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|
#!/usr/bin/env python
#
'''
Collect and plot latency-profiling data from a running gpsd.
Requires gnuplot, but gnuplot can be on another host.
'''
# This file is Copyright (c) 2010 by the GPSD project
# SPDX-License-Identifier: BSD-2-clause
#
# Updated to conform with RCC-219-00, RCC/IRIG Standard 261-00
# "STANDARD REPORT FORMAT FOR GLOBAL POSITIONING SYSTEM (GPS) RECEIVERS AND
# SYSTEMS ACCURACY TESTS AND EVALUATIONS"
#
# TODO: put date from data on plot, not time of replot.
# TODO: add lat/lon to polar plots
#
# This code runs compatibly under Python 2 and 3.x for x >= 2.
# Preserve this property!
from __future__ import absolute_import, print_function, division
import copy
import getopt
import math
import os
import signal
import socket
import sys
import time
# pylint wants local modules last
try:
import gps
except ImportError as e:
sys.stderr.write(
"gpsprof: can't load Python gps libraries -- check PYTHONPATH.\n")
sys.stderr.write("%s\n" % e)
sys.exit(1)
gps_version = '3.19-dev'
if gps.__version__ != gps_version:
sys.stderr.write("gpsprof: ERROR: need gps module version %s, got %s\n" %
(gps_version, gps.__version__))
sys.exit(1)
debug = False
def dist_2d(a, b):
"calculate distance between a[x,y] and b[x,y]"
# x and y are orthogonal, probably lat/lon in meters
# ignore altitude change.
return math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2)
def dist_3d(a, b):
"calculate distance between a[x,y,z] and b[x,y,z]"
# x, y, and z are othogonal, probably ECEF, probably in meters
return math.sqrt((a[0] - b[0]) ** 2 +
(a[1] - b[1]) ** 2 +
(a[2] - b[2]) ** 2)
def wgs84_to_ecef(wgs84):
"Convert wgs84 coordinates to ECEF ones"
# unpack args
(lat, lon, alt) = wgs84
# convert lat/lon/altitude in degrees and altitude in meters
# to ecef x, y, z in meters
# see
# http://www.mathworks.de/help/toolbox/aeroblks/llatoecefposition.html
lat = math.radians(lat)
lon = math.radians(lon)
rad = 6378137.0 # Radius of the Earth (in meters)
f = 1.0 / 298.257223563 # Flattening factor WGS84 Model
cosLat = math.cos(lat)
sinLat = math.sin(lat)
FF = (1.0 - f) ** 2
C = 1 / math.sqrt((cosLat ** 2) + (FF * sinLat ** 2))
S = C * FF
x = (rad * C + alt) * cosLat * math.cos(lon)
y = (rad * C + alt) * cosLat * math.sin(lon)
z = (rad * S + alt) * sinLat
return (x, y, z)
class Baton(object):
"Ship progress indication to stderr."
def __init__(self, prompt, endmsg=None):
self.stream = sys.stderr
self.stream.write(prompt + "...")
if os.isatty(self.stream.fileno()):
self.stream.write(" \b")
self.stream.flush()
self.count = 0
self.endmsg = endmsg
self.time = time.time()
return
def twirl(self, ch=None):
"Twirl the baton"
if self.stream is None:
return
if ch:
self.stream.write(ch)
elif os.isatty(self.stream.fileno()):
self.stream.write("-/|\\"[self.count % 4])
self.stream.write("\b")
self.count = self.count + 1
self.stream.flush()
return
def end(self, msg=None):
"Write the end message"
if msg is None:
msg = self.endmsg
if self.stream:
self.stream.write("...(%2.2f sec) %s.\n"
% (time.time() - self.time, msg))
return
class stats(object):
"Class for 1D stats: min, max, mean, sigma, skewness, kurtosis"
def __init__(self):
self.min = 0.0
self.max = 0.0
self.mean = 0.0
self.median = 0.0
self.sigma = 0.0
self.skewness = 0.0
self.kurtosis = 0.0
def __str__(self):
"return a nice string, for debug"
return ("min %f, max %f, mean %f, median %f, sigma %f, skewedness %f, "
"kurtosis %f" %
(self.min, self.max, self.mean, self.median,
self.sigma, self.skewness, self.kurtosis))
def min_max_mean(self, fixes, index):
"Find min, max, and mean of fixes[index]"
if not fixes:
return
# might be fast to go through list once?
if isinstance(fixes[0], tuple):
self.mean = (sum([x[index] for x in fixes]) / len(fixes))
self.min = min([x[index] for x in fixes])
self.max = max([x[index] for x in fixes])
else:
# must be float
self.mean = (sum([x for x in fixes]) / len(fixes))
self.min = min([x for x in fixes])
self.max = max([x for x in fixes])
return
def moments(self, fixes, index):
"Find and set the (sigma, skewness, kurtosis) of fixes[index]"
# The skewness of a random variable X is the third standardized
# moment and is a dimension-less ratio. ntpviz uses the Pearson's
# moment coefficient of skewness. Wikipedia describes it
# best: "The qualitative interpretation of the skew is complicated
# and unintuitive." A normal distribution has a skewness of zero.
self.skewness = float('nan')
# The kurtosis of a random variable X is the fourth standardized
# moment and is a dimension-less ratio. Here we use the Pearson's
# moment coefficient of kurtosis. A normal distribution has a
# kurtosis of three. NIST describes a kurtosis over three as
# "heavy tailed" and one under three as "light tailed".
self.kurtosis = float('nan')
if not fixes:
return
m3 = 0.0
m4 = 0.0
if isinstance(fixes[0], tuple):
sum_squares = [(x[index] - self.mean) ** 2 for x in fixes]
sigma = math.sqrt(sum(sum_squares) / (len(fixes) - 1))
for fix in fixes:
m3 += pow(fix[index] - sigma, 3)
m4 += pow(fix[index] - sigma, 4)
else:
# must be float
sum_squares = [(x - self.mean) ** 2 for x in fixes]
sigma = math.sqrt(sum(sum_squares) / (len(fixes) - 1))
for fix in fixes:
m3 += pow(fix - sigma, 3)
m4 += pow(fix - sigma, 4)
self.sigma = sigma
if sigma > 0.0001:
self.skewness = m3 / (len(fixes) * pow(sigma, 3))
self.kurtosis = m4 / (len(fixes) * pow(sigma, 4))
return
class plotter(object):
"Generic class for gathering and plotting sensor statistics."
def __init__(self):
self.device = None
self.fixes = []
self.in_replot = False
self.session = None
self.start_time = int(time.time())
self.watch = set(['TPV'])
def whatami(self):
"How do we identify this plotting run?"
desc = "%s, %s, " % \
(gps.misc.isotime(self.start_time),
self.device.get('driver', "unknown"))
if 'bps' in self.device:
desc += "%d %dN%d, cycle %.3gs" % \
(self.device['bps'], 9 - self.device['stopbits'],
self.device['stopbits'], self.device['cycle'])
else:
desc += self.device['path']
if 'subtype' in self.device:
desc += "\\n%s" % self.device['subtype']
return desc
def collect(self, verb, log_fp=None):
"Collect data from the GPS."
try:
self.session = gps.gps(host=host, port=port, verbose=verb)
except socket.error:
sys.stderr.write("gpsprof: gpsd unreachable.\n")
sys.exit(1)
# Initialize
self.session.read()
if self.session.version is None:
sys.stderr.write("gpsprof: requires gpsd to speak new protocol.\n")
sys.exit(1)
# Set parameters
flags = gps.WATCH_ENABLE | gps.WATCH_JSON
if self.requires_time:
flags |= gps.WATCH_TIMING
if device:
flags |= gps.WATCH_DEVICE
try:
signal.signal(signal.SIGUSR1,
lambda empty, unused: sys.stderr.write(
"%d of %d (%d%%)..."
% (wait - countdown, wait,
((wait - countdown) * 100.0 / wait))))
signal.siginterrupt(signal.SIGUSR1, False)
self.session.stream(flags, device)
baton = Baton("gpsprof: %d looking for fix" % os.getpid(), "done")
countdown = wait
basetime = time.time()
while countdown > 0:
if self.session.read() == -1:
sys.stderr.write("gpsprof: gpsd has vanished.\n")
sys.exit(1)
baton.twirl()
if self.session.data["class"] == "ERROR":
sys.stderr.write(" ERROR: %s.\n"
% self.session.data["message"])
sys.exit(1)
if self.session.data["class"] == "DEVICES":
if len(self.session.data["devices"]) != 1 and not device:
sys.stderr.write("ERROR: multiple devices connected, "
"you must explicitly specify the "
"device.\n")
sys.exit(1)
for i in range(len(self.session.data["devices"])):
self.device = copy.copy(
self.session.data["devices"][i])
if self.device['path'] == device:
break
if self.session.data["class"] == "WATCH":
if ((self.requires_time and
not self.session.data.get("timing"))):
sys.stderr.write("timing is not enabled.\n")
sys.exit(1)
# Log before filtering - might be good for post-analysis.
if log_fp:
log_fp.write(self.session.response)
# Ignore everything but what we're told to
if self.session.data["class"] not in self.watch:
continue
# We can get some funky artifacts at start of self.session
# apparently due to RS232 buffering effects. Ignore
# them.
if ((threshold and
time.time() - basetime < self.session.cycle * threshold)):
continue
if self.session.fix.mode <= gps.MODE_NO_FIX:
continue
if self.sample():
if countdown == wait:
sys.stderr.write("first fix in %.2fsec, gathering %d "
"samples..."
% (time.time() - basetime, wait))
countdown -= 1
baton.end()
finally:
self.session.stream(gps.WATCH_DISABLE | gps.WATCH_TIMING)
signal.signal(signal.SIGUSR1, signal.SIG_DFL)
def replot(self, infp):
"Replot from a JSON log file."
self.in_replot = True
baton = Baton("gpsprof: replotting", "done")
self.session = gps.gps(host=None)
for line in infp:
baton.twirl()
self.session.unpack(line)
if self.session.data["class"] == "DEVICES":
self.device = copy.copy(self.session.data["devices"][0])
elif self.session.data["class"] not in self.watch:
continue
self.sample()
baton.end()
def dump(self):
"Dump the raw data for post-analysis."
return self.header() + self.data()
class spaceplot(plotter):
"Spatial scattergram of fixes."
name = "space"
requires_time = False
def __init__(self):
"Initialize class spaceplot"
plotter.__init__(self)
self.centroid = None
self.centroid_ecef = None
self.recentered = []
def sample(self):
"Grab samples"
# Watch out for the NaN value from gps.py.
if (((self.in_replot or self.session.valid) and
self.session.data["class"] == "TPV")):
# get sat used count
sats_used = 0
for sat in self.session.satellites:
if sat.used:
sats_used += 1
if 'alt' not in self.session.data:
self.session.data['alt'] = gps.NaN
self.fixes.append((self.session.data['lat'],
self.session.data['lon'],
self.session.data['alt'], sats_used))
return True
def header(self):
"Return header"
return "\n# Position uncertainty, %s\n" % self.whatami()
def postprocess(self):
"Postprocess the sample data"
pass
def data(self):
"Format data for dump"
res = ""
for i in range(len(self.recentered)):
(lat, lon) = self.recentered[i][:2]
(raw1, raw2, alt) = self.fixes[i]
res += "%.9f\t%.9f\t%.9f\t%.9f\t%.9f\n" \
% (lat, lon, raw1, raw2, alt)
return res
def plot(self):
"Plot the data"
stat_lat = stats()
stat_lon = stats()
stat_alt = stats()
stat_used = stats()
sats_used = []
for x in self.fixes:
# skip missing sats, if any, often missing at start
if x[3] != 0:
sats_used.append(x[3])
# calc sats used data: mean, min, max, sigma
stat_used.min_max_mean(sats_used, 0)
stat_lat.moments(sats_used, 0)
# find min, max and mean of lat/lon
stat_lat.min_max_mean(self.fixes, 0)
stat_lon.min_max_mean(self.fixes, 1)
# centroid is just arithmetic avg of lat,lon
self.centroid = (stat_lat.mean, stat_lon.mean)
# Sort fixes by distance from centroid
# sorted to make getting CEP() easy
self.fixes.sort(key=lambda p: dist_2d(self.centroid, p[:2]))
# compute min/max as meters, ignoring altitude
# EarthDistance always returns a positve value
lat_min_o = -gps.EarthDistance((stat_lat.min, self.centroid[1]),
self.centroid[:2])
lat_max_o = gps.EarthDistance((stat_lat.max, self.centroid[1]),
self.centroid[:2])
lon_min_o = -gps.EarthDistance((self.centroid[0], stat_lon.min),
self.centroid[:2])
lon_max_o = gps.EarthDistance((self.centroid[0], stat_lon.max),
self.centroid[:2])
# Convert fixes to offsets from centroid in meters
self.recentered = [
gps.MeterOffset(fix[:2], self.centroid) for fix in self.fixes]
# compute sigma, skewness and kurtosis of lat/lon
stat_lat.moments(self.recentered, 0)
stat_lon.moments(self.recentered, 1)
# CEP(50) calculated per RCC 261-00, Section 3.1.1
calc_cep = 0.5887 * (stat_lat.sigma + stat_lon.sigma)
# 2DRMS calculated per RCC 261-00, Section 3.1.4
calc_2drms = 2 * math.sqrt(stat_lat.sigma ** 2 + stat_lon.sigma ** 2)
# Compute measured CEP(50%)
# same as median distance from centroid, 50% closer, 50% further
cep_meters = gps.misc.EarthDistance(
self.centroid[:2], self.fixes[int(len(self.fixes) * 0.50)][:2])
# Compute measured CEP(95%)
# distance from centroid, 95% closer, 5% further
cep95_meters = gps.misc.EarthDistance(
self.centroid[:2], self.fixes[int(len(self.fixes) * 0.95)][:2])
# Compute measured CEP(99%)
# distance from centroid, 99% closer, 1% further
cep99_meters = gps.misc.EarthDistance(
self.centroid[:2], self.fixes[int(len(self.fixes) * 0.99)][:2])
# Compute CEP(100%)
# max distance from centroid, 100% closer, 0% further
cep100_meters = gps.misc.EarthDistance(
self.centroid[:2], self.fixes[len(self.fixes) - 1][:2])
# init altitude data
alt_ep = gps.NaN
alt_ep95 = gps.NaN
alt_ep99 = gps.NaN
dist_3d_max = 0.0
alt_fixes = []
latlon_data = ""
alt_data = ""
# grab and format the fixes as gnuplot will use them
for i in range(len(self.recentered)):
# grab valid lat/lon data, recentered and raw
(lat, lon) = self.recentered[i][:2]
alt = self.fixes[i][2]
latlon_data += "%.9f\t%.9f\n" % (lat, lon)
if not math.isnan(alt):
# only keep good fixes
alt_fixes.append(alt)
# micro meters should be good enough
alt_data += "%.6f\n" % (alt)
if alt_fixes:
# got altitude data
# find min, max and mean of altitude
stat_alt.min_max_mean(alt_fixes, 0)
# centroid in ECEF
self.centroid_ecef = wgs84_to_ecef([stat_lat.mean,
stat_lon.mean,
stat_alt.mean])
# once more through the data, looking for 3D max
for i in range(len(self.fixes)):
fix_lla = self.fixes[i][:3]
if not math.isnan(fix_lla[2]):
fix_ecef = wgs84_to_ecef(fix_lla[:3])
dist3d = dist_3d(self.centroid_ecef, fix_ecef)
if dist_3d_max < dist3d:
dist_3d_max = dist3d
# Convert fixes to offsets from avg in meters
alt_centered = []
alt_data_centered = ""
alt_fixes.sort(key=lambda a: abs(a))
for i in alt_fixes:
alt_centered.append(i - stat_alt.mean)
alt_data_centered += "%.6f\n" % (i - stat_alt.mean)
# Sort fixes by distance from average altitude
alt_centered.sort(key=lambda a: abs(a))
# so we can rank fixes for EPs
alt_ep = abs(alt_centered[int(len(alt_centered) * 0.50)])
alt_ep95 = abs(alt_centered[int(len(alt_centered) * 0.95)])
alt_ep99 = abs(alt_centered[int(len(alt_centered) * 0.99)])
stat_alt.moments(alt_centered, 0)
# HEP(50) calculated per RCC 261-00, Section 3.1.2
calc_hep = 0.6745 * stat_alt.sigma
# SEP(50) calculated per RCC 261-00, Section 3.1.3 (3)
calc_sep = 0.51 * (stat_lat.sigma +
stat_lon.sigma +
stat_alt.sigma)
# MRSE calculated per RCC 261-00, Section 3.1.5
calc_mrse = math.sqrt(stat_lat.sigma ** 2 +
stat_lon.sigma ** 2 +
stat_alt.sigma ** 2)
fmt_lab11a = ('hep = %.3f meters\\n'
'sep = %.3f meters\\n'
'mrse = %.3f meters\\n'
) % (calc_hep, calc_sep, calc_mrse)
if stat_lat.mean < 0.0:
latstring = "%.9fS" % -stat_lat.mean
elif stat_lat.mean > 0.0:
latstring = "%.9fN" % stat_lat.mean
else:
latstring = "0.0"
if stat_lon.mean < 0.0:
lonstring = "%.9fW" % -stat_lon.mean
elif stat_lon.mean > 0.0:
lonstring = "%.9fE" % stat_lon.mean
else:
lonstring = "0.0"
# oh, this is fun, mixing gnuplot and python string formatting
# Grrr, python implements %s max width or precision incorrectly...
# and the old and new styles also disagree...
fmt = ('set xlabel "Meters east from %s"\n'
'set ylabel "Meters north from %s"\n'
'cep=%.9f\n'
'cep95=%.9f\n'
'cep99=%.9f\n'
) % (lonstring, latstring,
cep_meters, cep95_meters, cep99_meters)
fmt += ('set autoscale\n'
'set multiplot\n'
# plot to use 95% of width
# set x and y scales to same distance
'set size ratio -1 0.95,0.7\n'
# leave room at bottom for computed variables
'set origin 0.025,0.30\n'
'set format x "%.3f"\n'
'set format y "%.3f"\n'
'set key left at screen 0.6,0.30 vertical\n'
'set key noautotitle\n'
'set style line 2 pt 1\n'
'set style line 3 pt 2\n'
'set style line 5 pt 7 ps 1\n'
'set xtic rotate by -45\n'
'set border 15\n'
# now the CEP stuff
'set parametric\n'
'set trange [0:2*pi]\n'
'cx(t, r) = sin(t)*r\n'
'cy(t, r) = cos(t)*r\n'
'chlen = cep/20\n'
# what do the next two lines do??
'set arrow from -chlen,0 to chlen,0 nohead\n'
'set arrow from 0,-chlen to 0,chlen nohead\n')
fmt += ('set label 11 at screen 0.01, screen 0.30 '
'"RCC 261-00\\n'
'cep = %.3f meters\\n'
'2drms = %.3f meters\\n%s'
'2d max = %.3f meters\\n'
'3d max = %.3f meters"\n'
) % (calc_cep, calc_2drms, fmt_lab11a, cep100_meters,
dist_3d_max)
# row labels
fmt += ('set label 12 at screen 0.01, screen 0.12 '
'"RCC 261-00\\n'
'\\n'
'Lat\\n'
'Lon\\n'
'Alt\\n'
'Used"\n')
# mean
fmt += ('set label 13 at screen 0.06, screen 0.12 '
'"\\n'
' mean\\n'
'%s\\n'
'%s\\n'
'%s\\n'
'%s"\n'
) % ('{:>15}'.format(latstring),
'{:>15}'.format(lonstring),
'{:>15.3f}'.format(stat_alt.mean),
'{:>15.1f}'.format(stat_used.mean))
fmt += ('set label 14 at screen 0.23, screen 0.12 '
'"\\n'
' min max sigma '
'skewness kurtosis\\n'
'%s %s %s meters %s %s\\n'
'%s %s %s meters %s %s\\n'
'%s %s %s meters %s %s\\n'
'%12d %12d %s sats"\n'
) % ('{:>10.3f}'.format(lat_min_o),
'{:>10.3f}'.format(lat_max_o),
'{:>10.3f}'.format(stat_lat.sigma),
'{:>10.1f}'.format(stat_lat.skewness),
'{:>10.1f}'.format(stat_lat.kurtosis),
'{:>10.3f}'.format(lon_min_o),
'{:>10.3f}'.format(lon_max_o),
'{:>10.3f}'.format(stat_lon.sigma),
'{:>10.1f}'.format(stat_lon.skewness),
'{:>10.1f}'.format(stat_lon.kurtosis),
'{:>10.3f}'.format(stat_alt.min - stat_alt.mean),
'{:>10.3f}'.format(stat_alt.max - stat_alt.mean),
'{:>10.3f}'.format(stat_alt.sigma),
'{:>10.1f}'.format(stat_alt.skewness),
'{:>10.1f}'.format(stat_alt.kurtosis),
stat_used.min,
stat_used.max,
'{:>10.1f}'.format(stat_used.sigma))
if debug:
fmt += ('set label 15 at screen 0.6, screen 0.12 '
'"\\n'
' min\\n'
'%s\\n'
'%s\\n'
'%s"\n'
) % ('{:>15.9f}'.format(stat_lat.min),
'{:>15.9f}'.format(stat_lon.min),
'{:>15.3f}'.format(stat_alt.min))
fmt += ('set label 16 at screen 0.75, screen 0.12 '
'"\\n'
' max\\n'
'%s\\n'
'%s\\n'
'%s"\n'
) % ('{:>15.9f}'.format(stat_lat.max),
'{:>15.9f}'.format(stat_lon.max),
'{:>15.3f}'.format(stat_alt.max))
if len(self.fixes) > 1000:
plot_style = 'dots'
else:
plot_style = 'points'
# got altitude data?
if not math.isnan(stat_alt.mean):
fmt += ('set ytics nomirror\n'
'set y2tics\n'
'set format y2 "%.3f"\n')
fmt += (('set y2label "Altitude from %.3f meters"\n') %
(stat_alt.mean))
# add ep(50)s
altitude_x = cep100_meters * 1.2
fmt += ('$EPData << EOD\n'
'%.3f %.3f\n'
'%.3f %.3f\n'
'EOD\n'
) % (altitude_x, alt_ep,
altitude_x, -alt_ep)
fmt += ('$EP95Data << EOD\n'
'%.3f %.3f\n'
'%.3f %.3f\n'
'EOD\n'
) % (altitude_x, alt_ep95,
altitude_x, -alt_ep95)
fmt += ('$EP99Data << EOD\n'
'%.3f %.3f\n'
'%.3f %.3f\n'
'EOD\n'
) % (altitude_x, alt_ep99,
altitude_x, -alt_ep99)
# setup now done, plot it!
fmt += ('plot "-" using 1:2 with %s ls 3 title "%d GPS fixes" '
', cx(t,cep),cy(t,cep) ls 1 title "CEP (50%%) = %.3f meters"'
', cx(t,cep95),cy(t,cep95) title "CEP (95%%) = %.3f meters"'
', cx(t,cep99),cy(t,cep99) title "CEP (99%%) = %.3f meters"'
) % (plot_style, len(self.fixes),
cep_meters, cep95_meters, cep99_meters)
if not math.isnan(stat_alt.mean):
# add plot of altitude
fmt += (', "-" using ( %.3f ):( $1 - %.3f ) '
'axes x1y2 with points ls 2 lc "green"'
' title " %d Altitude fixes"'
) % (cep100_meters * 1.1, stat_alt.mean, len(alt_fixes))
# altitude EPs
fmt += (', $EPData using 1:2 '
'axes x1y2 with points ls 5 lc "dark-green"'
' title " EP(50%%) = %.3f meters"'
) % (alt_ep)
fmt += (', $EP95Data using 1:2 '
'axes x1y2 with points ls 5 lc "blue"'
' title " EP(95%%) = %.3f meters"'
) % (alt_ep95)
fmt += (', $EP99Data using 1:2 '
'axes x1y2 with points ls 5 lc "red"'
' title " EP(99%%) = %.3f meters"'
) % (alt_ep99)
fmt += self.header() + latlon_data
if not math.isnan(stat_alt.mean):
# add altitude samples
fmt += 'e\n' + alt_data
return fmt
class polarplot(plotter):
"Polar plot of signal strength"
name = "polar"
requires_time = False
seen_used = [] # count of seen and used in each SKY
def __init__(self):
plotter.__init__(self)
self.watch = set(['SKY'])
def sample(self):
"Grab samples"
if self.session.data["class"] == "SKY":
sats = self.session.data['satellites']
seen = 0
used = 0
for sat in sats:
seen += 1
# u'ss': 42, u'el': 15, u'PRN': 18, u'az': 80, u'used': True
if sat['used'] is True:
used += 1
if 'polarunused' == self.name:
continue
if (('polarused' == self.name) and (sat['used'] is False)):
continue
self.fixes.append((sat['PRN'], sat['ss'], sat['az'],
sat['el'], sat['used']))
self.seen_used.append((seen, used))
return True
def header(self):
"Return header"
return "# Polar plot of signal strengths, %s\n" % self.whatami()
def postprocess(self):
"Postprocess the sample data"
pass
def data(self):
"Format data for dump"
res = ""
for (prn, ss, az, el, used) in self.fixes:
res += "%d\t%d\t%d\t%d\t%s\n" % (prn, ss, az, el, used)
return res
def plot(self):
"Format data for dump"
# calc SNR: mean, min, max, sigma
stat_ss = stats()
stat_ss.min_max_mean(self.fixes, 1)
stat_ss.moments(self.fixes, 1)
# calc sats seen data: mean, min, max, sigma
stat_seen = stats()
stat_seen.min_max_mean(self.seen_used, 0)
stat_seen.moments(self.seen_used, 0)
# calc sats used data: mean, min, max, sigma
stat_used = stats()
stat_used.min_max_mean(self.seen_used, 1)
stat_used.moments(self.seen_used, 1)
fmt = '''\
unset border
set polar
set angles degrees # set gnuplot on degrees instead of radians
set style line 10 lt 1 lc 0 lw 0.3 #redefine a new line style for the grid
set grid polar 45 #set the grid to be displayed every 45 degrees
set grid ls 10
# x is angle, go from 0 to 360 degrees
# y is radius, go from 90 at middle to 0 at edge
set xrange [0:360]
set rrange [90:0] # 90 at center
set yrange [-90:90]
# set xtics axis #display the xtics on the axis instead of on the border
# set ytics axis
set xtics axis nomirror; set ytics axis nomirror
# "remove" the tics so that only the y tics are displayed
set xtics scale 0
# set the xtics only go from 0 to 90 with increment of 30
# but do not display anything. This has to be done otherwise the grid
# will not be displayed correctly.
set xtics ("" 90, "" 60, "" 30,)
# make the ytics go from the center (0) to 360 with incrment of 90
# set ytics 0, 45, 360
set ytics scale 0
# set the ytics only go from 0 to 90 with increment of 30
# but do not display anything. This has to be done otherwise the grid
# will not be displayed correctly.
set ytics ("" 90, "" 60, "" 30,)
set size square
set key lmargin
# this places a compass label on the outside
set_label(x, text) = sprintf("set label '%s' at (93*cos(%f)), (93*sin(%f)) center", text, x, x)
# here all labels are created
# we compute North (0) at top, East (90) at right
# bug gnuplot puts 0 at right, 90 at top
eval set_label(0, "E")
eval set_label(90, "N")
eval set_label(180, "W")
eval set_label(270, "S")
set style line 11 pt 2 ps 2 #set the line style for the plot
set style fill transparent solid 0.8 noborder
set cbrange [10:60]
set palette defined (100 "blue", 200 "green", 300 "red")
'''
count = len(self.fixes)
fmt += '''\
set label 10 at screen 0.01, screen 0.15 "%s plot, samples %d"
''' % (self.name, count)
fmt += '''\
set label 11 at screen 0.01, screen 0.10 "\\nSS\\nSeen\\nUsed"
'''
fmt += '''\
set label 12 at screen 0.11, screen 0.10 "min\\n%d\\n%d\\n%d" right
''' % (stat_ss.min, stat_seen.min, stat_used.min)
fmt += '''\
set label 13 at screen 0.21, screen 0.10 "max\\n%d\\n%d\\n%d" right
''' % (stat_ss.max, stat_seen.max, stat_used.max)
fmt += '''\
set label 14 at screen 0.31, screen 0.10 "mean\\n%.1f\\n%.1f\\n%.1f" right
''' % (stat_ss.mean, stat_seen.mean, stat_used.mean)
fmt += '''\
set label 15 at screen 0.41, screen 0.10 "sigma\\n%.1f\\n%.1f\\n%.1f" right
''' % (stat_ss.sigma, stat_seen.sigma, stat_used.sigma)
fmt += '''\
# and finally the plot
# flip azimuth to plot north up, east right
# plot "-" u (90 - $3):4 t "Sat" with points ls 11
plot "-" u (90 - $3):4:(1):($2) t "Sat" w circles lc palette
'''
# return fmt + self.header() + self.data()
return self.header() + fmt + self.data()
class polarplotunused(polarplot):
"Polar plot of unused sats signal strength"
name = "polarunused"
class polarplotused(polarplot):
"Polar plot of used sats signal strength"
name = "polarused"
class timeplot(plotter):
"Time drift against PPS."
name = "time"
requires_time = True
def __init__(self):
plotter.__init__(self)
self.watch = set(['PPS'])
def sample(self):
"Grab samples"
if self.session.data["class"] == "PPS":
self.fixes.append((self.session.data['real_sec'],
self.session.data['real_nsec'],
self.session.data['clock_sec'],
self.session.data['clock_nsec']))
return True
def header(self):
"Return header"
return "# Time drift against PPS, %s\n" % self.whatami()
def postprocess(self):
"Postprocess the sample data"
pass
def data(self):
"Format data for dump"
res = ""
for (real_sec, real_nsec, clock_sec, clock_nsec) in self.fixes:
res += "%d\t%d\t%d\t%d\n" % (real_sec, real_nsec, clock_sec,
clock_nsec)
return res
def plot(self):
"Format data for dump"
fmt = '''\
set autoscale
set key below
set ylabel "System clock delta from GPS time (nsec)"
plot "-" using 0:((column(1)-column(3))*1e9 + (column(2)-column(4))) \
title "Delta" with impulses
'''
return fmt + self.header() + self.data()
class uninstrumented(plotter):
"Total times without instrumentation."
name = "uninstrumented"
requires_time = False
def __init__(self):
plotter.__init__(self)
def sample(self):
"Grab samples"
if self.session.fix.time:
seconds = time.time() - gps.misc.isotime(self.session.data.time)
self.fixes.append(seconds)
return True
return False
def header(self):
"Return header"
return "# Uninstrumented total latency, " + self.whatami() + "\n"
def postprocess(self):
"Postprocess the sample data"
pass
def data(self):
"Format data for dump"
res = ""
for seconds in self.fixes:
res += "%2.6lf\n" % seconds
return res
def plot(self):
"Plot the data"
fmt = '''\
set autoscale
set key below
set key title "Uninstrumented total latency"
plot "-" using 0:1 title "Total time" with impulses
'''
return fmt + self.header() + self.data()
class instrumented(plotter):
"Latency as analyzed by instrumentation."
name = "instrumented"
requires_time = True
def __init__(self):
"Initialize class instrumented()"
plotter.__init__(self)
def sample(self):
"Grab the samples"
if 'rtime' in self.session.data:
self.fixes.append((gps.misc.isotime(self.session.data['time']),
self.session.data["chars"],
self.session.data['sats'],
self.session.data['sor'],
self.session.data['rtime'],
time.time()))
return True
return False
def header(self):
"Return the header"
res = "# Analyzed latency, " + self.whatami() + "\n"
res += "#-- Fix time -- - Chars - -- Latency - RS232- " \
"Analysis - Recv -\n"
return res
def postprocess(self):
"Postprocess the sample data"
pass
def data(self):
"Format data for dump"
res = ""
for (fix_time, chars, sats, start, xmit, recv) in self.fixes:
rs232_time = (chars * 10.0) / self.device['bps']
res += "%.3f %9u %2u %.6f %.6f %.6f %.6f\n" \
% (fix_time, chars, sats, start - fix_time,
(start - fix_time) + rs232_time, xmit - fix_time,
recv - fix_time)
return res
def plot(self):
"Do the plot"
legends = (
"Reception delta",
"Analysis time",
"RS232 time",
"Fix latency",
)
fmt = '''\
set autoscale
set key title "Analyzed latency"
set key below
plot \\\n'''
for (i, legend) in enumerate(legends):
j = len(legends) - i + 4
fmt += ' "-" using 0:%d title "%s" with impulses, \\\n' \
% (j, legend)
fmt = fmt[:-4] + "\n"
return fmt + self.header() + (self.data() + "e\n") * len(legends)
formatters = (polarplot, polarplotunused, polarplotused, spaceplot, timeplot,
uninstrumented, instrumented)
def usage():
"Print help, then exit"
sys.stderr.write('''\
usage: gpsprof [OPTION]... [server[:port[:device]]]
[-D debuglevel]
[-d dumpfile]
[-f {%s}]
[-h]
[-l logfile]
[-m threshold]
[-n samplecount]
[-r]
[-s speed]
[-S subtitle]
[-T terminal]
[-t title]
[-V]
''' % ("|".join([x.name for x in formatters])))
sys.exit(0)
if __name__ == '__main__':
try:
(options, arguments) = getopt.getopt(sys.argv[1:],
"d:f:hl:m:n:rs:t:D:S:T:V")
plotmode = "space"
raw = False
title = None
subtitle = None
threshold = 0
wait = 100
verbose = 0
terminal = None
dumpfile = None
logfp = None
redo = False
for (switch, val) in options:
if switch == '-d':
dumpfile = val
elif switch == '-D':
# set debug level, 0 off, 1 medium, 2 high
verbose = int(val)
elif switch == '-f':
plotmode = val
elif switch == '-h':
# print help, then exit
usage()
elif switch == '-l':
logfp = open(val, "w")
elif switch == '-m':
threshold = int(val)
elif switch == '-n':
if val[-1] == 'h':
wait = int(val[:-1]) * 360
else:
wait = int(val)
elif switch == '-r':
redo = True
elif switch == '-t':
# replace title
title = val
elif switch == '-S':
# add sub title
subtitle = val
elif switch == '-T':
terminal = val
elif switch == '-V':
sys.stderr.write("gpsprof: Version %s\n" % gps_version)
sys.exit(0)
(host, port, device) = ("localhost", "2947", None)
if arguments:
args = arguments[0].split(":")
if len(args) >= 1:
host = args[0]
if len(args) >= 2:
port = args[1]
if len(args) >= 3:
device = args[2]
# Select the plotting mode
if plotmode:
for formatter in formatters:
if formatter.name == plotmode:
plot = formatter()
break
else:
sys.stderr.write("gpsprof: no such formatter.\n")
sys.exit(1)
# Get fix data from the GPS
if redo:
plot.replot(sys.stdin)
else:
plot.collect(verbose, logfp)
plot.postprocess()
# Save the timing data (only) for post-analysis if required.
if dumpfile:
with open(dumpfile, "w") as fp:
fp.write(plot.dump())
if logfp:
logfp.close()
# Ship the plot to standard output
if not title:
title = plot.whatami()
# escape " for gnuplot
title = title.replace('"', '\\"')
if subtitle:
title += '\\n' + subtitle
if terminal:
sys.stdout.write("set terminal %s truecolor enhanced size"
" 800,950\n"
% terminal)
# double quotes on title so \n is parsed by gnuplot
sys.stdout.write('set title noenhanced "%s"\n' % title)
sys.stdout.write(plot.plot())
except KeyboardInterrupt:
pass
# The following sets edit modes for GNU EMACS
# Local Variables:
# mode:python
# End:
|