| 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) |
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| 513 | consos = [] |
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| 514 | consos.append(cumul[0]) |
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| 515 | consos[1:nb_items] = cumul[1:nb_items] - cumul[0:nb_items-1] |
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| 516 | consos = np.array(consos, dtype=float) |
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| 517 | |
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| 518 | if projet.project == "gencmip6": |
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| 519 | alloc1 = (1 * projet.alloc) / 3 |
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| 520 | alloc2 = (2 * projet.alloc) / 3 |
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| 521 | conso_per_day = 2 * alloc1 / projet.days |
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| 522 | conso_per_day_2 = 2 * alloc2 / projet.days |
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| 523 | else: |
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| 524 | conso_per_day = projet.alloc / projet.days |
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| 525 | conso_per_day_2 = None |
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| 526 | |
|---|
| 527 | theo_uses = np.array( |
|---|
| 528 | [100.*item.theo_use for item in selected_items], |
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| 529 | dtype=float |
|---|
| 530 | ) |
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| 531 | real_uses = np.array( |
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| 532 | [100.*item.real_use for item in selected_items], |
|---|
| 533 | dtype=float |
|---|
| 534 | ) |
|---|
| 535 | theo_equs = np.array( |
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| 536 | [100. * bilan.poly_theo(date.timetuple().tm_yday) |
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| 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 | |
|---|
