New rtc dump analyzing tool in Python

tools/py_event_log_analyzer/rtp_analyzer.py

+#  Copyright (c) 2016 The WebRTC project authors. All Rights Reserved.
+#
+#  Use of this source code is governed by a BSD-style license
+#  that can be found in the LICENSE file in the root of the source
+#  tree. An additional intellectual property rights grant can be found
+#  in the file PATENTS.  All contributing project authors may
+#  be found in the AUTHORS file in the root of the source tree.
+
+"""Displays statistics and plots graphs from RTC protobuf dump."""
+
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import sys
+import matplotlib.pyplot as plt
+import misc
+import numpy
+import pb_parse
+
+
+class RTPStatistics(object):
+  """Has methods for calculating and plotting statistics for RTP
+  packets.
+  """
+
+  BANDWIDTH_SMOOTHING_WINDOW_SIZE = 10
+
+  def __init__(self, data_points):
+    """Initializes object with data_points and computes simple statistics:
+    percentages of packets and packet sizes by SSRC.
+
+    Args:
+        data_points: list of pb_parse.DataPoints on which statistics are
+            calculated.
+
+    """
+
+    self.data_points = data_points
+    self.ssrc_frequencies = misc.normalize_counter(
+        collections.Counter([pt.ssrc for pt in self.data_points]))
+    self.ssrc_size_table = misc.ssrc_normalized_size_table(self.data_points)
+    self.bandwidth_kbps = None
+    self.smooth_bw_kbps = None
+
+  def print_ssrc_info(self, ssrc_id, ssrc):
+    """Prints packet and size statistics for a given SSRC.
+
+    Args:
+        ssrc_id: textual identifier of SSRC printed beside statistics for it.
+        ssrc: SSRC by which to filter data and display statistics
+    """
+    filtered_ssrc = [x for x in self.data_points if x.ssrc == ssrc]
+    payloads = misc.normalize_counter(
+        collections.Counter([x.payload_type for x in filtered_ssrc]))
+
+    payload_info = "payload type(s): {}".format(", ".join(str(x) for x
+                                                          in payloads))
+    print("{} 0x{:x} {}, {:.2f}% packets, {:.2f}% data".format(
+        ssrc_id, ssrc, payload_info, self.ssrc_frequencies[ssrc] * 100,
+        self.ssrc_size_table[ssrc] * 100))
+    print("  packet sizes:")
+    (bin_counts, bin_bounds) = numpy.histogram([x.size for x in
+                                                filtered_ssrc], bins=5,
+                                               density=False)
+    bin_proportions = bin_counts / sum(bin_counts)
+    print("\n".join([
+        " {:.1f} - {:.1f}: {:.2f}%".format(bin_bounds[i], bin_bounds[i + 1],
+                                           bin_proportions[i] * 100)
+        for i in range(len(bin_proportions))
+    ]))
+
+  def choose_ssrc(self):
+    """Queries user for SSRC."""
+
+    if len(self.ssrc_frequencies) == 1:
+      chosen_ssrc = self.ssrc_frequencies[0][-1]
+      self.print_ssrc_info("", chosen_ssrc)
+      return chosen_ssrc
+
+    for (i, ssrc) in enumerate(self.ssrc_frequencies):
+      self.print_ssrc_info(i, ssrc)
+
+    while True:
+      chosen_index = int(misc.get_input("choose one> "))
+      if 0 <= chosen_index < len(self.ssrc_frequencies):
+        return list(self.ssrc_frequencies)[chosen_index]
+      else:
+        print("Invalid index!")
+
+  def filter_ssrc(self, chosen_ssrc):
+    """Filters and wraps data points.
+
+    Removes data points with `ssrc != chosen_ssrc`. Unwraps sequence
+    numbers and timestamps for the chosen selection.
+    """
+    self.data_points = [x for x in self.data_points if x.ssrc ==
+                        chosen_ssrc]
+    unwrapped_sequence_numbers = misc.unwrap([x.sequence_number for x in
+                                              self.data_points],
+                                             2**16 - 1)
+    for (data_point, sequence_number) in zip(self.data_points,
+                                             unwrapped_sequence_numbers):
+      data_point.sequence_number = sequence_number
+
+    unwrapped_timestamps = misc.unwrap([x.timestamp for x in self.data_points],
+                                       2**32 - 1)
+
+    for (data_point, timestamp) in zip(self.data_points,
+                                       unwrapped_timestamps):
+      data_point.timestamp = timestamp
+
+  def print_sequence_number_statistics(self):
+    seq_no_set = set(x.sequence_number for x in self.data_points)
+    print("Missing sequence numbers: {} out of {}".format(
+        max(seq_no_set) - min(seq_no_set) + 1 - len(seq_no_set),
+        len(seq_no_set)
+    ))
+    print("Duplicated packets: {}".format(len(self.data_points) -
+                                          len(seq_no_set)))
+    print("Reordered packets: {}".format(
+        misc.count_reordered([x.sequence_number for x in  self.data_points])))
+
+  def estimate_frequency(self):
+    """Estimates frequency and updates data.
+
+    Guesses the most probable frequency by looking at changes in
+    timestamps (RFC 3550 section 5.1), calculates clock drifts and
+    sending time of packets. Updates `self.data_points` with changes
+    in delay and send time.
+    """
+    delta_timestamp = (self.data_points[-1].timestamp -
+                       self.data_points[0].timestamp)
+    delta_arr_timestamp = float((self.data_points[-1].arrival_timestamp_ms -
+                                 self.data_points[0].arrival_timestamp_ms))
+    fs_est = delta_timestamp / delta_arr_timestamp
+
+    fs_vec = [8, 16, 32, 48, 90]
+    fs = None
+    for f in fs_vec:
+      if abs((fs_est - f) / f) < 0.05:
+        fs = f
+
+    print("Estimated frequency: {}kHz".format(fs_est))
+    if fs is None:
+      fs = int(misc.get_input(
+          "Frequency could not be guessed. Input frequency (in kHz)> "))
+    else:
+      print("Guessed frequency: {}kHz".format(fs))
+
+    for f in self.data_points:
+      f.real_send_time_ms = (f.timestamp -
+                             self.data_points[0].timestamp) / fs
+      f.delay = f.arrival_timestamp_ms - f.real_send_time_ms
+
+  def print_duration_statistics(self):
+    """Prints delay, clock drift and bitrate statistics.
+    """
+
+    min_delay = min(f.delay for f in self.data_points)
+
+    for f in self.data_points:
+      f.absdelay = f.delay - min_delay
+
+    stream_duration_sender = self.data_points[-1].real_send_time_ms / 1000
+    print("Stream duration at sender: {:.1f} seconds".format(
+        stream_duration_sender
+    ))
+
+    arrival_timestamps_ms = [pt.arrival_timestamp_ms for pt in
+                             self.data_points]
+    stream_duration_receiver = (max(arrival_timestamps_ms) -
+                                min(arrival_timestamps_ms)) / 1000
+    print("Stream duration at receiver: {:.1f} seconds".format(
+        stream_duration_receiver
+    ))
+
+    print("Clock drift: {:.2f}%".format(
+        100 * (stream_duration_receiver / stream_duration_sender - 1)
+    ))
+
+    total_size = sum(x.size for x in self.data_points) * 8 / 1000
+    print("Send average bitrate: {:.2f} kbps".format(
+        total_size / stream_duration_sender))
+
+    print("Receive average bitrate: {:.2f} kbps".format(
+        total_size / stream_duration_receiver))
+
+  def remove_reordered(self):
+    last = self.data_points[0]
+    data_points_ordered = [last]
+    for x in self.data_points[1:]:
+      if x.sequence_number > last.sequence_number and (x.real_send_time_ms >
+                                                       last.real_send_time_ms):
+        data_points_ordered.append(x)
+        last = x
+    self.data_points = data_points_ordered
+
+  def compute_bandwidth(self):
+    """Computes bandwidth averaged over several consecutive packets.
+
+    The number of consecutive packets used in the average is
+    BANDWIDTH_SMOOTHING_WINDOW_SIZE. Averaging is done with
+    numpy.correlate.
+    """
+    self.bandwidth_kbps = []
+    for i in range(len(self.data_points) - 1):
+      self.bandwidth_kbps.append(self.data_points[i].size * 8 /
+                                 (self.data_points[i +
+                                                   1].real_send_time_ms -
+                                  self.data_points[i].real_send_time_ms)
+                                )
+    correlate_filter = (numpy.ones(
+        RTPStatistics.BANDWIDTH_SMOOTHING_WINDOW_SIZE) /
+                        RTPStatistics.BANDWIDTH_SMOOTHING_WINDOW_SIZE)
+    self.smooth_bw_kbps = numpy.correlate(self.bandwidth_kbps, correlate_filter)
+
+  def plot_statistics(self):
+    """Plots changes in delay and average bandwidth."""
+    plt.figure(1)
+    plt.plot([f.real_send_time_ms / 1000 for f in self.data_points],
+             [f.absdelay for f in self.data_points])
+    plt.xlabel("Send time [s]")
+    plt.ylabel("Relative transport delay [ms]")
+
+    plt.figure(2)
+    plt.plot([f.real_send_time_ms / 1000 for f in
+              self.data_points][:len(self.smooth_bw_kbps)],
+             self.smooth_bw_kbps[:len(self.data_points)])
+    plt.xlabel("Send time [s]")
+    plt.ylabel("Bandwidth [kbps]")
+
+    plt.show()
+
+
+def main():
+
+  if len(sys.argv) < 2:
+    print("Usage: python rtp_analyzer.py <filename of rtc event log>")
+    sys.exit(0)
+
+  data_points = pb_parse.parse_protobuf(sys.argv[1])
+  rtp_stats = RTPStatistics(data_points)
+  chosen_ssrc = rtp_stats.choose_ssrc()
+  print("Chosen SSRC: 0X{:X}".format(chosen_ssrc))
+
+  rtp_stats.filter_ssrc(chosen_ssrc)
+  print("Statistics:")
+  rtp_stats.print_sequence_number_statistics()
+  rtp_stats.estimate_frequency()
+  rtp_stats.print_duration_statistics()
+  rtp_stats.remove_reordered()
+  rtp_stats.compute_bandwidth()
+  rtp_stats.plot_statistics()
+
+if __name__ == "__main__":
+  main()