| 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 gencmip6 import * |
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| 16 | from gencmip6_path 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 | # delta = delta + dt.timedelta(days=1) |
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| 31 | delta = date_end - date_beg + dt.timedelta(days=1) |
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| 32 | |
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| 33 | (deb, fin) = (0, int(delta.total_seconds() / 3600)) |
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| 34 | |
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| 35 | dates = (date_beg + dt.timedelta(hours=i) |
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| 36 | for i in xrange(deb, fin, inc)) |
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| 37 | |
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| 38 | for date in dates: |
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| 39 | self.add_item(date) |
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| 40 | |
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| 41 | #--------------------------------------- |
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| 42 | def fill_data(self, file_list): |
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| 43 | """ |
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| 44 | """ |
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| 45 | for filein in sorted(file_list): |
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| 46 | try: |
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| 47 | data = np.genfromtxt( |
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| 48 | filein, |
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| 49 | skip_header=1, |
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| 50 | converters={ |
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| 51 | 0: string_to_datetime, |
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| 52 | 1: int, |
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| 53 | 2: int, |
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| 54 | }, |
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| 55 | missing_values="nan", |
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| 56 | ) |
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| 57 | except Exception as rc: |
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| 58 | print("Problem with file {} :\n{}".format(filein, rc)) |
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| 59 | exit(1) |
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| 60 | |
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| 61 | if len(data) == 24: |
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| 62 | run_mean = np.nanmean( |
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| 63 | np.array([run for _, run, _ in data]) |
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| 64 | ) |
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| 65 | pen_mean = np.nanmean( |
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| 66 | np.array([pen for _, _, pen in data]) |
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| 67 | ) |
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| 68 | run_std = np.nanstd( |
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| 69 | np.array([run for _, run, _ in data]) |
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| 70 | ) |
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| 71 | pen_std = np.nanstd( |
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| 72 | np.array([pen for _, _, pen in data]) |
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| 73 | ) |
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| 74 | else: |
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| 75 | run_mean = np.nan |
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| 76 | pen_mean = np.nan |
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| 77 | run_std = np.nan |
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| 78 | pen_std = np.nan |
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| 79 | |
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| 80 | for date, run, pen in data: |
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| 81 | if date.hour == 0: |
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| 82 | self.add_item( |
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| 83 | date, |
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| 84 | run, |
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| 85 | pen, |
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| 86 | run_mean, |
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| 87 | pen_mean, |
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| 88 | run_std, |
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| 89 | pen_std |
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| 90 | ) |
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| 91 | else: |
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| 92 | self.add_item(date, run, pen) |
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| 93 | self[date].fill() |
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| 94 | |
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| 95 | #--------------------------------------- |
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| 96 | def add_item(self, date, run=np.nan, pen=np.nan, |
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| 97 | run_mean=np.nan, pen_mean=np.nan, |
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| 98 | run_std=np.nan, pen_std=np.nan): |
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| 99 | """ |
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| 100 | """ |
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| 101 | self[date] = Conso( |
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| 102 | date, |
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| 103 | run, |
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| 104 | pen, |
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| 105 | run_mean, |
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| 106 | pen_mean, |
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| 107 | run_std, |
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| 108 | pen_std |
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| 109 | ) |
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| 110 | |
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| 111 | #--------------------------------------- |
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| 112 | def get_items_in_range(self, date_beg, date_end, inc=1): |
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| 113 | """ |
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| 114 | """ |
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| 115 | items = (item for item in self.itervalues() |
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| 116 | if item.date >= date_beg and |
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| 117 | item.date <= date_end) |
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| 118 | items = sorted(items, key=lambda item: item.date) |
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| 119 | |
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| 120 | return items[::inc] |
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| 121 | |
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| 122 | #--------------------------------------- |
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| 123 | def get_items_in_full_range(self, inc=1): |
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| 124 | """ |
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| 125 | """ |
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| 126 | items = (item for item in self.itervalues() |
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| 127 | if item.date.hour == 0) |
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| 128 | items = sorted(items, key=lambda item: item.date) |
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| 129 | |
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| 130 | return items[::inc] |
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| 131 | |
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| 132 | #--------------------------------------- |
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| 133 | def get_items(self, inc=1): |
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| 134 | """ |
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| 135 | """ |
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| 136 | items = (item for item in self.itervalues() |
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| 137 | if item.isfilled()) |
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| 138 | items = sorted(items, key=lambda item: item.date) |
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| 139 | |
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| 140 | return items[::inc] |
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| 141 | |
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| 142 | |
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| 143 | class Conso(object): |
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| 144 | #--------------------------------------- |
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| 145 | def __init__(self, date, run=np.nan, pen=np.nan, |
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| 146 | run_mean=np.nan, pen_mean=np.nan, |
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| 147 | run_std=np.nan, pen_std=np.nan): |
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| 148 | |
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| 149 | self.date = date |
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| 150 | self.run = run |
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| 151 | self.pen = pen |
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| 152 | self.run_mean = run_mean |
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| 153 | self.pen_mean = pen_mean |
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| 154 | self.run_std = run_std |
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| 155 | self.pen_std = pen_std |
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| 156 | self.filled = False |
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| 157 | |
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| 158 | #--------------------------------------- |
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| 159 | def __repr__(self): |
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| 160 | return "R{:.0f} ({:.0f}/{:.0f}) P{:.0f} ({:.0f}/{:.0f})".format( |
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| 161 | self.run, |
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| 162 | self.run_mean, |
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| 163 | self.run_std, |
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| 164 | self.pen, |
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| 165 | self.pen_mean, |
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| 166 | self.pen_std, |
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| 167 | ) |
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| 168 | |
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| 169 | #--------------------------------------- |
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| 170 | def isfilled(self): |
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| 171 | return self.filled |
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| 172 | |
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| 173 | #--------------------------------------- |
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| 174 | def fill(self): |
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| 175 | self.filled = True |
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| 176 | |
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| 177 | |
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| 178 | ######################################## |
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| 179 | def plot_init(): |
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| 180 | paper_size = np.array([29.7, 21.0]) |
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| 181 | fig, ax = plt.subplots(figsize=(paper_size/2.54)) |
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| 182 | |
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| 183 | return fig, ax |
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| 184 | |
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| 185 | |
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| 186 | ######################################## |
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| 187 | def plot_data(ax, xcoord, dates, run_jobs, pen_jobs, run_std, pen_std): |
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| 188 | """ |
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| 189 | """ |
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| 190 | line_width = 0. |
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| 191 | width = 1.05 |
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| 192 | |
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| 193 | ax.bar(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 | ax.bar(xcoord, pen_jobs, bottom=run_jobs, width=width, |
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| 198 | linewidth=line_width, align="center", |
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| 199 | color="indianred", antialiased=True, |
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| 200 | label="jobs pending") |
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| 201 | |
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| 202 | |
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| 203 | ######################################## |
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| 204 | def plot_config(fig, ax, xcoord, dates, title, conso_per_day): |
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| 205 | """ |
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| 206 | """ |
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| 207 | # ... Config axes ... |
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| 208 | # ------------------- |
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| 209 | # 1) Range |
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| 210 | xmin, xmax = xcoord[0]-1, xcoord[-1]+1 |
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| 211 | ax.set_xlim(xmin, xmax) |
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| 212 | |
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| 213 | # 2) Ticks labels |
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| 214 | (date_beg, date_end) = (dates[0], dates[-1]) |
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| 215 | |
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| 216 | if date_end - date_beg > dt.timedelta(weeks=9): |
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| 217 | date_fmt = "{:%d-%m}" |
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| 218 | maj_xticks = [x for x, d in zip(xcoord, dates) |
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| 219 | if d.weekday() == 0 and d.hour == 0] |
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| 220 | maj_xlabs = [date_fmt.format(d) for d in dates |
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| 221 | if d.weekday() == 0 and d.hour == 0] |
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| 222 | else: |
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| 223 | date_fmt = "{:%d-%m %Hh}" |
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| 224 | maj_xticks = [x for x, d in zip(xcoord, dates) |
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| 225 | if d.hour == 0 or d.hour == 12] |
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| 226 | maj_xlabs = [date_fmt.format(d) for d in dates |
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| 227 | if d.hour == 0 or d.hour == 12] |
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| 228 | |
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| 229 | ax.set_xticks(xcoord, minor=True) |
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| 230 | ax.set_xticks(maj_xticks, minor=False) |
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| 231 | ax.set_xticklabels(maj_xlabs, rotation="vertical", size="x-small") |
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| 232 | |
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| 233 | yticks = list(ax.get_yticks()) |
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| 234 | yticks.append(conso_per_day) |
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| 235 | ax.set_yticks(yticks) |
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| 236 | |
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| 237 | ax.axhline(y=conso_per_day, color="blue", alpha=0.5, |
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| 238 | label="conso journaliÚre idéale") |
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| 239 | |
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| 240 | for x, d in zip(xcoord, dates): |
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| 241 | if d.weekday() == 0 and d.hour == 0: |
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| 242 | ax.axvline(x=x, color="black", linewidth=1., linestyle=":") |
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| 243 | |
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| 244 | # 3) Define axes title |
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| 245 | ax.set_ylabel("cÅurs", fontweight="bold") |
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| 246 | ax.tick_params(axis="y", labelsize="small") |
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| 247 | |
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| 248 | # 4) Define plot size |
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| 249 | fig.subplots_adjust( |
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| 250 | left=0.08, |
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| 251 | bottom=0.09, |
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| 252 | right=0.93, |
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| 253 | top=0.93, |
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| 254 | ) |
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| 255 | |
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| 256 | # ... Main title and legend ... |
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| 257 | # ----------------------------- |
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| 258 | fig.suptitle(title, fontweight="bold", size="large") |
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| 259 | ax.legend(loc="upper right", fontsize="x-small", frameon=False) |
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| 260 | |
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| 261 | |
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| 262 | ######################################## |
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| 263 | def get_arguments(): |
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| 264 | parser = ArgumentParser() |
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| 265 | parser.add_argument("-v", "--verbose", action="store_true", |
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| 266 | help="verbose mode") |
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| 267 | parser.add_argument("-f", "--full", action="store_true", |
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| 268 | help="plot the whole period") |
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| 269 | parser.add_argument("-i", "--increment", action="store", |
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| 270 | type=int, default=1, dest="inc", |
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| 271 | help="sampling increment") |
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| 272 | parser.add_argument("-r", "--range", action="store", nargs=2, |
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| 273 | type=string_to_date, |
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| 274 | help="date range: ssaa-mm-jj ssaa-mm-jj") |
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| 275 | parser.add_argument("-s", "--show", action="store_true", |
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| 276 | help="interactive mode") |
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| 277 | parser.add_argument("-d", "--dods", action="store_true", |
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| 278 | help="copy output on dods") |
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| 279 | |
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| 280 | return parser.parse_args() |
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| 281 | |
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| 282 | |
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| 283 | ######################################## |
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| 284 | if __name__ == '__main__': |
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| 285 | |
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| 286 | # .. Initialization .. |
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| 287 | # ==================== |
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| 288 | # ... Command line arguments ... |
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| 289 | # ------------------------------ |
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| 290 | args = get_arguments() |
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| 291 | if args.verbose: |
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| 292 | print(args) |
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| 293 | |
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| 294 | # ... Turn interactive mode off ... |
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| 295 | # --------------------------------- |
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| 296 | if not args.show: |
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| 297 | import matplotlib |
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| 298 | matplotlib.use('Agg') |
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| 299 | |
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| 300 | import matplotlib.pyplot as plt |
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| 301 | # from matplotlib.backends.backend_pdf import PdfPages |
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| 302 | |
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| 303 | if not args.show: |
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| 304 | plt.ioff() |
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| 305 | |
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| 306 | # ... Files and directories ... |
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| 307 | # ----------------------------- |
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| 308 | (file_param, file_utheo) = \ |
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| 309 | get_input_files(DIR["SAVEDATA"], [OUT["PARAM"], OUT["UTHEO"]]) |
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| 310 | |
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| 311 | file_list = glob.glob(os.path.join(DIR["SAVEDATA"], |
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| 312 | "OUT_JOBS_PENDING_*")) |
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| 313 | |
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| 314 | img_name = "jobs" |
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| 315 | today = os.path.basename(file_param).strip(OUT["PARAM"]) |
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| 316 | |
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| 317 | if args.verbose: |
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| 318 | print(file_param) |
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| 319 | print(file_utheo) |
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| 320 | print(file_list) |
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| 321 | print(img_name) |
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| 322 | print(today) |
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| 323 | |
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| 324 | # .. Get project info .. |
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| 325 | # ====================== |
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| 326 | gencmip6 = Project() |
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| 327 | gencmip6.fill_data(file_param) |
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| 328 | gencmip6.get_date_init(file_utheo) |
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| 329 | |
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| 330 | # .. Fill in data .. |
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| 331 | # ================== |
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| 332 | # ... Initialization ... |
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| 333 | # ---------------------- |
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| 334 | bilan = DataDict() |
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| 335 | bilan.init_range(gencmip6.date_init, gencmip6.deadline) |
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| 336 | |
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| 337 | # ... Extract data from file ... |
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| 338 | # ------------------------------ |
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| 339 | bilan.fill_data(file_list) |
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| 340 | |
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| 341 | # # ... Compute theoratical use from known data ... |
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| 342 | # # ------------------------------------------------ |
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| 343 | # bilan.theo_equation() |
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| 344 | |
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| 345 | # .. Extract data depending on C.L. arguments .. |
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| 346 | # ============================================== |
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| 347 | if args.full: |
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| 348 | selected_items = bilan.get_items_in_full_range(args.inc) |
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| 349 | elif args.range: |
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| 350 | selected_items = bilan.get_items_in_range( |
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| 351 | args.range[0], args.range[1], args.inc |
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| 352 | ) |
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| 353 | else: |
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| 354 | selected_items = bilan.get_items(args.inc) |
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| 355 | |
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| 356 | # .. Compute data to be plotted .. |
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| 357 | # ================================ |
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| 358 | nb_items = len(selected_items) |
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| 359 | |
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| 360 | xcoord = np.linspace(1, nb_items, num=nb_items) |
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| 361 | dates = [item.date for item in selected_items] |
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| 362 | |
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| 363 | if args.full: |
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| 364 | run_jobs = np.array([item.run_mean for item in selected_items], |
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| 365 | dtype=float) |
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| 366 | pen_jobs = np.array([item.pen_mean for item in selected_items], |
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| 367 | dtype=float) |
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| 368 | run_std = np.array([item.run_std for item in selected_items], |
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| 369 | dtype=float) |
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| 370 | pen_std = np.array([item.pen_std for item in selected_items], |
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| 371 | dtype=float) |
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| 372 | else: |
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| 373 | run_jobs = np.array([item.run for item in selected_items], |
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| 374 | dtype=float) |
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| 375 | pen_jobs = np.array([item.pen for item in selected_items], |
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| 376 | dtype=float) |
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| 377 | run_std = np.nan |
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| 378 | pen_std = np.nan |
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| 379 | |
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| 380 | if args.verbose: |
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| 381 | for i in selected_items: |
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| 382 | if not np.isnan(i.run_mean): |
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| 383 | print( |
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| 384 | "{} {:13.2f} {:13.2f} {:13.2f} {:13.2f}".format( |
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| 385 | i.date, |
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| 386 | i.run_mean, i.pen_mean, |
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| 387 | i.run_std, i.pen_std |
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| 388 | ) |
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| 389 | ) |
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| 390 | |
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| 391 | conso_per_day = gencmip6.alloc / (gencmip6.days * 24.) |
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| 392 | |
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| 393 | # .. Plot stuff .. |
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| 394 | # ================ |
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| 395 | # ... Initialize figure ... |
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| 396 | # ------------------------- |
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| 397 | (fig, ax) = plot_init() |
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| 398 | |
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| 399 | # ... Plot data ... |
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| 400 | # ----------------- |
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| 401 | plot_data(ax, xcoord, dates, run_jobs, pen_jobs, run_std, pen_std) |
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| 402 | |
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| 403 | # # ... Tweak figure ... |
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| 404 | # # -------------------- |
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| 405 | # if args.max: |
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| 406 | # ymax = gencmip6.alloc |
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| 407 | # else: |
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| 408 | # ymax = np.nanmax(consos) + np.nanmax(consos)*.1 |
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| 409 | |
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| 410 | title = "Suivi des jobs {}\n({:%d/%m/%Y} - {:%d/%m/%Y})".format( |
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| 411 | gencmip6.project.upper(), |
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| 412 | gencmip6.date_init, |
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| 413 | gencmip6.deadline |
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| 414 | ) |
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| 415 | |
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| 416 | plot_config(fig, ax, xcoord, dates, title, conso_per_day) |
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| 417 | |
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| 418 | # ... Save figure ... |
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| 419 | # ------------------- |
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| 420 | img_in = os.path.join(DIR["PLOT"], "{}.pdf".format(img_name)) |
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| 421 | img_out = os.path.join(DIR["SAVEPLOT"], |
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| 422 | "{}_{}.pdf".format(img_name, today)) |
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| 423 | |
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| 424 | plot_save(img_in, img_out, title) |
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| 425 | |
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| 426 | # ... Publish figure on dods ... |
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| 427 | # ------------------------------ |
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| 428 | if args.dods: |
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| 429 | dods_cp(img_in) |
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| 430 | |
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| 431 | if args.show: |
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| 432 | plt.show() |
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| 433 | |
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| 434 | exit(0) |
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| 435 | |
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