def main(argv):
if len(argv) != 3:
print "Usage: python plot_ping_suffix.py mode file_suffix json_file"
exit()
mode = argv[0]
if mode not in MODE:
print "mode %s not recognized, set to default mode 'avg', all possible modes are %s" % (mode, MODE)
mode = 'avg'
suffix = argv[1]
measure_id = argv[-1]
filename = measure_id + '.json'
if not os.path.isfile(filename):
print"Measurement results for #%s doesn't exist." % measure_id
exit()
trace_dict = at.readPingJSON(filename)
pdId_dict = read_by_suffix(suffix)
for f in pdId_dict:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
y_lable = []
yi=1
for probe in pdId_dict[f]:
#print trace_dict[probe][mode]
#time.sleep(10)
trace = [-1 if x < 0 else x for x in trace_dict[probe][mode]]
ax.plot(xs=trace_dict[probe]['time_md'], ys=[yi]*len(trace_dict[probe]['time_md']),zs=trace, ls='-', lw=0.5, color='black', marker='None')
y_lable.append(probe)
yi += 1
ax.xaxis.set_major_locator(mdates.MinuteLocator(interval=1440))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
ax.yaxis.set_major_locator(MultipleLocator(1))
ax.set_yticklabels(y_lable)
ax.set_ylabel('Probe ID', fontsize=16)
ax.set_xlabel('Time', fontsize=16)
ax.set_zlabel(MODE_LABEL[mode], fontsize=16)
fig.set_size_inches(12, 9)
#plt.show()
fig_name = os.path.join(os.getcwd(), 'rtt3d_%s.pdf'% f)
plt.savefig(fig_name, bbox_inches='tight', format='pdf')
plt.close()
def main(argv):
traceflag =False
if len(argv) != 2:
print "Usage: python cleaning.py t/p(trace/ping) filename"
exit()
trace = argv[0]
if trace in TRACE:
traceflag = True
filename = argv[1]
if not os.path.isfile(filename):
print "Measurement file %s doesn't exist." % filename
exit()
pb_to_rm = set([])
if traceflag:
print "Traceroute trace.\n" + \
"An ip-path\n" + \
"- ends with *;\n" + \
"- or contains more than %f *;\n" % INV_PATH + \
"- or contains five or more consecutive *" + \
"is considered invalid."
trace_dict = at.readTraceJSON(filename)
min_len = LEN_P * TRACE_LEN
max_intv = INTV_MX * TRACE_INTV
inv_len = INV_TRACE * TRACE_LEN
#fsave = 'trace_rm.txt'
fsave = PROBE_ID_TRACE_RM_FILE
fval = PROBE_ID_TRACE_VAL_FILE
for pbid in trace_dict:
path_val_flag = []
for p in trace_dict[pbid]['ip_path']:
if path_val(p, INV_PATH):
path_val_flag.append(1)
else:
path_val_flag.append(-1)
trace_dict[pbid]['path_val'] = path_val_flag
val_check = 'path_val'
else:
print "Ping trace\n" + \
"An RTT measurement\n" + \
"- equals -1; \n" + \
"- missing value;\n" + \
"- contains err field; \n" + \
"is considered invalid."
trace_dict = at.readPingJSON(filename)
min_len = LEN_P * PING_LEN
max_intv = INTV_MX * PING_INTV
inv_len = INV_PING * PING_LEN
val_check = 'avg'
#fsave = 'ping_rm.txt'
fsave = PROBE_ID_PING_RM_FILE
fval = PROBE_ID_PING_VAL_FILE
print "\nCleaning criteria:\n\
Minimum length: %f,\n\
Maximum neighbour interval: %f,\n\
Maximum invalid values: %f." % (min_len, max_intv, inv_len)
for pbid in trace_dict:
if not plft_stab(trace_dict[pbid]['time_epc'], max_intv, min_len):
pb_to_rm.add(pbid)
if trace_dict[pbid][val_check].count(-1) > inv_len:
pb_to_rm.add(pbid)
if pb_to_rm:
print "Probes to be removed:"
print "{id:<7}{len_:>10}{intv:>10}{invd:>10}".format(id='ID', len_='Len.',
intv='Max. Intv', invd='# Invd.')
for pb in pb_to_rm:
print "{id:>7d}{len_:>10d}{intv:>10d}{invd:>10d}".format(id=pb,
len_=len(trace_dict[pb]['time_epc']),
intv=max(interv(trace_dict[pb]['time_epc'])),
invd=trace_dict[pb][val_check].count(-1))
clean_trace = removekey(trace_dict, list(pb_to_rm))
else:
clean_trace = trace_dict.copy()
print "%d probes in all, %d probes after cleaning" % (len(trace_dict), len(clean_trace))
f = open(fsave, 'w')
for pb in list(pb_to_rm):
f.write("%d\n" % pb)
f.close()
f = open(fval, 'w')
for pb in clean_trace:
f.write("%d\n" % pb)
f.close()
def main(argv):
mode = 'avg'
if len(argv) != 1:
print "Usage: python alignRTT.py mode\n\
all possible modes are %s." % MODE
exit()
mode = argv[0]
if mode not in MODE:
print "mode %s not recognized, set to default mode 'avg', \
all possible modes are %s" % (mode, MODE)
mode = 'avg'
valid_id = read_pdid(PROBE_ID_VALID_FILE)
alltrace = at.readPingJSON(MES_PING_FILE)
pbAct = alltrace.keys()
pbVal = list(set(valid_id) & set(pbAct))
clean_trace = {k: alltrace[k] for k in pbVal}
print len(clean_trace)
#Quantify measurment dis-sync in time with k-means
len_sort = sorted(clean_trace.items(), key=lambda s:len(s[1]['time_epc']), reverse=True)
#find the probe with maximum data length
max_len_pb = len_sort[0][0]
max_length = len(len_sort[0][1]['time_epc'])
all_time_epc = []
for pb in clean_trace:
for t in clean_trace[pb]['time_epc']:
all_time_epc.append(t)
all_time_epc = np.array(all_time_epc).astype(float).reshape(len(all_time_epc),1)
# use the timestamps of the probe with max data length as the beginning centroids
book = np.array(clean_trace[max_len_pb]['time_epc']).astype(float).reshape(max_length,1)
alTP, dist = kmeans(all_time_epc, book)
alTP = sorted(alTP)
alTP = [i[0] for i in alTP]
tpIntv = interv(alTP)
#print "In average measurements are dis-synchronized by %.3fsec." % dist
print "Calculating 'aligned' timestamps that minimize time distortion after alignment"
print "Aligned timestamps have:\n\
- mean interval of %.3fsec;\n\
- interval std of %.3fsec." % (np.mean(tpIntv), np.std(tpIntv))
print "RTT measurements of each probe will be aligned to the cloest 'aligned' timestamp."
filename = 'pingAL_'+mode+'.csv'
f = open(filename, 'w')
line = 'id,' + ','.join(['%d'%round(t) for t in alTP]) + '\n'
f.write(line)
mean_time_dist = 0
for pb in clean_trace:
align = np.zeros(max_length) # max_length equals the length of alTP
# for i in range(len(clean_trace[pb]['time_epc'])):
# distance = []
# for c in alTP:
# distance.append(np.linalg.norm(c - clean_trace[pb]['time_epc'][i]))
# lb = np.argmin(distance)
# # assign RTT measuremrnt to the cloest aligend timestamp
# align[lb] = clean_trace[pb][mode][i]
# it actually sounds like Dynamic Time Wrapping...
last_match = 0
time_dis = []
for i in range(len(clean_trace[pb]['time_epc'])):
last_dist = 10e6
last_pose = -1
for j in range(last_match, max_length, 1): # begin the search from last matched value
distance = abs(alTP[j] - clean_trace[pb]['time_epc'][i])
if distance < last_dist: # if the distance is deacreasing, continue
last_dist = distance
last_pose = j
else: # if otherwise, last searched al timestamp must be the closest
break
align[last_pose] = clean_trace[pb][mode][i] # assigne the measurment to the aligend postion
last_match = last_pose # update last match position
time_dis.append(last_dist)
if last_match == (max_length - 1):
# if already at the end of aligned ts, stop the procedure, even if there are still values left
break
clean_trace[pb]['time_dis'] = time_dis
print "Probe %d, time dist: avg. %8.3f, max. %8.3f at %d" % (pb, np.mean(time_dis), np.max(time_dis), np.argmax(time_dis))
mean_time_dist += np.mean(time_dis)
# in case 0 or -1 align list,
# assigne the cloest (in time both direction) valide value
for i in range(max_length):
#if align[i] == 0 or align[i] == -1:
if align[i] <= 0:
left_candi = -1
right_candi = -1
for j in range(1,5,1): # neighbour search range
if left_candi == -1:
if (i-j >= 0) and (align[i-j] != 0 and align[i-j] != -1):
left_candi = i-j
if right_candi == -1:
if (i+j < max_length) and (align[i+j] != 0 and align[i+j] != -1):
right_candi = i+j
if left_candi + right_candi > -2: #at least one candidate
break
if left_candi == -1:
align[i] = align[right_candi]
elif right_candi == -1:
align[i] = align[left_candi]
elif (alTP[i] - alTP[left_candi]) <= (alTP[right_candi] - alTP[i]):
align[i] = align[left_candi]
else:
align[i] = align[right_candi]
clean_trace[pb]['align'] = align
line = "%s," % pb + ",".join(map(str, clean_trace[pb]['align'])) + '\n'
f.write(line)
f.close()
print "mean time dist of all probes: %.3f" % (float(mean_time_dist)/len(clean_trace))
