def profline(statement, functions):
from line_profiler import LineProfiler
try:
len(functions)
except TypeError:
lp = LineProfiler(functions)
else:
lp = LineProfiler(*functions)
lp.run(statement)
lp.print_stats()
except:
pass
# print data.iloc[0:num, i]
frame = pd.DataFrame.from_items(d, orient="index", columns=["Median (0.25)", "Error (%)", "Median (0.05)", "Error (%)", "Truth"])
print(frame.to_latex(float_format=(lambda x: u"%1.1f" % x)))
if args.time:
test_median()
if args.size:
size_vs_error()
if args.quality:
no_test_DP_median()
if args.cprof:
import cProfile
cProfile.run("test_median()", sort="tottime")
if args.lprof:
from line_profiler import LineProfiler
profile = LineProfiler(test_median, CountSketchCt.estimate, CountSketchCt.aggregate,
Ct.__add__, EcPt.__add__, EcPt.__neg__, EcPt.__copy__,)
profile.run("test_median()")
profile.print_stats()
from twilio.rest import Client
from line_profiler import LineProfiler
send_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
# 短信收发费用0.028$
def send_message():
account_sid = 'ACd615e71b7ad5f4686943f7e716e95b0f'
auth_token = 'xxxxx'
client = Client(account_sid, auth_token)
message = client.messages \
.create(
body="Join Earth's mightiest heroes. Like Kevin Bacon.",
from_='+12187890888',
to='+8613871115289'
)
print('发送时间：%s \n状态：发送成功！' % send_time)
print('接收短信号码：' + message.to)
print('短信内容：\n' + message.body) # 打印短信内容
print('短信SID：' + message.sid) # 打印SID
#send_message() # 调用执行函数
lptest = LineProfiler(send_message)
lptest.run('send_message()')
lptest.print_stats()
action='store_true',
help='Run the c profiler')
parser.add_argument('--plot',
action='store_true',
help='Upload time plot to plotly')
args = parser.parse_args()
if args.time:
xxx = msg_mass()
test_full_client(xxx)
if args.cprof:
import cProfile
xxx = msg_mass()
cProfile.run("test_full_client(xxx)", sort="tottime")
if args.lprof:
from line_profiler import LineProfiler
#import rscoin.rscservice
profile = LineProfiler(rscoin.rscservice.RSCProtocol.handle_Query,
rscoin.rscservice.RSCFactory.process_TxQuery,
rscoin.Tx.check_transaction,
rscoin.Tx.check_transaction_utxo,
rscoin.Tx.parse)
xxx = msg_mass()
profile.run("test_full_client(xxx)")
profile.print_stats()
properties[i].append(objects[j].moments_hu())
properties[i].append(objects[j].image())
properties[i].append(objects[j].label)
properties[i].append(objects[j].major_axis_length())
properties[i].append(objects[j].max_intensity())
properties[i].append(objects[j].mean_intensity())
properties[i].append(objects[j].min_intensity())
properties[i].append(objects[j].minor_axis_length())
properties[i].append(objects[j].moments())
properties[i].append(objects[j].moments_normalized())
properties[i].append(objects[j].orientation())
properties[i].append(objects[j].perimeter())
properties[i].append(objects[j].solidity())
properties[i].append(objects[j].weighted_moments_central())
properties[i].append(objects[j].weighted_centroid())
properties[i].append(objects[j].weighted_moments_hu())
properties[i].append(objects[j].weighted_moments())
properties[i].append(objects[j].weighted_moments_normalized())
return properties, prop_names
if __name__ == '__main__':
image = io.imread('test-image.png')
green = image[..., 1].copy()
lp = LineProfiler()
lp.add_function(object_features)
lp.run('intensity_object_features(green, 100)')
lp.print_stats()
lp.dump_stats('profile.lprof')
print(__file__)
import app
from line_profiler import LineProfiler
import numpy as np
import time
from tqdm import tqdm
import matplotlib.pyplot as plt
profile = LineProfiler()
profile.add_module(app)
profile.run("app.Application()")
profile.print_stats()
args = parser.parse_args()
if args.time:
notest_timing(31)
if args.cprof:
import cProfile
cProfile.run("notest_timing(51)", sort="tottime")
if args.lprof:
from line_profiler import LineProfiler
profile = LineProfiler(VerifyOneOfN, ProveOneOfN, Bn.__init__,
Bn.__del__)
profile.run("notest_timing(31)")
profile.print_stats()
if args.plot:
all_sizes, prove_time, verify_time = notest_timing()
import plotly.plotly as py
from plotly.graph_objs import *
trace0 = Scatter(
x=all_sizes,
y=prove_time,
name='Proving',
)
trace1 = Scatter(
from line_profiler import LineProfiler
from sklearn_seco import SimpleSeCoEstimator
from sklearn_seco.common import match_rule, RuleContext
from sklearn_seco.tests import conftest
def tcn2(dataset):
with warnings.catch_warnings():
from _pytest.deprecated import RemovedInPytest4Warning
warnings.simplefilter("ignore", RemovedInPytest4Warning)
est = SimpleSeCoEstimator()
est.fit(dataset.x_train, dataset.y_train)
ypred = est.predict(dataset.x_test)
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(dataset.y_test, ypred))
print(classification_report(dataset.y_test, ypred))
try:
from numba import NumbaWarning
warnings.simplefilter("error", NumbaWarning)
except ImportError:
pass
profile = LineProfiler()
profile.add_function(match_rule)
profile.add_function(RuleContext._count_matches)
profile.add_function(RuleContext.pn)
profile.run('tcn2(conftest.sklearn_make_moons())')
profile.print_stats()
if stats[l][j]['case'] == 'L' and stats[k][i]['case'] == 'U': continue
if stats[l][j]['alph'] == 'A' and stats[k][i]['alph'] == 'N': continue
if stats[l][j]['alph'] == 'N' and stats[k][i]['alph'] == 'A': continue
y = tabs[l][j]
if len(y) < 3: continue # avoid boolean
# common types
comtyp = typsets[k][i] & typsets[l][j]
# if len(comtyp) == 0: continue # not good, miss most refs
# if y <= x: # (strict) subset # instead: 90% subset
fracsubs = fracsubset(y, x)
if fracsubs >= 0.9:
# table l, column j is subset of table k, column i: table l references table k ?
fout.write("insert into sub (l, j, k, i, comtyp, fracsubset, nchild, nparent, fnchild, fnparent)"
+ " values (%d, %d, %d, %d, '%s', %f, %d, %d, '%s', '%s');\n"
% (l, j, k, i, len(comtyp), fracsubs, len(y), len(x), cleanfn(sam[l]), cleanfn(sam[k])))
fout.write("commit;\n")
fout.close()
t3 = time()
print('references done in %.1f seconds' % (t3-t2,), file=sys.stderr)
print('total run time %.1f seconds or %.1f minutes' % (t3-t0, (t3-t0)/60), file=sys.stderr)
sys.exit(0)
if '--profile' in sys.argv:
print('profiling..', file=sys.stderr)
prof = LineProfiler(main)
prof.run('main()')
prof.print_stats()
else:
main()
"""
Line-by-line profiling of CPU use of Python scripts in
:mod:`mandelbrot.implementations` using :mod:`line_profiler`.
"""
from line_profiler import LineProfiler
import mandelbrot
if __name__ == "__main__":
args = mandelbrot.parsed_mandelbrot_args()
for impl in mandelbrot.all_implementations():
print(f"About to profile {impl.id_}")
profile = LineProfiler()
profile.add_function(impl.callable)
cmd = (
f"{impl.fully_qualified_name}(grid_side_size={args.grid_side_size}, "
f"max_iter={args.max_iter})"
)
profile.run(cmd)
profile.print_stats()
# !/usr/bin/env python
# -*- coding: utf-8 -*-
from line_profiler import LineProfiler
"""
分析时间耗时,
这个包需要下载到本地进行安装:
pip3 install ./python_module/memory_profiler_module/line_profiler-3.1.0-cp37-cp37m-win_amd64.whl
"""
def operation1():
num = 0
for i in range(10000):
num += 1
def operation2():
num = 0
while (num < 10000):
num += 1
if __name__ == "__main__":
lprofiler = LineProfiler(operation1, operation2)
lprofiler.run('operation1()')
lprofiler.run('operation2()')
lprofiler.print_stats()
args = parser.parse_args()
if args.time:
notest_timing(31)
if args.cprof:
import cProfile
cProfile.run("notest_timing(51)", sort="tottime")
if args.lprof:
from line_profiler import LineProfiler
profile = LineProfiler(VerifyOneOfN, ProveOneOfN, Bn.__init__, Bn.__del__)
profile.run("notest_timing(31)")
profile.print_stats()
if args.plot:
all_sizes, prove_time, verify_time = notest_timing()
import plotly.plotly as py
from plotly.graph_objs import *
trace0 = Scatter(
x=all_sizes,
y=prove_time,
name='Proving',
)
# has 3 expensive lines - check_array, np.asarray, np.average
#https://github.com/scikit-learn/scikit-learn/blob/1495f69242646d239d89a5713982946b8ffcf9d9/sklearn/utils/validation.py#L600
# check_X_y
# checks for array for certain characteristics and lengths
#
df = pd.read_pickle('generated_ols_data.pickle')
print(f"Loaded {df.shape} rows")
est = LinearRegression()
row = df.iloc[0]
X = np.arange(row.shape[0]).reshape(-1, 1).astype(np.float_)
lp = LineProfiler(est.fit)
print("Run on a single row")
lp.run("est.fit(X, row.values)")
lp.print_stats()
print("Run on 5000 rows")
lp.run("df[:5000].apply(ols_sklearn, axis=1)")
lp.print_stats()
lp = LineProfiler(base._preprocess_data)
lp.run("base._preprocess_data(X, row, fit_intercept=True)")
lp.print_stats()
lp = LineProfiler(base.check_X_y)
lp.run(
"base.check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], y_numeric=True, multi_output=True)"
)
lp.print_stats()
parser.add_argument('--plot', action='store_true', help='Upload time plot to plotly')
args = parser.parse_args()
if args.time:
xxx = msg_mass()
test_full_client(xxx)
if args.cprof:
import cProfile
xxx = msg_mass()
cProfile.run("test_full_client(xxx)", sort="tottime")
if args.lprof:
from line_profiler import LineProfiler
#import rscoin.rscservice
profile = LineProfiler(rscoin.rscservice.RSCProtocol.handle_Query,
rscoin.rscservice.RSCFactory.process_TxQuery,
rscoin.Tx.check_transaction,
rscoin.Tx.check_transaction_utxo,
rscoin.Tx.parse)
xxx = msg_mass()
profile.run("test_full_client(xxx)")
profile.print_stats()
for timestamp in bit_2_events[-5:]:
print "unstrobed bit 2 t=%f" % timestamp
print "found %d bit 3 events. Last 5 events are:" %(len(bit_3_events))
for timestamp in bit_3_events[-5:]:
print "unstrobed bit 3 t=%f" % timestamp
unstrobed_word = pu.GetExtEvents(data, event='unstrobed_word', online=False)
print "found %d unstrobed word events in which 10 events are:" %(len(unstrobed_word['value']))
indices = np.arange(0,len(unstrobed_word['value']),len(unstrobed_word['value'])/10)
for value,timestamp in zip(unstrobed_word['value'][indices],unstrobed_word['timestamp'][indices]) :
binary_value = bin(value)
print "unstrobed word:%s t=%f" % (binary_value,timestamp)
if __name__ == "__main__":
#run()
profile = LineProfiler()
profile.add_function(run)
profile.add_function(PlexUtil.GetExtEvents)
profile.add_function(reconstruct_word)
profile.run('run()')
profile.print_stats()
profile.dump_stats("testPlexFile_profile.lprof")
#cProfile.run('run()','PlexFile_profile')
#p = pstats.Stats('testPlexFile_profile.lprof')
#p.sort_stats('cumulative')
#p.print_stats()
#print h.heap()
# Test and profile TimeHistogram
#
# Copyright (C) 2010-2012 Huang Xin
#
# See LICENSE.TXT that came with this file.
#import cProfile,pstats
from line_profiler import LineProfiler
import TimeHistogram
def run():
psth = TimeHistogram.PSTHAverage(
'/home/chrox/dev/plexon_data/c04-stim-timing-8ms-rand-1.plx')
psth.get_data()
if __name__ == '__main__':
#cProfile.run('psth.get_data()','hist_profile')
#p = pstats.Stats('hist_profile')
#p.sort_stats('cumulative')
#p.print_stats()
profile = LineProfiler()
profile.add_function(run)
profile.add_function(TimeHistogram.PSTHAverage._process_unit)
profile.run('run()')
profile.print_stats()
profile.dump_stats("hist_profile.lprof")
# =============================================================================
# =============================================================================
# Functions
# =============================================================================
# =============================================================================
# Init
# =============================================================================
if __name__ == '__main__':
profiler = LineProfiler()
#profiler.add_function(translations.TranslationFile.get_translation)
#profiler.add_function(translations.TranslationFile._read)
profiler.add_function(translations.TranslationString._set_string)
#profiler.add_function(translations.Translation.get_language)
#profiler.add_function(translations.TranslationQuantifier.handle)
#profiler.add_function(translations.TranslationRange.in_range)
#profiler.add_function(translations.TranslationLanguage.get_string)
profiler.run(
"s = translations.TranslationFile('C:/Temp/MetaData/stat_descriptions.txt')"
)
profiler.run(
"for i in range(0, 100): t = s.get_translation(tags=['additional_chance_to_take_critical_strike_%', 'additional_chance_to_take_critical_strike_%'], values=((3, 5), 6))"
)
profiler.print_stats()
print('translations.Translation:', t)
