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annotate python/pavement.py @ 449:1e4c1b6b0e0b
added comments
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 10 Feb 2014 18:32:43 -0500 |
parents | 0b15fdeb903e |
children | c0786fe4ad94 |
rev | line source |
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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 |
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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 |
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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 |
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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 |
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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, minProportionMeasures = 0.): |
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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 |
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127 minProportionMeasures is proportion of measures necessary to consider the indicators''' |
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128 from matplotlib.mlab import find |
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129 nbre_jours_T_negatif,nbre_jours_gel_degel,pluie_tot,neige_tot,ecart_type_T = 0,0,0,0,0 |
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130 compteur,nbre_jours_gel_consecutifs=0,0 |
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131 tmoys = [] |
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132 seuils_T = [20,15,10,5] |
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133 deltas_T = [0,0,0,0] |
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134 startIndex = find(data['date'] == startDate) |
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135 nDays = (endDate - startDate).days+1 |
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136 for i in range(startIndex, startIndex+int(nDays)): |
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137 if data['tmax'][i] != '' and data['tmax'][i] != None: |
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138 tmax = float(data['tmax'][i].replace(',','.')) |
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139 else: |
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140 tmax = None |
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141 if data['tmin'][i] != '' and data['tmin'][i] != None: |
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142 tmin = float(data['tmin'][i].replace(',','.')) |
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143 if data['pluie_tot'][i] != '' and data['pluie_tot'][i] != None: |
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144 pluie_tot += float(data['pluie_tot'][i].replace(',','.')) |
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145 if data['neige_tot'][i] != '' and data['neige_tot'][i] != None: |
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146 neige_tot += float(data['neige_tot'][i].replace(',','.')) |
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147 if tmax != None: |
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148 if tmax < 0: |
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149 nbre_jours_T_negatif += 1 |
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150 if tmax != None and data['tmin'][i] != '' and data['tmin'][i] != None: |
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151 if tmax > 0 and tmin < 0: |
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152 nbre_jours_gel_degel += 1 |
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153 for l in range(len(seuils_T)): |
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154 if tmax - tmin >=seuils_T[l]: |
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155 deltas_T[l] += 1 |
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156 if data['tmoy'][i] != '' and data['tmoy'][i] != None: |
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157 tmoys.append(float(data['tmoy'][i].replace(',','.'))) |
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158 if tmax != None: |
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159 if tmax < 0: |
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160 compteur += 1 |
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161 elif tmax >= 0 and compteur >= nbre_jours_gel_consecutifs: |
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162 nbre_jours_gel_consecutifs = compteur |
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163 compteur = 0 |
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164 else: |
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165 compteur = 0 |
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166 nbre_jours_gel_consecutifs = max(nbre_jours_gel_consecutifs,compteur) |
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167 if tmoys != [] and float(len(tmoys))/nDays >= minProportionMeasures: |
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168 ecart_type_T = np.std(tmoys) |
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169 if neige_tot < snowThreshold: |
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170 neigeEC_sup_seuil = 0 |
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171 else: |
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172 neigeEC_sup_seuil = 1 |
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173 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) |
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174 else: |
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175 return [None]*2+[[None]*len(seuils_T)]+[None]*5 |
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176 |
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177 class RTSS: |
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178 'class for data related to a RTSS: |
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179 - agregating pavement marking measurements |
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180 - RTSS characteristics from FMR: pavement type, age, AADT, truck AADT |
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181 - winter maintenance level from V155' |
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182 |
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183 def __init__(self, id): |
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184 self.id = id |
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185 |
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186 class MarkingTest: |
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187 '''class for a test site for a given product |
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188 |
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189 including the series of measurements over the years''' |
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190 |
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191 def __init__(self, id, paintingDate, paintingType, color, data): |
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192 self.id = id |
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193 self.paintingDate = paintingDate |
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194 self.paintingType = paintingType |
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195 self.color = color |
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196 self.data = data |
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197 self.nMeasures = len(data) |
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198 |
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199 def getSite(self): |
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200 return int(self.id[:2]) |
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201 |
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202 def getTestAttributes(self): |
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203 return [self.paintingType, self.color, self.paintingDate.year] |
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204 |
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205 def plot(self, measure, options = 'o', dayRatio = 1., **kwargs): |
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206 from matplotlib.pyplot import plot |
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207 plot(self.data['jours']/float(dayRatio), |
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208 self.data[measure], options, **kwargs) |
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209 |
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210 def getMarkingMeasures(self, dataLabel): |
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211 nonZeroIndices = ~np.isnan(self.data[dataLabel]) |
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212 return self.data[nonZeroIndices]['jours'], self.data[nonZeroIndices][dataLabel] |
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213 |
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214 def plotMarkingMeasures(self, measure, options = 'o', dayRatio = 1., **kwargs): |
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215 for i in range(1,7): |
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216 self.plot('{}_{}'.format(measure, i), options, dayRatio, **kwargs) |
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217 |
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218 def computeMarkingMeasureVariations(self, dataLabel, lanePositions, weatherData, snowThreshold, minProportionMeasures = 0.): |
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219 '''Computes for each successive measurement |
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220 lanePositions = None |
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221 measure variation, initial measure, time duration, weather indicators |
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222 |
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223 TODO if measurements per lane, add a variable for lane position (position1 to 6) |
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224 lanePositions = list of integers (range(1,7)) |
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225 measure variation, initial measure, time duration, lane position1, weather indicators |
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226 measure variation, initial measure, time duration, lane position2, weather indicators |
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227 ...''' |
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228 variationData = [] |
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229 if lanePositions == None: |
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230 nonZeroIndices = ~np.isnan(self.data[dataLabel]) |
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231 days = self.data[nonZeroIndices]['jours'] |
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232 dates = self.data[nonZeroIndices]['date_mesure'] |
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233 measures = self.data[nonZeroIndices][dataLabel] |
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234 for i in range(1, len(dates)): |
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235 nDaysTNegative, nDaysThawFreeze, deltaTemp, nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp = ecWeatherIndicators(weatherData, dates[i-1], dates[i], snowThreshold, minProportionMeasures) |
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236 variationData.append([measures[i-1]-measures[i], measures[i-1], days[i]-days[i-1], days[i-1], nDaysTNegative, nDaysThawFreeze] + deltaTemp + [nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp]) |
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237 return variationData |