107 lines
3.3 KiB
Python
Executable file
107 lines
3.3 KiB
Python
Executable file
#!/usr/bin/python3
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import os
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import sys
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import numpy as np
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import matplotlib
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from matplotlib import pyplot as plt
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plt.style.use('ggplot')
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matplotlib.rcParams['font.size'] = 12
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matplotlib.rcParams['font.family'] = ['Exo 2', 'sans-serif']
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from bench import NSERIES
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def get_xlabels(x):
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xlabels = []
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for value in x:
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b = int(2**value)
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if b < 1024:
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xlabels.append(f'{b}B')
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elif b < 1048576:
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xlabels.append(f'{b//1024}K')
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elif b < 1073741824:
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xlabels.append(f'{b//1024//1024}M')
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else:
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xlabels.append(f'{b//1024//1024//1024}G')
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return xlabels
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def get_ylabels(y):
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ylabels = []
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for power in y:
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power = int(np.log10(power))
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if power < -6:
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value = 10**(power+9)
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ylabels.append(f'{value}ns')
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elif power < -3:
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value = 10**(power+6)
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ylabels.append(f'{value}μs')
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elif power < 0:
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value = 10**(power+3)
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ylabels.append(f'{value}ms')
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else:
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value = 10**power
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ylabels.append(f'{value}s')
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return ylabels
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prefix = 'results_ns'
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maxy = 1e1
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miny = 1e-6
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for results in (f for f in os.listdir('.') if f.startswith(prefix)):
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num_series = results.removeprefix(prefix)
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sizes, bads, goods = [], [], []
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with open(results, 'r') as fh:
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for line in fh:
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size, good, bad = line.split()
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bads.append(float(bad))
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goods.append(float(good))
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sizes.append(int(size))
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goods = np.array(goods)
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bads = np.array(bads)
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x = np.log2(sizes)
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y1 = goods
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y2 = bads
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# generate two plots: good+bad timings and slowdown plot
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plt.figure(figsize=(8.5, 7.5))
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p1, = plt.semilogy(x, y1, 'o')
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p2, = plt.semilogy(x, y2, 'o')
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plt.xlabel('size of one time series')
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plt.ylabel('loading time')
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plt.grid(None)
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plt.grid(which='both', axis='both')
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plt.xticks(x, get_xlabels(x), rotation=60)
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plt.ylim(miny, maxy)
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yticks = np.logspace(int(np.log10(miny)),
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int(np.log10(maxy)),
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num=int(np.log10(maxy/miny))+1)
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plt.yticks(yticks, get_ylabels(yticks))
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plt.tick_params(axis='y', labelright=True, right=True)
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lgd = plt.legend((p1, p2), ('good', 'bad'), frameon=True)
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lgd.get_frame().set_edgecolor('black')
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plt.title(f'Timings\n{num_series} time series')
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plt.savefig(f'loading-timings-ns{num_series}.svg')
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# slowdown plot
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plt.figure(figsize=(8.5, 7.5))
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p1, = plt.plot(x, bads/goods, 'og', label=r'$\frac{\mathrm{time\_bad}}{\mathrm{time\_good}}$')
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plt.xlabel('size of one time series')
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plt.ylabel('slowdown')
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plt.grid(None)
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plt.grid(which='both', axis='both')
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plt.xticks(x, get_xlabels(x), rotation=60)
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plt.tick_params(axis='y', which='both', reset=True, labelright=True, right=True)
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lmaxy = (bads/goods).max()
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yticks = range(0, int(np.ceil(lmaxy))+1)
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yticks_labels = []
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for i in yticks:
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if not i%5:
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yticks_labels.append(str(i))
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else:
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yticks_labels.append('')
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plt.yticks(yticks, yticks_labels)
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#plt.legend((p1,), ('time_bad/time_good',))
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lgd = plt.legend(frameon=True, fontsize=16)
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lgd.get_frame().set_edgecolor('black')
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plt.title(f'Slowdown\n{num_series} time series')
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plt.savefig(f'loading-slowdown-ns{num_series}.svg')
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