# 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 numpy import misc import pb_parse class RTPStatistics(object): """Has methods for calculating and plotting RTP stream statistics.""" BANDWIDTH_SMOOTHING_WINDOW_SIZE = 10 def __init__(self, data_points): """Initializes object with data_points and computes simple statistics. Computes percentages of number 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 = [point for point in self.data_points if point.ssrc == ssrc] payloads = misc.normalize_counter( collections.Counter([point.payload_type for point in filtered_ssrc])) payload_info = "payload type(s): {}".format( ", ".join(str(payload) for payload 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([point.size for point 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 = [point for point in self.data_points if point.ssrc == chosen_ssrc] unwrapped_sequence_numbers = misc.unwrap( [point.sequence_number for point 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([point.timestamp for point 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(point.sequence_number for point 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([point.sequence_number for point 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)) freq_est = delta_timestamp / delta_arr_timestamp freq_vec = [8, 16, 32, 48, 90] freq = None for f in freq_vec: if abs((freq_est - f) / f) < 0.05: freq = f print("Estimated frequency: {}kHz".format(freq_est)) if freq is None: freq = int(misc.get_input( "Frequency could not be guessed. Input frequency (in kHz)> ")) else: print("Guessed frequency: {}kHz".format(freq)) for point in self.data_points: point.real_send_time_ms = (point.timestamp - self.data_points[0].timestamp) / freq point.delay = point.arrival_timestamp_ms -point.real_send_time_ms def print_duration_statistics(self): """Prints delay, clock drift and bitrate statistics.""" min_delay = min(point.delay for point in self.data_points) for point in self.data_points: point.absdelay = point.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 = [point.arrival_timestamp_ms for point 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(point.size for point 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 point in self.data_points[1:]: if point.sequence_number > last.sequence_number and ( point.real_send_time_ms > last.real_send_time_ms): data_points_ordered.append(point) last = point 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 ") 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()