Saravanan666/pypi-org huggingface dataset:
Package: logop
pylogoppylogop 是一个轻量型的日志模块，用于记录日志信息。特点多样化的日志输出格式：pylogop 提供了不同的日志输出格式，可以根据需求选择适合的格式，例如标准输出格式、文件输出格式等。灵活的日志级别设置：可以根据需要设置不同的日志级别，从而控制日志消息的详细程度。常见的日志级别包括DEBUG、INFO、WARNING、ERROR和CRITICAL。多种日志输出方式：pylogop 支持将日志消息输出到标准输出、文件等多种目标。这样可以根据实际需求选择合适的输出方式，方便日志的收集和查看。异步日志记录：pylogop 支持异步日志记录，可以在后台线程中处理日志消息。这种方式可以提高性能，尤其是在高并发或频繁日志记录的情况下。简单易用的接口：pylogop 的接口设计简单明了，使用起来非常方便。通过调用相应的方法或设置相应的参数，就可以完成日志记录的配置和操作。可扩展性：pylogop 的代码结构清晰，采用面向对象的设计，易于扩展和定制。可以根据需要添加新的日志输出方式或定制日志记录逻辑。安装安装 pylogop 的首选方式是通过 pippip install logop或更新 pylogop 以使用新版本的特性pip install --upgrade logop快速上手导入 pylogop 模块importlogop希望将日志消息输出到控制台# 初始化一个日志管理对象logging=logop.logging.Logging()或希望将带有颜色的日志消息输出到控制台# 拒绝日志管理对象自动初始化一个标准型日志输出对象logging=logop.logging.Logging(stdout=False)# 初始化一个带有颜色的标准型日志输出对象logoutput=logop.logoutput.LogopStandardPlus()# 将日志输出对象添加到日志管理对象logging.add_op(logoutput)或希望将日志消息输出到文件# 初始化一个日志管理对象logging=logop.logging.Logging()# 初始化一个文件型日志输出对象logoutput=logop.logoutput.LogopFile()# 将日志输出对象添加到日志管理对象logging.add_op(logoutput)输出日志logging.info("Hello word")logging.warn("WARN")logging.error("ERROR")logging.fatal("FATAL ERROR")设计Logging 类是日志管理器，它可以添加和删除日志输出对象，并根据日志级别过滤日志消息。它还支持异步日志记录，可以在后台线程中处理日志消息，以提高性能。BaseLogop、LogopStandard、LogopStandardPlus 和 LogopFile，它们都是日志输出对象的不同实现方式。BaseLogop 是基础类，其他类继承自它并实现了具体的日志输出逻辑。改动1.0.0 版本模块的结构发生了改动，但仍可以通过以下方式直接使用旧版本的代码importlogop.oldaslogop文档日志等级日志格式日志管理器日志输出对象
Package: logo-qrcode
logo-qrcodeA simple Python library for generating QR codes with logo一、直接拷贝脚本文件使用logo_qrcode.py -data http://www.google.com -logo logo.png -save qecode.png二、使用 pip 安装pip install logo-qrcode查看帮助使用pip install logo-qrcode安装成功可以直接使用cli工具使用logo-qrcode-cli -h或python3 -m logo_qrcode -h使用 logo-qrcode-cli使用pip install logo-qrcode安装成功可以直接使用cli工具logo-qrcode-cli -data http://www.baidu.com -logo logo.png -save qecode.png使用--base64直接在控制台输出图片base64logo-qrcode-cli -data http://www.baidu.com -logo logo.png --base64
Package: logos-cdi
Simple and powerful python container dependency injection moduleinstallation>poetryaddlogos_cdiusagecreatemain.pyfile and createlogos_cdi.application:Applicationinstance with a modules for usage.fromlogos_cdi.applicationimportApplicationapp=Application(['logos_cdi','logos_cdi.command'])in your terminal with venv actived typelogos -hcommand and press enter.> logos -h
usage: logos [-h] {} ...

options:
  -h, --help  show this help message and exit

command:
  {}          command to executethis is your app's command manager all your commands you can see herePS. your commands are loaded from the modules used in the application, you can implement them, see./logos_cdi/command.pyfile to understand how to create a module.
Package: logo-standardizer
logo-standardizerDescriptionA smart logo standardizer that detects both the logo and the background of an unstandardized logo picture and  transforms it into the desired logo standards for app circled or general front-end display.Installation$ pip install logo-standardizer==1.0.1UsageA brief example of how to use the package:fromlogo_standardizerimportlogo_standardize# call the function directly and fill the required argumentslogo_path="example/import_dir/img_name"export_dir="example/export_dir"standard_dimension=300# optionalmessage=logo_standardize.standardize(logo_path,export_dir,standard_dimension)#print the message to confirm the completion of the processprint(message)
Package: logot
Log-based testing 🪵logotmakes it easy to test whether your code is logging correctly:fromlogotimportLogot,loggeddeftest_something(logot:Logot)->None:do_something()logot.assert_logged(logged.info("Something was done"))logotintegrates with popular testing (pytest,unittest), asynchronous (asyncio,3rd-party) and logging frameworks (logging,3rd-party). It can be extended to support many others. 💪Documentation 📖Full documentation is published onRead the Docs.Bugs / feedback 🐛Issue tracking is hosted onGitHub.Changelog 🏗️Release notes are published onGitHub.License ⚖️logotis published as open-source software under theMIT license.
Package: logover
PLEASE READ THIS SECTION CAREFULLYThis module provides you three main methods to use in your application:
1.sendlog(message):this method send your string type log messages with appropriate buffering and time out facility. Your messages will remain in buffer for fixrd time or till buffer gets complelety filled and then batch of messages will be send to DB and bufferes get cleared for next batch.2.setBufferLength(len):this method is used to customize buffer length.(default length is 5)3.setTimerTime(time):this methods enables you to set time interval, after the expiry of this time,buffer data will be send,irrespective of buffer length.(default value=10)NOTE:first set bufferlength and timeout interval using method number 2 and 3 and then use sendlog() method in your code as many time as you wish.
Package: logpandas
logpandasParse various logfiles commonly found in/var/logasPandasDataFrames.InstallationTo install the package, type:pipinstalllogpandasUsageFor more information on the command-line interface, type:logpandas --helpusage: logpandas [-h] input_filepath output_filepath

positional arguments:
  input_filepath
  output_filepath

optional arguments:
  -h, --help       show this help message and exitLicenseThis project is licensed under the MIT license. See theLICENSEfor details.
Package: logpass-pytest-plugins
LogPass pytest pluginsA few pytest plugins used by LogPass.InstallationTo uselogpass_pytest_pluginsinstall it with your package manager,
e.g. via pip:pipinstalllogpass_pytest_pluginsTo install plugin with all its dependencies use one of following extras:auto_pytest_factoryboychannelsrest_frameworkFor instance, to installchannelsandrest_frameworkplugins with all
dependencies:pipinstalllogpass_pytest_plugins[channels,rest_framework]And finally add plugin import path topytest_pluginsin your rootconftest.pyfile, e.g. to usechannelsandrest_frameworkplugins:# root `conftest.py`pytest_plugins=('logpass_pytest_plugins.contrib.channels','logpass_pytest_plugins.contrib.rest_framework',)Available pluginsNOTE: None plugin isnotused by default - you need to enable them viapytest_pluginslogpass_pytest_plugins.contrib.auto_pytest_factoryboyPlugin that automatically registersfactory_boyfactories topytest-factoryboy, so factories and models instances will be available
as pytest fixtures.ConfigurationFollowing INI options can be used to configureauto_pytest_factoryboyplugin:auto_pytest_factoryboy_root_dir- directory where factories declarations
searching starts (defaults to.- pytest config path)auto_pytest_factoryboy_globs- list ofglobpatterns used to find files
withfactoryboyfactories declarations starting from theauto_pytest_factoryboy_root_dirdirectory (defaults to**/factories*.py)logpass_pytest_plugins.contrib.channelsPlugin that simplifieschannelsconsumers testing by providing following
fixtures:websocket_commmunicator_factory- factory ofWebSocketCommunicatorinstances, that will automatically disconnect at the end of a test.
Using this fixture also automatically flush all used channel layershttp_commmunicator_factory- factory ofHttpCommunicatorinstances. Using this fixture also automatically flush all used
channel layerslogpass_pytest_plugins.contrib.flaskPlugin that simplifiesflaskviews and other components testing
by providing following fixtures:flask_app-Flaskapp instanceclient-FlaskClientinstance to use in testsFollowing INI options can be used to configureflaskplugin:FLASK_SETTINGS_MODULE- import path to settings module when using
flask's config from object. OverridesFLASK_SETTINGS_MODULEenvironment
variable.FLASK_APP- import path to flask app factory or flask app instance.
OverridesFLASK_APPenvironment variable.To useflaskplugin you need to do one of following:setFLASK_APPINI optionsetFLASK_APPenvironment variabledefineflask_appfunction-scoped fixture in rootconftest.pylogpass_pytest_plugins.contrib.rest_frameworkPlugin that simplifiesrest_frameworkviews and other components testing
by providing following fixtures:api_rf-APIRequestFactoryinstanceapi_client-APIClientinstance
Package: logpilot
logpilotlogpilot is a project to extend python standard logging to offer customizing ability.InstallpipinstalllogpilotLog formatwe only support format like this:#--------------------------------------------------# Log Format#--------------------------------------------------# %(asctime)s.%(msecs)03d      yyyy-mm-dd HH:MM:SS 2016-01-01 00:00:00.000# %(filename)s# %(pathname)s      Full path# %(funcName)s# %(levelname)s# %(lineno)d# %(module)s# %(message)s# %(name)s# %(processName)s# %(threadName)s# %(hostname)s      FQDN# %(uuid)s          custom uuid# %(elapsed)s       (ms)%(asctime)s.%(msecs)03d|%(levelname)s|%(name)s|%(filename)s|%(lineno)d|%(module)s|%(funcName)s|%(processName)s|%(threadName)s|%(message)s|%(hostname)s|%(uuid)s|%(elapsed)sHow to usefromlogpilot.logimportLoglog=Log.get_logger(__name__)classExample(object):def__init__(self):log.info(msg="this is a test for logpilot",uuid="2b24bad1c5df6b4551768fe09ae877b893fc35505847e80f119c395bca27",elapsed=256)example log2016-11-1714:35:59|DEBUG|__main__|test2_log.py|11|test2_log|__init__|MainProcess|MainThread|thisisatestforlogpilot|localhost|2b24bad1c5df6b4551768fe09ae877b893fc35505847e80f119c395bca27|256How to set global uuidfromlogpilot.logimportLoglog=Log.get_logger(__name__,uuid='hsdfhkd')log.info(msg="this is a test logpilot",elapsed=8888)log.info(msg='hahah',uuid='5678')log.debug(msg="android world",elapsed=234)# notice that uuid also can be overridelog.error(msg="ios world",uuid='jaj',elapsed=99)How to change log leveldefault DEBUGimportloggingfromlogpilot.logimportLoglog=Log.get_logger('test2222')log.logger.setLevel(logging.INFO)classTest(object):def__init__(self):log.info(msg="this is a test logpilot",hahah='hooooo.',age=10000,fake_key='not valid')if__name__=='__main__':fromlogpilot.logimportLoga=Test()log.debug(msg="this is a test logpilot",hahah='hooooo.',uuid="1234567890")TODO:-[ ] custom format
Package: logPoints
log_pointsDocumentationlog_pointlog_point(message=None)Returns the file and line called from and the message if one is given.log_point()also keeps track of how many times it has been called from each line and if this exceeds a maximum will kill the program.pointerpointer(message=None, back_colour=0, font_colour=0)Returns the file and line called from and the message if one is given but coloured with a given background and font colour.
Package: log-points
log_pointsDocumentationlog_pointlog_point(message=None)Returns the file and line called from and the message if one is given.log_point()also keeps track of how many times it has been called from each line and if this exceeds a maximum will kill the program.pointerpointer(message=None, back_colour=0, font_colour=0)Returns the file and line called from and the message if one is given but coloured with a given background and font colour.
Package: logPPP
日志记录简介日志记录模块是一个简单的日志输出工具，它可以将不同等级（INFO、WARNING、ERROR、DEBUG、CRITICAL）的日志输出到控制台。使用方法要使用日志记录模块，请在您的Python脚本或模块中导入它，如下所示：pip install logPPP示例fromlogPPPimport*logger=get_logger(logging_level=INFO,logging_is_output_sys_stdout=True,logging_file="log.log",logger_name=None,logging_fmt=None,logging_date_fmt=None)logger.info("info")logger.debug("debug")logger.warning("warning")logger.error("error")logger.critical("critical")
Package: logprism
A command line log colouriser utility.InstallationFrom Python package index (Pypi):(sudo) pip install logprismor fromGithub:(sudo) pip install watchdog  # optionalgit clonehttps://github.com/peterhil/prism.gitcd prism(sudo) python setyp.py installDependenciesAll dependencies can be installed withpip, usually by:pip install some-packageRequired (automatically handled by pip):ordereddict (if using Python 2.6)Optional (install manually with pip):watchdog (watch files and directories with the -w option. Recommended for Python 2.x only – doesn’t work with Python 3)Developers only:pytest (test framework)tox (continuous integration)Usage examplesColourise stdin:tail -f /opt/local/var/log/couchdb/couch.log | prismGrep some logs for messages with levels:prism -g /var/log/*.logCombine with grep command to find just specific levels:grep -iE ‘(warn|error)’ /var/log/*.log | prism -gMatch multiple log levels per line:prism -m /var/log/apache2/vhost*logprism -m <(echo “Dribble me a debug or info lest I notice no create nor alter nor drop of your critical tables.”)Watch for a whole directory of logs for changes (and new files):prism -m -w /opt/local/var/log/nginx/Some programs output normally to stderr, grab that output for prism:python run.py 2>&1 | prismTestRun unit tests:python setup.py testRun continuous integration with tox (from an activated virtualenv, use tox -r to recreate CI envs):toxTodoShow some tail lines when using -w: Make a line cache (count total lines withwc -l) from the end of the file using some blocksize.Can be done with readlines() first. Then seek and read blocksize bytes from len(file)-blocksize and update cache.Later enable ncurses scrolling for earlier lines?Show n lines by default (a screenful / filecount?)
Package: logprof
logprof - Simple logging profilerSimple logging of timing and memory usage with decorator or context manager.InstallationpipinstalllogprofUsageBelow is an example usageimportloggingfromlogprofimportlogproflogging.basicConfig(level="INFO")# tf can be seconds (default) or "full"@logprof("bar",tf="breakdown")deffunction():print("The bit in the middle")function()# INFO:logprof:>>> 'bar' started..# The bit in the middle# INFO:logprof:<<< 'bar' finished. Took 15us 860nsclassFoo:@logprof("class func",tf="nanoseconds")defbar(self,x=1):print(f"bar got x ={x}")Foo().bar()# INFO:logprof:>>> 'class func' started..# bar got x = 1# INFO:logprof:<<< 'class func' finished. Took 15339nswithlogprof("foo",tf="breakdown",mf="breakdown"):fromtimeimportsleepsleep(1)x=[1]*2**25## 256MB?x.append(1000)print("The bit in the middle")# INFO:logprof:>>> 'foo' started..# The bit in the middle# INFO:logprof:<<< 'foo' finished. Took 1s 111ms 947us 614ns. Used 255mib 1004kib.DevelopmentBefore commit run following format commands in project folder:poetryrunblack.
poetryrunisort.--profileblack
Package: log-proxy
log-proxyThis package implements a logging server which can be secured with TLS. The server can forward the
logs to another server allowing the definition of gateways. The transmission happens using JSON
instead of pickle used by pythons SocketHandler to prevent code execution.The main purpose of this project is to aggregate logs via the network inside of a database like
PostgreSQL or MongoDB. The client is able to collect the logs from different sources.This tool is not designed to later process, view, or manage the logs inside of the database.FeaturesLog aggregation and proxy serversForward the logs in a server to another server or database (MongoDB, PostgreSQL)Logging handlers to send logs to the logging server from existing appsClient tool for testingSecure the transmission with TLS and token authenticationUsage examplesStart a logging server and forward the logs to a MongoDB database$ python3 -m log_proxy server mongodb --db logs --db-table log <...>Start a logging server and forward the logs to the next server$ python3 -m log_proxy server socket --forward <host>Start client for testing$ python3 -m log_proxy client --forward <host> --log-stdin
Package: log.py
log.py📖 About:A basic logging library for Python with the capability to:save to files.have custom formats.have custom levels.be used instantiated.be used non-instantiated.📝 Basic Usage:(For more advanced uses, please refer to the given examples or the documentation.)Instantiated:Code:fromlogpyimportLoggerfromlogpy.logimportLevelslogger=Logger()logger.log("Hello World!")logger.log("Oh no, something went wrong!",Levels.error)Terminal:Non-instantiated:Code:fromlogpyimportLoggerfromlogpy.logimportLevelsLogger.slog("Hello World!")Logger.slog("Oh no, something went wrong!",Levels.error)Terminal:🔧 Examples:Click me!📚 Documentation:❗ Requirements:Python 3.10
Package: logquicky
LogquickyNicer python logging in one lineThe python logging package from the standard library is awesome and its certainly much better than sprinklingprint()statement all over your code.
However, even hough setting it up takes just a few lines of code, configuring it to make it look nice across projects, just adds up to doing the same thing over and over.Therefore, I createdlogquicky.Because this is basically just a very simple configuration on the logging module, which also makes it very easy to fall back to it once you decide your project needs more advanced functionalities.
However, it hopes to save you some time when quickly building scripts or when getting started in a new project.Finally, this is also my first (hopefully useful) little OpenSource contribution up on PyPI, so I figured it would be a good exercise.FeaturesColored log levels make it easy to identify different levels.Pre-configured formatting of log lines,Optional logging to a log file,Ability to rewrite lines for nicer progress bars etc.Based on Python's logging module.InstallationpipinstalllogquickyNoteslogquicky is supported for python 3.6+ (due to usage of f-strings)How to useBasic exampleimportlogquicky# Add this line to create your logger.log=logquicky.load('my-logger')# Start logging!log.info("This is a log message")See how it looks in actionLogging from multiple files#!/usr/bin/env python# --- example2.py ---importlogquickyimportother_file# Creates a new logger and returns it.log=logquicky.load('my-logger')log.info("I can log from here...")# --- example2_other_file.py ---importlogquickylog=logquicky.load('my-logger')defrun():log.info("And from here as well!")Result:2019-02-2812:16:56my-logger[INFO]Icanlogfromhere...2019-02-2812:16:56my-logger[INFO]Andfromhereaswell!
Package: logram
logramPython logger with different handlerspip install logramAdd LOGGING configurationsOnly different part is telegram handlerimportloggingfromlogging.configimportdictConfigfromlogram.handlersimportTelegramTelegram.token="20**:AA*****"Telegram.chat_id="-6***"LOGGING={"version":1,"disable_existing_loggers":False,"formatters":{"verbose":{"format":"[%(levelname)s] [%(asctime)s] [%(module)s] [line:%(lineno)d]%(message)s"},"simple":{"format":"%(levelname)s%(message)s"},},"handlers":{"file":{"level":"INFO","class":"logging.FileHandler","filename":"logs/log","formatter":"verbose","encoding":"UTF-8",},"console":{"level":"INFO","class":"logging.StreamHandler","formatter":"verbose",},"telegram":{"level":"WARNING","class":"logram.handlers.Telegram","formatter":"verbose",},},"loggers":{"django":{"handlers":["console"],"level":"INFO","propagate":True},"offices":{"handlers":["console","file"],"level":"INFO","propagate":False},"smsreport":{"handlers":["console","telegram","file"],"level":"INFO","propagate":True},"qinspect":{"handlers":["console"],"level":"INFO","propagate":True,},},}dictConfig(LOGGING)Then this will send messages right into the Telegram Grouplogging.warning("Send directly to the Telegram group")
Package: lograptor
Lograptor is a command-line interface for system’s logs processing.Pattern matching searches can be performed together with filtering
rules and scope delimitation options. Each run can produce data and
a report that can be easily sent by e-mail or saved into a file system
directory.
The program can parse logs written in RFC 3164 and RFC 5424 formats.
Lograptor is provided with a base configuration for a set of well known
applications. You can easily extend this set adding new applications
with specific pattern search rules.
Package: logredactor
Log RedactorRedacts data in python logs based on regex filters passed in by the user.This will work with json logging formats as well and also with nested data in theextraargument.Examplesimportreimportloggingimportlogredactor# Create a loggerlogger=logging.getLogger()# Add the redact filter to the logger with your custom filtersredact_patterns=[re.compile(r'\d+')]# if no `default_mask` is passed in, 4 asterisks will be usedlogger.addFilter(logredactor.RedactingFilter(redact_patterns,default_mask='xx'))logger.warning("This is a test 123...")# Output: This is a test xx...Python only applies the filter on that logger, so any other files using logging will not get the filter applied. To have this filter applied to all loggers do the followingimportreimportloggingimportlogredactorfrompythonjsonloggerimportjsonlogger# Create a pattern to hide api key in url. This uses a _Positive Lookbehind_redact_patterns=[re.compile(r'(?<=api_key=)[\w-]+')]# Override the logging handler that you want redactedclassRedactStreamHandler(logging.StreamHandler):def__init__(self,*args,**kwargs):logging.StreamHandler.__init__(self,*args,**kwargs)self.addFilter(logredactor.RedactingFilter(redact_patterns))root_logger=logging.getLogger()sys_stream=RedactStreamHandler()# Also set the formatter to use json, this is optional and all nested keys will get redacted toosys_stream.setFormatter(jsonlogger.JsonFormatter('%(name)s%(message)s'))root_logger.addHandler(sys_stream)logger=logging.getLogger(__name__)logger.error("Request Failed",extra={'url':'https://example.com?api_key=my-secret-key'})# Output: {"name": "__main__", "message": "Request Failed", "url": "https://example.com?api_key=****"}
Package: log-reg-dre
LogReg DRE (Logistic Regression for Density Ratio Estimation)LogReg DRE is a Python package for implementing regression models with a focus on handling covariate shift via density ratio estimation using logistic regression. This approach is particularly useful in scenarios where the training and test data distributions differ significantly.FeaturesData Generation: Customizable data generation for simulating training and testing datasets with different distributions.Density Ratio Estimation: Utilizes logistic regression to estimate density ratios between training and test datasets, which are then used as weights in the loss function to mitigate covariate shift.Regression Models: Includes implementation of standard regression models and RU regression models that leverage estimated density ratios.Visualization: Functionality to visualize training loss and model predictions to evaluate performance and the impact of covariate shift correction.Installationhttps://github.com/szhang120/log-reg-DRE.gitcd log_reg_dre
pip install .UsageThe main components of LogReg DRE are contained within the package directory, which includes modules for data generation, model definition, density ratio estimation, and training routines.Data Generationfrom package.data import generate_x_vals
x_train = generate_x_vals(P_TRAIN, NUM_PTS)
x_test = generate_x_vals(P_TEST, NUM_PTS)Density Ratio Estimationfrom package.density_ratio_estimation import train_logistic_regression, estimate_density_ratio
classifier = train_logistic_regression(x_train, x_test)
density_ratios = estimate_density_ratio(x_train, classifier)Model Trainingfrom package.training import train_standard_model, train_RU_model
std_model, std_loss = train_standard_model(X_train, Y_train, classifier)
ru_models, ru_loss = train_RU_model(X_train, Y_train, gamma_values, classifier)VisualizationRefer to the visualization section in main.py for examples on how to plot training losses and model predictions.LicenseThis project is licensed under the MIT License - see the LICENSE file for details.