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# Licensed under the Apache License: http://www.apache.org/licenses/LICENSE-2.0
# For details: https://bitbucket.org/ned/coveragepy/src/default/NOTICE.txt
import csv
from collections import namedtuple
import os
import shutil
import statistics
import sys
import time
import coverage
from tests.coveragetest import CoverageTest
class StressResult(namedtuple('StressResult', ['files', 'calls', 'lines', 'baseline', 'covered'])):
@property
def overhead(self):
return self.covered - self.baseline
NANOS = 1e9
TEST_FILE = """\
def parent(call_count, line_count):
for _ in range(call_count):
child(line_count)
def child(line_count):
for i in range(line_count):
x = 1
"""
def mk_main(file_count, call_count, line_count):
lines = []
lines.extend("import test{}".format(idx) for idx in range(file_count))
lines.extend("test{}.parent({}, {})".format(idx, call_count, line_count) for idx in range(file_count))
return "\n".join(lines)
class StressTest(CoverageTest):
def _compute_overhead(self, file_count, call_count, line_count):
self.clean_local_file_imports()
for idx in range(file_count):
self.make_file('test{}.py'.format(idx), TEST_FILE)
self.make_file('testmain.py', mk_main(file_count, call_count, line_count))
# Run it once just to get the disk caches loaded up.
self.import_local_file("testmain")
self.clean_local_file_imports()
# Run it to get the baseline time.
start = time.perf_counter()
self.import_local_file("testmain")
baseline = time.perf_counter() - start
self.clean_local_file_imports()
# Run it to get the covered time.
start = time.perf_counter()
cov = coverage.Coverage()
cov.start()
try: # pragma: nested
# Import the Python file, executing it.
mod = self.import_local_file("testmain")
finally: # pragma: nested
# Stop coverage.py.
covered = time.perf_counter() - start
stats = cov.collector.tracers[0].get_stats()
if stats:
stats = stats.copy()
cov.stop()
print("baseline = {:.2f}, covered = {:.2f}".format(baseline, covered))
# Empirically determined to produce the same numbers as the collected
# stats from get_stats().
actual_file_count = 6 + file_count
actual_call_count = 85 + file_count * (call_count + 98)
actual_line_count = (
343 +
390 * file_count +
3 * file_count * call_count +
2 * file_count * call_count * line_count
)
if stats is not None:
assert actual_file_count == stats['files']
assert actual_call_count == stats['calls']
assert actual_line_count == stats['lines']
print("File counts", file_count, actual_file_count, stats['files'])
print("Call counts", call_count, actual_call_count, stats['calls'])
print("Line counts", line_count, actual_line_count, stats['lines'])
print()
return StressResult(
actual_file_count,
actual_call_count,
actual_line_count,
baseline,
covered,
)
def stress_test(self):
# For checking the calculation of actual stats:
if 0:
for f in range(3):
for c in range(3):
for l in range(3):
self._compute_overhead(100*f+1, 100*c+1, 100*l+1)
# For checking the overhead for each component:
fixed = 900
step = 500
def time_thing(thing):
per_thing = []
pct_thing = []
for runs in range(5):
for n in range(100, 1000, step):
kwargs = {
"file_count": fixed,
"call_count": fixed,
"line_count": fixed,
}
kwargs[thing+"_count"] = n
res = self._compute_overhead(**kwargs)
per_thing.append(res.overhead / getattr(res, "{}s".format(thing)))
pct_thing.append(res.covered / res.baseline * 100)
print("Per {}: mean = {:.5f}us, stddev = {:0.5f}us".format(thing, statistics.mean(per_thing)*1e6, statistics.stdev(per_thing)*1e6))
print(" pct = {:.3f}%, stddev = {:.5f}".format(statistics.mean(pct_thing), statistics.stdev(pct_thing)))
time_thing("file")
time_thing("call")
time_thing("line")
return
line_result = self._compute_overhead(1, 1, int(1e8))
call_result = self._compute_overhead(1, int(1e7), 1)
file_result = self._compute_overhead(int(1e4), 1, 1)
line_overhead_estimate = 0
call_overhead_estimate = 0
file_overhead_estimate = 0
for i in range(20):
line_overhead_estimate = (
line_result.overhead * NANOS -
call_overhead_estimate * line_result.calls -
file_overhead_estimate * line_result.files
) / line_result.lines
call_overhead_estimate = (
call_result.overhead * NANOS -
line_overhead_estimate * call_result.lines -
file_overhead_estimate * call_result.files
) / call_result.calls
file_overhead_estimate = (
file_result.overhead * NANOS -
call_overhead_estimate * file_result.calls -
line_overhead_estimate * file_result.lines
) / file_result.files
line_baseline_estimate = 0
call_baseline_estimate = 0
file_baseline_estimate = 0
for i in range(20):
line_baseline_estimate = (
line_result.baseline * NANOS -
call_baseline_estimate * line_result.calls -
file_baseline_estimate * line_result.files
) / line_result.lines
call_baseline_estimate = (
call_result.baseline * NANOS -
line_baseline_estimate * call_result.lines -
file_baseline_estimate * call_result.files
) / call_result.calls
file_baseline_estimate = (
file_result.baseline * NANOS -
call_baseline_estimate * file_result.calls -
line_baseline_estimate * file_result.lines
) / file_result.files
print("Line: {:.2f} ns baseline, {:.2f} ns overhead, {:.2%} overhead".format(
line_baseline_estimate,
line_overhead_estimate,
line_overhead_estimate/line_baseline_estimate,
))
print("Call: {:.2f} ns baseline, {:.2f} ns overhead, {:.2%} overhead".format(
call_baseline_estimate,
call_overhead_estimate,
call_overhead_estimate/call_baseline_estimate,
))
print("File: {:.2f} ns baseline, {:.2f} ns overhead, {:.2%} overhead".format(
file_baseline_estimate,
file_overhead_estimate,
file_overhead_estimate/file_baseline_estimate,
))
assert False
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