def analyse_cmpexp(
exp_list='experiments_completed.txt',
res_dir='',
out_dir='',
source_filter='',
min_values='3',
omit_const='0',
metric='throughput',
ptype='box',
variables='',
out_name='',
ymin='0',
ymax='0',
lnames='',
group_by_prefix='0',
omit_const_xlab_vars='0',
replot_only='0',
pdf_dir='',
stime='0.0',
etime='0.0',
ts_correct='1',
smoothed='1',
link_len='0',
plot_params='',
plot_script='',
stat_index='',
dupacks='0',
cum_ackseq='1',
merge_data='0',
sburst='1',
#eburst='0', test_id_prefix='[0-9]{8}\-[0-9]{6}_experiment_',
eburst='0',
test_id_prefix='exp_[0-9]{8}\-[0-9]{6}_',
slowest_only='0',
res_time_mode='0',
query_host=''):
"Compare metrics for different experiments"
if ptype != 'box' and ptype != 'mean' and ptype != 'median':
abort('ptype must be either box, mean or median')
check = get_metric_params(metric, smoothed, ts_correct)
if check == None:
abort('Unknown metric %s specified' % metric)
if source_filter == '':
abort('Must specify at least one source filter')
if len(source_filter.split(';')) > 12:
abort('Cannot have more than 12 filters')
# prevent wrong use of res_time_mode
if metric != 'restime' and res_time_mode != '0':
res_time_mode = '0'
if ptype == 'box' and res_time_mode == '2':
res_time_mode = '0'
# XXX more param checking
# Initialise source filter data structure
sfil = SourceFilter(source_filter)
# read test ids
experiments = read_experiment_ids(exp_list)
# get path based on first experiment id
dir_name = get_first_experiment_path(experiments)
# if we haven' got the extracted data run extract method(s) first
if res_dir == '':
for experiment in experiments:
(ex_function,
kwargs) = get_extract_function(metric,
link_len,
stat_index,
sburst=sburst,
eburst=eburst,
slowest_only=slowest_only,
query_host=query_host)
(dummy, out_files,
out_groups) = ex_function(test_id=experiment,
out_dir=out_dir,
source_filter=source_filter,
replot_only=replot_only,
ts_correct=ts_correct,
**kwargs)
if out_dir == '' or out_dir[0] != '/':
res_dir = dir_name + '/' + out_dir
else:
res_dir = out_dir
else:
if res_dir[0] != '/':
res_dir = dir_name + '/' + res_dir
# make sure we have trailing slash
res_dir = valid_dir(res_dir)
if pdf_dir == '':
pdf_dir = res_dir
else:
if pdf_dir[0] != '/':
pdf_dir = dir_name + '/' + pdf_dir
pdf_dir = valid_dir(pdf_dir)
# if pdf_dir specified create if it doesn't exist
mkdir_p(pdf_dir)
#
# build match string from variables
#
(match_str, match_str2) = build_match_strings(experiments[0], variables,
test_id_prefix)
#
# filter out the experiments to plot, generate x-axis labels, get test id prefix
#
(fil_experiments, test_id_pfx,
xlabs) = filter_experiments(experiments, match_str, match_str2)
#
# get out data files based on filtered experiment list and source_filter
#
(ext, ylab, yindex, yscaler, sep, aggr,
diff) = get_metric_params(metric, smoothed, ts_correct, stat_index,
dupacks, cum_ackseq, slowest_only)
if res_time_mode == '1':
plot_params += ' NOMINAL_RES_TIME="1"'
if res_time_mode == '2':
if ptype == 'median':
ylab = 'Median resp time / nominal resp time'
elif ptype == 'mean':
ylab = 'Mean resp time / nominal resp time'
plot_params += ' RATIO_RES_TIME="1"'
leg_names = source_filter.split(';')
# if we merge responders make sure we only use the merged files
if merge_data == '1':
# set label to indicate merged data
leg_names = ['Merged data']
# reset source filter so we match the merged file
sfil.clear()
source_filter = 'S_0.0.0.0_0'
sfil = SourceFilter(source_filter)
file_names = []
for experiment in fil_experiments:
out_files = {}
_ext = ext
files = get_testid_file_list('', experiment, '%s' % _ext,
'LC_ALL=C sort', res_dir)
if merge_data == '1':
# change extension
_ext += '.all'
files = merge_data_files(files)
#print(files)
match_str = '.*_([0-9\.]*_[0-9]*_[0-9\.]*_[0-9]*)[0-9a-z_.]*' + _ext
for f in files:
# print(f)
res = re.search(match_str, f)
#print(res.group(1))
if res and sfil.is_in(res.group(1)):
# only add file if enough data points
rows = int(
local('wc -l %s | awk \'{ print $1 }\'' % f, capture=True))
if rows > int(min_values):
out_files[res.group(1)] = f
#print(out_files)
#print(leg_names)
if len(out_files) < len(leg_names):
abort(
'No data files for some of the source filters for experiment %s'
% experiment)
sorted_files = sort_by_flowkeys(out_files, source_filter)
for name, file_name in sorted_files:
file_names.append(file_name)
if group_by_prefix == '1':
# group by test prefix (and flow)
# first, get all test id prefixes
test_id_pfxs = {}
for experiment in fil_experiments:
res = re.search(match_str2, experiment)
if res:
test_id_pfxs[res.group(1)] = 1
# second, sort files so that same parameter combinations for different
# prefixes are together
# if we have multiple prefixes, create legend entry for each
# prefix+flow combination
_file_names = [''] * len(file_names)
_leg_names = []
pfx_cnt = len(test_id_pfxs)
i = 0
j = -1
last_pfx = ''
for name in file_names:
for p in test_id_pfxs:
if name.find(p) > -1:
curr_pfx = p
break
if curr_pfx != last_pfx:
i = 0
j += 1
for l in leg_names:
_leg_names.append(curr_pfx + '-' + l)
_file_names[i * pfx_cnt + j] = name
i += 1
last_pfx = curr_pfx
file_names = _file_names
leg_names = _leg_names
# remove duplicates in the x-axis labels
xlabs = list(set(xlabs))
if lnames != '':
lnames_arr = lnames.split(';')
if len(lnames_arr) != len(leg_names):
abort(
'Number of legend names must be qual to the number of source filters'
)
leg_names = lnames_arr
# filter out unchanged variables in the x labels (need at least 2 labels)
if omit_const_xlab_vars == '1' and len(xlabs) > 1:
xlabs_arrs = {}
xlabs_changed = {}
for i in range(len(xlabs)):
xlabs_arrs[i] = xlabs[i].split('\n')
for i in range(len(xlabs_arrs[0])):
changed = False
xlab_var = xlabs_arrs[0][i]
for j in range(1, len(xlabs)):
if xlabs_arrs[j][i] != xlab_var:
changed = True
break
xlabs_changed[i] = changed
for i in range(len(xlabs)):
tmp = []
for j in range(len(xlabs_arrs[i])):
if xlabs_changed[j]:
tmp.append(xlabs_arrs[i][j].replace('_', ' ', 1))
xlabs[i] = '\n'.join(tmp)
print(leg_names)
print(file_names)
#
# pass the data files and auxilary info to plot function
#
if out_name != '':
oprefix = out_name + '_' + test_id_pfx + '_' + metric + '_' + ptype
else:
oprefix = test_id_pfx + '_' + metric + '_' + ptype
title = oprefix
plot_cmpexp(title, file_names, xlabs, ylab, yindex, yscaler, 'pdf',
oprefix, pdf_dir, sep, aggr, diff, omit_const, ptype, ymin,
ymax, leg_names, stime, etime, plot_params, plot_script)
# done
puts('\n[MAIN] COMPLETED analyse_cmpexp %s \n' % test_id_pfx)
def analyse_2d_density(
exp_list='experiments_completed.txt',
res_dir='',
out_dir='',
source_filter='',
min_values='3',
xmetric='throughput',
ymetric='tcprtt',
variables='',
out_name='',
xmin='0',
xmax='0',
ymin='0',
ymax='0',
lnames='',
group_by='aqm',
replot_only='0',
pdf_dir='',
stime='0.0',
etime='0.0',
ts_correct='1',
smoothed='1',
link_len='0',
plot_params='',
plot_script='',
xstat_index='',
ystat_index='',
dupacks='0',
cum_ackseq='1',
merge_data='0',
#sburst='1', eburst='0', test_id_prefix='[0-9]{8}\-[0-9]{6}_experiment_',
sburst='1',
eburst='0',
test_id_prefix='exp_[0-9]{8}\-[0-9]{6}_',
slowest_only='0',
query_host=''):
"2d density / ellipse plot for different experiments"
test_id_pfx = ''
check = get_metric_params(xmetric, smoothed, ts_correct)
if check == None:
abort('Unknown metric %s specified with xmetric' % xmetric)
check = get_metric_params(ymetric, smoothed, ts_correct)
if check == None:
abort('Unknown metric %s specified with ymetric' % ymetric)
#if source_filter == '':
# abort('Must specify at least one source filter')
if len(source_filter.split(';')) > 12:
abort('Cannot have more than 12 filters')
# XXX more param checking
# make sure res_dir has valid form (out_dir is handled by extract methods)
res_dir = valid_dir(res_dir)
# Initialise source filter data structure
sfil = SourceFilter(source_filter)
# read test ids
experiments = read_experiment_ids(exp_list)
# get path based on first experiment id
dir_name = get_first_experiment_path(experiments)
# if we haven' got the extracted data run extract method(s) first
if res_dir == '':
for experiment in experiments:
(ex_function,
kwargs) = get_extract_function(xmetric,
link_len,
xstat_index,
sburst=sburst,
eburst=eburst,
slowest_only=slowest_only,
query_host=query_host)
(dummy, out_files,
out_groups) = ex_function(test_id=experiment,
out_dir=out_dir,
source_filter=source_filter,
replot_only=replot_only,
ts_correct=ts_correct,
**kwargs)
(ex_function,
kwargs) = get_extract_function(ymetric,
link_len,
ystat_index,
sburst=sburst,
eburst=eburst,
slowest_only=slowest_only,
query_host=query_host)
(dummy, out_files,
out_groups) = ex_function(test_id=experiment,
out_dir=out_dir,
source_filter=source_filter,
replot_only=replot_only,
ts_correct=ts_correct,
**kwargs)
if out_dir == '' or out_dir[0] != '/':
res_dir = dir_name + '/' + out_dir
else:
res_dir = out_dir
else:
if res_dir[0] != '/':
res_dir = dir_name + '/' + res_dir
# make sure we have trailing slash
res_dir = valid_dir(res_dir)
if pdf_dir == '':
pdf_dir = res_dir
else:
if pdf_dir[0] != '/':
pdf_dir = dir_name + '/' + pdf_dir
pdf_dir = valid_dir(pdf_dir)
# if pdf_dir specified create if it doesn't exist
mkdir_p(pdf_dir)
#
# build match string from variables
#
(match_str, match_str2) = build_match_strings(experiments[0], variables,
test_id_prefix)
#
# filter out the experiments to plot, generate x-axis labels, get test id prefix
#
(fil_experiments, test_id_pfx,
dummy) = filter_experiments(experiments, match_str, match_str2)
#
# get groups based on group_by variable
#
group_idx = 1
levels = {}
groups = []
leg_names = []
_experiments = []
for experiment in fil_experiments:
level = ''
add_exp = True
for g in group_by.split(';'):
p = experiment.find(g)
if p > -1:
s = experiment.find('_', p)
s += 1
e = experiment.find('_', s)
level += g + ':' + experiment[s:e] + ' '
else:
add_exp = False
break
# remove the final space from the string
level = level[:-1]
if add_exp == True:
_experiments.append(experiment)
#print('level: ' + level)
if level not in levels:
levels[level] = group_idx
group_idx += 1
leg_names.append(level)
if merge_data == '1':
groups.append(levels[level])
else:
for i in range(len(source_filter.split(';'))):
groups.append(levels[level])
fil_experiments = _experiments
#
# get metric parameters and list of data files
#
# get the metric parameter for both x and y
x_axis_params = get_metric_params(xmetric, smoothed, ts_correct,
xstat_index, dupacks, cum_ackseq,
slowest_only)
y_axis_params = get_metric_params(ymetric, smoothed, ts_correct,
ystat_index, dupacks, cum_ackseq,
slowest_only)
x_ext = x_axis_params[0]
y_ext = y_axis_params[0]
# if we merge responders make sure we only use the merged files
if merge_data == '1':
# reset source filter so we match the merged file
sfil.clear()
sfil = SourceFilter('S_0.0.0.0_0')
x_files = []
y_files = []
for experiment in fil_experiments:
_x_files = []
_y_files = []
_x_ext = x_ext
_y_ext = y_ext
_files = get_testid_file_list('', experiment, _x_ext, 'LC_ALL=C sort',
res_dir)
if merge_data == '1':
_x_ext += '.all'
_files = merge_data_files(_files)
_x_files += _files
_files = get_testid_file_list('', experiment, _y_ext, 'LC_ALL=C sort',
res_dir)
if merge_data == '1':
_y_ext += '.all'
_files = merge_data_files(_files)
_y_files += _files
match_str = '.*_([0-9\.]*_[0-9]*_[0-9\.]*_[0-9]*)[0-9a-z_.]*' + _x_ext
for f in _x_files:
#print(f)
res = re.search(match_str, f)
#print(res.group(1))
if res and sfil.is_in(res.group(1)):
# only add file if enough data points
rows = int(
local('wc -l %s | awk \'{ print $1 }\'' % f, capture=True))
if rows > int(min_values):
x_files.append(f)
match_str = '.*_([0-9\.]*_[0-9]*_[0-9\.]*_[0-9]*)[0-9a-z_.]*' + _y_ext
for f in _y_files:
# print(f)
res = re.search(match_str, f)
if res and sfil.is_in(res.group(1)):
# only add file if enough data points
rows = int(
local('wc -l %s | awk \'{ print $1 }\'' % f, capture=True))
if rows > int(min_values):
y_files.append(f)
yindexes = [str(x_axis_params[2]), str(y_axis_params[2])]
yscalers = [str(x_axis_params[3]), str(y_axis_params[3])]
aggr_flags = [x_axis_params[5], y_axis_params[5]]
diff_flags = [x_axis_params[6], y_axis_params[6]]
if lnames != '':
lnames_arr = lnames.split(';')
if len(lnames_arr) != len(leg_names):
abort(
'Number of legend names must be qual to the number of source filters'
)
leg_names = lnames_arr
print(x_files)
print(y_files)
print(groups)
print(leg_names)
#
# pass the data files and auxilary info to plot function
#
if out_name != '':
oprefix = out_name + '_' + test_id_pfx + '_' + xmetric + '_' + ymetric
else:
oprefix = test_id_pfx + '_' + xmetric + '_' + ymetric
title = oprefix
plot_2d_density(title, x_files, y_files, x_axis_params[1],
y_axis_params[1], yindexes, yscalers, 'pdf', oprefix,
pdf_dir, x_axis_params[4], y_axis_params[4], aggr_flags,
diff_flags, xmin, xmax, ymin, ymax, stime, etime, groups,
leg_names, plot_params, plot_script)
# done
puts('\n[MAIN] COMPLETED analyse_2d_density %s \n' % test_id_pfx)
def _extract_owd_pktloss(test_id='', out_dir='', replot_only='0', source_filter='',
ts_correct='1', burst_sep='0.0', sburst='1', eburst='0',
seek_window='16000', log_loss='0', anchor_map='', owd_midpoint='0'):
"Extract OWD or PKTLOSS of flows"
ifile_ext = '.dmp.gz'
if log_loss == '0':
ofile_ext = '.owds2.gz'
else:
ofile_ext = '.loss2.gz'
already_done = {}
out_files = {}
out_groups = {}
test_id_arr = test_id.split(';')
if len(test_id_arr) == 0 or test_id_arr[0] == '':
abort('Must specify test_id parameter')
if ts_correct == '0' and log_loss == '0':
abort('Must use ts_correct=1 when calculating OWD')
# Initialise source filter data structure
sfil = SourceFilter(source_filter)
# EXPERIMENTAL: anchor_map="<srcip1>:<dstip1>;<srcip2>:<dstip2>;..."
# Normally a packet's OWD is logged as having occurred at the time the
# packet is seen in pcap file associated with <srcip> (src_extname)
# An 'anchor_map' entry allows you to specify that a packet's OWD be
# logged as having occurred at the time the packet from <srcip> was
# seen in the pcap file associated with <dstip> (dst_extname)
# This is only operates on packets between <srcip>:<dstip>, other
# flows in the same testID are unaffected.
anchor_map_list = {}
if anchor_map != '':
if replot_only == '1':
abort("Must specify replot_only=0 in conjunction with anchor_map")
entries = anchor_map.split(';')
for entry in entries:
k, v = entry.split(':')
anchor_map_list[k] = v
group = 1
for test_id in test_id_arr:
# first process tcpdump files (ignore router and ctl interface tcpdumps)
tcpdump_files = get_testid_file_list('', test_id,
ifile_ext,
'grep -v "router.dmp.gz" | grep -v "ctl.dmp.gz"')
for tcpdump_file in tcpdump_files:
# get input directory name and create result directory if necessary
out_dirname = get_out_dir(tcpdump_file, out_dir)
dir_name = os.path.dirname(tcpdump_file)
# get unique flows
flows = lookup_flow_cache(tcpdump_file)
if flows == None:
# If not previously found in flow_cache,
# extract and identify the tcp and udp flows contained in tcpdump_file
flows = _list(local('zcat %s | tcpdump -nr - "tcp" | '
'awk \'{ if ( $2 == "IP" ) { print $3 " " $5 " tcp" } }\' | '
'sed "s/://" | '
'sed "s/\.\([0-9]*\) /,\\1 /g" | sed "s/ /,/g" | '
'LC_ALL=C sort -u' %
tcpdump_file, capture=True))
flows += _list(local('zcat %s | tcpdump -nr - "udp" | '
'awk \'{ if ( $2 == "IP" ) { print $3 " " $5 " udp" } }\' | '
'sed "s/://" | '
'sed "s/\.\([0-9]*\) /,\\1 /g" | sed "s/ /,/g" | '
'LC_ALL=C sort -u' %
tcpdump_file, capture=True))
# Add them to the flow cache
append_flow_cache(tcpdump_file, flows)
# Walk through and process the flows identified in this current tcpdump_file
for flow in flows:
# First extract src & dst as IP addr on experiment networks
src, src_port, dst, dst_port, proto = flow.split(',')
# Map src & dst (IP addr) to testbed-specific external (control network)
# host names from config.py ({src,dst}_extname) and internal (experiment network)
# hostnames/addresses from config.py ({src,dst}_internal)
src_extname, src_internal = get_address_pair_analysis(test_id, src, do_abort='0')
dst_extname, dst_internal = get_address_pair_analysis(test_id, dst, do_abort='0')
# Skip cases of random (broadcast) traffic involving an IP address for
# which no experimental network NIC (and hence control network hostname)
# is directly related
if src_extname == '' or dst_extname == '':
continue
# flow name
name = src_internal + '_' + src_port + '_' + dst_internal + '_' + dst_port
# test id plus flow name
if len(test_id_arr) > 1:
long_name = test_id + '_' + name
else:
long_name = name
if long_name not in already_done:
# Construct filenames for files containing final <time> <owd|loss> pairs
out_final = out_dirname + test_id + '_' + name + ofile_ext
# Only embark on actual filtering/extraction if we're asked to regenerate
# the intermediate OWD values, or for some reason the intermediate OWD
# file is missing...
if replot_only == '0' or not os.path.isfile(out_final):
# Per flow/host:
# Create intermediate file of timestamps + uniqString from pcap files,
# THEN call adjust_timestamps to construct a version with timestamps adjusted
# relative to a single reference host in the testbed
# THEN use the adjusted timestamps in the subsequent owd/loss calculations.
# To extract packets in in FORWARD direction from both src and dst pcap files,
# construct dpkt flow filter in form <src_ip>:<src_port>:<dst_ip>:<dst_port>
# (and more specifically, from src_internal:src_port to dst_internal:dst_port).
filter_dpkt = src_internal + ':' + src_port + ':' + dst_internal + ':' + dst_port
src_port_int = int(src_port)
dst_port_int = int(dst_port)
# Loop across the src and dst dmp files
tmp_fwd_out_adj = {}
for dmpfile_host, dirsuffix in ([src_extname,"src"],[dst_extname,"dst"]):
# Construct the file name of the dump file that contains this
# flow's packets. 'src' captured at (near) the flow's source, and 'dst'
# captured at (near) the flow's destination.
dmp_file = dir_name + '/' + test_id + '_' + dmpfile_host + ifile_ext
print "Extracting packets for " + name + " from:" + dmp_file
# Construct filename for intermediate "<time> <uniqueString>" output files
# whose timestamps will be adjusted by adjust_timestamps()
# before being used for owd calculations
# (NOTE: Due to adjust_timestamps making assumptions about out_dir parameter,
# we currently can't place these tmp files under /tmp)
tmp_fwd_out = tempfile.mktemp(suffix=test_id + '_' + name +'_fwd_out_' + dirsuffix+".gz", dir=out_dirname)
# Extract packet id info
if dmp_file.endswith('.gz'):
f_dmp_file = gzip.open(dmp_file)
else:
f_dmp_file = open(dmp_file)
pcap_reader = dpkt.pcap.Reader(f_dmp_file)
pcap_reader.setfilter(filter_dpkt)
#pcap_reader.setfilter('')
# Create a compressed temporary intermediate file
f_tmp_fwd_out = gzip.open(tmp_fwd_out,'wb',1)
# Walk across every packet in this pcap file
for ts, pkt in pcap_reader:
# get pointer to ethernet layer and check that we have IP
eth = dpkt.ethernet.Ethernet(pkt)
if eth.type != dpkt.ethernet.ETH_TYPE_IP:
continue
# get pointer to IP layer
ip_pkt = eth.data
# ignore if src or dst IP not the ones specified in filter
if socket.inet_ntoa(ip_pkt.src) != src_internal or \
socket.inet_ntoa(ip_pkt.dst) != dst_internal:
continue
# ignore if UDP/TCP src or dst ports not the ones specified in filter
# get pointer to payload
if type(ip_pkt.data) == dpkt.udp.UDP:
udp_frame = ip_pkt.data
if udp_frame.sport != src_port_int or udp_frame.dport != dst_port_int:
continue
# Add IP ID field to the payload to ensure
# at least something semi-unique is hashed
# if UDP payload is invariant
payload = str(ip_pkt.id) +udp_frame.data
elif type(ip_pkt.data) == dpkt.tcp.TCP:
tcp_frame = ip_pkt.data
if tcp_frame.sport != src_port_int or tcp_frame.dport != dst_port_int:
continue
# Use IP ID field, TCP Sequence number and ACK number to
# construct a mostly unique string within context of this flow
payload = str(ip_pkt.id) + str(tcp_frame.seq) + str(tcp_frame.ack)
else:
continue
# Write <timestamp> <crc32 hash of uniqueString bytes>
# (hashing here eliminates any later problems parsing payloads
# containing null bytes)
f_tmp_fwd_out.write("%f %s\n" % (ts,zlib.crc32(payload)))
f_tmp_fwd_out.close()
f_dmp_file.close()
# Apply timestamp corrections to the data thus extracted, prior to
# calculating OWDs. Correction is MANDATORY otherwise the
# 'calculated' OWDs are essentially useless.
tmp_fwd_out_adj[dirsuffix] = adjust_timestamps(test_id, tmp_fwd_out, dmpfile_host, ' ', out_dir)
# Remove pre-adjustment files.
os.remove(tmp_fwd_out)
# Now we have unique packet hashes seen at both src and dst locations,
# and timestamps have been adjusted for clockoffsets.
# Begin calculating OWD or identifying when packet losses occurred
# Read into memory the <adjusted_timestamp> <uniqString> datasets captured
# at dst (2nd place packet seen, "destination"). The src is (1st place packet
# seen, "source")
dst_data_time=list()
dst_data_uniqString=list()
for line in gzip.open(tmp_fwd_out_adj["dst"]).read().splitlines():
sline = line.split(" ")
dst_data_time.append(float(sline[0]))
dst_data_uniqString.append(sline[1])
# Walk through tmp_fwd_out_adj["src"] looking for matches to packets
# in dst_data_uniqString, and write <time> <owd|loss> pairs in plain
# ASCII to out_final
# To limit potential duplicate matches to packets received forward
# in time from previous match in dst_data_uniqString, maintain
# index next_j pointing to next row in dst_data_uniqString to start
# matching next packet from tmp_fwd_out_adj["src"]
next_j = 0
last_j = len(dst_data_uniqString)-1
# As a speed-up hack, assume match in dst_data_uniqString is
# within sk_window entries of next_j (saves searching all the
# way to the end of dst_data_uniqString when seeking a lost packet)
# Keeping seek_window in the low 1000s also minimises chances of
# duplicate matches.
if seek_window != '':
sk_window = int(seek_window)
else:
sk_window = last_j
# Create gzipped output file (rough experiments showed over reduction
# in on-disk file size easily 100s of K down to 10s of K).
# R automagically reads gzipped data files, so no changes required
# to subsequent analyse_* plotting scripts.
f = gzip.open(out_final, 'w')
cumulative_loss = 0
# Decide whether to use timestamp at src or dst for OWD
# (Default to src, unless anchor_map indicates using dst for
# this particular traffic pattern)
anchor = 0 # Default print timestamp at src
if log_loss == '0': # Only relevant for OWD calculations
if src_internal in anchor_map_list.keys():
#print "*** Found " + src_internal + " in anchor_map"
if anchor_map_list[src_internal] == dst_internal:
# Only relevant if the map relates to both src_internal and dst_internal
#print "*** " + src_internal + " points to " + anchor_map_list[src_internal] + " in anchor_map"
anchor = 1 # Print timestamp at dst
for line in gzip.open(tmp_fwd_out_adj["src"]).read().splitlines():
i = line.split(" ")
try:
# The following search will raise a 'ValueError' exception if i[1] does not occur in dst_data_uniqString[next_j:]
j = dst_data_uniqString[next_j:min((next_j+sk_window),last_j+1)].index(i[1])
if log_loss == '0':
# OWD is diff between i[0] and dst_data_time[next_j+j]
ts = float(i[0])
owd = dst_data_time[next_j+j]-float(i[0])
# If required, print event as occuring at dst timestamp rather than src timestamp
if anchor:
ts = dst_data_time[next_j+j]
# If we want to imply the OWD "existed" at some mid-point
# between pkt seen at src and seen at dst
if owd_midpoint == '1':
ts += owd/2
f.write('%f %f\n' % (ts, owd))
if log_loss == '1':
# No lost packet, emit "0"
f.write('%s 0\n' % (i[0]))
if log_loss == '2':
# No lost packet, emit previous cumulative count
f.write('%s %i\n' % (i[0], cumulative_loss))
next_j = min(next_j+j+1,last_j)
except ValueError:
# No match means a packet loss
if log_loss == '1':
# Single loss event, emit "1"
f.write('%s 1\n' % (i[0]))
if log_loss == '2':
# Single loss event, increment cumulative count, emit cumulative count
cumulative_loss += 1
f.write('%s %i\n' % (i[0], cumulative_loss))
pass
f.close()
dst_data_time=[]
dst_data_uniqString=[]
# Clean up temporary post-adjustment files
os.remove(tmp_fwd_out_adj["src"])
os.remove(tmp_fwd_out_adj["dst"])
already_done[long_name] = 1
if sfil.is_in(name):
(out_files,
out_groups) = select_bursts(long_name, group, out_final, burst_sep, sburst, eburst,
out_files, out_groups)
group += 1
return (test_id_arr, out_files, out_groups)