Include script to generate plot

merge-performance/graph.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+
+#from matplotlib.ticker import FuncFormatter
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.arange(2)
+#money = [1.5e5, 2.5e6, 5.5e6, 2.0e7]
+timings = [5977.214, 31.815]
+
+
+#def millions(x, pos):
+#    'The two args are the value and tick position'
+#    return '$%1.1fM' % (x * 1e-6)
+#formatter = FuncFormatter(millions)
+fig, ax = plt.subplots()
+#ax.yaxis.set_major_formatter(formatter)
+
+plt.bar(x, timings)
+logscale = False
+if logscale:
+  ax.set_yscale('log')
+  plt.yticks((10, 100, 1000, 10000), ('1', '2', '3', '4'))
+  plt.ylabel('log$_{10}$(seconds)')
+else:
+  plt.ylabel('seconds')
+plt.xticks(x, ('git-2.25.0 (Before)', 'git-devel (After)'))
+plt.title('Rebase 35 patches after renaming drivers/ $\Rightarrow$ pilots/')
+plt.savefig('timing.pdf')
+#plt.show()
+
+
+#N = 5
+#menMeans = (20, 35, 30, 35, 27)
+#womenMeans = (25, 32, 34, 20, 25)
+#menStd = (2, 3, 4, 1, 2)
+#womenStd = (3, 5, 2, 3, 3)
+#ind = np.arange(N)    # the x locations for the groups
+#width = 0.35       # the width of the bars: can also be len(x) sequence
+#
+#p1 = plt.bar(ind, menMeans, width, yerr=menStd)
+#p2 = plt.bar(ind, womenMeans, width,
+#             bottom=menMeans, yerr=womenStd)
+#
+#plt.ylabel('Scores')
+#plt.title('Scores by group and gender')
+#plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
+#plt.yticks(np.arange(0, 81, 10))
+#plt.legend((p1[0], p2[0]), ('Men', 'Women'))
+#
+#plt.show()