def main():
global from_time
global cap_time
# Suppress future warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
parser = OptionParser(usage="usage: %prog [options] <cdict_file1> [cdict_file2...]")
parser.add_option("-t", "--task",
dest="task",
metavar="task name (regex)",
help="selected task(s) (regex on task name)"
)
parser.add_option("--label",
dest="label",
metavar="label",
help="label for the title (defaults to the cdict file name)"
)
parser.add_option("--map",
dest="map",
action="store",
metavar="mapping csv file",
help="remap task names from mapping csv file"
)
parser.add_option("--output-dir",
dest="output_dir",
action="store",
metavar="dir pathname",
help="output all html files to the provided directory"
)
parser.add_option("--heatmaps",
dest="heatmaps",
action="store_true",
help="generate heatmap charts only (default: generate basic charts only)"
)
parser.add_option("--headless",
dest="headless",
action="store_true",
help="do not show chart in the browser (default=False)"
)
parser.add_option("-c", "--cap",
dest="cap_time",
help="(optional) cap the analysis to first <cap_time> msec"
" of capture (default=all)"
)
parser.add_option("-f", "--from",
dest="from_time",
help="(optional) start the analysis after first <from_time> msec"
" of capture (default=0)"
)
parser.add_option("--merge-sys-tasks",
dest="merge_sys_tasks",
action="store_true",
help="group all system tasks (e.g. swapper/0 -> swapper)"
)
parser.add_option("--successors-of",
dest="successor_of_task",
help="only show list of successors of given tid or task name"
)
parser.add_option("--list",
dest="list",
action="store_true",
default=False,
help="only show list of all tasks with event count"
)
(options, args) = parser.parse_args()
if options.from_time:
from_time = int(options.from_time) * 1000
if options.cap_time:
# convert to usec
cap_time = int(options.cap_time) * 1000 + from_time
if not args:
print 'Missing cdict file'
sys.exit(1)
if options.output_dir:
if not os.path.isdir(options.output_dir):
print('Invalid output directory: ' + options.output_dir)
sys.exit(1)
dfds = []
cdict_files = args
if len(cdict_files):
if len(cdict_files) > 1:
html_filename = cdict_files[0] + '-diff'
else:
html_filename = cdict_files[0]
else:
html_filename = cdict_files[0] if len(cdict_files) else 'perfwhiz'
set_html_file(html_filename, options.headless, options.label, options.output_dir)
# get smallest capture window of all cdicts
min_cap_usec = 0
for cdict_file in cdict_files:
perf_dict = open_cdict(cdict_file, options.map)
df = DataFrame(perf_dict)
dfd = DfDesc(cdict_file, df, options.merge_sys_tasks)
dfds.append(dfd)
last_usec = df['usecs'].iloc[-1]
if min_cap_usec == 0:
min_cap_usec = last_usec
else:
min_cap_usec = min(min_cap_usec, last_usec)
if from_time and from_time >= min_cap_usec:
print 'Error: from time cannot be larger than %d msec' % (min_cap_usec / 1000)
sys.exit(2)
# if a cap time is provided, always use that value (even if it is >min_cap_usec)
if not cap_time:
cap_time = min_cap_usec
# normalize all dataframes
for dfd in dfds:
dfd.normalize(from_time, cap_time)
# at this point some cdict entries may have "missing" data
# if the requested cap_time is > the cdict cap time
# the relevant processing will extrapolate when needed (and if possible)
# reduce all names to minimize the length o;f the cdict file name
set_short_names(dfds)
if not options.label:
if len(dfds) > 1:
options.label = 'diff'
else:
options.label = os.path.splitext(os.path.basename(cdict_file))[0]
if options.list:
print 'List of tids and task names sorted by context switches and kvm event count'
for dfd in dfds:
print dfd.name + ':'
res = dfd.df.groupby(['pid', 'task_name']).size()
res.sort_values(ascending=False, inplace=True)
pandas.set_option('display.max_rows', len(res))
print res
sys.exit(0)
if options.successor_of_task:
for dfd in dfds:
print dfd.name + ':'
show_successors(dfd.df, options.successor_of_task, options.label)
sys.exit(0)
# These options can be cumulative and all require a --task parameter to select tasks
if not options.task:
print '--task <task_regex> is required'
sys.exit(1)
# A common mistake is to forget the head "." before a star ("*.vcpu0")
# Better detect and fix to avoid frustration
if options.task.startswith("*"):
options.task = "." + options.task
# create heatmaps only if one cdict was given
if options.heatmaps:
if len(dfds) == 1:
create_heatmaps(dfds[0], cap_time, options.task, options.label)
else:
print 'Error: --heat-maps requires 1 cdict file only'
sys.exit(1)
else:
create_charts(dfds, cap_time, options.task, options.label)
with open(cdict_file, 'r') as ff:
cdict = ff.read()
decomp = zlib.decompress(cdict)
try:
perf_dict = unpackb(decomp)
except Exception:
# old serialization format
perf_dict = marshal.loads(decomp)
if options.map:
remap(perf_dict, options.map)
df = DataFrame(perf_dict)
set_html_file(cdict_file)
# filter on usecs
if from_time:
df = df[df['usecs'] >= from_time]
if cap_time:
df = df[df['usecs'] <= cap_time]
if not options.label:
options.label = os.path.splitext(os.path.basename(cdict_file))[0]
if options.convert:
convert(df, options.convert)
sys.exit(0)
if options.from_time:
from_time = int(options.from_time) * 1000
if options.cap_time:
# convert to usec
cap_time = int(options.cap_time) * 1000 + from_time
if not args:
print 'Missing cdict file'
sys.exit(1)
cdict_file = args[0]
perf_dict = open_cdict(cdict_file, options.map)
df = DataFrame(perf_dict)
set_html_file(cdict_file, options.headless)
# filter on usecs
if from_time:
df = df[df['usecs'] >= from_time]
if cap_time:
df = df[df['usecs'] <= cap_time]
if not options.label:
options.label = os.path.splitext(os.path.basename(cdict_file))[0]
if options.convert:
convert(df, options.convert)
sys.exit(0)
if options.show_tids:
if options.from_time:
from_time = int(options.from_time) * 1000
if options.cap_time:
# convert to usec
cap_time = int(options.cap_time) * 1000 + from_time
if not args:
print 'Missing cdict file'
sys.exit(1)
cdict_file = args[0]
perf_dict = open_cdict(cdict_file, options.map)
df = DataFrame(perf_dict)
set_html_file(cdict_file, options.headless)
# filter on usecs
if from_time:
df = df[df['usecs'] >= from_time]
if cap_time:
df = df[df['usecs'] <= cap_time]
if not options.label:
options.label = os.path.splitext(os.path.basename(cdict_file))[0]
if options.convert:
convert(df, options.convert)
sys.exit(0)
if options.show_tids:
