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new plot timestamps

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pull/32380/head
Shane Smiskol 1 year ago
parent 2d47613208
commit 4110e17c75
1 changed files with 196 additions and 4 deletions
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  1. 200
      tools/latencylogger/plot_timestamps.py

200
tools/latencylogger/plot_timestamps.py
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@ -3,6 +3,9 @@ import argparse
import json
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
import numpy as np
import sys
from collections import defaultdict
@ -33,7 +36,7 @@ def plot(lr):
start_time = next(msg.logMonoTime for msg in lr)
for jmsg in timestamps:
if len(times[PLOT_SERVICES[0]]) > 400:
if len(times[PLOT_SERVICES[0]]) > 1400:
continue
# print()
@ -107,7 +110,8 @@ def plot(lr):
status, pts = scatter.contains(event)
txt.set_visible(status)
if status:
txt.set_text(points['labels'][pts['ind'][0]])
pt_idx = pts['ind'][0]
txt.set_text(f"{points['labels'][pt_idx]} ({points['x'][pt_idx]:0.2f} ms)")
txt.set_position((event.xdata, event.ydata + 1))
event.canvas.draw()
@ -118,6 +122,179 @@ def plot(lr):
return times, points
def plot_dist(lr, poll):
lr = list(lr)
# carState_service_times = []
# prev_time = None
# for msg in lr:
# if msg.which() == 'carState':
# if prev_time is not None:
# carState_service_times.append((msg.logMonoTime - prev_time) * 1e-6)
# prev_time = msg.logMonoTime
# logMonoTime is from logmessaged, not when the timestamp was created. some messages are out of order
timestamps = [json.loads(msg.logMessage) for msg in tqdm(lr) if msg.which() == 'logMessage' and '"timestamp"' in msg.logMessage]
timestamps = sorted(timestamps, key=lambda m: float(m['msg']['timestamp']['time']))
timestamps = [m for m in tqdm(timestamps) if m['ctx']['daemon'] in PLOT_SERVICES]
initialized = False
ready = False
start_card_loop = None
received_can = None
state_updated = None
sent_carState = None
state_published = None
sent_carControl = None
card_e2e_loop_times = []
card_carInterface_update_times = []
carState_recv_times = []
carControl_recv_times = []
carState_to_carControl_times = []
card_controls_times = []
card_loop_times = []
for jmsg in tqdm(timestamps):
time = int(jmsg['msg']['timestamp']['time'])
service = jmsg['ctx']['daemon']
event = jmsg['msg']['timestamp']['event']
if event == 'Initialized' and service == 'card':
initialized = True
if initialized and event == 'Start card':
ready = True
if not ready:
continue
if event == 'Start card' and service == 'card':
if start_card_loop is not None:
card_e2e_loop_times.append((time - start_card_loop) * 1e-6)
start_card_loop = time
elif event == 'Received can' and service == 'card':
# measuring from this time does not include wait time for can packet, so this measures true card loop time taken
received_can = time
elif event == 'State updated' and service == 'card':
state_updated = time
card_carInterface_update_times.append((time - received_can) * 1e-6)
elif event == 'Sent carState' and service == 'card':
sent_carState = time
elif event == 'Got carState' and service == 'controlsd':
# TODO why none
if sent_carState is not None:
carState_recv_times.append((time - sent_carState) * 1e-6)
elif event == 'Logs published' and service == 'controlsd':
sent_carControl = time
elif event == 'State published' and service == 'card':
state_published = time
if poll: # only makes sense when polling
# from carState sent to carControl received
carControl_recv_times.append((time - sent_carControl) * 1e-6)
carState_to_carControl_times.append((time - sent_carState) * 1e-6)
elif event == 'Controls updated' and service == 'card':
card_controls_times.append((time - state_published) * 1e-6)
card_loop_times.append((time - received_can) * 1e-6) # this is time NOT spent waiting for can
fig, ax = plt.subplots(3)
# plt.figure()
# plt.title('card end to end loop times')
# sns.distplot(card_e2e_loop_times)
# plt.xlabel('ms')
# plt.figure()
# plt.title('card loop times (timestamps)')
# sns.distplot(card_loop_times)
# plt.xlabel('ms')
#
# plt.figure()
# plt.title('card CI.update times')
# sns.distplot(card_carInterface_update)
# plt.xlabel('ms')
#
# plt.figure()
# plt.title('card loop times (logMonoTime)')
# sns.distplot(carState_service_times)
# plt.xlabel('ms')
#
# plt.figure()
# plt.title('carState to controlsd times')
# sns.distplot(carState_recv_times)
# plt.xlabel('ms')
#
# plt.figure()
# plt.title('carState to carControl times1')
# sns.distplot(carState_to_carControl_times, bins=50)
# plt.xlabel('ms')
#
# plt.figure()
# plt.title('carState to carControl times2')
# sns.displot(carState_to_carControl_times, bins=50, kind='hist', kde=True)
# plt.xlabel('ms')
# plt.figure()
# ax.set_title('carState to carControl times3')
fig.suptitle('Polling/waiting on carControl from controlsd' if poll else 'Not polling on carControl')
ax[0].set_title('cereal communication times')
ax[0].set_xlim(0, 16)
sns.histplot(carState_recv_times, kde=True, ax=ax[0],
label=f'carState->controlsd recv time: \n minmax: {min(carState_recv_times):0.2f}, {max(carState_recv_times):>5.2f}, ' +
f'med: {np.median(carState_recv_times):0.2f}, mean: {np.mean(carState_recv_times):0.2f}, ' +
f'95th: {np.percentile(carState_recv_times, 95):0.2f}')
if poll:
sns.histplot(carControl_recv_times, kde=True, ax=ax[0],
label=f'carControl->card recv time (polling on carControl): \n minmax: {min(carControl_recv_times):0.2f}, {max(carControl_recv_times):>5.2f}, ' +
f'med: {np.median(carControl_recv_times):0.2f}, mean: {np.mean(carControl_recv_times):0.2f}, ' +
f'95th: {np.percentile(carControl_recv_times, 95):0.2f}')
ax[0].legend()
ax[1].set_title('card loop times')
ax[1].set_xlim(0, 16)
if poll:
sns.histplot(carState_to_carControl_times, kde=True, ax=ax[1],
label=f'waiting on carControl (polling on carControl): \n minmax: {min(carState_to_carControl_times):0.2f}, {max(carState_to_carControl_times):>5.2f}, ' +
f'med: {np.median(carState_to_carControl_times):0.2f}, mean: {np.mean(carState_to_carControl_times):0.2f}, ' +
f'95th: {np.percentile(carState_to_carControl_times, 95):0.2f}')
sns.histplot(card_controls_times, kde=True, ax=ax[1], label=f'CI.apply(): \n minmax: {min(card_controls_times):0.2f}, {max(card_controls_times):>6.2f}, ' +
f'med: {np.median(card_controls_times):0.2f}, mean: {np.mean(card_controls_times):0.2f}, ' +
f'95th: {np.percentile(card_controls_times, 95):0.2f}')
sns.histplot(card_carInterface_update_times, kde=True, ax=ax[1],
label=f'CI.update(): \n minmax: {min(card_carInterface_update_times):0.2f}, {max(card_carInterface_update_times):>5.2f}, ' +
f'med: {np.median(card_carInterface_update_times):0.2f}, mean: {np.mean(card_carInterface_update_times):0.2f}, ' +
f'95th: {np.percentile(card_carInterface_update_times, 95):0.2f}')
ax[1].legend()
ax[2].set_title('total card loop time')
ax[2].set_xlim(0, 16)
sns.histplot(card_loop_times, kde=True, ax=ax[2], label=f'entire card loop time: \n minmax: {min(card_loop_times):0.2f}, {max(card_loop_times):>5.2f}, ' +
f'med: {np.median(card_loop_times):0.2f}, mean: {np.mean(card_loop_times):0.2f}, ' +
f'95th: {np.percentile(card_loop_times, 95):0.2f}')
ax[2].legend()
ax[2].set_xlabel('ms')
#
# plt.figure()
# plt.title('card CI.apply (controls) times')
# sns.distplot(card_controls_times)
# plt.xlabel('ms')
return timestamps, card_loop_times, carState_recv_times, carState_to_carControl_times
if __name__ == "__main__":
# parser = argparse.ArgumentParser(description="A tool for analyzing openpilot's end-to-end latency",
# formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@ -131,9 +308,24 @@ if __name__ == "__main__":
# r = DEMO_ROUTE if args.demo else args.route_or_segment_name.strip()
# lr = LogReader(r, sort_by_time=True)
lr = LogReader('08e4c2a99df165b1/00000016--c3a4ca99ec/0', sort_by_time=True) # normal
# lr = LogReader('08e4c2a99df165b1/00000016--c3a4ca99ec/0', sort_by_time=True) # normal
# lr = LogReader('08e4c2a99df165b1/00000017--e2d24ab118/0', sort_by_time=True) # polls on carControl
# lr = LogReader('08e4c2a99df165b1/00000018--cf65e47c24/0', sort_by_time=True) # polls on carControl, sends it earlier
# lr = LogReader('08e4c2a99df165b1/00000019--e73e3ab4df/0', sort_by_time=True) # polls on carControl, more logging
times, points = plot(lr)
# lr = LogReader('08e4c2a99df165b1/0000002c--b40eb82d6d/0:-1', sort_by_time=True) # polls on carControl
# lr = LogReader('08e4c2a99df165b1/0000002d--ccebe8b617/0:1', sort_by_time=True) # no poll on carControl
# lr = LogReader('08e4c2a99df165b1/0000002e--fd98f6603b/:7', sort_by_time=True) # no poll on carControl (no timestamps)
POLL = False # carControl polling or not
if POLL:
# lr = LogReader('08e4c2a99df165b1/00000032--2c1d57d894/0', sort_by_time=True) # carControl poll, w/ reduced timestamps
# lr = LogReader('08e4c2a99df165b1/00000033--1e2720e55b/0', sort_by_time=True) # carControl poll, w/ poll flag (FINAL)
lr = LogReader('08e4c2a99df165b1/00000036--4eb8126f04', sort_by_time=True) # carControl poll, w/ poll flag & Received can (FINAL v2)
else:
# lr = LogReader('08e4c2a99df165b1/00000031--f6f38d1ccf/0', sort_by_time=True) # no carControl poll, w/ reduced timestamps
# lr = LogReader('08e4c2a99df165b1/00000035--0abfde9c4a/0', sort_by_time=True) # no carControl poll, w/ poll flag (FINAL)
lr = LogReader('08e4c2a99df165b1/00000037--f6294815ac', sort_by_time=True) # no carControl poll, w/ poll flag & Received can (FINAL v2)
timestamps = plot_dist(lr, poll=POLL)
# times, points = plot(lr)

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