def main(path):
from act_parser import ActParser
print "Reading activity data from %s..." % (path),
ap = ActParser(path)
print "done"
for n, s in enumerate(normalize(ap.samples())):
print "%6d: %s" % (n, s)
return 0
def main(path):
from act_parser import ActParser
print "Reading activity data from %s..." % (path),
ap = ActParser(path)
print "done"
distances = [0.0, 0.0]
for n, d in enumerate(ActSampleDiff.diffs_between_samples(ap.samples())):
distances[0] += d.gps_d
distances[1] += d.d
print "%6d: %s" % (n, d)
print "Total distance: %5.2fm vs. %5.2fm" % (distances[0], distances[1])
return 0
def main(path1, path2):
print "Reading activity data from %s..." % (path1),
p1 = ActParser(path1)
print "done"
print "Reading activity data from %s..." % (path2),
p2 = ActParser(path2)
print "done"
compfunc = ActSample.comparator(alt=0.25, d=0.1, v=0.1, hr=0.1, cad=0.1)
for s1, s2 in zip(p1.samples(), p2.samples()):
if compfunc(s1, s2):
print "-%s" % (s1)
print "+%s" % (s2)
return 0
def main(path1, path2):
from act_parser import ActParser
print "# Reading activity data from %s..." % (path1),
p1 = ActParser(path1)
l1 = list(normalize(p1.samples(), ignore_gps=True))
print "done, read %d samples" % (len(l1))
print "# Reading activity data from %s..." % (path2),
p2 = ActParser(path2)
l2 = list(normalize(p2.samples(), ignore_gps=True))
print "done, read %d samples" % (len(l2))
by_hr = by_key(lambda x: x.hr)
for i, (a, b) in enumerate(correlated_samples(l1, l2, score=by_hr)):
print "%6d: %s" % (i, a)
print " %s" % (b)
def main(path1, path2, smooth=1, skip=0, stop=sys.maxint, low=0, high=9999):
from act_parser import ActParser
print "# Reading activity data from %s..." % (path1),
p1 = ActParser(path1)
l1 = list(normalize(p1.samples(), ignore_gps=True))
print "done, read %d samples" % (len(l1))
print "# Reading activity data from %s..." % (path2),
p2 = ActParser(path2)
l2 = list(normalize(p2.samples(), ignore_gps=True))
print "done, read %d samples" % (len(l2))
smooth = int(smooth)
assert smooth >= 1
skip = parse_seconds(skip)
assert skip >= 0
stop = parse_seconds(stop)
assert stop > skip
low = int(low)
high = int(high)
assert high > low
# Correlate by HR (assumes both activities picked up the same HR data.)
by_hr = by_key(lambda x: x.hr)
sum1 = sum2 = 0
n = 0
for i, (pwr1, pwr2) in enumerate(smoother(extract_pwr(l1, l2, score=by_hr), smooth)):
if i < skip or (pwr1 + pwr2 < 2 * low) or (pwr1 + pwr2 > 2 * high):
continue
if i >= stop:
break
sum1 += pwr1
sum2 += pwr2
n += 1
try:
percent = float(pwr2) / pwr1 * 100
except ZeroDivisionError:
percent = float("inf")
print "%6d: %4dW, %4dW => %3.1f%%" % (i, pwr1, pwr2, percent)
percent = float(sum2) / sum1 * 100
print "Average across %d samples: %4.2fW, %4.2fW => %3.3f%%" % (
n,
float(sum1) / n,
float(sum2) / n,
percent)
print "Difference from %s to %s: %4.2fW/%3.3f%%" % (
path1, path2,
float(sum2) / n - float(sum1) / n, percent - 100)
def main(path1, path2, logfile=None):
from act_parser import ActParser
from act_sample_norm import normalize
print_samples = False
if logfile:
log = open(logfile, "w")
def log_score_enabled(o, s):
print >> log, o, s
global log_score
log_score = log_score_enabled
else:
log = sys.stdout
print_samples = True
print >> log, "# Reading activity data from %s..." % (path1),
p1 = ActParser(path1)
l1 = list(normalize(p1.samples()))
print >> log, "done, read %d samples" % (len(l1))
print >> log, "# Reading activity data from %s..." % (path2),
p2 = ActParser(path2)
l2 = list(normalize(p2.samples()))
print >> log, "done, read %d samples" % (len(l2))
o = int(l1[0].t - l2[0].t)
print >> log, "# Timestamps are offset by %ds" % (o)
if abs(o) > min(len(l1), len(l2)):
print >> log, "# Offset is too big. Assuming clocks out-of-whack..."
o = 0
o, score = find_local_max_correlation_offset(l1, l2, o)
print >> log, "# Minimum score at offset %d is %.2f m" % (o, score)
if print_samples:
for s, t in zip_samples_at_offset(l1, l2, o):
print >> log, "{}\n\t{}\n\t{}".format(geodistance(s, t), s, t)
return 0
def main(path):
print "Reading activity data from %s..." % (path),
ap = ActParser(path)
print "done"
seconds, records = 0, 0
gps_dist, int_dist = 0, 0
dist_diff = 0
for d in ActSampleDiff.diffs_between_samples(ap.samples()):
if d.t > 1:
logger.warn("Lost %ds after %ds" % (d.t - 1, seconds))
records += 1
seconds += d.t
gps_dist += d.gps_d
int_dist += d.d
dd = gps_dist - int_dist
if abs(dd - dist_diff) > 1:
print "GPS - Int. distance -> %.1fm after %ds" % (dd, seconds)
dist_diff = dd
missing = seconds - records
print "Found %d records in %ds, %d records (=%.2f%%) missing)" % (
records, seconds, missing, 100.0 * missing / seconds)
print "Internal distance: %.3fkm" % (int_dist / 1000.0)
print "Calc. distance from GPS coords: %.3fkm" % (gps_dist / 1000.0)
print "Int./GPS distance is %.2f%%" % (100.0 * int_dist / gps_dist)