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 | run_mean, pen_mean, run_std, pen_std, \ |
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69 | _, _, _, _ 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 | run_mean, |
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77 | pen_mean, |
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78 | run_std, |
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79 | pen_std, |
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80 | ) |
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81 | self[date].fill() |
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82 | |
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83 | #--------------------------------------- |
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84 | def add_item(self, date, conso=np.nan, |
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85 | real_use=np.nan, theo_use=np.nan, |
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86 | run_mean=np.nan, pen_mean=np.nan, |
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87 | run_std=np.nan, pen_std=np.nan): |
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88 | """ |
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89 | """ |
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90 | self[date] = Conso(date, conso, real_use, theo_use, |
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91 | run_mean, pen_mean, run_std, pen_std) |
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92 | |
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93 | #--------------------------------------- |
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94 | def theo_equation(self): |
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95 | """ |
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96 | """ |
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97 | (dates, theo_uses) = \ |
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98 | zip(*((item.date, item.theo_use) |
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99 | for item in self.get_items_in_full_range())) |
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100 | |
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101 | (idx_min, idx_max) = \ |
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102 | (np.nanargmin(theo_uses), np.nanargmax(theo_uses)) |
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103 | |
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104 | x1 = dates[idx_min].timetuple().tm_yday |
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105 | x2 = dates[idx_max].timetuple().tm_yday |
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106 | |
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107 | y1 = theo_uses[idx_min] |
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108 | y2 = theo_uses[idx_max] |
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109 | |
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110 | m = np.array([[x1, 1.], [x2, 1.]], dtype="float") |
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111 | n = np.array([y1, y2], dtype="float") |
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112 | |
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113 | poly_ok = True |
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114 | try: |
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115 | poly_theo = np.poly1d(np.linalg.solve(m, n)) |
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116 | except np.linalg.linalg.LinAlgError: |
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117 | poly_ok = False |
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118 | |
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119 | if poly_ok: |
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120 | delta = (dates[0] + relativedelta(months=2) - dates[0]).days |
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121 | |
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122 | poly_delay = np.poly1d( |
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123 | [poly_theo[1], poly_theo[0] - poly_theo[1] * delta] |
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124 | ) |
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125 | |
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126 | self.poly_theo = poly_theo |
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127 | self.poly_delay = poly_delay |
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128 | |
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129 | #--------------------------------------- |
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130 | def get_items_in_range(self, date_beg, date_end, inc=1): |
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131 | """ |
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132 | """ |
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133 | items = (item for item in self.itervalues() |
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134 | if item.date >= date_beg and |
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135 | item.date <= date_end) |
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136 | items = sorted(items, key=lambda item: item.date) |
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137 | |
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138 | return items[::inc] |
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139 | |
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140 | #--------------------------------------- |
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141 | def get_items_in_full_range(self, inc=1): |
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142 | """ |
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143 | """ |
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144 | items = (item for item in self.itervalues()) |
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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(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 | if item.isfilled()) |
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155 | items = sorted(items, key=lambda item: item.date) |
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156 | |
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157 | return items[::inc] |
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158 | |
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159 | |
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160 | class Conso(object): |
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161 | #--------------------------------------- |
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162 | def __init__(self, date, conso=np.nan, |
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163 | real_use=np.nan, theo_use=np.nan, |
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164 | run_mean=np.nan, pen_mean=np.nan, |
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165 | run_std=np.nan, pen_std=np.nan): |
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166 | self.date = date |
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167 | self.conso = conso |
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168 | self.real_use = real_use |
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169 | self.theo_use = theo_use |
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170 | self.poly_theo = np.poly1d([]) |
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171 | self.poly_delay = np.poly1d([]) |
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172 | self.run_mean = run_mean |
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173 | self.pen_mean = pen_mean |
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174 | self.run_std = run_std |
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175 | self.pen_std = pen_std |
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176 | self.filled = False |
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177 | |
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178 | #--------------------------------------- |
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179 | def __repr__(self): |
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180 | return "{:.2f} ({:.2%})".format(self.conso, self.real_use) |
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181 | |
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182 | #--------------------------------------- |
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183 | def isfilled(self): |
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184 | return self.filled |
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185 | |
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186 | #--------------------------------------- |
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187 | def fill(self): |
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188 | self.filled = True |
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189 | |
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190 | |
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191 | ######################################## |
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192 | def plot_init(): |
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193 | paper_size = np.array([29.7, 21.0]) |
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194 | fig, (ax_conso, ax_jobs) = plt.subplots( |
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195 | nrows=2, |
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196 | ncols=1, |
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197 | sharex=True, |
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198 | squeeze=True, |
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199 | figsize=(paper_size/2.54) |
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200 | ) |
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201 | ax_theo = ax_conso.twinx() |
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202 | |
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203 | return fig, ax_conso, ax_theo, ax_jobs |
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204 | |
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205 | |
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206 | ######################################## |
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207 | def plot_data(ax_conso, ax_theo, ax_jobs, xcoord, dates, |
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208 | consos, theo_uses, real_uses, theo_equs, theo_delay, |
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209 | run_mean, pen_mean, run_std, pen_std): |
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210 | """ |
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211 | """ |
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212 | line_style = "-" |
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213 | if args.full: |
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214 | line_width = 0.05 |
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215 | else: |
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216 | line_width = 0.1 |
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217 | |
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218 | ax_conso.bar( |
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219 | xcoord, consos, width=1, align="center", color="linen", |
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220 | linewidth=line_width, label="conso (heures)" |
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221 | ) |
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222 | |
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223 | ax_theo.plot( |
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224 | xcoord, real_uses, line_style, |
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225 | color="forestgreen", linewidth=1, markersize=8, |
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226 | solid_capstyle="round", solid_joinstyle="round", |
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227 | label="conso\nréelle (%)" |
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228 | ) |
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229 | ax_theo.plot( |
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230 | xcoord, theo_equs, "--", |
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231 | color="firebrick", linewidth=0.5, |
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232 | solid_capstyle="round", solid_joinstyle="round" |
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233 | ) |
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234 | ax_theo.plot( |
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235 | xcoord, theo_uses, line_style, |
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236 | color="firebrick", linewidth=1, markersize=8, |
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237 | solid_capstyle="round", solid_joinstyle="round", |
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238 | label="conso\nthéorique (%)" |
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239 | ) |
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240 | ax_theo.plot( |
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241 | xcoord, theo_delay, ":", |
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242 | color="firebrick", linewidth=0.5, |
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243 | solid_capstyle="round", solid_joinstyle="round", |
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244 | label="retard de\ndeux mois (%)" |
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245 | ) |
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246 | |
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247 | line_width = 0. |
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248 | width = 1.05 |
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249 | |
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250 | ax_jobs.bar( |
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251 | xcoord, run_mean, width=width, align="center", |
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252 | # yerr=run_std/2, ecolor="green", |
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253 | color="lightgreen", linewidth=line_width, |
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254 | antialiased=True, label="jobs running" |
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255 | ) |
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256 | ax_jobs.bar( |
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257 | xcoord, pen_mean, bottom=run_mean, width=width, align="center", |
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258 | # yerr=pen_std/2, ecolor="darkred", |
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259 | color="firebrick", linewidth=line_width, |
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260 | antialiased=True, label="jobs pending" |
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261 | ) |
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262 | |
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263 | |
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264 | ######################################## |
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265 | def plot_config(fig, ax_conso, ax_theo, xcoord, dates, title, |
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266 | conso_per_day, conso_per_day_2): |
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267 | """ |
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268 | """ |
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269 | from matplotlib.ticker import AutoMinorLocator |
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270 | |
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271 | # ... Config axes ... |
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272 | # ------------------- |
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273 | # 1) Range |
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274 | conso_max = np.nanmax(consos) |
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275 | jobs_max = np.nanmax(run_mean) |
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276 | if args.max: |
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277 | ymax = conso_max # * 1.1 |
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278 | ymax_jobs = jobs_max # * 1.1 |
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279 | else: |
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280 | ymax = 3. * max(conso_per_day, conso_per_day_2) |
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281 | ymax_jobs = max(conso_per_day, conso_per_day_2)/24. |
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282 | |
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283 | if conso_max > ymax: |
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284 | ax_conso.annotate( |
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285 | "{:.2e} heures".format(conso_max), |
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286 | ha="left", |
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287 | va="top", |
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288 | fontsize="xx-small", |
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289 | bbox=dict(boxstyle="round", fc="w", ec="0.5", color="gray",), |
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290 | # xy=(np.nanargmax(consos)+1.2, ymax), |
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291 | xy=(np.nanargmax(consos)+1.2, ymax*0.9), |
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292 | textcoords="axes fraction", |
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293 | xytext=(0.01, 0.8), |
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294 | arrowprops=dict( |
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295 | arrowstyle="->", |
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296 | shrinkA=0, |
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297 | shrinkB=0, |
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298 | color="gray", |
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299 | ), |
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300 | ) |
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301 | |
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302 | xmin, xmax = xcoord[0]-1, xcoord[-1]+1 |
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303 | ax_conso.set_xlim(xmin, xmax) |
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304 | ax_conso.set_ylim(0., ymax) |
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305 | ax_jobs.set_ylim(0., ymax_jobs) |
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306 | ax_theo.set_ylim(0., 100) |
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307 | |
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308 | # 2) Plot ideal daily consumption in hours |
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309 | line_color = "blue" |
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310 | line_alpha = 0.5 |
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311 | line_label = "conso journaliÚre\nidéale ({})" |
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312 | for ax, y_div, label in ( |
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313 | (ax_conso, 1., line_label.format("heures")), |
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314 | (ax_jobs, 24., line_label.format("cÅurs")), |
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315 | ): |
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316 | if conso_per_day_2: |
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317 | list_x = [0, xmax/2, xmax/2, xmax] |
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318 | list_y = np.array( |
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319 | [conso_per_day, conso_per_day, conso_per_day_2, conso_per_day_2], |
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320 | dtype=float |
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321 | ) |
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322 | ax.plot( |
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323 | list_x, list_y/y_div, |
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324 | color=line_color, alpha=line_alpha, label=label, |
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325 | ) |
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326 | else: |
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327 | ax.axhline( |
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328 | y=conso_per_day/y_div, |
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329 | color=line_color, alpha=line_alpha, label=label, |
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330 | ) |
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331 | |
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332 | # 3) Ticks labels |
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333 | (date_beg, date_end) = (dates[0], dates[-1]) |
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334 | date_fmt = "{:%d-%m}" |
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335 | |
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336 | if date_end - date_beg > dt.timedelta(weeks=9): |
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337 | maj_xticks = [x for x, d in zip(xcoord, dates) |
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338 | if d.weekday() == 0] |
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339 | maj_xlabs = [date_fmt.format(d) for d in dates |
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340 | if d.weekday() == 0] |
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341 | else: |
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342 | maj_xticks = [x for x, d in zip(xcoord, dates)] |
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343 | maj_xlabs = [date_fmt.format(d) for d in dates] |
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344 | |
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345 | ax_conso.ticklabel_format(axis="y", style="sci", scilimits=(0, 0)) |
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346 | |
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347 | ax_jobs.set_xticks(xcoord, minor=True) |
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348 | ax_jobs.set_xticks(maj_xticks, minor=False) |
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349 | ax_jobs.set_xticklabels( |
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350 | maj_xlabs, rotation="vertical", size="x-small" |
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351 | ) |
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352 | |
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353 | for ax, y, label in ( |
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354 | (ax_conso, conso_per_day, "heures"), |
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355 | (ax_jobs, conso_per_day / 24., "cÅurs"), |
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356 | ): |
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357 | minor_locator = AutoMinorLocator() |
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358 | ax.yaxis.set_minor_locator(minor_locator) |
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359 | |
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360 | yticks = list(ax.get_yticks()) |
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361 | yticks.append(y) |
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362 | ax.set_yticks(yticks) |
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363 | |
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364 | if conso_per_day_2: |
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365 | yticks.append(conso_per_day_2) |
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366 | |
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367 | ax_theo.spines["right"].set_color("firebrick") |
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368 | ax_theo.tick_params(colors="firebrick") |
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369 | ax_theo.yaxis.label.set_color("firebrick") |
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370 | |
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371 | for x, d in zip(xcoord, dates): |
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372 | if d.weekday() == 0 and d.hour == 0: |
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373 | for ax in (ax_conso, ax_jobs): |
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374 | ax.axvline(x=x, color="black", alpha=0.5, |
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375 | linewidth=0.5, linestyle=":") |
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376 | |
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377 | # 4) Define axes title |
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378 | for ax, label in ( |
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379 | (ax_conso, "heures"), |
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380 | (ax_theo, "%"), |
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381 | (ax_jobs, "cÅurs"), |
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382 | ): |
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383 | ax.set_ylabel(label, fontweight="bold") |
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384 | ax.tick_params(axis="y", labelsize="small") |
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385 | |
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386 | # 5) Define plot size |
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387 | fig.subplots_adjust( |
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388 | left=0.08, |
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389 | bottom=0.09, |
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390 | right=0.93, |
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391 | top=0.93, |
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392 | hspace=0.1, |
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393 | wspace=0.1, |
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394 | ) |
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395 | |
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396 | # ... Main title and legend ... |
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397 | # ----------------------------- |
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398 | fig.suptitle(title, fontweight="bold", size="large") |
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399 | for ax, loc in ( |
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400 | (ax_conso, "upper left"), |
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401 | (ax_theo, "upper right"), |
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402 | (ax_jobs, "upper left"), |
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403 | ): |
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404 | ax.legend(loc=loc, fontsize="x-small", frameon=False) |
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405 | |
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406 | |
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407 | ######################################## |
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408 | def get_arguments(): |
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409 | parser = ArgumentParser() |
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410 | parser.add_argument("-v", "--verbose", action="store_true", |
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411 | help="verbose mode") |
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412 | parser.add_argument("-f", "--full", action="store_true", |
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413 | help="plot the whole period") |
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414 | parser.add_argument("-i", "--increment", action="store", |
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415 | type=int, default=1, dest="inc", |
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416 | help="sampling increment") |
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417 | parser.add_argument("-r", "--range", action="store", nargs=2, |
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418 | type=string_to_date, |
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419 | help="date range: ssaa-mm-jj ssaa-mm-jj") |
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420 | parser.add_argument("-m", "--max", action="store_true", |
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421 | help="plot with y_max = allocation") |
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422 | parser.add_argument("-s", "--show", action="store_true", |
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423 | help="interactive mode") |
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424 | parser.add_argument("-d", "--dods", action="store_true", |
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425 | help="copy output on dods") |
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426 | |
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427 | return parser.parse_args() |
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428 | |
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429 | |
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430 | ######################################## |
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431 | if __name__ == '__main__': |
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432 | |
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433 | # .. Initialization .. |
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434 | # ==================== |
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435 | # ... Command line arguments ... |
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436 | # ------------------------------ |
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437 | args = get_arguments() |
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438 | |
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439 | # ... Turn interactive mode off ... |
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440 | # --------------------------------- |
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441 | if not args.show: |
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442 | import matplotlib |
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443 | matplotlib.use('Agg') |
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444 | |
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445 | import matplotlib.pyplot as plt |
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446 | # from matplotlib.backends.backend_pdf import PdfPages |
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447 | |
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448 | if not args.show: |
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449 | plt.ioff() |
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450 | |
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451 | # ... Files and directories ... |
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452 | # ----------------------------- |
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453 | project_name, DIR, OUT = parse_config("bin/config.ini") |
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454 | |
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455 | (file_param, file_utheo, file_data) = \ |
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456 | get_input_files(DIR["SAVEDATA"], |
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457 | [OUT["PARAM"], OUT["UTHEO"], OUT["BILAN"]]) |
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458 | |
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459 | img_name = os.path.splitext( |
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460 | os.path.basename(__file__) |
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461 | )[0].replace("plot_", "") |
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462 | |
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463 | today = os.path.basename(file_param).strip(OUT["PARAM"]) |
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464 | |
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465 | if args.verbose: |
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466 | fmt_str = "{:10s} : {}" |
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467 | print(fmt_str.format("args", args)) |
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468 | print(fmt_str.format("today", today)) |
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469 | print(fmt_str.format("file_param", file_param)) |
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470 | print(fmt_str.format("file_utheo", file_utheo)) |
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471 | print(fmt_str.format("file_data", file_data)) |
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472 | print(fmt_str.format("img_name", img_name)) |
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473 | |
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474 | # .. Get project info .. |
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475 | # ====================== |
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476 | projet = Project(project_name) |
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477 | projet.fill_data(file_param) |
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478 | projet.get_date_init(file_utheo) |
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479 | |
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480 | # .. Fill in data .. |
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481 | # ================== |
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482 | # ... Initialization ... |
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483 | # ---------------------- |
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484 | bilan = DataDict() |
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485 | bilan.init_range(projet.date_init, projet.deadline) |
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486 | # ... Extract data from file ... |
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487 | # ------------------------------ |
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488 | bilan.fill_data(file_data) |
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489 | # ... Compute theoratical use from known data ... |
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490 | # ------------------------------------------------ |
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491 | bilan.theo_equation() |
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492 | |
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493 | # .. Extract data depending on C.L. arguments .. |
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494 | # ============================================== |
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495 | if args.full: |
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496 | selected_items = bilan.get_items_in_full_range(args.inc) |
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497 | elif args.range: |
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498 | selected_items = bilan.get_items_in_range( |
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499 | args.range[0], args.range[1], args.inc |
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500 | ) |
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501 | else: |
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502 | selected_items = bilan.get_items(args.inc) |
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503 | |
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504 | # .. Compute data to be plotted .. |
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505 | # ================================ |
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506 | nb_items = len(selected_items) |
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507 | |
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508 | xcoord = np.linspace(1, nb_items, num=nb_items) |
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509 | dates = [item.date for item in selected_items] |
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510 | |
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511 | cumul = np.array([item.conso for item in selected_items], |
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512 | dtype=float) |
---|
513 | consos = [] |
---|
514 | consos.append(cumul[0]) |
---|
515 | consos[1:nb_items] = cumul[1:nb_items] - cumul[0:nb_items-1] |
---|
516 | consos = np.array(consos, dtype=float) |
---|
517 | |
---|
518 | if projet.project == "gencmip6": |
---|
519 | alloc1 = (1 * projet.alloc) / 3 |
---|
520 | alloc2 = (2 * projet.alloc) / 3 |
---|
521 | conso_per_day = 2 * alloc1 / projet.days |
---|
522 | conso_per_day_2 = 2 * alloc2 / projet.days |
---|
523 | else: |
---|
524 | conso_per_day = projet.alloc / projet.days |
---|
525 | conso_per_day_2 = None |
---|
526 | |
---|
527 | theo_uses = np.array( |
---|
528 | [100.*item.theo_use for item in selected_items], |
---|
529 | dtype=float |
---|
530 | ) |
---|
531 | real_uses = np.array( |
---|
532 | [100.*item.real_use for item in selected_items], |
---|
533 | dtype=float |
---|
534 | ) |
---|
535 | theo_equs = np.array( |
---|
536 | [100. * bilan.poly_theo(date.timetuple().tm_yday) |
---|
537 | for date in dates], |
---|
538 | dtype=float |
---|
539 | ) |
---|
540 | theo_delay = np.array( |
---|
541 | [100. * bilan.poly_delay(date.timetuple().tm_yday) |
---|
542 | for date in dates], |
---|
543 | dtype=float |
---|
544 | ) |
---|
545 | |
---|
546 | run_mean = np.array([item.run_mean for item in selected_items], |
---|
547 | dtype=float) |
---|
548 | pen_mean = np.array([item.pen_mean for item in selected_items], |
---|
549 | dtype=float) |
---|
550 | run_std = np.array([item.run_std for item in selected_items], |
---|
551 | dtype=float) |
---|
552 | pen_std = np.array([item.pen_std for item in selected_items], |
---|
553 | dtype=float) |
---|
554 | |
---|
555 | # .. Plot stuff .. |
---|
556 | # ================ |
---|
557 | # ... Initialize figure ... |
---|
558 | # ------------------------- |
---|
559 | (fig, ax_conso, ax_theo, ax_jobs) = plot_init() |
---|
560 | |
---|
561 | # ... Plot data ... |
---|
562 | # ----------------- |
---|
563 | plot_data(ax_conso, ax_theo, ax_jobs, xcoord, dates, |
---|
564 | consos, theo_uses, real_uses, theo_equs, theo_delay, |
---|
565 | run_mean, pen_mean, run_std, pen_std) |
---|
566 | |
---|
567 | # ... Tweak figure ... |
---|
568 | # -------------------- |
---|
569 | title = "{} : Conso globale et suivi des jobs " \ |
---|
570 | "(moyenne journaliÚre)\n({:%d/%m/%Y} - {:%d/%m/%Y})".format( |
---|
571 | projet.project.upper(), |
---|
572 | projet.date_init, |
---|
573 | projet.deadline |
---|
574 | ) |
---|
575 | |
---|
576 | plot_config( |
---|
577 | fig, ax_conso, ax_theo, xcoord, dates, title, |
---|
578 | conso_per_day, conso_per_day_2 |
---|
579 | ) |
---|
580 | |
---|
581 | # ... Save figure ... |
---|
582 | # ------------------- |
---|
583 | img_in = os.path.join(DIR["PLOT"], "{}.pdf".format(img_name)) |
---|
584 | img_out = os.path.join(DIR["SAVEPLOT"], |
---|
585 | "{}_{}.pdf".format(img_name, today)) |
---|
586 | |
---|
587 | plot_save(img_in, img_out, title, DIR) |
---|
588 | |
---|
589 | # ... Publish figure on dods ... |
---|
590 | # ------------------------------ |
---|
591 | if args.dods: |
---|
592 | if args.verbose: |
---|
593 | print("Publish figure on dods") |
---|
594 | dods_cp(img_in, DIR) |
---|
595 | |
---|
596 | if args.show: |
---|
597 | plt.show() |
---|
598 | |
---|
599 | exit(0) |
---|
600 | |
---|