Add some statistics to the monsoon profile run

tools/telemetry/telemetry/core/platform/profiler/monsoon_profiler.py

 # Copyright 2013 The Chromium Authors. All rights reserved.
 # Use of this source code is governed by a BSD-style license that can be
 # found in the LICENSE file.
 
 """Profiler using data collected from a Monsoon power meter.
 
 http://msoon.com/LabEquipment/PowerMonitor/
 Data collected is a namedtuple of (amps, volts), at 5000 samples/second.
 Output graph plots power in watts over time in seconds.
 """
 
 import csv
 import multiprocessing
-
 from telemetry.core import exceptions
 from telemetry.core.platform import profiler
 from telemetry.core.platform.profiler import monsoon
+from telemetry.util import statistics
 
 
 def _CollectData(output_path, is_collecting):
   mon = monsoon.Monsoon(wait=False)
   # Note: Telemetry requires the device to be connected by USB, but that
   # puts it in charging mode. This increases the power consumption.
   mon.SetUsbPassthrough(1)
   # Nominal Li-ion voltage is 3.7V, but it puts out 4.2V at max capacity. Use
   # 4.0V to simulate a "~80%" charged battery. Google "li-ion voltage curve".
   # This is true only for a single cell. (Most smartphones, some tablets.)
@@ skipping 14 matching lines @@
   # Add x-axis labels.
   plot_data = [(i / 5000., sample.amps * sample.volts)
                for i, sample in enumerate(samples)]
 
   # Print data in csv.
   with open(output_path, 'w') as output_file:
     output_writer = csv.writer(output_file)
     output_writer.writerows(plot_data)
     output_file.flush()
 
+  power = []
+  for sample in samples:
+    power.append(sample.amps * sample.volts)

[tonyg, 2014/03/12 15:28:38]

Neat pythonism:

power_samples = [s.amps * s.volts for s in samples]

+
+  print ('Power total=%f' % statistics.TrapezoidalRule(power, 1/5000.) +

[tonyg, 2014/03/12 15:28:38]

I'm wondering whether the labels would be better as:

Energy consumption=...
Average power=...
Peak power=...
Profile duration=...

Also, recommend line breaks between each.

+      ' average=%f' % statistics.ArithmeticMean(power) +
+      '+-%f' % statistics.StandardDeviation(power) +
+      ' peak=%f' % max(power) +
+      ' time=%f' % (len(power) / 5000.))
+
   print 'To view the Monsoon profile, run:'
   print ('  echo "set datafile separator \',\'; plot \'%s\' with lines" | '
       'gnuplot --persist' % output_path)
 
 
 class MonsoonProfiler(profiler.Profiler):
   def __init__(self, browser_backend, platform_backend, output_path, state):
     super(MonsoonProfiler, self).__init__(
         browser_backend, platform_backend, output_path, state)
     # We collect the data in a separate process, so we can continuously
@@ skipping 16 matching lines @@
       monsoon.Monsoon(wait=False)
     except EnvironmentError:
       return False
     else:
       return True
 
   def CollectProfile(self):
     self._is_collecting.clear()
     self._collector.join()
     return [self._output_path]