def summary(request):
P = 15
def fmt_ms(s):
return ('{:.3f}ms'.format(1000 * s)).rjust(P)
r = http.HttpResponse()
r.write(''.ljust(P))
r.write('TIMES'.rjust(P))
r.write('AVERAGE'.rjust(P))
r.write('MEDIAN'.rjust(P))
r.write('STDDEV'.rjust(P))
r.write('\n')
avgs = []
medians = []
for CACHE in settings.CACHE_NAMES:
data = caches[CACHE].get('benchmarking')
if data is None:
r.write('Nothing for {}\n'.format(CACHE))
else:
# Always chop off the first 10 measurements because it's usually
# way higher than all the others. That way we're only comparing
# configurations once they're all warmed up
data = data[10:]
median, avg, stddev = _stats(data)
avgs.append((CACHE, avg * 1000))
medians.append((CACHE, median * 1000))
r.write('{}{}{}{}{}\n'.format(
CACHE.ljust(P),
str(len(data)).rjust(P),
fmt_ms(avg),
fmt_ms(median),
fmt_ms(stddev),
))
r.write('\n')
graph = Pyasciigraph(float_format='{0:,.3f}')
for line in graph.graph('Best Averages (shorter better)', avgs):
print(line, file=r)
for line in graph.graph('Best Medians (shorter better)', medians):
print(line, file=r)
print('\n', file=r)
sizes = []
for name in settings.CACHE_NAMES:
connection = get_redis_connection(name)
sizes.append((name, connection.strlen(":1:benchmarking")))
graph = Pyasciigraph(human_readable='si', )
for line in graph.graph('Size of Data Saved (shorter better)', sizes):
print(line, file=r)
print('\n', file=r)
return r
def output(self, args, begin_ns, end_ns, final=0):
count = 0
limit = args.top
total_ns = end_ns - begin_ns
graph = Pyasciigraph()
values = []
print('%s to %s' % (ns_to_asctime(begin_ns), ns_to_asctime(end_ns)))
for tid in sorted(self.state.tids.values(),
key=operator.attrgetter('cpu_ns'), reverse=True):
if len(args.proc_list) > 0 and tid.comm not in args.proc_list:
continue
pc = float("%0.02f" % ((tid.cpu_ns * 100) / total_ns))
if tid.migrate_count > 0:
migrations = ", %d migrations" % (tid.migrate_count)
else:
migrations = ""
values.append(("%s (%d)%s" % (tid.comm, tid.tid, migrations), pc))
count = count + 1
if limit > 0 and count >= limit:
break
for line in graph.graph("Per-TID CPU Usage", values, unit=" %"):
print(line)
values = []
total_cpu_pc = 0
for cpu in sorted(self.state.cpus.values(),
key=operator.attrgetter('cpu_ns'), reverse=True):
cpu_pc = float("%0.02f" % cpu.cpu_pc)
total_cpu_pc += cpu_pc
values.append(("CPU %d" % cpu.cpu_id, cpu_pc))
for line in graph.graph("Per-CPU Usage", values, unit=" %"):
print(line)
print("\nTotal CPU Usage: %0.02f%%\n" %
(total_cpu_pc / len(self.state.cpus.keys())))
def graph_intervals(tl_videos, interval=timedelta(hours=1)):
"""
Plot ascii frequency of photos per bin
"""
bins = {}
for video in tl_videos:
# round bin start
start = prev_mark(interval, video.start)
end = next_mark(interval, video.end)
# generate a list of marks
video_extents = list(
rrule(SECONDLY,
dtstart=start,
until=end,
interval=int(interval.total_seconds())))
for bin_start in video_extents:
images_in_slice = [
im for im in video.images
if bin_start <= im.taken < bin_start + interval
]
bins[bin_start] = len(images_in_slice)
graphable = []
for h in sorted(bins):
# print("{}:{}".format(h,freq[h]))
graphable.append(tuple((h.isoformat(), bins[h])))
# print (graphable)
graph = Pyasciigraph()
for line in graph.graph('Frequency per {}'.format(interval), graphable):
print(line)
def calculate_text_coverage(self):
"""
Prints CLI stats about percentage of matched dbpedia facts in wiki raw text.
"""
matched_count = self.count_matches()
total_count = {}
for entity, relation_types in self.dbpedia.iteritems():
for relation, values in relation_types.iteritems():
target_resources = values.get('resources', [])
total_count.setdefault(relation, 0)
total_count[relation] += len(target_resources)
occurrence_count = {}
for relation in total_count:
occurrence_count[relation] = {
'total':
total_count[relation],
'matched':
min(total_count[relation],
matched_count.setdefault(relation, 0))
} # there might be more occurrences of a fact in an article, thus, resulting in a coverage above 100%
# print bar chart
data = [
('% ' + str(vals['matched']) + '/' + str(vals['total']) + ' ' +
rel.split('/')[-1], vals['matched'] / vals['total'] * 100)
for rel, vals in occurrence_count.iteritems()
]
graph = Pyasciigraph()
for line in graph.graph('occurred facts in percentage', data):
print(line)
def graph_thresholds(test_data):
# H color test
# Multicolor on one line
print('\nMultiColor example:')
# Color lines according to Thresholds
thresholds = {
51: Gre,
100: Blu,
350: Yel,
500: Red,
}
data = hcolor(test_data, thresholds)
# graph with colors, power of 1000, different graph symbol,
# float formatting and a few tweaks
graph = Pyasciigraph(
line_length=120,
min_graph_length=50,
separator_length=4,
multivalue=True,
human_readable='si',
graphsymbol='*', # comment out if you want to use solid bars
float_format='{0:,.2f}',
force_max_value=2000,
)
for line in graph.graph(label='With Thresholds', data=data):
print(line)
return graph, data
def test_human_readable_si(self):
test = [('long_labe☭', 1234), ('sl', 1231234), ('line3', 1231231234),
('line4', 1231231231234), ('line5', 1231231231231234),
('line6', 1231231231231231234),
('line7', 1231231231231231231234),
('line8', 1231231231231231231231234),
('line9', 123231231231231231231231234)]
graph = Pyasciigraph(human_readable='si')
res = graph.graph('☭test print', test)
expected = [
'☭test print',
'###############################################################################',
' 1K long_labe☭',
' 1M sl ',
' 1G line3 ',
' 1T line4 ',
' 1P line5 ',
' 1E line6 ',
' 1Z line7 ',
' 1Y line8 ',
'█████████████████████████████████████████████████████████████ 123Y line9 ',
]
gprint(res)
gprint(expected)
assert res == expected
def irq_list_to_freq(self, irq, _min, _max, res, name, nr):
step = (_max - _min) / res
if step == 0:
return
buckets = []
values = []
graph = Pyasciigraph()
for i in range(res):
buckets.append(i * step)
values.append(0)
for i in irq["list"]:
v = (i.stop_ts - i.start_ts) / 1000
b = min(int((v - _min) / step), res - 1)
values[b] += 1
g = []
i = 0
for v in values:
g.append(("%0.03f" % (i * step + _min), v))
i += 1
for line in graph.graph('Handler duration frequency distribution %s '
'(%s) (usec)' % (name, nr),
g,
info_before=True,
count=True):
print(line)
print("")
def plot(self):
graph = Pyasciigraph()
print()
for line in graph.graph(self.name, list(self.data.items())):
print(line)
print()
print("-" * 80)
def test_neg_multicolor(self):
test = [('testval0', 600), ('testval1', 400, Red),
('testval2', [(600, Gre), (500, Blu)]),
('testval3', [(200, Yel), (100, )]), ('testval4', -170, Cya),
('testval5', 50, Blu), ('testval6', [(-300, Gre),
(-230, Red)]),
('testval7', [(-100, Gre), (-230, Red), (200, Yel),
(600, Blu)])]
graph = Pyasciigraph()
res = graph.graph('☭test print', test)
expected = [
u'\u262dtest print',
u'###############################################################################',
u' \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 600 testval0',
u' \x1b[0;31m\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\x1b[0m \x1b[0;31m400\x1b[0m testval1',
u' \x1b[0;34m\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\x1b[0m\x1b[0;32m\u2588\u2588\u2588\u2588\u2588\x1b[0m \x1b[0;32m600\x1b[0m,\x1b[0;34m500\x1b[0m testval2',
u' \u2588\u2588\u2588\u2588\u2588\x1b[0;33m\u2588\u2588\u2588\u2588\u2588\x1b[0m \x1b[0;33m200\x1b[0m,100 testval3',
u' \x1b[0;36m\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\x1b[0m \x1b[0;36m-170\x1b[0m testval4',
u' \x1b[0;34m\u2588\u2588\x1b[0m \x1b[0;34m50\x1b[0m testval5',
u' \x1b[0;32m\u2588\u2588\u2588\x1b[0m\x1b[0;31m\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\x1b[0m \x1b[0;32m-300\x1b[0m,\x1b[0;31m-230\x1b[0m testval6',
u' \x1b[0;31m\u2588\u2588\u2588\u2588\u2588\u2588\u2588\x1b[0m\x1b[0;32m\u2588\u2588\u2588\u2588\u2588\x1b[0m\x1b[0;33m\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\x1b[0m\x1b[0;34m\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\x1b[0m \x1b[0;32m-100\x1b[0m,\x1b[0;31m-230\x1b[0m,\x1b[0;33m200\x1b[0m,\x1b[0;34m600\x1b[0m testval7'
]
gprint(res)
gprint(expected)
assert res == expected
def iotop_output_print_file_write(self, files):
# Compute files read
count = 0
limit = self._arg_limit
graph = Pyasciigraph()
values = []
sorted_f = sorted(files.items(),
key=lambda files: files[1]['write'],
reverse=True)
for f in sorted_f:
if f[1]["write"] == 0:
continue
info_fmt = "{:>10}".format(
common.convert_size(f[1]["write"], padding_after=True))
values.append(("%s %s %s" %
(info_fmt, f[1]["name"], str(f[1]["other"])[1:-1]),
f[1]["write"]))
count = count + 1
if limit > 0 and count >= limit:
break
for line in graph.graph('Files Write',
values,
sort=2,
with_value=False):
print(line)
def output(self, args, begin_ns, end_ns, final=0):
count = 0
limit = args.top
graph = Pyasciigraph()
values = []
print('%s to %s' % (ns_to_asctime(begin_ns), ns_to_asctime(end_ns)))
for tid in sorted(self.state.tids.values(),
key=operator.attrgetter('allocated_pages'),
reverse=True):
values.append(
("%s (%d)" % (tid.comm, tid.tid), tid.allocated_pages))
count = count + 1
if limit > 0 and count >= limit:
break
for line in graph.graph("Per-TID Memory Allocations",
values,
unit=" pages"):
print(line)
values = []
count = 0
for tid in sorted(self.state.tids.values(),
key=operator.attrgetter('freed_pages'),
reverse=True):
values.append(("%s (%d)" % (tid.comm, tid.tid), tid.freed_pages))
count = count + 1
if limit > 0 and count >= limit:
break
for line in graph.graph("Per-TID Memory Deallocation",
values,
unit=" pages"):
print(line)
print("\nTotal memory usage:\n- %d pages allocated\n- %d pages freed" %
(self.state.mm["allocated_pages"], self.state.mm["freed_pages"]))
def print_graph(dataset, title):
graph = Pyasciigraph(
separator_length=4,
multivalue=False,
human_readable='si',
)
chart = []
keys = sorted(dataset.keys())
mean = np.mean(list(dataset.values()))
# median = np.median(list(dataset.values()))
for key in keys:
chart.append((key, dataset[key]))
if (not args.no_color):
thresholds = {
int(mean): Gre,
int(mean * 2): Yel,
int(mean * 3): Red,
}
data = hcolor(chart, thresholds)
else:
data = chart
for line in graph.graph(title, data):
print(line)
def show_index(self):
graphArray = []
for coin in IndexedCoinModel.select():
graphArray.append((coin.Ticker, coin.DesiredPercentage))
pyGraph = Pyasciigraph(line_length=50,
min_graph_length=50,
separator_length=4,
multivalue=False,
human_readable='si',
graphsymbol='*',
float_format='{0:,.2f}')
thresholds = {
15: Gre,
30: Blu,
50: Yel,
60: Red,
}
data = hcolor(graphArray, thresholds)
sys.stdout.write("\n")
for line in pyGraph.graph('Index Distribution', data=data):
sys.stdout.write(line + "\n")
sys.stdout.write("\n")
def print_charts(dataset, title, args, weekday=False):
chart = []
keys = sorted(dataset.keys())
mean = numpy.mean(list(dataset.values()))
median = numpy.median(list(dataset.values()))
for key in keys:
if dataset[key] >= median * 1.33:
displayed_key = "%s (\033[92m+\033[0m)" % (int_to_weekday(key) if weekday else key)
elif dataset[key] <= median * 0.66:
displayed_key = "%s (\033[91m-\033[0m)" % (int_to_weekday(key) if weekday else key)
else:
displayed_key = (int_to_weekday(key) if weekday else key)
chart.append((displayed_key, dataset[key]))
thresholds = {
int(mean): Gre, int(mean * 2): Yel, int(mean * 3): Red,
}
data = hcolor(chart, thresholds)
graph = Pyasciigraph(
separator_length=4,
multivalue=False,
human_readable='si',
)
for line in graph.graph(title, data):
if args.no_color:
ansi_escape = re.compile(r'\x1B\[[0-?]*[ -/]*[@-~]')
line = ansi_escape.sub('', line)
print(line)
print("")
def print_charts(dataset, title, weekday=False):
""" Prints nice charts based on a dict {(key, value), ...} """
chart = []
keys = sorted(dataset.keys())
mean = numpy.mean(list(dataset.values()))
median = numpy.median(list(dataset.values()))
for key in keys:
if (dataset[key] >= median * 1.33):
displayed_key = "%s (\033[92m+\033[0m)" % (int_to_weekday(key) if weekday else key)
elif (dataset[key] <= median * 0.66):
displayed_key = "%s (\033[91m-\033[0m)" % (int_to_weekday(key) if weekday else key)
else:
displayed_key = (int_to_weekday(key) if weekday else key)
chart.append((displayed_key, dataset[key]))
thresholds = {
int(mean): Gre, int(mean * 2): Yel, int(mean * 3): Red,
}
data = hcolor(chart, thresholds)
graph = Pyasciigraph(
separator_length=4,
multivalue=False,
human_readable='si',
)
for line in graph.graph(title, data):
print(line)
print("")
def __init__(self):
self._cur_oxygen = 0
self._cur_systolic = 0
self._cur_diastolic = 0
self._cur_pulse = 0
self._graph_lock = threading.Lock()
self._graph = Pyasciigraph()
def iotop_output_write(self):
count = 0
limit = self._arg_limit
graph = Pyasciigraph()
values = []
for tid in sorted(self.state.tids.values(),
key=operator.attrgetter('write'), reverse=True):
if not self.filter_process(tid):
continue
info_fmt = "{:>10} {:<25} {:>9} file {:>9} net {:>9} unknown "
values.append((info_fmt.format(
common.convert_size(tid.write, padding_after=True),
"%s (%d)" % (tid.comm, tid.pid),
common.convert_size(tid.disk_write,
padding_after=True),
common.convert_size(tid.net_write,
padding_after=True),
common.convert_size(tid.unk_write,
padding_after=True)),
tid.write))
count = count + 1
if limit > 0 and count >= limit:
break
for line in graph.graph('Per-process I/O Write', values,
with_value=False):
print(line)
def _print_results(self, begin_ns, end_ns, final=0):
count = 0
limit = self._arg_limit
graph = Pyasciigraph()
values = []
self.state = self._automaton.state
alloc = 0
freed = 0
print('Timerange: [%s, %s]' %
(common.ns_to_hour_nsec(
begin_ns, gmt=self._arg_gmt, multi_day=True),
common.ns_to_hour_nsec(
end_ns, gmt=self._arg_gmt, multi_day=True)))
for tid in sorted(self.state.tids.values(),
key=operator.attrgetter('allocated_pages'),
reverse=True):
if not self.filter_process(tid):
continue
values.append(
("%s (%d)" % (tid.comm, tid.tid), tid.allocated_pages))
count = count + 1
if limit > 0 and count >= limit:
break
for line in graph.graph("Per-TID Memory Allocations",
values,
unit=" pages"):
print(line)
values = []
count = 0
for tid in sorted(self.state.tids.values(),
key=operator.attrgetter('freed_pages'),
reverse=True):
if not self.filter_process(tid):
continue
values.append(("%s (%d)" % (tid.comm, tid.tid), tid.freed_pages))
count = count + 1
freed += tid.freed_pages
if limit > 0 and count >= limit:
break
for line in graph.graph("Per-TID Memory Deallocation",
values,
unit=" pages"):
print(line)
for tid in sorted(self.state.tids.values(),
key=operator.attrgetter('allocated_pages'),
reverse=True):
if not self.filter_process(tid):
continue
alloc += tid.allocated_pages
for tid in sorted(self.state.tids.values(),
key=operator.attrgetter('freed_pages'),
reverse=True):
if not self.filter_process(tid):
continue
freed += tid.freed_pages
print("\nTotal memory usage:\n- %d pages allocated\n- %d pages freed" %
(alloc, freed))
def synthesize_trials(block: Block) -> List[dict]:
# TODO: Do this in separate thread, and output some kind of progress indicator.
json_data = __generate_json_request(block)
solutions = cast(List[dict], [])
# Make sure the local image is up-to-date.
update_docker_image("sweetpea/server")
# 1. Start a container for the sweetpea server, making sure to use -d and -p to map the port.
container = start_docker_container("sweetpea/server", 8080)
# 2. POST to /experiments/generate using the backend request json as the body.
# TOOD: Do this in separate thread, and output some kind of progress indicator.
print("Sending formula to backend... ", end='', flush=True)
t_start = datetime.now()
try:
__check_server_health()
experiments_request = requests.post(
'http://localhost:8080/experiments/generate', data=json_data)
if experiments_request.status_code != 200 or not experiments_request.json(
)['ok']:
tmp_filename = ""
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write(str.encode(json_data))
tmp_filename = f.name
raise RuntimeError(
"Received non-200 response from experiment generation! LowLevelRequest body saved to temp file '"
+ tmp_filename + "' status_code=" +
str(experiments_request.status_code) + " response_body=" +
str(experiments_request.text))
solutions = experiments_request.json()['solutions']
t_end = datetime.now()
print(str((t_end - t_start).seconds) + "s")
# 3. Stop and then remove the docker container.
finally:
stop_docker_container(container)
# 4. Decode the results
result = list(map(lambda s: __decode(block, s['assignment']), solutions))
# Dump histogram of frequency distribution, just to make sure it's somewhat even.
print()
print("Found " + str(len(solutions)) + " distinct solutions.")
print()
hist_data = [("Solution #" + str(idx + 1), sol['frequency'])
for idx, sol in enumerate(solutions)]
hist_data.sort(key=lambda tup: tup[1], reverse=True)
graph = Pyasciigraph()
for line in graph.graph('Most Frequently Sampled Solutions',
hist_data[:15]):
print(line)
return result
def makeGraph(fList):
docs = 0
vids = 0
songs = 0
images = 0
others = 0
compressed = 0
codess = 0
ll = len(fList)
if (oss == 1):
print("Total no of files in system: ", ll)
else:
print("Total no of files in user home: ", ll)
for file in fList:
if file.name.lower().endswith('.pdf') or file.name.lower().endswith(
'.docx') or file.name.lower().endswith(
'.doc') or file.name.lower().endswith('.txt'):
docs += 1
if file.name.lower().endswith('.mp4') or file.name.endswith(
'.mkv') or file.name.endswith('.avi'):
vids += 1
if file.name.lower().endswith('.jpeg') or file.name.endswith(
'.png') or file.name.endswith('.jpg') or file.name.endswith(
'.gif'):
images += 1
if file.name.lower().endswith('.mp3') or file.name.endswith(
'.ogg') or file.name.endswith('.wav'):
songs += 1
if file.name.endswith('.apk') or file.name.endswith(
'.jar') or file.name.endswith('.exe') or file.name.endswith(
'.iso') or file.name.endswith(
'.dmg') or file.name.endswith(
'.csv') or file.name.endswith(
'.log') or file.name.endswith('.db'):
others += 1
if file.name.lower().endswith('.zip') or file.name.endswith(
'.7z') or file.name.endswith('.deb') or file.name.endswith(
'.tar.gz') or file.name.endswith('.rpm'):
compressed += 1
if file.name.endswith('.c') or file.name.endswith(
'.py') or file.name.endswith('.java') or file.name.endswith(
'.cpp'):
codess += 1
data = [('docs', docs), ('songs', songs), ('videos', vids),
('images', images), ('codes', codess), ("compressed", compressed),
('others', others)]
pattern = [Gre, Yel, Red]
data = vcolor(data, pattern)
graph = Pyasciigraph()
for line in graph.graph('Files on PC', data):
print(line)
def dumptAll(self, context=""):
global distriDuration
self.logger.info("Dump all perfmon recorded timings")
with open(myConfig['DEBUG']['PARTS']['PERFMON']['FILE'],
"a+") as myfile:
for part in distriDuration:
sorted_distriDuration = self.getSortedDuration(part)
#self.logger.info('Timing for parts :'+part)
#self.logger.info (sorted_distriDuration)
graph = Pyasciigraph(graphsymbol='#')
myfile.write("----------------------- {}\n".format(context))
myfile.write("Duration Timing for parts : {}\n".format(part))
for line in graph.graph(part, sorted_distriDuration):
myfile.write("{}\n".format(
line.encode('ascii', 'ignore').decode('utf-8')))
totalDur = 0
totalNb = 0
for measure in distriDuration[part].items():
totalDur = totalDur + (measure[0] * measure[1])
totalNb = totalNb + measure[1]
myfile.write("Average {} ms/processing\n".format(totalDur /
totalNb))
#self.logger.info(line.encode('ascii','ignore').decode('utf-8'))
for part in distriCycle:
sorted_distriCycle = self.getSortedCycle(part)
graph = Pyasciigraph(graphsymbol='#')
myfile.write("----------------------- {}\n".format(context))
myfile.write("Cycle Timing for parts : {}\n".format(part))
for line in graph.graph(part, sorted_distriCycle):
myfile.write("{}\n".format(
line.encode('ascii', 'ignore').decode('utf-8')))
totalDur = 0
totalNb = 0
for measure in distriCycle[part].items():
totalDur = totalDur + (measure[0] * measure[1])
totalNb = totalNb + measure[1]
myfile.write("Averazge {} events/s\n".format(
1000 / (totalDur / totalNb)))
myfile.close()
with open(myConfig['DEBUG']['PARTS']['TRACER']['FILE'],
"a+") as myfile:
myfile.write("----------------------- {}\n".format(context))
for evt in timeline:
myfile.write("{0} {1} {2} {3}\n".format(
evt['ts'], evt['thread'], evt['tag'], evt['state']))
myfile.close()
def print_distribution_graph(data, title):
y = np.bincount(data)
ii = np.nonzero(y)[0]
dist = [(str(x[0]), x[1]) for x in zip(ii, y[ii])]
graph = Pyasciigraph(human_readable='si')
for line in graph.graph(title, dist):
print line
print ""
def histogram(data, name):
name = 'count of %s response time by week' % name
g = Pyasciigraph()
buckets = defaultdict(int)
for item in data:
buckets[int(item // (86400 * 7))] += 1
for line in g.graph(name, ((k, v) for k, v in buckets.iteritems())):
print line
def output_latencies(self, args):
graph = Pyasciigraph()
for proc in self.latency_hist.keys():
values = []
for v in self.latency_hist[proc]:
values.append(("%s" % (v[0]), v[1]))
for line in graph.graph('%s requests latency (ms)' % proc,
values,
unit=" ms"):
print(line)
def barGraph(title, data):
"""
Dado un titulo y una tupla de datos formados por (titulo, numero)
imprime un gráfico de barra en la terminal
"""
print()
for line in Pyasciigraph().graph(title, data):
print(line)
return
def print_stats_diagram(self, total_entries):
data = []
graph = Pyasciigraph(separator_length=4)
for resolver_name in sorted(self.resolver_names):
item = (resolver_name, self.statistics[resolver_name])
data.append(item)
item = ('TOTAL', total_entries)
data.append(item)
for line in graph.graph('Blocking Statistics:', data):
print(line)
def test_type_output(self):
test = [('long_labe☭', 423), ('sl', 1234), ('line3', 531), ('line4', 200), ('line5', 834)]
graph = Pyasciigraph()
res = graph.graph('test print', test)
if sys.version < '3':
expected = unicode
else:
expected = str
for line in res:
assert type(line) == expected
def print_graph(header, data, is_bytes=False):
graph = Pyasciigraph(float_format='{:,.1f}',
min_graph_length=20,
separator_length=1,
line_length=shutil.get_terminal_size(
(DEFAULT_TERMINAL_WIDTH, 20)).columns,
human_readable='cs' if is_bytes else None)
for line in graph.graph(header, data):
print(line)
print()
def iotop_output_nr_sector(self):
graph = Pyasciigraph()
values = []
for disk in sorted(self.state.disks.values(),
key=operator.attrgetter('nr_sector'), reverse=True):
if disk.nr_sector == 0:
continue
values.append((disk.prettyname, disk.nr_sector))
for line in graph.graph('Disk nr_sector', values, unit=" sectors"):
print(line)
def graph_output(self, args, begin_ns, end_ns, final=0):
for comm in args.proc_list:
graph = Pyasciigraph()
values = []
for sec in sorted(self.history.keys()):
if comm not in self.history[sec]["proc"].keys():
break
pc = float("%0.02f" %
((self.history[sec]["proc"][comm] * 100) /
self.history[sec]["total_ns"]))
values.append(("%s" % sec_to_hour(sec), pc))
for line in graph.graph("%s CPU Usage" % comm, values, unit=" %"):
print(line)
graph = Pyasciigraph()
values = []
for sec in sorted(self.history.keys()):
pc = float("%0.02f" % (self.history[sec]["cpu"]))
values.append(("%s" % sec_to_hour(sec), pc))
for line in graph.graph("Total CPU Usage", values, unit=" %"):
print(line)
