Implementing a Monsoon power monitor trace agent, utilizing the UI infrastructure that the BattOr a…

common/monsoon/monsoon/monsoon_utils.py

Index: common/monsoon/monsoon/monsoon_utils.py
diff --git a/common/monsoon/monsoon/monsoon_utils.py b/common/monsoon/monsoon/monsoon_utils.py
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index 0000000000000000000000000000000000000000..905792f2a12afb1f19695bb6a648b2add7d2a93f
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+++ b/common/monsoon/monsoon/monsoon_utils.py
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+# Copyright 2017 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.
+import collections
+import re
+
+# Named tuple for all monsoon samples.
+MonsoonSample = collections.namedtuple('MonsoonSample', 'Timestamp Amps')
+
+# Regular expression to extract timestamp and amperage from Monsoon's log.
+SampleRegex = re.compile(r"(\d+)\s+(?<![a-zA-Z:])([-+]?\d*\.?\d+)")
+
+def ReadSamplesFromFile(file):
+  """ Reads raw samples from a file in the form 'Timestamp Amps', returning
+  a list of MonsoonSample tuples.
+  """
+  samples = []
+  for line in file:
+    value_match = SampleRegex.match(line)
+    # Sometimes there can be transient USB communication errors.  They can be
+    # safely ignored.
+    if value_match is not None:
+      # Some versions of monsoon.py supply timestamp in integer.  Some later
+      # versions could in theory supply timestamp as a float.  Collapse all
+      # and treat timestamps as if they were integer seconds.
+      timestamp_second = int(value_match.group(1))
+      amps = float(value_match.group(2))
+      samples.append(MonsoonSample(Timestamp = timestamp_second, Amps = amps))
+  return samples
+
+def TransformSamplesWithFrequency(samples, frequency):
+  """ Transforms a list of MonsoonSample (with integer second accuracy) into a
+  list of MonsoonSample with fractional accuracy, assuming a fixed frequency.
+  """
+  result = []
+  second_samples = []
+  second = -1
+  last_index = len(samples) - 1
+  for i,source_sample in enumerate(samples):
+    if second == -1:
+      second = source_sample.Timestamp
+    if (source_sample.Timestamp != second) or (i == last_index):
+      # Now and then the data may have more samples than the given frequency
+      # (a desktop OS isn't an RTOS after all). Lets allow it and average out
+      # the samples.
+      if len(second_samples) > frequency:
+        second_frequency = len(second_samples)
+      else:
+        second_frequency = frequency
+      period = 1.0 / second_frequency
+      # Samples are represented as halfway through the period.
+      time_offset = -(period / 2.0)
+      if len(result) == 0:
+        time_offset += (second_frequency - len(second_samples) - 1) * period
+      for second_sample in second_samples:
+        result.append(MonsoonSample(Timestamp = second + time_offset, Amps =
+          second_sample.Amps))
+        time_offset += period
+      del second_samples[:]
+      second = source_sample.Timestamp
+    second_samples.append(source_sample)
+  return result