[dependentVariable].quantile([.25, .75]) |
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429 #plt.ylim(ymax = q75+1.5*(q75-q25)) |
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430 plt.xticks(range(1,len(independentVariableValues)+1), independentVariableValues) |
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431 plt.title('{} vs {}'.format(dependentVariable, independentVariable)) |
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432 if filenamePrefix is not None: |
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433 plt.savefig(filenamePrefix+'-{}-{}.{}'.format(dependentVariable, independentVariable, figureFileType)) |
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434 table = tmp.groupby([independentVariable])[dependentVariable].describe().unstack().sort(['50%'], ascending = False) |
676 | 435 table['count'] = table['count'].astype(int) |
436 testResult = kruskal(*[tmp.loc[tmp[independentVariable] == x, dependentVariable] for x in independentVariableValues]) | |
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437 if saveLatex: |
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438 out.write('\\begin{minipage}{\\linewidth}\n' |
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439 +'\\centering\n' |
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440 +'\\captionof{table}{'+(kwCaption.format(dependentVariable, independentVariable, *testResult))+'}\n' |
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441 +table.to_latex(float_format = lambda x: '{:.3f}'.format(x)).encode('ascii')+'\n' |
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442 +'\\end{minipage}\n' |
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443 +'\\ \\vspace{0.5cm}\n') |
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444 else: |
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445 print(table) |
676 | 446 return testResult |
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447 else: |
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448 return None |
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449 |
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450 def prepareRegression(data, dependentVariable, independentVariables, maxCorrelationThreshold, correlations, maxCorrelationP, correlationFunc, stdoutText = ['Removing {} (constant: {})', 'Removing {} (correlation {} with {})', 'Removing {} (no correlation: {}, p={})'], saveFiles = False, filenamePrefix = None, latexHeader = '', latexTable = None, latexFooter=''): |
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451 '''Removes variables from candidate independent variables if |
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452 - if two independent variables are correlated (> maxCorrelationThreshold), one is removed |
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453 - if an independent variable is not correlated with the dependent variable (p>maxCorrelationP) |
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454 Returns the remaining non-correlated variables, correlated with the dependent variable |
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455 |
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456 correlationFunc is spearmanr or pearsonr from scipy.stats |
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457 text is the template to display for the two types of printout (see default): 3 elements if no saving to latex file, 8 otherwise |
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458 |
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459 TODO: pass the dummies for nominal variables and remove if all dummies are correlated, or none is correlated with the dependentvariable''' |
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460 from copy import copy |
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461 from pandas import DataFrame |
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462 result = copy(independentVariables) |
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463 table1 = '' |
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464 table2 = {} |
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465 # constant variables |
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466 for var in independentVariables: |
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467 uniqueValues = data[var].unique() |
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468 if (len(uniqueValues) == 1) or (len(uniqueValues) == 2 and uniqueValues.dtype != dtype('O') and len(data.loc[~isnan(data[var]), var].unique()) == 1): |
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469 print(stdoutText[0].format(var, uniqueValues)) |
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470 if saveFiles: |
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471 table1 += latexTable[0].format(var, *uniqueValues) |
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472 result.remove(var) |
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473 # correlated variables |
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474 for v1 in copy(result): |
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475 if v1 in correlations.index: |
677
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476 for v2 in copy(result): |
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477 if v2 != v1 and v2 in correlations.index: |
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478 if abs(correlations.loc[v1, v2]) > maxCorrelationThreshold: |
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479 if v1 in result and v2 in result: |
677
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480 if saveFiles: |
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481 table1 += latexTable[1].format(v2, v1, correlations.loc[v1, v2]) |
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482 print(stdoutText[1].format(v2, v1, correlations.loc[v1, v2])) |
668
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483 result.remove(v2) |
677
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484 # not correlated with dependent variable |
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485 table2['Correlations'] = [] |
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486 table2['Valeurs p'] = [] |
668
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487 for var in copy(result): |
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488 if data.dtypes[var] != dtype('O'): |
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489 cor, p = correlationFunc(data[dependentVariable], data[var]) |
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490 if p > maxCorrelationP: |
677
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491 if saveFiles: |
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492 table1 += latexTable[2].format(var, cor, p) |
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493 print(stdoutText[2].format(var, cor, p)) |
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494 result.remove(var) |
677
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495 else: |
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496 table2['Correlations'].append(cor) |
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497 table2['Valeurs p'].append(p) |
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498 |
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499 if saveFiles: |
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500 from storage import openCheck |
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501 out = openCheck(filenamePrefix+'-removed-variables.tex', 'w') |
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502 out.write(latexHeader) |
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503 out.write(table1) |
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504 out.write(latexFooter) |
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505 out.close() |
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506 out = openCheck(filenamePrefix+'-correlations.html', 'w') |
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507 table2['Variables'] = [var for var in result if data.dtypes[var] != dtype('O')] |
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508 out.write(DataFrame(table2)[['Variables', 'Correlations', 'Valeurs p']].to_html(formatters = {'Correlations': lambda x: '{:.2f}'.format(x), 'Valeurs p': lambda x: '{:.3f}'.format(x)}, index = False)) |
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509 out.close() |
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510 return result |
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511 |
841 | 512 def saveDokMatrix(filename, m, lowerTriangle = False): |
840
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513 'Saves a dok_matrix using savez' |
841 | 514 if lowerTriangle: |
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515 keys = [k for k in m if k[0] > k[1]] |
841 | 516 savez(filename, shape = m.shape, keys = keys, values = [m[k[0],k[1]] for k in keys]) |
517 else: | |
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518 savez(filename, shape = m.shape, keys = list(m.keys()), values = list(m.values())) |
840
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519 |
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520 def loadDokMatrix(filename): |
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521 'Loads a dok_matrix saved using the above saveDokMatrix' |
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522 data = npload(filename) |
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523 m = dok_matrix(tuple(data['shape'])) |
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524 for k, v in zip(data['keys'], data['values']): |
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525 m[tuple(k)] = v |
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526 return m |
667
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527 |
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528 ######################### |
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529 # regression analysis using statsmodels (and pandas) |
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530 ######################### |
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531 |
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532 # TODO make class for experiments? |
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533 # TODO add tests with public dataset downloaded from Internet (IRIS et al) |
667
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534 def modelString(experiment, dependentVariable, independentVariables): |
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535 return dependentVariable+' ~ '+' + '.join([independentVariable for independentVariable in independentVariables if experiment[independentVariable]]) |
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536 |
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537 def runModel(experiment, data, dependentVariable, independentVariables, regressionType = 'ols'): |
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538 import statsmodels.formula.api as smf |
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539 modelStr = modelString(experiment, dependentVariable, independentVariables) |
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540 if regressionType == 'ols': |
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541 model = smf.ols(modelStr, data = data) |
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542 elif regressionType == 'gls': |
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543 model = smf.gls(modelStr, data = data) |
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544 elif regressionType == 'rlm': |
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545 model = smf.rlm(modelStr, data = data) |
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546 else: |
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547 print('Unknown regression type {}. Exiting'.format(regressionType)) |
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548 import sys |
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549 sys.exit() |
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550 return model.fit() |
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551 |
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552 def runModels(experiments, data, dependentVariable, independentVariables, regressionType = 'ols'): |
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553 '''Runs several models and stores 3 statistics |
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554 adjusted R2, condition number (should be small, eg < 1000) |
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555 and p-value for Shapiro-Wilk test of residual normality''' |
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556 for i,experiment in experiments.iterrows(): |
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557 if experiment[independentVariables].any(): |
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558 results = runModel(experiment, data, dependentVariable, independentVariables, regressionType = 'ols') |
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559 experiments.loc[i,'r2adj'] = results.rsquared_adj |
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560 experiments.loc[i,'condNum'] = results.condition_number |
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561 experiments.loc[i, 'shapiroP'] = shapiro(results.resid)[1] |
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562 experiments.loc[i,'nobs'] = int(results.nobs) |
667
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563 return experiments |
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|
564 |
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|
565 def generateExperiments(independentVariables): |
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566 '''Generates all possible models for including or not each independent variable''' |
670
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567 from pandas import DataFrame |
667
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568 experiments = {} |
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|
569 nIndependentVariables = len(independentVariables) |
669 | 570 if nIndependentVariables != len(set(independentVariables)): |
667
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|
571 print("Duplicate variables. Exiting") |
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|
572 import sys |
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|
573 sys.exit() |
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|
574 nModels = 2**nIndependentVariables |
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575 for i,var in enumerate(independentVariables): |
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576 pattern = [False]*(2**i)+[True]*(2**i) |
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577 experiments[var] = pattern*(2**(nIndependentVariables-i-1)) |
670
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578 experiments = DataFrame(experiments) |
667
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|
579 experiments['r2adj'] = 0. |
677
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580 experiments['condNum'] = NaN |
667
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581 experiments['shapiroP'] = -1 |
668
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|
582 experiments['nobs'] = -1 |
667
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diff
changeset
|
583 return experiments |
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diff
changeset
|
584 |
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585 def findBestModel(data, dependentVariable, independentVariables, regressionType = 'ols', nProcesses = 1): |
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586 '''Generates all possible model with the independentVariables |
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587 and runs them, saving the results in experiments |
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|
588 with multiprocess option''' |
670
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589 from pandas import concat |
671
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diff
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590 from multiprocessing import Pool |
667
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|
591 experiments = generateExperiments(independentVariables) |
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592 nModels = len(experiments) |
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|
593 print("Running {} models with {} processes".format(nModels, nProcesses)) |
674
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diff
changeset
|
594 print("IndependentVariables: {}".format(independentVariables)) |
667
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595 if nProcesses == 1: |
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596 return runModels(experiments, data, dependentVariable, independentVariables, regressionType) |
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597 else: |
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|
598 pool = Pool(processes = nProcesses) |
670
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|
599 chunkSize = int(ceil(nModels/nProcesses)) |
667
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|
600 jobs = [pool.apply_async(runModels, args = (experiments[i*chunkSize:(i+1)*chunkSize], data, dependentVariable, independentVariables, regressionType)) for i in range(nProcesses)] |
670
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|
601 return concat([job.get() for job in jobs]) |
667
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|
602 |
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603 def findBestModelFwd(data, dependentVariable, independentVariables, modelFunc, experiments = None): |
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604 '''Forward search for best model (based on adjusted R2) |
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605 Randomly starting with one variable and adding randomly variables |
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606 if they improve the model |
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|
607 |
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608 The results are added to experiments if provided as argument |
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changeset
|
609 Storing in experiment relies on the index being the number equal |
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610 to the binary code derived from the independent variables''' |
670
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611 from numpy.random import permutation as nppermutation |
667
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612 if experiments is None: |
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613 experiments = generateExperiments(independentVariables) |
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614 nIndependentVariables = len(independentVariables) |
997
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615 permutation = nppermutation(list(range(nIndependentVariables))) |
667
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616 variableMapping = {j: independentVariables[i] for i,j in enumerate(permutation)} |
997
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617 print('Tested variables '+', '.join([variableMapping[i] for i in range(nIndependentVariables)])) |
667
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618 bestModel = [False]*nIndependentVariables |
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619 currentVarNum = 0 |
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620 currentR2Adj = 0. |
997
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621 for currentVarNum in range(nIndependentVariables): |
667
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622 currentModel = [i for i in bestModel] |
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623 currentModel[currentVarNum] = True |
997
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624 rowIdx = sum([0]+[2**i for i in range(nIndependentVariables) if currentModel[permutation[i]]]) |
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625 #print currentVarNum, sum(currentModel), ', '.join([independentVariables[i] for i in range(nIndependentVariables) if currentModel[permutation[i]]]) |
667
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626 if experiments.loc[rowIdx, 'shapiroP'] < 0: |
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627 modelStr = modelString(experiments.loc[rowIdx], dependentVariable, independentVariables) |
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628 model = modelFunc(modelStr, data = data) |
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629 results = model.fit() |
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630 experiments.loc[rowIdx, 'r2adj'] = results.rsquared_adj |
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changeset
|
631 experiments.loc[rowIdx, 'condNum'] = results.condition_number |
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changeset
|
632 experiments.loc[rowIdx, 'shapiroP'] = shapiro(results.resid)[1] |
668
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parents:
667
diff
changeset
|
633 experiments.loc[rowIdx, 'nobs'] = int(results.nobs) |
667
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diff
changeset
|
634 if currentR2Adj < experiments.loc[rowIdx, 'r2adj']: |
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diff
changeset
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635 currentR2Adj = experiments.loc[rowIdx, 'r2adj'] |
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diff
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636 bestModel[currentVarNum] = True |
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637 return experiments |
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|
638 |
677
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639 def displayModelResults(results, model = None, plotFigures = True, filenamePrefix = None, figureFileType = 'pdf', text = {'title-shapiro': 'Shapiro-Wilk normality test for residuals: {:.2f} (p={:.3f})', 'true-predicted.xlabel': 'Predicted values', 'true-predicted.ylabel': 'True values', 'residuals-predicted.xlabel': 'Predicted values', 'residuals-predicted.ylabel': 'Residuals'}): |
667
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changeset
|
640 import statsmodels.api as sm |
677
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diff
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641 '''Displays some model results |
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|
642 |
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643 3 graphics, true-predicted, residuals-predicted, ''' |
676 | 644 print(results.summary()) |
677
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676
diff
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|
645 shapiroResult = shapiro(results.resid) |
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diff
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|
646 print(shapiroResult) |
676 | 647 if plotFigures: |
677
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diff
changeset
|
648 fig = plt.figure(figsize=(7,6.3*(2+int(model is not None)))) |
676 | 649 if model is not None: |
677
ae07c7b4cf87
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diff
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|
650 ax = fig.add_subplot(3,1,1) |
676 | 651 plt.plot(results.predict(), model.endog, 'x') |
652 x=plt.xlim() | |
653 y=plt.ylim() | |
654 plt.plot([max(x[0], y[0]), min(x[1], y[1])], [max(x[0], y[0]), min(x[1], y[1])], 'r') | |
677
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diff
changeset
|
655 #plt.axis('equal') |
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|
656 if text is not None: |
ae07c7b4cf87
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diff
changeset
|
657 plt.title(text['title-shapiro'].format(*shapiroResult)) |
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|
658 #plt.title(text['true-predicted.title']) |
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changeset
|
659 plt.xlabel(text['true-predicted.xlabel']) |
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diff
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660 plt.ylabel(text['true-predicted.ylabel']) |
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diff
changeset
|
661 fig.add_subplot(3,1,2, sharex = ax) |
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662 plt.plot(results.predict(), results.resid, 'x') |
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663 nextSubplotNum = 3 |
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diff
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|
664 else: |
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665 fig.add_subplot(2,1,1) |
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diff
changeset
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666 plt.plot(results.predict(), results.resid, 'x') |
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diff
changeset
|
667 nextSubplotNum = 2 |
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diff
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|
668 if text is not None: |
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diff
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|
669 if model is None: |
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diff
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670 plt.title(text['title-shapiro'].format(*shapiroResult)) |
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671 plt.xlabel(text['residuals-predicted.xlabel']) |
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diff
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|
672 plt.ylabel(text['residuals-predicted.ylabel']) |
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diff
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673 qqAx = fig.add_subplot(nextSubplotNum,1,nextSubplotNum) |
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diff
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674 sm.qqplot(results.resid, fit = True, line = '45', ax = qqAx) |
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676
diff
changeset
|
675 plt.axis('equal') |
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676
diff
changeset
|
676 if text is not None and 'qqplot.xlabel' in text: |
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676
diff
changeset
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677 plt.xlabel(text['qqplot.xlabel']) |
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diff
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|
678 plt.ylabel(text['qqplot.ylabel']) |
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diff
changeset
|
679 plt.tight_layout() |
676 | 680 if filenamePrefix is not None: |
677
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681 from storage import openCheck |
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682 out = openCheck(filenamePrefix+'-coefficients.html', 'w') |
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diff
changeset
|
683 out.write(results.summary().as_html()) |
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diff
changeset
|
684 plt.savefig(filenamePrefix+'-model-results.'+figureFileType) |
667
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diff
changeset
|
685 |
27
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diff
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686 ######################### |
455
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diff
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687 # iterable section |
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diff
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|
688 ######################### |
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diff
changeset
|
689 |
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diff
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|
690 def mostCommon(L): |
456 | 691 '''Returns the most frequent element in a iterable |
692 | |
693 taken from http://stackoverflow.com/questions/1518522/python-most-common-element-in-a-list''' | |
455
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changeset
|
694 from itertools import groupby |
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diff
changeset
|
695 from operator import itemgetter |
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|
696 # get an iterable of (item, iterable) pairs |
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diff
changeset
|
697 SL = sorted((x, i) for i, x in enumerate(L)) |
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diff
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|
698 # print 'SL:', SL |
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changeset
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699 groups = groupby(SL, key=itemgetter(0)) |
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diff
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|
700 # auxiliary function to get "quality" for an item |
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diff
changeset
|
701 def _auxfun(g): |
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diff
changeset
|
702 item, iterable = g |
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703 count = 0 |
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diff
changeset
|
704 min_index = len(L) |
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added most common utility function
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diff
changeset
|
705 for _, where in iterable: |
abe0b2347d4c
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diff
changeset
|
706 count += 1 |
abe0b2347d4c
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434
diff
changeset
|
707 min_index = min(min_index, where) |
abe0b2347d4c
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diff
changeset
|
708 # print 'item %r, count %r, minind %r' % (item, count, min_index) |
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diff
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|
709 return count, -min_index |
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diff
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|
710 # pick the highest-count/earliest item |
abe0b2347d4c
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diff
changeset
|
711 return max(groups, key=_auxfun)[0] |
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diff
changeset
|
712 |
abe0b2347d4c
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434
diff
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|
713 ######################### |
370
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|
714 # sequence section |
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|
715 ######################### |
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|
716 |
665
15e244d2a1b5
corrected bug with circular import for VideoFilenameAddable, moved to base module
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
659
diff
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|
717 class LCSS(object): |
370
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|
718 '''Class that keeps the LCSS parameters |
686
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allowing alternate database and filename for classify-objects
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diff
changeset
|
719 and puts together the various computations |
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
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|
720 |
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680
diff
changeset
|
721 the methods with names starting with _ are not to be shadowed |
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680
diff
changeset
|
722 in child classes, who will shadow the other methods, |
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680
diff
changeset
|
723 ie compute and computeXX methods''' |
689
9990ef119bce
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diff
changeset
|
724 def __init__(self, similarityFunc = None, metric = None, epsilon = None, delta = float('inf'), aligned = False, lengthFunc = min): |
9990ef119bce
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diff
changeset
|
725 '''One should provide either a similarity function |
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diff
changeset
|
726 that indicates (return bool) whether elements in the compares lists are similar |
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diff
changeset
|
727 |
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diff
changeset
|
728 eg distance(p1, p2) < epsilon |
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diff
changeset
|
729 |
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diff
changeset
|
730 or a type of metric usable in scipy.spatial.distance.cdist with an epsilon''' |
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diff
changeset
|
731 if similarityFunc is None and metric is None: |
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diff
changeset
|
732 print("No way to compute LCSS, similarityFunc and metric are None. Exiting") |
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diff
changeset
|
733 import sys |
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diff
changeset
|
734 sys.exit() |
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diff
changeset
|
735 elif metric is not None and epsilon is None: |
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diff
changeset
|
736 print("Please provide a value for epsilon if using a cdist metric. Exiting") |
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diff
changeset
|
737 import sys |
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diff
changeset
|
738 sys.exit() |
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diff
changeset
|
739 else: |
741 | 740 if similarityFunc is None and metric is not None and not isinf(delta): |
737
fb60b54e1041
added warning for finite delta
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
733
diff
changeset
|
741 print('Warning: you are using a cdist metric and a finite delta, which will make probably computation slower than using the equivalent similarityFunc (since all pairwise distances will be computed by cdist).') |
689
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diff
changeset
|
742 self.similarityFunc = similarityFunc |
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diff
changeset
|
743 self.metric = metric |
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diff
changeset
|
744 self.epsilon = epsilon |
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diff
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|
745 self.aligned = aligned |
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diff
changeset
|
746 self.delta = delta |
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diff
changeset
|
747 self.lengthFunc = lengthFunc |
9990ef119bce
added version of LCSS with cdist computations
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diff
changeset
|
748 self.subSequenceIndices = [(0,0)] |
370
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|
749 |
373
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|
750 def similarities(self, l1, l2, jshift=0): |
370
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|
751 n1 = len(l1) |
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|
752 n2 = len(l2) |
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|
753 self.similarityTable = zeros((n1+1,n2+1), dtype = npint) |
689
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
754 if self.similarityFunc is not None: |
997
4f3387a242a1
updated utils to python 3
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
990
diff
changeset
|
755 for i in range(1,n1+1): |
4f3387a242a1
updated utils to python 3
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
990
diff
changeset
|
756 for j in range(max(1,i-jshift-self.delta),min(n2,i-jshift+self.delta)+1): |
689
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
757 if self.similarityFunc(l1[i-1], l2[j-1]): |
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
758 self.similarityTable[i,j] = self.similarityTable[i-1,j-1]+1 |
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
759 else: |
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
760 self.similarityTable[i,j] = max(self.similarityTable[i-1,j], self.similarityTable[i,j-1]) |
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
761 elif self.metric is not None: |
9990ef119bce
added version of LCSS with cdist computations
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parents:
687
diff
changeset
|
762 similarElements = distance.cdist(l1, l2, self.metric) <= self.epsilon |
997
4f3387a242a1
updated utils to python 3
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
990
diff
changeset
|
763 for i in range(1,n1+1): |
4f3387a242a1
updated utils to python 3
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
990
diff
changeset
|
764 for j in range(max(1,i-jshift-self.delta),min(n2,i-jshift+self.delta)+1): |
689
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
765 if similarElements[i-1, j-1]: |
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
766 self.similarityTable[i,j] = self.similarityTable[i-1,j-1]+1 |
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
767 else: |
9990ef119bce
added version of LCSS with cdist computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
687
diff
changeset
|
768 self.similarityTable[i,j] = max(self.similarityTable[i-1,j], self.similarityTable[i,j-1]) |
9990ef119bce
added version of LCSS with cdist computations
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diff
changeset
|
769 |
370
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diff
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|
770 |
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diff
changeset
|
771 def subSequence(self, i, j): |
97e8fa0ee9a1
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diff
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|
772 '''Returns the subsequence of two sequences |
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diff
changeset
|
773 http://en.wikipedia.org/wiki/Longest_common_subsequence_problem''' |
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diff
changeset
|
774 if i == 0 or j == 0: |
97e8fa0ee9a1
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diff
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|
775 return [] |
97e8fa0ee9a1
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diff
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|
776 elif self.similarityTable[i][j] == self.similarityTable[i][j-1]: |
97e8fa0ee9a1
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|
777 return self.subSequence(i, j-1) |
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|
778 elif self.similarityTable[i][j] == self.similarityTable[i-1][j]: |
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|
779 return self.subSequence(i-1, j) |
97e8fa0ee9a1
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diff
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|
780 else: |
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|
781 return self.subSequence(i-1, j-1) + [(i-1,j-1)] |
97e8fa0ee9a1
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parents:
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diff
changeset
|
782 |
373
d0b86ed50f32
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parents:
372
diff
changeset
|
783 def _compute(self, _l1, _l2, computeSubSequence = False): |
370
97e8fa0ee9a1
work in progress for complete alignment
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diff
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|
784 '''returns the longest common subsequence similarity |
689
9990ef119bce
added version of LCSS with cdist computations
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parents:
687
diff
changeset
|
785 l1 and l2 should be the right format |
9990ef119bce
added version of LCSS with cdist computations
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parents:
687
diff
changeset
|
786 eg list of tuple points for cdist |
9990ef119bce
added version of LCSS with cdist computations
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parents:
687
diff
changeset
|
787 or elements that can be compare using similarityFunc |
370
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diff
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|
788 |
607 | 789 if aligned, returns the best matching if using a finite delta by shifting the series alignments |
370
97e8fa0ee9a1
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369
diff
changeset
|
790 ''' |
372
349eb1e09f45
Cleaned the methods/functions indicating if a point is in a polygon
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parents:
371
diff
changeset
|
791 if len(_l2) < len(_l1): # l1 is the shortest |
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Cleaned the methods/functions indicating if a point is in a polygon
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diff
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|
792 l1 = _l2 |
349eb1e09f45
Cleaned the methods/functions indicating if a point is in a polygon
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371
diff
changeset
|
793 l2 = _l1 |
349eb1e09f45
Cleaned the methods/functions indicating if a point is in a polygon
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371
diff
changeset
|
794 revertIndices = True |
349eb1e09f45
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parents:
371
diff
changeset
|
795 else: |
349eb1e09f45
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371
diff
changeset
|
796 l1 = _l1 |
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diff
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|
797 l2 = _l2 |
349eb1e09f45
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diff
changeset
|
798 revertIndices = False |
349eb1e09f45
Cleaned the methods/functions indicating if a point is in a polygon
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diff
changeset
|
799 n1 = len(l1) |
349eb1e09f45
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371
diff
changeset
|
800 n2 = len(l2) |
349eb1e09f45
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parents:
371
diff
changeset
|
801 |
370
97e8fa0ee9a1
work in progress for complete alignment
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369
diff
changeset
|
802 if self.aligned: |
97e8fa0ee9a1
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diff
changeset
|
803 lcssValues = {} |
97e8fa0ee9a1
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diff
changeset
|
804 similarityTables = {} |
997
4f3387a242a1
updated utils to python 3
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parents:
990
diff
changeset
|
805 for i in range(-n2-self.delta+1, n1+self.delta): # interval such that [i-shift-delta, i-shift+delta] is never empty, which happens when i-shift+delta < 1 or when i-shift-delta > n2 |
373
d0b86ed50f32
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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372
diff
changeset
|
806 self.similarities(l1, l2, i) |
d0b86ed50f32
work in progress on LCSS
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diff
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|
807 lcssValues[i] = self.similarityTable.max() |
370
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work in progress for complete alignment
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diff
changeset
|
808 similarityTables[i] = self.similarityTable |
374
a7af3519687e
finished implementation of aligned LCSS with matching sequence decoded
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parents:
373
diff
changeset
|
809 #print self.similarityTable |
521
3707eeb20f25
changed argMaxDict name to argmaxDict
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
518
diff
changeset
|
810 alignmentShift = argmaxDict(lcssValues) # ideally get the medium alignment shift, the one that minimizes distance |
389
6d26dcc7bba0
modifications to compute alignment for None indicators
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parents:
381
diff
changeset
|
811 self.similarityTable = similarityTables[alignmentShift] |
372
349eb1e09f45
Cleaned the methods/functions indicating if a point is in a polygon
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371
diff
changeset
|
812 else: |
389
6d26dcc7bba0
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parents:
381
diff
changeset
|
813 alignmentShift = 0 |
372
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diff
changeset
|
814 self.similarities(l1, l2) |
373
d0b86ed50f32
work in progress on LCSS
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372
diff
changeset
|
815 |
374
a7af3519687e
finished implementation of aligned LCSS with matching sequence decoded
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373
diff
changeset
|
816 # threshold values for the useful part of the similarity table are n2-n1-delta and n1-n2-delta |
389
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817 self.similarityTable = self.similarityTable[:min(n1, n2+alignmentShift+self.delta)+1, :min(n2, n1-alignmentShift+self.delta)+1] |
373
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diff
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|
818 |
372
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371
diff
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|
819 if computeSubSequence: |
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diff
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|
820 self.subSequenceIndices = self.subSequence(self.similarityTable.shape[0]-1, self.similarityTable.shape[1]-1) |
371
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diff
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|
821 if revertIndices: |
374
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diff
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|
822 self.subSequenceIndices = [(j,i) for i,j in self.subSequenceIndices] |
372
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diff
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|
823 return self.similarityTable[-1,-1] |
370
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|
824 |
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|
825 def compute(self, l1, l2, computeSubSequence = False): |
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|
826 '''get methods are to be shadowed in child classes ''' |
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|
827 return self._compute(l1, l2, computeSubSequence) |
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diff
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|
828 |
375
2ea8584aa80a
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diff
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|
829 def computeAlignment(self): |
374
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|
830 return mean([j-i for i,j in self.subSequenceIndices]) |
370
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diff
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|
831 |
376
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diff
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|
832 def _computeNormalized(self, l1, l2, computeSubSequence = False): |
370
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diff
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|
833 ''' compute the normalized LCSS |
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diff
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|
834 ie, the LCSS divided by the min or mean of the indicator lengths (using lengthFunc) |
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diff
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|
835 lengthFunc = lambda x,y:float(x,y)/2''' |
376
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diff
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|
836 return float(self._compute(l1, l2, computeSubSequence))/self.lengthFunc(len(l1), len(l2)) |
370
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|
837 |
376
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diff
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|
838 def computeNormalized(self, l1, l2, computeSubSequence = False): |
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839 return self._computeNormalized(l1, l2, computeSubSequence) |
370
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|
840 |
376
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|
841 def _computeDistance(self, l1, l2, computeSubSequence = False): |
370
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|
842 ''' compute the LCSS distance''' |
376
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diff
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|
843 return 1-self._computeNormalized(l1, l2, computeSubSequence) |
370
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|
844 |
376
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diff
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|
845 def computeDistance(self, l1, l2, computeSubSequence = False): |
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|
846 return self._computeDistance(l1, l2, computeSubSequence) |
370
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|
847 |
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|
848 ######################### |
45
74d2de078baf
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diff
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|
849 # plotting section |
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diff
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|
850 ######################### |
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diff
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|
851 |
940
d8ab183a7351
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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876
diff
changeset
|
852 def plotPolygon(poly, options = '', **kwargs): |
332
a6ca86107f27
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parents:
324
diff
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|
853 'Plots shapely polygon poly' |
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
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|
854 from matplotlib.pyplot import plot |
940
d8ab183a7351
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
855 x,y = poly.exterior.xy |
d8ab183a7351
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876
diff
changeset
|
856 plot(x, y, options, **kwargs) |
332
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
324
diff
changeset
|
857 |
324 | 858 def stepPlot(X, firstX, lastX, initialCount = 0, increment = 1): |
859 '''for each value in X, increment by increment the initial count | |
297
f6f423e25c7f
adding function to generate step plots (for cumulative number of vehicles)
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286
diff
changeset
|
860 returns the lists that can be plotted |
324 | 861 to obtain a step plot increasing by one for each value in x, from first to last value |
862 firstX and lastX should be respectively smaller and larger than all elements in X''' | |
297
f6f423e25c7f
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diff
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|
863 |
f6f423e25c7f
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286
diff
changeset
|
864 sortedX = [] |
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diff
changeset
|
865 counts = [initialCount] |
f6f423e25c7f
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286
diff
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|
866 for x in sorted(X): |
f6f423e25c7f
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diff
changeset
|
867 sortedX += [x,x] |
f6f423e25c7f
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diff
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|
868 counts.append(counts[-1]) |
324 | 869 counts.append(counts[-1]+increment) |
297
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diff
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|
870 counts.append(counts[-1]) |
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diff
changeset
|
871 return [firstX]+sortedX+[lastX], counts |
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286
diff
changeset
|
872 |
665
15e244d2a1b5
corrected bug with circular import for VideoFilenameAddable, moved to base module
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
659
diff
changeset
|
873 class PlottingPropertyValues(object): |
45
74d2de078baf
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42
diff
changeset
|
874 def __init__(self, values): |
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42
diff
changeset
|
875 self.values = values |
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42
diff
changeset
|
876 |
116
2bf5b76320c0
moved intersection plotting and added markers for scatter plots
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
115
diff
changeset
|
877 def __getitem__(self, i): |
45
74d2de078baf
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42
diff
changeset
|
878 return self.values[i%len(self.values)] |
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42
diff
changeset
|
879 |
74d2de078baf
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parents:
42
diff
changeset
|
880 markers = PlottingPropertyValues(['+', '*', ',', '.', 'x', 'D', 's', 'o']) |
116
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moved intersection plotting and added markers for scatter plots
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parents:
115
diff
changeset
|
881 scatterMarkers = PlottingPropertyValues(['s','o','^','>','v','<','d','p','h','8','+','x']) |
45
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parents:
42
diff
changeset
|
882 |
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parents:
42
diff
changeset
|
883 linestyles = PlottingPropertyValues(['-', '--', '-.', ':']) |
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parents:
42
diff
changeset
|
884 |
74d2de078baf
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42
diff
changeset
|
885 colors = PlottingPropertyValues('brgmyck') # 'w' |
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diff
changeset
|
886 |
990 | 887 def monochromeCycler(withMarker = False): |
888 from cycler import cycler | |
889 if withMarker: | |
890 monochrome = (cycler('color', ['k']) * cycler('linestyle', ['-', '--', ':', '-.']) * cycler('marker', ['^',',', '.'])) | |
891 else: | |
892 monochrome = (cycler('color', ['k']) * cycler('linestyle', ['-', '--', ':', '-.'])) | |
893 plt.rc('axes', prop_cycle=monochrome) | |
894 | |
115
550556378466
added functionalities to indicator maps
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
86
diff
changeset
|
895 def plotIndicatorMap(indicatorMap, squareSize, masked = True, defaultValue=-1): |
65
75cf537b8d88
moved and generalized map making functions to the library
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
48
diff
changeset
|
896 from matplotlib.pyplot import pcolor |
997
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
990
diff
changeset
|
897 coords = array(list(indicatorMap.keys())) |
65
75cf537b8d88
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48
diff
changeset
|
898 minX = min(coords[:,0]) |
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48
diff
changeset
|
899 minY = min(coords[:,1]) |
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48
diff
changeset
|
900 X = arange(minX, max(coords[:,0])+1.1)*squareSize |
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diff
changeset
|
901 Y = arange(minY, max(coords[:,1])+1.1)*squareSize |
115
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86
diff
changeset
|
902 C = defaultValue*ones((len(Y), len(X))) |
997
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
990
diff
changeset
|
903 for k,v in indicatorMap.items(): |
65
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diff
changeset
|
904 C[k[1]-minY,k[0]-minX] = v |
115
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parents:
86
diff
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|
905 if masked: |
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diff
changeset
|
906 pcolor(X, Y, ma.masked_where(C==defaultValue,C)) |
550556378466
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86
diff
changeset
|
907 else: |
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86
diff
changeset
|
908 pcolor(X, Y, C) |
65
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48
diff
changeset
|
909 |
45
74d2de078baf
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diff
changeset
|
910 ######################### |
637
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parents:
615
diff
changeset
|
911 # Data download |
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diff
changeset
|
912 ######################### |
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diff
changeset
|
913 |
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diff
changeset
|
914 def downloadECWeather(stationID, years, months = [], outputDirectoryname = '.', english = True): |
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diff
changeset
|
915 '''Downloads monthly weather data from Environment Canada |
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diff
changeset
|
916 If month is provided (number 1 to 12), it means hourly data for the whole month |
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diff
changeset
|
917 Otherwise, means the data for each day, for the whole year |
c9a0b72979fd
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diff
changeset
|
918 |
c9a0b72979fd
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615
diff
changeset
|
919 Example: MONTREAL MCTAVISH 10761 |
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615
diff
changeset
|
920 MONTREALPIERRE ELLIOTT TRUDEAU INTL A 5415 |
856
e310577cc0b8
updated function (url) for weather data
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855
diff
changeset
|
921 see ftp://client_climate@ftp.tor.ec.gc.ca/Pub/Get_More_Data_Plus_de_donnees/Station%20Inventory%20EN.csv |
637
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615
diff
changeset
|
922 |
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diff
changeset
|
923 To get daily data for 2010 and 2011, downloadECWeather(10761, [2010,2011], [], '/tmp') |
973 | 924 To get hourly data for 2009 and 2012, January, March and October, downloadECWeather(10761, [2009,2012], [1,3,10], '/tmp') |
925 | |
926 for annee in `seq 2016 2017`;do wget --content-disposition "http://climat.meteo.gc.ca/climate_data/bulk_data_f.html?format=csv&stationID=10761&Year=${annee}&timeframe=2&submit=++T%C3%A9l%C3%A9charger+%0D%0Ades+donn%C3%A9es" ;done | |
927 for annee in `seq 2016 2017`;do for mois in `seq 1 12`;do wget --content-disposition "http://climat.meteo.gc.ca/climate_data/bulk_data_f.html?format=csv&stationID=10761&Year=${annee}&Month=${mois}&timeframe=1&submit=++T%C3%A9l%C3%A9charger+%0D%0Ades+donn%C3%A9es" ;done;done | |
928 ''' | |
997
4f3387a242a1
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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990
diff
changeset
|
929 import urllib.request |
637
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615
diff
changeset
|
930 if english: |
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615
diff
changeset
|
931 language = 'e' |
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diff
changeset
|
932 else: |
c9a0b72979fd
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diff
changeset
|
933 language = 'f' |
c9a0b72979fd
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diff
changeset
|
934 if len(months) == 0: |
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parents:
615
diff
changeset
|
935 timeFrame = 2 |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
936 months = [1] |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
937 else: |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
938 timeFrame = 1 |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
939 |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
940 for year in years: |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
941 for month in months: |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
942 outFilename = '{}/{}-{}'.format(outputDirectoryname, stationID, year) |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
943 if timeFrame == 1: |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
944 outFilename += '-{}-hourly'.format(month) |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
945 else: |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
946 outFilename += '-daily' |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
947 outFilename += '.csv' |
997
4f3387a242a1
updated utils to python 3
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
990
diff
changeset
|
948 url = urllib.request.urlretrieve('http://climate.weather.gc.ca/climate_data/bulk_data_{}.html?format=csv&stationID={}&Year={}&Month={}&Day=1&timeframe={}&submit=Download+Data'.format(language, stationID, year, month, timeFrame), outFilename) |
637
c9a0b72979fd
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
949 |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
950 ######################### |
c9a0b72979fd
added function to get canadian public weather data
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
615
diff
changeset
|
951 # File I/O |
27
44689029a86f
updated segmentIntersection and other
Nicolas Saunier <nico@confins.net>
parents:
24
diff
changeset
|
952 ######################### |
24
6fb59cfb201e
first version of segmentIntersection
Nicolas Saunier <nico@confins.net>
parents:
19
diff
changeset
|
953 |
0
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
954 def removeExtension(filename, delimiter = '.'): |
31
c000f37c316d
moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents:
29
diff
changeset
|
955 '''Returns the filename minus the extension (all characters after last .)''' |
0
aed8eb63cdde
initial commit with non-functional python code for NGSIM
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parents:
diff
changeset
|
956 i = filename.rfind(delimiter) |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
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parents:
diff
changeset
|
957 if i>0: |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
958 return filename[:i] |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
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parents:
diff
changeset
|
959 else: |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
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parents:
diff
changeset
|
960 return filename |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
961 |
969
5d788d2e8ffc
work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
940
diff
changeset
|
962 def getExtension(filename, delimiter = '.'): |
5d788d2e8ffc
work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
940
diff
changeset
|
963 '''Returns the filename minus the extension (all characters after last .)''' |
5d788d2e8ffc
work in progress
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parents:
940
diff
changeset
|
964 i = filename.rfind(delimiter) |
5d788d2e8ffc
work in progress
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parents:
940
diff
changeset
|
965 if i>0: |
5d788d2e8ffc
work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
940
diff
changeset
|
966 return filename[i+1:] |
5d788d2e8ffc
work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
940
diff
changeset
|
967 else: |
5d788d2e8ffc
work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
940
diff
changeset
|
968 return '' |
5d788d2e8ffc
work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
940
diff
changeset
|
969 |
46
b5d007612e16
added filename util
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parents:
45
diff
changeset
|
970 def cleanFilename(s): |
b5d007612e16
added filename util
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parents:
45
diff
changeset
|
971 'cleans filenames obtained when contatenating figure characteristics' |
739 | 972 return s.replace(' ','-').replace('.','').replace('/','-').replace(',','') |
46
b5d007612e16
added filename util
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parents:
45
diff
changeset
|
973 |
0
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
974 def listfiles(dirname, extension, remove = False): |
14
e7bbe8465591
homography and other utils
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parents:
7
diff
changeset
|
975 '''Returns the list of files with the extension in the directory dirname |
e7bbe8465591
homography and other utils
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parents:
7
diff
changeset
|
976 If remove is True, the filenames are stripped from the extension''' |
0
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
977 from os import listdir |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
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parents:
diff
changeset
|
978 tmp = [f for f in listdir(dirname) if f.endswith(extension)] |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
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parents:
diff
changeset
|
979 tmp.sort() |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
980 if remove: |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
981 return [removeExtension(f, extension) for f in tmp] |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
982 else: |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
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parents:
diff
changeset
|
983 return tmp |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
984 |
266
aba9711b3149
small modificatons and reorganization
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
262
diff
changeset
|
985 def mkdir(dirname): |
aba9711b3149
small modificatons and reorganization
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
262
diff
changeset
|
986 'Creates a directory if it does not exist' |
aba9711b3149
small modificatons and reorganization
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
262
diff
changeset
|
987 import os |
aba9711b3149
small modificatons and reorganization
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parents:
262
diff
changeset
|
988 if not os.path.exists(dirname): |
aba9711b3149
small modificatons and reorganization
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
262
diff
changeset
|
989 os.mkdir(dirname) |
aba9711b3149
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parents:
262
diff
changeset
|
990 else: |
aba9711b3149
small modificatons and reorganization
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parents:
262
diff
changeset
|
991 print(dirname+' already exists') |
aba9711b3149
small modificatons and reorganization
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parents:
262
diff
changeset
|
992 |
14
e7bbe8465591
homography and other utils
Nicolas Saunier <nico@confins.net>
parents:
7
diff
changeset
|
993 def removeFile(filename): |
e7bbe8465591
homography and other utils
Nicolas Saunier <nico@confins.net>
parents:
7
diff
changeset
|
994 '''Deletes the file while avoiding raising an error |
e7bbe8465591
homography and other utils
Nicolas Saunier <nico@confins.net>
parents:
7
diff
changeset
|
995 if the file does not exist''' |
266
aba9711b3149
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
262
diff
changeset
|
996 import os |
14
e7bbe8465591
homography and other utils
Nicolas Saunier <nico@confins.net>
parents:
7
diff
changeset
|
997 if (os.path.exists(filename)): |
e7bbe8465591
homography and other utils
Nicolas Saunier <nico@confins.net>
parents:
7
diff
changeset
|
998 os.remove(filename) |
266
aba9711b3149
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
262
diff
changeset
|
999 else: |
aba9711b3149
small modificatons and reorganization
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
262
diff
changeset
|
1000 print(filename+' does not exist') |
14
e7bbe8465591
homography and other utils
Nicolas Saunier <nico@confins.net>
parents:
7
diff
changeset
|
1001 |
42
1a2ac2d4f53a
added loading of the rest of the data for objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
35
diff
changeset
|
1002 def line2Floats(l, separator=' '): |
1a2ac2d4f53a
added loading of the rest of the data for objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
35
diff
changeset
|
1003 '''Returns the list of floats corresponding to the string''' |
1a2ac2d4f53a
added loading of the rest of the data for objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
35
diff
changeset
|
1004 return [float(x) for x in l.split(separator)] |
1a2ac2d4f53a
added loading of the rest of the data for objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
35
diff
changeset
|
1005 |
1a2ac2d4f53a
added loading of the rest of the data for objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
35
diff
changeset
|
1006 def line2Ints(l, separator=' '): |
1a2ac2d4f53a
added loading of the rest of the data for objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
35
diff
changeset
|
1007 '''Returns the list of ints corresponding to the string''' |
1a2ac2d4f53a
added loading of the rest of the data for objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
35
diff
changeset
|
1008 return [int(x) for x in l.split(separator)] |
31
c000f37c316d
moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents:
29
diff
changeset
|
1009 |
c000f37c316d
moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents:
29
diff
changeset
|
1010 ######################### |
553
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1011 # Profiling |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1012 ######################### |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1013 |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1014 def analyzeProfile(profileFilename, stripDirs = True): |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1015 '''Analyze the file produced by cProfile |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1016 |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1017 obtained by for example: |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1018 - call in script (for main() function in script) |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1019 import cProfile, os |
3622a5653ee9
added basic info and function to profile code
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parents:
547
diff
changeset
|
1020 cProfile.run('main()', os.path.join(os.getcwd(),'main.profile')) |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1021 |
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added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1022 - or on the command line: |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1023 python -m cProfile [-o profile.bin] [-s sort] scriptfile [arg]''' |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1024 import pstats, os |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1025 p = pstats.Stats(os.path.join(os.pardir, profileFilename)) |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1026 if stripDirs: |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1027 p.strip_dirs() |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1028 p.sort_stats('time') |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1029 p.print_stats(.2) |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1030 #p.sort_stats('time') |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1031 # p.print_callees(.1, 'int_prediction.py:') |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1032 return p |
3622a5653ee9
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1033 |
3622a5653ee9
added basic info and function to profile code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
547
diff
changeset
|
1034 ######################### |
31
c000f37c316d
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Nicolas Saunier <nico@confins.net>
parents:
29
diff
changeset
|
1035 # running tests |
c000f37c316d
moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents:
29
diff
changeset
|
1036 ######################### |
c000f37c316d
moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents:
29
diff
changeset
|
1037 |
0
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
1038 if __name__ == "__main__": |
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
1039 import doctest |
2
de5642925615
started implementation of TimeInterval and Spatio-temporal object
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
0
diff
changeset
|
1040 import unittest |
31
c000f37c316d
moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents:
29
diff
changeset
|
1041 suite = doctest.DocFileSuite('tests/utils.txt') |
c000f37c316d
moved tests to independent file, added chi2 computation
Nicolas Saunier <nico@confins.net>
parents:
29
diff
changeset
|
1042 #suite = doctest.DocTestSuite() |
2
de5642925615
started implementation of TimeInterval and Spatio-temporal object
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
0
diff
changeset
|
1043 unittest.TextTestRunner().run(suite) |
de5642925615
started implementation of TimeInterval and Spatio-temporal object
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
0
diff
changeset
|
1044 #doctest.testmod() |
0
aed8eb63cdde
initial commit with non-functional python code for NGSIM
Nicolas Saunier <nico@confins.net>
parents:
diff
changeset
|
1045 #doctest.testfile("example.txt") |