scripts: Add benchmark scripts and distribution logs

scripts/discardoutiers

@@ -0,0 +1,24 @@
+#!/bin/python
+
+import numpy as np
+
+def trimvalues(values, cutoff):
+    sv = sorted(values)
+    n = round(len(values) * cutoff)
+
+    var = []
+    for i in range(len(values) - n):
+        var += [np.std(sv[i:n+i])]
+
+    i = var.index(min(var))
+    return sv[i:n+i]
+#
+#A = [ 0, 10, 11, 10, 11, 20, 18, 9, 8, 10, 18,6,7,8,9,12]
+#tA = trimvalues(A, 0.7)
+#
+#
+#print("    mean   stddev")
+#print("A:  %.2f  %.2f" % (np.mean(A), np.std(A)))
+#print("tA: %.2f   %.2f" % (np.mean(tA), np.std(tA)))
+#
+#print(tA)

scripts/parse-depmod

@@ -0,0 +1,52 @@
+#!/usr/bin/python
+
+import sys
+
+
+def parse_table(f):
+    curr = 0
+    x = []
+    y = []
+
+    while (True):
+        line = f.readline()
+
+        if line == '#####\n':
+            if curr == 3:
+                break
+            else:
+                curr += 1
+        elif curr == 2:
+            v = [int(i) for i in line.strip().split()]
+            x += [v[0]]
+            y += [v[1]]
+    return x,  y
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+fig = plt.figure()
+
+with open(sys.argv[1]) as f:
+    for i in range(0, 3):
+        x, y = parse_table(f)
+        fit = np.polyfit(x, y, 1)
+        fit_fn = np.poly1d(fit)
+
+        ax = fig.add_subplot(2, 2, i)
+        ax.plot(x, y, 'b', x, fit_fn(x), 'r')
+        ax.set_xlabel('bucket')
+        ax.set_ylabel('entries')
+        ax.axis('tight')
+        ax.text(0.03, 0.97,
+                'mean=%.2f\nstddev=%.2f' % (np.mean(y), np.std(y)),
+                transform=ax.transAxes,
+                verticalalignment='top',
+                horizontalalignment='left'
+                )
+        ax.grid(True)
+
+fig.suptitle('Hash distribution on buckets', weight='bold', size='large')
+fig.tight_layout()
+plt.subplots_adjust(top=0.9)
+plt.show()