def make_styler(col_map):
try:
return FieldStyle(col_map.get_values())
except ExcessVarietyException:
# Fallback, don't style by field values, instead create
# a unique style for every combination of field values possible
# This is significantly harder to visually parse
log_once("Too many columns and/or column values to create pretty "
"and simple graphs!\nGiving each combination of properties "
"its own line.")
return CombinationStyle(col_map)
def make_styler(col_map):
try:
return FieldStyle(col_map.get_values())
except ExcessVarietyException:
# Fallback, don't style by field values, instead create
# a unique style for every combination of field values possible
# This is significantly harder to visually parse
log_once("Too many columns and/or column values to create pretty "
"and simple graphs!\nGiving each combination of properties "
"its own line.")
return CombinationStyle(col_map)
def extract_sched_data(result, data_dir, work_dir):
task_dict = create_task_dict(data_dir, work_dir)
stat_data = defaultdict(list)
# Group per-task values
for tdata in task_dict.itervalues():
if not tdata.params:
# Currently unknown where these invalid tasks come from...
continue
miss = tdata.misses
record_loss = float(miss.disjoints)/(miss.matches + miss.disjoints)
stat_data["record-loss"].append(record_loss)
if record_loss > conf.MAX_RECORD_LOSS:
log_once(LOSS_MSG)
continue
miss_ratio = float(miss.num) / miss.matches
avg_tard = miss.avg * miss_ratio
stat_data["miss-ratio" ].append(miss_ratio)
stat_data["max-tard"].append(miss.max / tdata.params.period)
stat_data["avg-tard"].append(avg_tard / tdata.params.period)
stat_data["avg-block"].append(tdata.blocks.avg / NSEC_PER_MSEC)
stat_data["max-block"].append(tdata.blocks.max / NSEC_PER_MSEC)
# Summarize value groups
for name, data in stat_data.iteritems():
if not data or not sum(data):
log_once(SKIP_MSG, SKIP_MSG % name)
continue
result[name] = Measurement(str(name)).from_array(data)
def extract_sched_data(result, data_dir, work_dir):
task_dict = create_task_dict(data_dir, work_dir)
stat_data = defaultdict(list)
# Group per-task values
for tdata in task_dict.itervalues():
if not tdata.params:
# Currently unknown where these invalid tasks come from...
continue
miss = tdata.misses
record_loss = float(miss.disjoints())/(miss.matches + miss.disjoints())
stat_data["record-loss"].append(record_loss)
if record_loss > conf.MAX_RECORD_LOSS:
log_once("dir = {2}, miss.disjoints = {0}, miss.matches = {1} ratio= {3}%".format(unicode(miss.disjoints()), unicode(miss.matches), unicode(data_dir), unicode(100*record_loss)))
if record_loss > conf.MAX_RECORD_LOSS:
log_once(LOSS_MSG)
continue
miss_ratio = float(miss.num) / miss.matches
avg_tard = miss.avg * miss_ratio
stat_data["miss-ratio" ].append(miss_ratio)
stat_data["tard-max"].append(float(miss.max) / tdata.params.period)
stat_data["tard-avg"].append(avg_tard / tdata.params.period)
stat_data["block-avg"].append(tdata.blocks.avg / NSEC_PER_MSEC)
stat_data["block-max"].append(tdata.blocks.max / NSEC_PER_MSEC)
preemptions = tdata.preemptions.get_preemptions()
filtered_preemptions = tdata.preemptions.get_filtered_preemptions()
migrations = tdata.preemptions.get_migrations()
jobs = tdata.preemptions.get_jobs()
stat_data["jobs"].append(jobs)
stat_data["preemptions"].append(preemptions)
stat_data["filtered-preemptions"].append(filtered_preemptions)
stat_data["migrations"].append(migrations)
stat_data["preemptions-per-job"].append(float(preemptions)/jobs)
stat_data["migrations-per-job"].append(float(migrations)/jobs)
stat_data["filtered-preemptions-per-job"].append(float(filtered_preemptions)/jobs)
# Summarize value groups
for name, data in stat_data.iteritems():
if not data:
log_once(SKIP_MSG, SKIP_MSG % name)
continue
result[name] = Measurement(str(name)).from_array(data)
