Add nanobench stats scripts to Skia repo.
These are the scripts I've been homegrowing for measuring perf impact. I think we found them useful today as a way of sifting through the noise.
BUG=skia:
Review URL: https://codereview.chromium.org/703713002
diff --git a/bin/ac b/bin/ac
new file mode 100755
index 0000000..d9e0fd3
--- /dev/null
+++ b/bin/ac
@@ -0,0 +1,24 @@
+#!/bin/sh
+
+set -e
+
+BRANCH=$(git branch | grep \* | cut -d" " -f 2)
+CLEAN=${CLEAN-clean}
+SAMPLES=100
+
+if [ $BRANCH == $CLEAN ]; then
+ echo "Comparing $BRANCH to itself."
+ exit 1
+fi
+
+git checkout $CLEAN
+./gyp_skia >/dev/null
+platform_tools/android/bin/android_ninja -t Release nanobench
+platform_tools/android/bin/android_run_skia -t Release nanobench $@ --skps /data/local/tmp/skps -i /data/local/tmp/resources --samples $SAMPLES -v > $CLEAN.log
+
+git checkout $BRANCH
+./gyp_skia >/dev/null
+platform_tools/android/bin/android_ninja -t Release nanobench
+platform_tools/android/bin/android_run_skia -t Release nanobench $@ --skps /data/local/tmp/skps -i /data/local/tmp/resources --samples $SAMPLES -v > $BRANCH.log
+
+compare $CLEAN.log $BRANCH.log
diff --git a/bin/c b/bin/c
new file mode 100755
index 0000000..a7b752c
--- /dev/null
+++ b/bin/c
@@ -0,0 +1,24 @@
+#!/bin/sh
+
+set -e
+
+BRANCH=$(git branch | grep \* | cut -d" " -f 2)
+CLEAN=${CLEAN-clean}
+SAMPLES=100
+
+if [ $BRANCH == $CLEAN ]; then
+ echo "Comparing $BRANCH to itself."
+ exit 1
+fi
+
+git checkout $CLEAN
+./gyp_skia >/dev/null
+ninja -C out/Release nanobench
+out/Release/nanobench $@ --samples $SAMPLES -v 2> $CLEAN.log
+
+git checkout $BRANCH
+./gyp_skia >/dev/null
+ninja -C out/Release nanobench
+out/Release/nanobench $@ --samples $SAMPLES -v 2> $BRANCH.log
+
+compare $CLEAN.log $BRANCH.log
diff --git a/bin/compare b/bin/compare
new file mode 100755
index 0000000..fe489ab
--- /dev/null
+++ b/bin/compare
@@ -0,0 +1,39 @@
+#!/usr/bin/env python
+
+import sys
+from scipy.stats import mannwhitneyu
+
+SIGNIFICANCE_THRESHOLD = 0.0001
+
+a,b = {},{}
+for (path, d) in [(sys.argv[1], a), (sys.argv[2], b)]:
+ for line in open(path):
+ try:
+ tokens = line.split()
+ samples = tokens[:-1]
+ label = tokens[-1]
+ d[label] = map(float, samples)
+ except:
+ pass
+
+common = set(a.keys()).intersection(b.keys())
+
+ps = []
+for key in common:
+ _, p = mannwhitneyu(a[key], b[key]) # Non-parametric t-test. Doesn't assume normal dist.
+ am, bm = min(a[key]), min(b[key])
+ ps.append((bm/am, p, key, am, bm))
+ps.sort(reverse=True)
+
+def humanize(ns):
+ for threshold, suffix in [(1e9, 's'), (1e6, 'ms'), (1e3, 'us'), (1e0, 'ns')]:
+ if ns > threshold:
+ return "%.3g%s" % (ns/threshold, suffix)
+
+maxlen = max(map(len, common))
+
+# We print only signficant changes in benchmark timing distribution.
+bonferroni = SIGNIFICANCE_THRESHOLD / len(ps) # Adjust for the fact we've run multiple tests.
+for ratio, p, key, am, bm in ps:
+ if p < bonferroni:
+ print '%*s\t%6s -> %6s\t%.2gx' % (maxlen, key, humanize(am), humanize(bm), ratio)