1 | #!/usr/bin/env python |
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2 | # -*- coding: utf-8 -*- |
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3 | |
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4 | # this must come first |
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5 | from __future__ import print_function, unicode_literals, division |
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6 | |
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7 | # standard library imports |
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8 | from argparse import ArgumentParser |
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9 | import os |
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10 | import os.path |
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11 | import datetime as dt |
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12 | from dateutil.relativedelta import relativedelta |
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13 | import numpy as np |
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14 | |
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15 | # Application library imports |
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16 | from libconso import * |
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17 | |
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18 | |
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19 | ######################################## |
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20 | class DataDict(dict): |
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21 | #--------------------------------------- |
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22 | def __init__(self): |
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23 | self = {} |
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24 | |
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25 | #--------------------------------------- |
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26 | def init_range(self, date_beg, date_end, inc=1): |
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27 | """ |
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28 | """ |
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29 | delta = date_end - date_beg |
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30 | |
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31 | (deb, fin) = (0, delta.days+1) |
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32 | |
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33 | dates = (date_beg + dt.timedelta(days=i) |
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34 | for i in xrange(deb, fin, inc)) |
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35 | |
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36 | for date in dates: |
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37 | self.add_item(date) |
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38 | |
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39 | #--------------------------------------- |
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40 | def fill_data(self, filein): |
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41 | """ |
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42 | """ |
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43 | try: |
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44 | data = np.genfromtxt( |
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45 | filein, |
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46 | skip_header=1, |
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47 | converters={ |
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48 | 0: string_to_date, |
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49 | 1: string_to_float, |
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50 | 2: string_to_percent, |
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51 | 3: string_to_percent, |
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52 | 4: string_to_float, |
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53 | 5: string_to_float, |
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54 | 6: string_to_float, |
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55 | 7: string_to_float, |
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56 | 8: string_to_float, |
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57 | 9: string_to_float, |
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58 | 10: string_to_float, |
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59 | 11: string_to_float, |
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60 | }, |
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61 | missing_values="nan", |
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62 | ) |
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63 | except Exception as rc: |
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64 | print("Empty file {}:\n{}".format(filein, rc)) |
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65 | exit(1) |
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66 | |
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67 | for date, conso, real_use, theo_use, \ |
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68 | runp_mean, penp_mean, runp_std, penp_std, \ |
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69 | runf_mean, penf_mean, runf_std, penf_std in data: |
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70 | if date in self: |
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71 | self.add_item( |
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72 | date, |
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73 | conso, |
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74 | real_use, |
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75 | theo_use, |
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76 | runp_mean, |
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77 | penp_mean, |
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78 | runp_std, |
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79 | penp_std, |
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80 | runf_mean, |
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81 | penf_mean, |
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82 | runf_std, |
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83 | penf_std, |
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84 | ) |
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85 | self[date].fill() |
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86 | |
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87 | #--------------------------------------- |
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88 | def add_item(self, date, conso=np.nan, |
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89 | real_use=np.nan, theo_use=np.nan, |
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90 | runp_mean=np.nan, penp_mean=np.nan, |
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91 | runp_std=np.nan, penp_std=np.nan, |
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92 | runf_mean=np.nan, penf_mean=np.nan, |
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93 | runf_std=np.nan, penf_std=np.nan): |
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94 | """ |
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95 | """ |
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96 | self[date] = Conso( |
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97 | date, conso, real_use, theo_use, |
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98 | runp_mean, penp_mean, runp_std, penp_std, |
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99 | runf_mean, penf_mean, runf_std, penf_std, |
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100 | ) |
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101 | |
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102 | #--------------------------------------- |
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103 | def theo_equation(self): |
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104 | """ |
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105 | """ |
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106 | (dates, theo_uses) = \ |
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107 | zip(*((item.date, item.theo_use) |
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108 | for item in self.get_items_in_full_range())) |
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109 | |
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110 | (idx_min, idx_max) = \ |
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111 | (np.nanargmin(theo_uses), np.nanargmax(theo_uses)) |
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112 | |
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113 | x1 = dates[idx_min].timetuple().tm_yday |
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114 | x2 = dates[idx_max].timetuple().tm_yday |
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115 | |
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116 | y1 = theo_uses[idx_min] |
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117 | y2 = theo_uses[idx_max] |
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118 | |
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119 | m = np.array([[x1, 1.], [x2, 1.]], dtype="float") |
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120 | n = np.array([y1, y2], dtype="float") |
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121 | |
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122 | poly_ok = True |
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123 | try: |
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124 | poly_theo = np.poly1d(np.linalg.solve(m, n)) |
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125 | except np.linalg.linalg.LinAlgError: |
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126 | poly_ok = False |
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127 | |
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128 | if poly_ok: |
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129 | delta = (dates[0] + relativedelta(months=2) - dates[0]).days |
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130 | |
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131 | poly_delay = np.poly1d( |
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132 | [poly_theo[1], poly_theo[0] - poly_theo[1] * delta] |
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133 | ) |
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134 | |
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135 | self.poly_theo = poly_theo |
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136 | self.poly_delay = poly_delay |
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137 | |
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138 | #--------------------------------------- |
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139 | def get_items_in_range(self, date_beg, date_end, inc=1): |
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140 | """ |
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141 | """ |
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142 | items = (item for item in self.itervalues() |
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143 | if item.date >= date_beg and |
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144 | item.date <= date_end) |
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145 | items = sorted(items, key=lambda item: item.date) |
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146 | |
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147 | return items[::inc] |
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148 | |
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149 | #--------------------------------------- |
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150 | def get_items_in_full_range(self, inc=1): |
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151 | """ |
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152 | """ |
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153 | items = (item for item in self.itervalues()) |
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154 | items = sorted(items, key=lambda item: item.date) |
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155 | |
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156 | return items[::inc] |
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157 | |
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158 | #--------------------------------------- |
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159 | def get_items(self, inc=1): |
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160 | """ |
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161 | """ |
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162 | items = (item for item in self.itervalues() |
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163 | if item.isfilled()) |
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164 | items = sorted(items, key=lambda item: item.date) |
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165 | |
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166 | return items[::inc] |
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167 | |
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168 | |
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169 | class Conso(object): |
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170 | #--------------------------------------- |
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171 | def __init__( |
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172 | self, date, conso=np.nan, |
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173 | real_use=np.nan, theo_use=np.nan, |
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174 | runp_mean=np.nan, penp_mean=np.nan, |
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175 | runp_std=np.nan, penp_std=np.nan, |
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176 | runf_mean=np.nan, penf_mean=np.nan, |
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177 | runf_std=np.nan, penf_std=np.nan, |
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178 | ): |
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179 | self.date = date |
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180 | self.conso = conso |
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181 | self.real_use = real_use |
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182 | self.theo_use = theo_use |
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183 | self.poly_theo = np.poly1d([]) |
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184 | self.poly_delay = np.poly1d([]) |
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185 | self.runp_mean = runp_mean |
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186 | self.penp_mean = penp_mean |
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187 | self.runp_std = runp_std |
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188 | self.penp_std = penp_std |
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189 | self.runf_mean = runf_mean |
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190 | self.penf_mean = penf_mean |
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191 | self.runf_std = runf_std |
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192 | self.penf_std = penf_std |
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193 | self.filled = False |
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194 | |
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195 | #--------------------------------------- |
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196 | def __repr__(self): |
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197 | return "{:.2f} ({:.2%})".format(self.conso, self.real_use) |
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198 | |
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199 | #--------------------------------------- |
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200 | def isfilled(self): |
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201 | return self.filled |
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202 | |
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203 | #--------------------------------------- |
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204 | def fill(self): |
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205 | self.filled = True |
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206 | |
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207 | |
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208 | ######################################## |
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209 | def plot_init(): |
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210 | paper_size = np.array([29.7, 21.0]) |
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211 | fig, (ax_jobsp, ax_jobsf) = plt.subplots( |
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212 | nrows=2, |
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213 | ncols=1, |
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214 | sharex=True, |
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215 | squeeze=True, |
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216 | figsize=(paper_size/2.54) |
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217 | ) |
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218 | |
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219 | return fig, ax_jobsp, ax_jobsf |
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220 | |
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221 | |
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222 | ######################################## |
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223 | def plot_data(ax_jobsp, ax_jobsf, xcoord, dates, |
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224 | runp_mean, penp_mean, runp_std, penp_std, |
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225 | runf_mean, penf_mean, runf_std, penf_std): |
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226 | """ |
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227 | """ |
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228 | |
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229 | line_width = 0. |
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230 | width = 1.05 |
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231 | |
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232 | ax_jobsp.bar( |
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233 | xcoord, runp_mean, width=width, align="center", |
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234 | # yerr=runp_std/2, ecolor="green", |
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235 | color="lightgreen", linewidth=line_width, |
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236 | antialiased=True, label="jobs running" |
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237 | ) |
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238 | ax_jobsp.bar( |
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239 | xcoord, penp_mean, bottom=runp_mean, width=width, align="center", |
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240 | # yerr=penp_std/2, ecolor="darkred", |
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241 | color="firebrick", linewidth=line_width, |
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242 | antialiased=True, label="jobs pending" |
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243 | ) |
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244 | |
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245 | ax_jobsf.bar( |
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246 | xcoord, runf_mean, width=width, align="center", |
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247 | # yerr=runf_std/2, ecolor="green", |
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248 | color="lightgreen", linewidth=line_width, |
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249 | antialiased=True, label="jobs running" |
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250 | ) |
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251 | ax_jobsf.bar( |
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252 | xcoord, penf_mean, bottom=runf_mean, width=width, align="center", |
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253 | # yerr=penf_std/2, ecolor="darkred", |
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254 | color="firebrick", linewidth=line_width, |
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255 | antialiased=True, label="jobs pending\n(Ressources & Priority)" |
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256 | ) |
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257 | |
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258 | |
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259 | ######################################## |
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260 | def plot_config(fig, ax_conso, ax_theo, xcoord, dates, title, |
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261 | conso_per_day, conso_per_day_2): |
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262 | """ |
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263 | """ |
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264 | from matplotlib.ticker import AutoMinorLocator |
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265 | |
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266 | # ... Config axes ... |
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267 | # ------------------- |
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268 | # 1) Range |
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269 | jobsp_max = np.nanmax(runp_mean) |
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270 | jobsf_max = np.nanmax(runf_mean) |
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271 | conso_jobsf = 80000. |
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272 | if args.max: |
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273 | ymax_jobsp = jobsp_max # * 1.1 |
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274 | ymax_jobsf = jobsf_max # * 1.1 |
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275 | else: |
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276 | ymax_jobsp = 2. * (max(conso_per_day, conso_per_day_2)/24.) |
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277 | ymax_jobsf = 3. * conso_jobsf |
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278 | |
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279 | xmin, xmax = xcoord[0]-1, xcoord[-1]+1 |
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280 | ax_jobsp.set_xlim(xmin, xmax) |
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281 | ax_jobsp.set_ylim(0., ymax_jobsp) |
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282 | ax_jobsf.set_ylim(0., ymax_jobsf) |
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283 | |
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284 | # 2) Plot ideal daily consumption in hours |
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285 | line_color = "blue" |
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286 | line_alpha = 0.5 |
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287 | line_label = "conso journaliÚre\nidéale ({})" |
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288 | for ax, y_div, label in ( |
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289 | (ax_jobsp, 24., line_label.format("cÅurs")), |
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290 | ): |
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291 | if conso_per_day_2: |
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292 | list_x = [0, xmax/2, xmax/2, xmax] |
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293 | list_y = np.array( |
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294 | [conso_per_day, conso_per_day, conso_per_day_2, conso_per_day_2], |
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295 | dtype=float |
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296 | ) |
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297 | ax.plot( |
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298 | list_x, list_y/y_div, |
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299 | color=line_color, alpha=line_alpha, label=label, |
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300 | ) |
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301 | else: |
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302 | ax.axhline( |
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303 | y=conso_per_day/y_div, |
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304 | color=line_color, alpha=line_alpha, label=label, |
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305 | ) |
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306 | ax_jobsf.axhline( |
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307 | y=conso_jobsf, |
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308 | color=line_color, alpha=line_alpha |
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309 | ) |
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310 | |
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311 | # 3) Ticks labels |
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312 | (date_beg, date_end) = (dates[0], dates[-1]) |
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313 | date_fmt = "{:%d-%m}" |
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314 | |
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315 | if date_end - date_beg > dt.timedelta(weeks=9): |
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316 | maj_xticks = [x for x, d in zip(xcoord, dates) |
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317 | if d.weekday() == 0] |
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318 | maj_xlabs = [date_fmt.format(d) for d in dates |
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319 | if d.weekday() == 0] |
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320 | else: |
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321 | maj_xticks = [x for x, d in zip(xcoord, dates)] |
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322 | maj_xlabs = [date_fmt.format(d) for d in dates] |
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323 | |
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324 | ax_jobsf.set_xticks(xcoord, minor=True) |
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325 | ax_jobsf.set_xticks(maj_xticks, minor=False) |
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326 | ax_jobsf.set_xticklabels( |
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327 | maj_xlabs, rotation="vertical", size="x-small" |
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328 | ) |
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329 | |
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330 | for ax, y, label in ( |
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331 | (ax_jobsp, conso_per_day / 24., "cÅurs (projet)"), |
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332 | (ax_jobsf, conso_jobsf, "cÅurs (machine)"), |
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333 | ): |
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334 | minor_locator = AutoMinorLocator() |
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335 | ax.yaxis.set_minor_locator(minor_locator) |
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336 | |
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337 | yticks = list(ax.get_yticks()) |
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338 | yticks.append(y) |
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339 | ax.set_yticks(yticks) |
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340 | |
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341 | if conso_per_day_2: |
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342 | yticks.append(conso_per_day_2) |
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343 | |
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344 | for x, d in zip(xcoord, dates): |
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345 | if d.weekday() == 0 and d.hour == 0: |
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346 | for ax in (ax_jobsp, ax_jobsf): |
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347 | ax.axvline(x=x, color="black", alpha=0.5, |
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348 | linewidth=0.5, linestyle=":") |
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349 | |
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350 | # 4) Define axes title |
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351 | for ax, label in ( |
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352 | (ax_jobsp, "cÅurs (projet)"), |
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353 | (ax_jobsf, "cÅurs (machine)"), |
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354 | ): |
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355 | ax.set_ylabel(label, fontweight="bold") |
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356 | ax.tick_params(axis="y", labelsize="small") |
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357 | |
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358 | # 5) Define plot size |
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359 | fig.subplots_adjust( |
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360 | left=0.08, |
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361 | bottom=0.09, |
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362 | right=0.93, |
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363 | top=0.93, |
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364 | hspace=0.1, |
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365 | wspace=0.1, |
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366 | ) |
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367 | |
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368 | # ... Main title and legend ... |
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369 | # ----------------------------- |
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370 | fig.suptitle(title, fontweight="bold", size="large") |
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371 | for ax, subtitle, loc_legend in ( |
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372 | (ax_jobsp, "Projet", "upper right"), |
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373 | (ax_jobsf, "Tout Curie", "upper right"), |
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374 | ): |
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375 | ax.legend(loc=loc_legend, fontsize="x-small", frameon=False) |
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376 | ax.set_title(subtitle, loc="left") |
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377 | |
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378 | |
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379 | ######################################## |
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380 | def get_arguments(): |
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381 | parser = ArgumentParser() |
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382 | parser.add_argument("-v", "--verbose", action="store_true", |
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383 | help="verbose mode") |
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384 | parser.add_argument("-f", "--full", action="store_true", |
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385 | help="plot the whole period") |
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386 | parser.add_argument("-i", "--increment", action="store", |
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387 | type=int, default=1, dest="inc", |
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388 | help="sampling increment") |
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389 | parser.add_argument("-r", "--range", action="store", nargs=2, |
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390 | type=string_to_date, |
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391 | help="date range: ssaa-mm-jj ssaa-mm-jj") |
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392 | parser.add_argument("-m", "--max", action="store_true", |
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393 | help="plot with y_max = allocation") |
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394 | parser.add_argument("-s", "--show", action="store_true", |
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395 | help="interactive mode") |
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396 | parser.add_argument("-d", "--dods", action="store_true", |
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397 | help="copy output on dods") |
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398 | |
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399 | return parser.parse_args() |
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400 | |
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401 | |
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402 | ######################################## |
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403 | if __name__ == '__main__': |
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404 | |
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405 | # .. Initialization .. |
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406 | # ==================== |
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407 | # ... Command line arguments ... |
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408 | # ------------------------------ |
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409 | args = get_arguments() |
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410 | |
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411 | # ... Turn interactive mode off ... |
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412 | # --------------------------------- |
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413 | if not args.show: |
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414 | import matplotlib |
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415 | matplotlib.use('Agg') |
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416 | |
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417 | import matplotlib.pyplot as plt |
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418 | # from matplotlib.backends.backend_pdf import PdfPages |
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419 | |
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420 | if not args.show: |
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421 | plt.ioff() |
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422 | |
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423 | # ... Files and directories ... |
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424 | # ----------------------------- |
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425 | project_name, DIR, OUT = parse_config("bin/config.ini") |
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426 | |
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427 | (file_param, file_utheo, file_data) = \ |
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428 | get_input_files(DIR["SAVEDATA"], |
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429 | [OUT["PARAM"], OUT["UTHEO"], OUT["BILAN"]]) |
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430 | |
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431 | img_name = os.path.splitext( |
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432 | os.path.basename(__file__) |
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433 | )[0].replace("plot_", "") |
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434 | |
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435 | today = os.path.basename(file_param).strip(OUT["PARAM"]) |
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436 | |
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437 | if args.verbose: |
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438 | fmt_str = "{:10s} : {}" |
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439 | print(fmt_str.format("args", args)) |
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440 | print(fmt_str.format("today", today)) |
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441 | print(fmt_str.format("file_param", file_param)) |
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442 | print(fmt_str.format("file_utheo", file_utheo)) |
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443 | print(fmt_str.format("file_data", file_data)) |
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444 | print(fmt_str.format("img_name", img_name)) |
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445 | |
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446 | # .. Get project info .. |
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447 | # ====================== |
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448 | projet = Project(project_name) |
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449 | projet.fill_data(file_param) |
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450 | projet.get_date_init(file_utheo) |
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451 | |
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452 | # .. Fill in data .. |
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453 | # ================== |
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454 | # ... Initialization ... |
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455 | # ---------------------- |
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456 | bilan = DataDict() |
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457 | bilan.init_range(projet.date_init, projet.deadline) |
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458 | # ... Extract data from file ... |
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459 | # ------------------------------ |
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460 | bilan.fill_data(file_data) |
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461 | # ... Compute theoratical use from known data ... |
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462 | # ------------------------------------------------ |
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463 | bilan.theo_equation() |
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464 | |
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465 | # .. Extract data depending on C.L. arguments .. |
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466 | # ============================================== |
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467 | if args.full: |
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468 | selected_items = bilan.get_items_in_full_range(args.inc) |
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469 | elif args.range: |
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470 | selected_items = bilan.get_items_in_range( |
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471 | args.range[0], args.range[1], args.inc |
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472 | ) |
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473 | else: |
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474 | selected_items = bilan.get_items(args.inc) |
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475 | |
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476 | # .. Compute data to be plotted .. |
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477 | # ================================ |
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478 | nb_items = len(selected_items) |
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479 | |
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480 | xcoord = np.linspace(1, nb_items, num=nb_items) |
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481 | dates = [item.date for item in selected_items] |
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482 | |
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483 | if projet.project == "gencmip6": |
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484 | alloc1 = (1 * projet.alloc) / 3 |
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485 | alloc2 = (2 * projet.alloc) / 3 |
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486 | conso_per_day = 2 * alloc1 / projet.days |
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487 | conso_per_day_2 = 2 * alloc2 / projet.days |
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488 | else: |
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489 | conso_per_day = projet.alloc / projet.days |
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490 | conso_per_day_2 = None |
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491 | |
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492 | runp_mean = np.array( |
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493 | [item.runp_mean for item in selected_items], dtype=float |
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494 | ) |
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495 | penp_mean = np.array( |
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496 | [item.penp_mean for item in selected_items], dtype=float |
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497 | ) |
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498 | runp_std = np.array( |
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499 | [item.runp_std for item in selected_items], dtype=float |
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500 | ) |
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501 | penp_std = np.array( |
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502 | [item.penp_std for item in selected_items], dtype=float |
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503 | ) |
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504 | |
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505 | runf_mean = np.array( |
---|
506 | [item.runf_mean for item in selected_items], dtype=float |
---|
507 | ) |
---|
508 | penf_mean = np.array( |
---|
509 | [item.penf_mean for item in selected_items], dtype=float |
---|
510 | ) |
---|
511 | runf_std = np.array( |
---|
512 | [item.runf_std for item in selected_items], dtype=float |
---|
513 | ) |
---|
514 | penf_std = np.array( |
---|
515 | [item.penf_std for item in selected_items], dtype=float |
---|
516 | ) |
---|
517 | |
---|
518 | # .. Plot stuff .. |
---|
519 | # ================ |
---|
520 | # ... Initialize figure ... |
---|
521 | # ------------------------- |
---|
522 | (fig, ax_jobsp, ax_jobsf) = plot_init() |
---|
523 | |
---|
524 | # ... Plot data ... |
---|
525 | # ----------------- |
---|
526 | plot_data( |
---|
527 | ax_jobsp, ax_jobsf, xcoord, dates, |
---|
528 | runp_mean, penp_mean, runp_std, penp_std, |
---|
529 | runf_mean, penf_mean, runf_std, penf_std, |
---|
530 | ) |
---|
531 | |
---|
532 | # ... Tweak figure ... |
---|
533 | # -------------------- |
---|
534 | title = ( |
---|
535 | "{} : Suivi des jobs (moyenne journaliÚre)\n" |
---|
536 | "({:%d/%m/%Y} - {:%d/%m/%Y})" |
---|
537 | ).format( |
---|
538 | projet.project.upper(), |
---|
539 | projet.date_init, |
---|
540 | projet.deadline |
---|
541 | ) |
---|
542 | |
---|
543 | plot_config( |
---|
544 | fig, ax_jobsp, ax_jobsf, |
---|
545 | xcoord, dates, title, |
---|
546 | conso_per_day, conso_per_day_2 |
---|
547 | ) |
---|
548 | |
---|
549 | # ... Save figure ... |
---|
550 | # ------------------- |
---|
551 | img_in = os.path.join(DIR["PLOT"], "{}.pdf".format(img_name)) |
---|
552 | img_out = os.path.join(DIR["SAVEPLOT"], |
---|
553 | "{}_{}.pdf".format(img_name, today)) |
---|
554 | |
---|
555 | plot_save(img_in, img_out, title, DIR) |
---|
556 | |
---|
557 | # ... Publish figure on dods ... |
---|
558 | # ------------------------------ |
---|
559 | if args.dods: |
---|
560 | if args.verbose: |
---|
561 | print("Publish figure on dods") |
---|
562 | dods_cp(img_in, DIR) |
---|
563 | |
---|
564 | if args.show: |
---|
565 | plt.show() |
---|
566 | |
---|
567 | exit(0) |
---|
568 | |
---|