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annotate python/pavement.py @ 446:a65a14c90834

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adding weather info to pavement data
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 04 Feb 2014 02:13:27 -0500
parents b5cc6b001ae6
children 7ef40014236c
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
431
0bcfa49d179a began module for pavement marking data processing
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1 #! /usr/bin/env python
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2 '''Tools for processing and analyzing pavement marking data'''
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3
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4 import numpy as np
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5
431
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6 __metaclass__ = type
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7
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8 def occ_max(a):
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9 if a != []:
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10 s = set(a)
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11 l = list(s)
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12 occ = []
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13 for i in range(len(l)):
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14 b = 0
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15 for j in range(len(a)):
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16 if a[j] == l[i]:
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17 b += 1
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18 occ.append([l[i], b])
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19 nbre_occs = []
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20 for i in range(len(occ)):
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21 nbre_occs.append(occ[i][1])
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22 return occ[nbre_occs.index(max(nbre_occs))][0]
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23 else:
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24 return ""
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25
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26 def caracteristiques(rtss, maintenanceLevel, rtssWeatherStation, fmr, paintType):
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27 '''Computes characteristic data for the RTSS (class rtss)
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28 maintenanceLevel = pylab.csv2rec('C:\Users\Alexandre\Desktop\Projet_maitrise_recherche\BDD_access\\analyse_donnees_deneigement\\exigence_circuits.txt', delimiter = ';')
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29 rtssWeatherStation = pylab.csv2rec('C:\Users\Alexandre\Desktop\Projet_maitrise_recherche\stations_environnement_canada\\rtssWeatherStation\juste_pour_rtss_avec_donnees_entretien_hiv\\rtssWeatherStation_EC3.txt', delimiter = ',')
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30 fmr = pylab.csv2rec('C:\Users\Alexandre\Desktop\Projet_maitrise_recherche\BDD_access\\analyse_donnees_deneigement\\fmr.txt', delimiter = ';')
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31 paintType = pylab.csv2rec('C:\Users\Alexandre\Desktop\Projet_maitrise_recherche\BDD_access\\analyse_donnees_deneigement\\type_peinture.txt', delimiter = ';')
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32 '''
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33 # determination exigence deneigement
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34 if rtss.id in maintenanceLevel['rtss_debut']:
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35 for i in range(len(maintenanceLevel)):
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36 if maintenanceLevel['rtss_debut'][i] == rtss.id:
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37 exigence = maintenanceLevel['exigence'][i]
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38 else:
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39 exigence = ''
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40
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41 # determination x/y
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42 if rtss.id in rtssWeatherStation['rtss']:
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43 for i in range(len(rtssWeatherStation)):
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44 if rtssWeatherStation['rtss'][i] == rtss.id:
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45 x_moy = rtssWeatherStation['x_moy'][i]
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46 y_moy = rtssWeatherStation['y_moy'][i]
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47 else:
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48 x_moy, y_moy = '',''
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49
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50 # determination info fmr
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51 age_revtm, classe_fonct, type_revtm, milieu, djma, pourc_camions = [], [], [], [], [], []
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52 if rtss.id in fmr['rtss_debut']:
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53 for i in range(len(fmr)):
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54 if fmr['rtss_debut'][i] == rtss.id:
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55 age_revtm.append(fmr['age_revtm'][i])
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56 classe_fonct.append(fmr['des_clasf_fonct'][i])
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57 type_revtm.append(fmr['des_type_revtm'][i])
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58 milieu.append(fmr['des_cod_mil'][i])
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59 djma.append(fmr['val_djma'][i])
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60 pourc_camions.append(fmr['val_pourc_camns'][i])
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61 age_revtm = occ_max(age_revtm)
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62 classe_fonct = occ_max(classe_fonct)
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63 type_revtm = occ_max(type_revtm)
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64 milieu = occ_max(milieu)
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65 djma = occ_max(djma)
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66 pourc_camions = occ_max(pourc_camions)
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67 if pourc_camions == "" or pourc_camions < 0:
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68 djma_camions = ""
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69 else:
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70 djma_camions = pourc_camions*djma/100
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71 else:
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72 age_revtm, classe_fonct, type_revtm, milieu, djma, djma_camions = '','','','','',''
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73
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74 # determination type peinture
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75 peinture_rd, peinture_rg, peinture_cl = [], [], []
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76 peinture_lrd, peinture_lrg, peinture_lc = 0,0,0
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77 if rtss.id in paintType['rtss_debut_orig']:
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78 for i in range(len(paintType)):
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79 if paintType['rtss_debut_orig'][i] == rtss.id:
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80 peinture_rd.append((paintType['peinture_rd'][i]))
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81 peinture_rg.append((paintType['peinture_rg'][i]))
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82 peinture_cl.append((paintType['peinture_cl'][i]))
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83 peinture_lrd = occ_max(peinture_rd)
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84 peinture_lrg = occ_max(peinture_rg)
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85 peinture_lc = occ_max(peinture_cl)
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86 else:
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87 peinture_lrd, peinture_lrg, peinture_lc = '','',''
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88
440
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89 return (exigence, x_moy, y_moy, age_revtm, classe_fonct, type_revtm, milieu, djma, djma_camions, peinture_lrd, peinture_lrg, peinture_lc)
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90
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91 def winterMaintenanceIndicators(data, startDate, endDate, circuitReference, snowThreshold):
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92 '''Computes several winter maintenance indicators
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93 data = entretien_hivernal = pylab.csv2rec('C:\Users\Alexandre\Documents\Cours\Poly\Projet\mesures_entretien_hivernal\mesures_deneigement.txt', delimiter = ',')'''
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94 import datetime
437
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95 somme_eau, somme_neige, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, compteur_somme_abrasif = 0,0,0,0,0,0,0,0,0
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96
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97 if circuitReference in data['ref_circuit']:
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98 for i in range(len(data)):
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99 if data['ref_circuit'][i] == circuitReference and (data['date'][i] + datetime.timedelta(days = 6)) <= endDate and (data['date'][i] + datetime.timedelta(days = 6)) > startDate:
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100 premiere_neige += data['premiere_neige'][i]
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101 somme_neige += float(data['neige'][i])
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102 somme_eau += float(data['eau'][i])
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103 somme_abrasif += float(data['abrasif'][i])
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104 somme_sel += float(data['sel'][i])
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105 somme_lc += float(data['lc'][i])
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106 somme_lrg += float(data['lrg'][i])
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107 somme_lrd += float(data['lrd'][i])
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108 compteur_somme_abrasif += float(data['autre_abrasif_binaire'][i])
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109 if compteur_somme_abrasif >= 1:
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110 autres_abrasifs = 1
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111 else:
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112 autres_abrasifs = 0
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113 if somme_neige < snowThreshold:
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114 neigeMTQ_sup_seuil = 0
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115 else:
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116 neigeMTQ_sup_seuil = 1
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117 else:
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118 somme_eau, somme_neige, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs, neigeMTQ_sup_seuil = '','','','','','','','','',''
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119
440
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120 return (somme_eau, somme_neige, neigeMTQ_sup_seuil, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs)
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121
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122 def ecWeatherIndicators(data, startDate, endDate, snowThreshold):
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123 '''Computes the indicators from Environment Canada files
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124 (loaded as a recarray using csv2rec in data),
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125 between start and end dates (datetime.datetime objects)'''
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126 nbre_jours_T_negatif,nbre_jours_gel_degel,pluie_tot,neige_tot,ecart_type_T = 0,0,0,0,0
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127 compteur,nbre_jours_gel_consecutifs=0,0
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128 tmoys = []
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129 seuils_T = [20,15,10,5]
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130 deltas_T = [0,0,0,0]
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131 for i in range(int((endDate - startDate).days)+1):
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132 if data['tmax'][i] != '' and data['tmax'][i] != None:
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133 tmax = float(data['tmax'][i].replace(',','.'))
440
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134 else:
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135 tmax = None
437
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136 if data['tmin'][i] != '' and data['tmin'][i] != None:
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137 tmin = float(data['tmin'][i].replace(',','.'))
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138 if data['pluie_tot'][i] != '' and data['pluie_tot'][i] != None:
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139 pluie_tot += float(data['pluie_tot'][i].replace(',','.'))
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140 if data['neige_tot'][i] != '' and data['neige_tot'][i] != None:
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141 neige_tot += float(data['neige_tot'][i].replace(',','.'))
440
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142 if tmax != None:
437
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143 if tmax < 0:
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144 nbre_jours_T_negatif += 1
440
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145 if tmax != None and data['tmin'][i] != '' and data['tmin'][i] != None:
437
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146 if tmax > 0 and tmin < 0:
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147 nbre_jours_gel_degel += 1
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148 for l in range(len(seuils_T)):
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149 if tmax - tmin >=seuils_T[l]:
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150 deltas_T[l] += 1
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151 if data['tmoy'][i] != '' and data['tmoy'][i] != None:
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152 tmoys.append(float(data['tmoy'][i].replace(',','.')))
440
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153 if tmax != None:
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154 if tmax < 0:
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155 compteur += 1
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156 elif tmax >= 0 and compteur >= nbre_jours_gel_consecutifs:
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157 nbre_jours_gel_consecutifs = compteur
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158 compteur = 0
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159 else:
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160 compteur = 0
437
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161 nbre_jours_gel_consecutifs = max(nbre_jours_gel_consecutifs,compteur)
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162 ecart_type_T = np.std(tmoys)
440
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163 if neige_tot < snowThreshold:
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164 neigeEC_sup_seuil = 0
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165 else:
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166 neigeEC_sup_seuil = 1
437
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167
440
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168 return (nbre_jours_T_negatif,nbre_jours_gel_degel, deltas_T, nbre_jours_gel_consecutifs, pluie_tot, neige_tot, neigeEC_sup_seuil, ecart_type_T)
446
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169
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170
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171 class RTSS:
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172 'class for data related to a RTSS, including agregating pavement marking measurements'
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173
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174 def __init__(self, id):
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175 self.id = id
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176
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177 class MarkingTest:
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178 '''class for a test site for a given product
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179
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180 including the series of measurements over the years'''
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181
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182 def __init__(self, id, paintingDate, paintingType, color, data):
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183 self.id = id
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184 self.paintingDate = paintingDate
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185 self.paintingType = paintingType
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186 self.color = color
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187 self.data = data
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188 self.nMeasures = len(data)
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189
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190 def getSite(self):
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191 return int(self.id[:2])
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192
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193 def getTestAttributes(self):
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194 return [self.paintingType, self.color]
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195
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196 def plot(self, measure, options = 'o', dayRatio = 1., **kwargs):
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197 from matplotlib.pyplot import plot
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198 plot(self.data['jours']/float(dayRatio),
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199 self.data[measure], options, **kwargs)
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200
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201 def getMarkingMeasures(self, dataLabel):
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202 from numpy import isnan
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203 nonZeroIndices = ~isnan(self.data[dataLabel])
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diff changeset
204 return self.data[nonZeroIndices]['jours'], self.data[nonZeroIndices][dataLabel]
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205
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206 def plotMarkingMeasures(self, measure, options = 'o', dayRatio = 1., **kwargs):
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diff changeset
207 for i in range(1,7):
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diff changeset
208 self.plot('{}_{}'.format(measure, i), options, dayRatio, **kwargs)
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diff changeset
209
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diff changeset
210 def computeMarkingMeasureVariations(self, dataLabel, lanePositions, weatherData, snowThreshold):
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diff changeset
211 '''Computes for each successive measurement
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212 lanePositions = None
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diff changeset
213 measure variation, initial measure, time duration, weather indicators
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diff changeset
214
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215 TODO if measurements per lane, add a variable for lane position (position1 to 6)
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diff changeset
216 lanePositions = list of integers (range(1,7))
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diff changeset
217 measure variation, initial measure, time duration, lane position1, weather indicators
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diff changeset
218 measure variation, initial measure, time duration, lane position2, weather indicators
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219 ...'''
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220 from numpy import isnan
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diff changeset
221 variationData = []
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diff changeset
222 if lanePositions == None:
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diff changeset
223 nonZeroIndices = ~isnan(self.data[dataLabel])
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diff changeset
224 days = self.data[nonZeroIndices]['jours']
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diff changeset
225 dates = self.data[nonZeroIndices]['date_mesure']
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diff changeset
226 measures = self.data[nonZeroIndices][dataLabel]
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diff changeset
227 for i in range(1, len(dates)):
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diff changeset
228 nDaysTNegative, nDaysThawFreeze, deltaTemp, nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp = ecWeatherIndicators(weatherData, dates[i-1], dates[i], snowThreshold)
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diff changeset
229 variationData.append([measures[i-1]-measures[i], measures[i-1], days[i]-days[i-1], nDaysTNegative, nDaysThawFreeze] + deltaTemp + [nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp])
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diff changeset
230 return variationData