| 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 | import numpy as np |
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| 13 | |
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| 14 | # Application library imports |
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| 15 | from libconso import * |
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| 16 | |
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| 17 | |
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| 18 | ######################################## |
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| 19 | class DataDict(dict): |
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| 20 | #--------------------------------------- |
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| 21 | def __init__(self): |
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| 22 | self = {} |
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| 23 | |
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| 24 | #--------------------------------------- |
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| 25 | def init_range(self, date_beg, date_end, inc=1): |
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| 26 | """ |
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| 27 | """ |
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| 28 | delta = date_end - date_beg + dt.timedelta(days=1) |
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| 29 | |
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| 30 | (deb, fin) = (0, int(delta.total_seconds() / 3600)) |
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| 31 | |
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| 32 | dates = (date_beg + dt.timedelta(hours=i) |
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| 33 | for i in xrange(deb, fin, inc)) |
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| 34 | |
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| 35 | for date in dates: |
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| 36 | self.add_item(date) |
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| 37 | |
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| 38 | #--------------------------------------- |
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| 39 | def fill_data(self, file_list): |
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| 40 | """ |
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| 41 | """ |
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| 42 | for filein in sorted(file_list): |
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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_datetime, |
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| 49 | 1: float, |
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| 50 | 2: float, |
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| 51 | }, |
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| 52 | missing_values="nan", |
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| 53 | ) |
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| 54 | except Exception as rc: |
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| 55 | print("Problem with file {} :\n{}".format(filein, rc)) |
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| 56 | exit(1) |
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| 57 | |
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| 58 | if len(data) == 24: |
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| 59 | run_mean = np.nanmean( |
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| 60 | np.array([run for _, run, _ in data]) |
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| 61 | ) |
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| 62 | pen_mean = np.nanmean( |
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| 63 | np.array([pen for _, _, pen in data]) |
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| 64 | ) |
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| 65 | run_std = np.nanstd( |
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| 66 | np.array([run for _, run, _ in data]) |
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| 67 | ) |
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| 68 | pen_std = np.nanstd( |
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| 69 | np.array([pen for _, _, pen in data]) |
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| 70 | ) |
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| 71 | else: |
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| 72 | run_mean = np.nan |
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| 73 | pen_mean = np.nan |
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| 74 | run_std = np.nan |
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| 75 | pen_std = np.nan |
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| 76 | |
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| 77 | for date, run, pen in data: |
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| 78 | if date.hour == 0: |
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| 79 | self.add_item( |
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| 80 | date, |
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| 81 | run, |
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| 82 | pen, |
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| 83 | run_mean, |
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| 84 | pen_mean, |
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| 85 | run_std, |
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| 86 | pen_std |
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| 87 | ) |
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| 88 | else: |
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| 89 | self.add_item(date, run, pen) |
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| 90 | self[date].fill() |
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| 91 | |
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| 92 | #--------------------------------------- |
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| 93 | def add_item(self, date, run=np.nan, pen=np.nan, |
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| 94 | run_mean=np.nan, pen_mean=np.nan, |
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| 95 | run_std=np.nan, pen_std=np.nan): |
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| 96 | """ |
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| 97 | """ |
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| 98 | self[date] = Conso( |
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| 99 | date, |
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| 100 | run, |
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| 101 | pen, |
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| 102 | run_mean, |
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| 103 | pen_mean, |
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| 104 | run_std, |
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| 105 | pen_std |
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| 106 | ) |
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| 107 | |
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| 108 | #--------------------------------------- |
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| 109 | def get_items_in_range(self, date_beg, date_end, inc=1): |
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| 110 | """ |
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| 111 | """ |
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| 112 | items = (item for item in self.itervalues() |
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| 113 | if item.date >= date_beg and |
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| 114 | item.date <= date_end) |
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| 115 | items = sorted(items, key=lambda item: item.date) |
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| 116 | |
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| 117 | return items[::inc] |
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| 118 | |
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| 119 | #--------------------------------------- |
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| 120 | def get_items_in_full_range(self, inc=1): |
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| 121 | """ |
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| 122 | """ |
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| 123 | items = (item for item in self.itervalues() |
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| 124 | if item.date >= projet.date_init and |
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| 125 | item.date <= projet.deadline and |
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| 126 | item.date.hour == 0) |
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| 127 | items = sorted(items, key=lambda item: item.date) |
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| 128 | |
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| 129 | return items[::inc] |
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| 130 | |
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| 131 | #--------------------------------------- |
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| 132 | def get_items(self, inc=1): |
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| 133 | """ |
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| 134 | """ |
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| 135 | items = (item for item in self.itervalues() |
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| 136 | if item.isfilled()) |
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| 137 | items = sorted(items, key=lambda item: item.date) |
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| 138 | |
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| 139 | return items[::inc] |
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| 140 | |
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| 141 | |
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| 142 | class Conso(object): |
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| 143 | #--------------------------------------- |
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| 144 | def __init__(self, date, run=np.nan, pen=np.nan, |
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| 145 | run_mean=np.nan, pen_mean=np.nan, |
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| 146 | run_std=np.nan, pen_std=np.nan): |
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| 147 | |
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| 148 | self.date = date |
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| 149 | self.run = run |
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| 150 | self.pen = pen |
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| 151 | self.run_mean = run_mean |
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| 152 | self.pen_mean = pen_mean |
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| 153 | self.run_std = run_std |
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| 154 | self.pen_std = pen_std |
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| 155 | self.filled = False |
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| 156 | |
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| 157 | #--------------------------------------- |
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| 158 | def __repr__(self): |
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| 159 | return "R{:.0f} ({:.0f}/{:.0f}) P{:.0f} ({:.0f}/{:.0f})".format( |
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| 160 | self.run, |
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| 161 | self.run_mean, |
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| 162 | self.run_std, |
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| 163 | self.pen, |
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| 164 | self.pen_mean, |
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| 165 | self.pen_std, |
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| 166 | ) |
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| 167 | |
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| 168 | #--------------------------------------- |
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| 169 | def isfilled(self): |
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| 170 | return self.filled |
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| 171 | |
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| 172 | #--------------------------------------- |
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| 173 | def fill(self): |
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| 174 | self.filled = True |
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| 175 | |
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| 176 | |
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| 177 | ######################################## |
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| 178 | def plot_init(): |
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| 179 | paper_size = np.array([29.7, 21.0]) |
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| 180 | fig, ax = plt.subplots(figsize=(paper_size/2.54)) |
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| 181 | |
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| 182 | return fig, ax |
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| 183 | |
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| 184 | |
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| 185 | ######################################## |
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| 186 | def plot_data(ax, xcoord, dates, run_jobs, pen_jobs, run_std, pen_std): |
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| 187 | """ |
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| 188 | """ |
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| 189 | line_width = 0. |
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| 190 | width = 1.05 |
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| 191 | |
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| 192 | ax.bar( |
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| 193 | xcoord, run_jobs, width=width, yerr=run_std/2., |
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| 194 | linewidth=line_width, align="center", |
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| 195 | color="lightgreen", ecolor="green", antialiased=True, |
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| 196 | label="jobs running" |
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| 197 | ) |
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| 198 | if args.mode == "machine": |
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| 199 | label = "jobs pending\n(Ressources & Priority)" |
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| 200 | else: |
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| 201 | label = "jobs pending" |
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| 202 | ax.bar( |
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| 203 | xcoord, pen_jobs, bottom=run_jobs, width=width, |
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| 204 | linewidth=line_width, align="center", |
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| 205 | color="firebrick", antialiased=True, |
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| 206 | label=label |
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| 207 | ) |
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| 208 | |
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| 209 | |
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| 210 | ######################################## |
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| 211 | def plot_config( |
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| 212 | fig, ax, xcoord, dates, title, conso_per_day, conso_per_day_2 |
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| 213 | ): |
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| 214 | """ |
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| 215 | """ |
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| 216 | from matplotlib.ticker import AutoMinorLocator |
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| 217 | |
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| 218 | # ... Compute useful stuff ... |
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| 219 | # ---------------------------- |
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| 220 | multialloc = False |
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| 221 | |
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| 222 | if args.mode == "machine": |
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| 223 | xi = 0 |
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| 224 | xn = xi |
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| 225 | xf = len(dates) |
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| 226 | yi = 80000. |
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| 227 | yf = yi |
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| 228 | else: |
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| 229 | if conso_per_day_2: |
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| 230 | date_inter = projet.date_init + dt.timedelta(days=projet.days//2) |
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| 231 | if projet.date_init in dates: |
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| 232 | xi = dates.index(projet.date_init) |
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| 233 | else: |
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| 234 | xi = 0 |
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| 235 | |
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| 236 | if projet.deadline in dates: |
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| 237 | xf = dates.index(projet.deadline) |
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| 238 | else: |
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| 239 | xf = len(dates) |
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| 240 | |
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| 241 | if date_inter in dates: |
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| 242 | xn = dates.index(date_inter) |
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| 243 | yi = conso_per_day |
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| 244 | yf = conso_per_day_2 |
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| 245 | multialloc = True |
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| 246 | else: |
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| 247 | if dates[-1] < date_inter: |
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| 248 | xn = xf |
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| 249 | yi = conso_per_day |
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| 250 | yf = conso_per_day |
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| 251 | elif dates[0] > date_inter: |
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| 252 | xn = xi |
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| 253 | yi = conso_per_day_2 |
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| 254 | yf = conso_per_day_2 |
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| 255 | |
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| 256 | # ... Config axes ... |
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| 257 | # ------------------- |
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| 258 | # 1) Range |
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| 259 | xmin, xmax = xcoord[0]-1, xcoord[-1]+1 |
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| 260 | if multialloc: |
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| 261 | ymax = 4. * max(yi, yf) |
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| 262 | else: |
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| 263 | ymax = 4. * yi |
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| 264 | ax.set_xlim(xmin, xmax) |
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| 265 | ax.set_ylim(0, ymax) |
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| 266 | |
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| 267 | # 2) Plot ideal daily consumption |
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| 268 | line_color = "blue" |
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| 269 | line_alpha = 0.5 |
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| 270 | line_label = "conso journaliÚre idéale" |
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| 271 | ax.plot( |
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| 272 | [xi, xn, xn, xf], [yi, yi, yf, yf], |
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| 273 | color=line_color, alpha=line_alpha, label=line_label, |
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| 274 | ) |
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| 275 | |
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| 276 | # 3) Ticks labels |
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| 277 | (date_beg, date_end) = (dates[0], dates[-1]) |
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| 278 | |
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| 279 | if date_end - date_beg > dt.timedelta(weeks=9): |
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| 280 | date_fmt = "{:%d-%m}" |
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| 281 | maj_xticks = [x for x, d in zip(xcoord, dates) |
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| 282 | if d.weekday() == 0 and d.hour == 0] |
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| 283 | maj_xlabs = [date_fmt.format(d) for d in dates |
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| 284 | if d.weekday() == 0 and d.hour == 0] |
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| 285 | else: |
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| 286 | date_fmt = "{:%d-%m %Hh}" |
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| 287 | maj_xticks = [x for x, d in zip(xcoord, dates) |
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| 288 | if d.hour == 0 or d.hour == 12] |
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| 289 | maj_xlabs = [date_fmt.format(d) for d in dates |
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| 290 | if d.hour == 0 or d.hour == 12] |
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| 291 | |
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| 292 | ax.set_xticks(xcoord, minor=True) |
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| 293 | ax.set_xticks(maj_xticks, minor=False) |
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| 294 | ax.set_xticklabels(maj_xlabs, rotation="vertical", size="x-small") |
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| 295 | |
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| 296 | minor_locator = AutoMinorLocator() |
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| 297 | ax.yaxis.set_minor_locator(minor_locator) |
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| 298 | |
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| 299 | yticks = list(ax.get_yticks()) |
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| 300 | if args.mode == "machine": |
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| 301 | yticks.append(yi) |
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| 302 | else: |
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| 303 | yticks.append(conso_per_day) |
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| 304 | if multialloc: |
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| 305 | yticks.append(conso_per_day_2) |
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| 306 | ax.set_yticks(yticks) |
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| 307 | |
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| 308 | for x, d in zip(xcoord, dates): |
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| 309 | if d.weekday() == 0 and d.hour == 0: |
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| 310 | ax.axvline(x=x, color="black", linewidth=1., linestyle=":") |
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| 311 | |
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| 312 | # 4) Define axes title |
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| 313 | ax.set_ylabel("cÅurs", fontweight="bold") |
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| 314 | ax.tick_params(axis="y", labelsize="small") |
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| 315 | |
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| 316 | # 5) Define plot size |
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| 317 | fig.subplots_adjust( |
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| 318 | left=0.08, |
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| 319 | bottom=0.09, |
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| 320 | right=0.93, |
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| 321 | top=0.93, |
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| 322 | ) |
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| 323 | |
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| 324 | # ... Main title and legend ... |
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| 325 | # ----------------------------- |
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| 326 | fig.suptitle(title, fontweight="bold", size="large") |
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| 327 | ax.legend(loc="upper right", fontsize="x-small", frameon=False) |
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| 328 | |
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| 329 | |
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| 330 | ######################################## |
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| 331 | def get_arguments(): |
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| 332 | parser = ArgumentParser() |
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| 333 | parser.add_argument("-v", "--verbose", action="store_true", |
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| 334 | help="verbose mode") |
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| 335 | parser.add_argument("-f", "--full", action="store_true", |
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| 336 | help="plot the whole period") |
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| 337 | parser.add_argument("-i", "--increment", action="store", |
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| 338 | type=int, default=1, dest="inc", |
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| 339 | help="sampling increment") |
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| 340 | parser.add_argument("-r", "--range", action="store", nargs=2, |
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| 341 | type=string_to_date, |
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| 342 | help="date range: ssaa-mm-jj ssaa-mm-jj") |
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| 343 | parser.add_argument("-s", "--show", action="store_true", |
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| 344 | help="interactive mode") |
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| 345 | parser.add_argument("-d", "--dods", action="store_true", |
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| 346 | help="copy output on dods") |
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| 347 | parser.add_argument("-m", "--mode", action="store", |
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| 348 | choices=["project", "machine"], |
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| 349 | default="project", |
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| 350 | help="copy output on dods") |
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| 351 | |
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| 352 | return parser.parse_args() |
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| 353 | |
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| 354 | |
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| 355 | ######################################## |
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| 356 | if __name__ == '__main__': |
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| 357 | |
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| 358 | # .. Initialization .. |
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| 359 | # ==================== |
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| 360 | # ... Command line arguments ... |
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| 361 | # ------------------------------ |
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| 362 | args = get_arguments() |
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| 363 | |
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| 364 | # ... Constants ... |
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| 365 | # ----------------- |
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| 366 | WEEK_NB = 3 |
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| 367 | |
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| 368 | # ... Turn interactive mode off ... |
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| 369 | # --------------------------------- |
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| 370 | if not args.show: |
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| 371 | import matplotlib |
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| 372 | matplotlib.use('Agg') |
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| 373 | |
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| 374 | import matplotlib.pyplot as plt |
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| 375 | # from matplotlib.backends.backend_pdf import PdfPages |
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| 376 | |
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| 377 | if not args.show: |
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| 378 | plt.ioff() |
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| 379 | |
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| 380 | # ... Files and directories ... |
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| 381 | # ----------------------------- |
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| 382 | project_name, DIR, OUT = parse_config("bin/config.ini") |
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| 383 | |
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| 384 | (file_param, file_utheo) = \ |
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| 385 | get_input_files(DIR["SAVEDATA"], [OUT["PARAM"], OUT["UTHEO"]]) |
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| 386 | |
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| 387 | if args.mode == "project": |
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| 388 | pattern = "OUT_JOBS_PENDING_" |
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| 389 | else: |
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| 390 | pattern = "OUT_JOBS_PEN_FULL_" |
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| 391 | file_list = glob.glob( |
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| 392 | os.path.join(DIR["SAVEDATA"], pattern + "*") |
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| 393 | ) |
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| 394 | |
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| 395 | img_name = "jobs_{}".format(args.mode) |
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| 396 | |
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| 397 | today = dt.datetime.today() |
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| 398 | weeknum = today.isocalendar()[1] |
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| 399 | |
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| 400 | if args.verbose: |
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| 401 | fmt_str = "{:10s} : {}" |
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| 402 | print(fmt_str.format("args", args)) |
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| 403 | print(fmt_str.format("today", today)) |
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| 404 | print(fmt_str.format("file_param", file_param)) |
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| 405 | print(fmt_str.format("file_utheo", file_utheo)) |
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| 406 | print(fmt_str.format("file_list", file_list)) |
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| 407 | print(fmt_str.format("img_name", img_name)) |
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| 408 | |
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| 409 | # .. Get project info .. |
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| 410 | # ====================== |
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| 411 | projet = Project(project_name) |
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| 412 | projet.fill_data(file_param) |
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| 413 | projet.get_date_init(file_utheo) |
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| 414 | |
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| 415 | # .. Fill in data .. |
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| 416 | # ================== |
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| 417 | # ... Initialization ... |
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| 418 | # ---------------------- |
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| 419 | bilan = DataDict() |
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| 420 | bilan.init_range(projet.date_init, projet.deadline) |
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| 421 | # ... Extract data from file ... |
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| 422 | # ------------------------------ |
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| 423 | bilan.fill_data(file_list) |
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| 424 | |
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| 425 | # .. Extract data depending on C.L. arguments .. |
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| 426 | # ============================================== |
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| 427 | if args.full: |
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| 428 | selected_items = bilan.get_items_in_full_range(args.inc) |
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| 429 | elif args.range: |
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| 430 | selected_items = bilan.get_items_in_range( |
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| 431 | args.range[0], args.range[1], args.inc |
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| 432 | ) |
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| 433 | else: |
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| 434 | range_end = today |
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| 435 | range_start = ( |
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| 436 | range_end + dt.timedelta(weeks=-WEEK_NB) - dt.timedelta(days=1) |
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| 437 | ) |
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| 438 | selected_items = bilan.get_items_in_range( |
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| 439 | range_start, range_end, args.inc |
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| 440 | ) |
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| 441 | |
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| 442 | # .. Compute data to be plotted .. |
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| 443 | # ================================ |
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| 444 | nb_items = len(selected_items) |
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| 445 | |
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| 446 | xcoord = np.linspace(1, nb_items, num=nb_items) |
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| 447 | dates = [item.date for item in selected_items] |
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| 448 | |
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| 449 | if args.full: |
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| 450 | run_jobs = np.array([item.run_mean for item in selected_items], |
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| 451 | dtype=float) |
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| 452 | pen_jobs = np.array([item.pen_mean for item in selected_items], |
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| 453 | dtype=float) |
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| 454 | run_std = np.array([item.run_std for item in selected_items], |
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| 455 | dtype=float) |
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| 456 | pen_std = np.array([item.pen_std for item in selected_items], |
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| 457 | dtype=float) |
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| 458 | else: |
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| 459 | run_jobs = np.array([item.run for item in selected_items], |
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| 460 | dtype=float) |
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| 461 | pen_jobs = np.array([item.pen for item in selected_items], |
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| 462 | dtype=float) |
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| 463 | run_std = np.nan |
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| 464 | pen_std = np.nan |
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| 465 | |
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| 466 | if args.verbose: |
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| 467 | for i in selected_items: |
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| 468 | if not np.isnan(i.run_mean): |
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| 469 | print( |
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| 470 | "{} {:13.2f} {:13.2f} {:13.2f} {:13.2f}".format( |
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| 471 | i.date, |
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| 472 | i.run_mean, i.pen_mean, |
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| 473 | i.run_std, i.pen_std |
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| 474 | ) |
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| 475 | ) |
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| 476 | |
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| 477 | if projet.project == "gencmip6": |
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| 478 | alloc1 = (1 * projet.alloc) / 3 |
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| 479 | alloc2 = (2 * projet.alloc) / 3 |
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| 480 | conso_per_day = 2 * alloc1 / (projet.days * 24.) |
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| 481 | conso_per_day_2 = 2 * alloc2 / (projet.days * 24.) |
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| 482 | else: |
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| 483 | conso_per_day = projet.alloc / (projet.days * 24.) |
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| 484 | conso_per_day_2 = None |
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| 485 | |
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| 486 | # .. Plot stuff .. |
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| 487 | # ================ |
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| 488 | # ... Initialize figure ... |
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| 489 | # ------------------------- |
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| 490 | (fig, ax) = plot_init() |
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| 491 | |
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| 492 | # ... Plot data ... |
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| 493 | # ----------------- |
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| 494 | plot_data(ax, xcoord, dates, run_jobs, pen_jobs, run_std, pen_std) |
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| 495 | |
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| 496 | # ... Tweak figure ... |
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| 497 | # -------------------- |
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| 498 | title = "Suivi des jobs {}\n({:%d/%m/%Y} - {:%d/%m/%Y})".format( |
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| 499 | projet.project.upper(), |
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| 500 | projet.date_init, |
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| 501 | projet.deadline |
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| 502 | ) |
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| 503 | |
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| 504 | plot_config( |
|---|
| 505 | fig, ax, xcoord, dates, title, conso_per_day, conso_per_day_2 |
|---|
| 506 | ) |
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| 507 | |
|---|
| 508 | # ... Save figure ... |
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| 509 | # ------------------- |
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| 510 | img_in = os.path.join(DIR["PLOT"], "{}.pdf".format(img_name)) |
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| 511 | img_out = os.path.join(DIR["SAVEPLOT"], |
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| 512 | "{}_w{:02d}.pdf".format(img_name, weeknum)) |
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| 513 | |
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| 514 | plot_save(img_in, img_out, title, DIR) |
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| 515 | |
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| 516 | # ... Publish figure on dods ... |
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| 517 | # ------------------------------ |
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| 518 | if args.dods: |
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| 519 | dods_cp(img_in, DIR) |
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| 520 | |
|---|
| 521 | if args.show: |
|---|
| 522 | plt.show() |
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| 523 | |
|---|
| 524 | exit(0) |
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