add all materials

benchmark_python/bench_plot.py

@@ -0,0 +1,106 @@
+#!/usr/bin/python3
+import os
+import sys
+
+import numpy as np
+import matplotlib
+from matplotlib import pyplot as plt
+plt.style.use('ggplot')
+matplotlib.rcParams['font.size'] = 12
+matplotlib.rcParams['font.family'] = ['Exo 2', 'sans-serif']
+
+from bench import NSERIES
+
+def get_xlabels(x):
+    xlabels = []
+    for value in x:
+        b = int(2**value)
+        if b < 1024:
+            xlabels.append(f'{b}B')
+        elif b < 1048576:
+            xlabels.append(f'{b//1024}K')
+        elif b < 1073741824:
+            xlabels.append(f'{b//1024//1024}M')
+        else:
+            xlabels.append(f'{b//1024//1024//1024}G')
+    return xlabels
+
+def get_ylabels(y):
+    ylabels = []
+    for power in y:
+        power = int(np.log10(power))
+        if power < -6:
+            value = 10**(power+9)
+            ylabels.append(f'{value}ns')
+        elif power < -3:
+            value = 10**(power+6)
+            ylabels.append(f'{value}μs')
+        elif power < 0:
+            value = 10**(power+3)
+            ylabels.append(f'{value}ms')
+        else:
+            value = 10**power
+            ylabels.append(f'{value}s')
+    return ylabels
+
+prefix = 'results_ns'
+maxy = 1e1
+miny = 1e-6
+for results in (f for f in os.listdir('.') if f.startswith(prefix)):
+    num_series = results.removeprefix(prefix)
+
+    sizes, bads, goods = [], [], []
+    with open(results, 'r') as fh:
+        for line in fh:
+            size, good, bad = line.split()
+            bads.append(float(bad))
+            goods.append(float(good))
+            sizes.append(int(size))
+    goods = np.array(goods)
+    bads = np.array(bads)
+    x = np.log2(sizes)
+    y1 = goods
+    y2 = bads
+    # generate two plots: good+bad timings and slowdown plot
+    plt.figure(figsize=(8.5, 7.5))
+    p1, = plt.semilogy(x, y1, 'o')
+    p2, = plt.semilogy(x, y2, 'o')
+    plt.xlabel('size of one time series')
+    plt.ylabel('loading time')
+    plt.grid(None)
+    plt.grid(which='both', axis='both')
+    plt.xticks(x, get_xlabels(x), rotation=60)
+    plt.ylim(miny, maxy)
+    yticks = np.logspace(int(np.log10(miny)),
+                         int(np.log10(maxy)),
+                         num=int(np.log10(maxy/miny))+1)
+    plt.yticks(yticks, get_ylabels(yticks))
+    plt.tick_params(axis='y', labelright=True, right=True)
+    lgd = plt.legend((p1, p2), ('good', 'bad'), frameon=True)
+    lgd.get_frame().set_edgecolor('black')
+    plt.title(f'Timings\n{num_series} time series')
+    plt.savefig(f'loading-timings-ns{num_series}.svg')
+
+    # slowdown plot
+    plt.figure(figsize=(8.5, 7.5))
+    p1, = plt.plot(x, bads/goods, 'og', label=r'$\frac{\mathrm{time\_bad}}{\mathrm{time\_good}}$')
+    plt.xlabel('size of one time series')
+    plt.ylabel('slowdown')
+    plt.grid(None)
+    plt.grid(which='both', axis='both')
+    plt.xticks(x, get_xlabels(x), rotation=60)
+    plt.tick_params(axis='y', which='both', reset=True, labelright=True, right=True)
+    lmaxy = (bads/goods).max()
+    yticks = range(0, int(np.ceil(lmaxy))+1)
+    yticks_labels = []
+    for i in yticks:
+        if not i%5:
+            yticks_labels.append(str(i))
+        else:
+            yticks_labels.append('')
+    plt.yticks(yticks, yticks_labels)
+    #plt.legend((p1,), ('time_bad/time_good',))
+    lgd = plt.legend(frameon=True, fontsize=16)
+    lgd.get_frame().set_edgecolor('black')
+    plt.title(f'Slowdown\n{num_series} time series')
+    plt.savefig(f'loading-slowdown-ns{num_series}.svg')