Saravanan666/pypi-org huggingface dataset:
Package: loginUtil
Login Utillogin util is a python package to help adding a login screen to your python file and it has (will have) everything it needseasy to useencryption modeprivacy modecakeHow to useto use you just need to do:importloginUtilUsernameList=['user1','user2']passwordlist=['password1','password2']ifloginUtil.login(unl,pwl,uni,pwi):print('correct')else:print('wrong')More optionsolder versions than 0.0.4:https://pypi.org/manage/project/login-util/
Package: login-workhours
# Compute working hours from logins and logoutsI never know how much time I’m spending at work per week, so I wrote a little script that gets me this information out of logins / logouts.It obviously only work if you have a separated computer for work and for your personal use. But that’s my case!InstallationIt’s pretty simple to install, here are a few lines for you:$ pip install login-workhours
$ workhours
  Week #    Duration (hours)
--------  ------------------
    19                3.66
    21                4.19
    23                0.18
    24                0.01
    25               63.96
    28               23.99
    29               23.96
    30                1.61
Package: logio
logioParse log file as input and export the data to database as output.Installpip install logioInstalled Command UtilslogioUsageC:\Workspace\logio>logio --help
Usage: logio [OPTIONS] COMMAND [ARGS]...

  Parse log file as input and export the data to database as output.

Options:
  -c, --config TEXT  Config file path. The config file must in yaml format.
                     [required]

  --help             Show this message and exit.

Commands:
  scan    Try example settings on a test file.
  server  Start log handler server.
  test    Parse the example text and print out parse result.Settingsinputtype: stdin, file, tailignore-blank-lines: true, falseencoding: utf-8, gb18030, ...for type=filefilenamefor type=tailfilenameoffset-fileread-from-endbackup-patternssleep-intervalnon-blockingblockingoutputtype: mysql, stdout, print-not-matched-linebuffer-sizefor type=mysqlignore-not-matched-lineskeep-failed-linesinserts: list<string, string>key: DEFAULT, some other keysql_templateparsertype: regex, jsonkeeep-not-matched-lines: boolfor type=regexuse-default-rulestransformsrulesmatches: list<string, string>matched_nameregexExample configinput:
  type: tail
  filename: tests\nginx.access.log
parser:
  type: regex
  matches:
    simple: "{SIMPLE_NGINX_ACCESS}"
  transforms:
    - field: time_local
      type: strftime
      strptime: "%d/%b/%Y:%H:%M:%S %z"
    - field: request_time
      type: str2number
    - field: request_time
      type: formula
      formula: "{request_time} * 1000000"
    - field: request_time
      type: int
output:
  type: mysql
  mysql:
    host: 127.0.0.1
    port: 3306
    user: root
    password: password
    database: database
  inserts:
    simple:
      sql: insert into table_name (add_time, path, response_time) values (%s, %s, %s)
      fields:
        - time_local
        - request_path
        - request_timeReleasesv0.1.19 2022/01/18Add sympy in requirements.txt.Remove mysqlclient from requirements.txt.Fix license file missing problem.v0.1.18 2021/02/22Set default buffer-size to 10.Add log in loop.Use fastutils.logutils.setup.V0.1.13 2021/02/18Add Nginx Access Log Rules.Add LogFilter subsystem.Add transforms support in LogParser.v0.1.10 2020/10/10Add json parser.v0.1.9 2020/09/10Change package's home url.v0.1.8 2020/09/09Translate help informations to english.Add License.Add LogToStdout.v0.1.7Some bad old release, ignore them...
Package: log_io_handler
Handler for the python logging framework for log.io stateless TCP APIhttp://logio.org/Usage:from log_io_handler import LogIOHandler

Logger.addHandler(LogIOHandler)

#or dict_config
'handlers'
        'log_io': {
            'level': 'DEBUG',
            'class': 'log_io_handler.LogIOHandler',
            #optional configs
            'logstream': 'EXAMPLE_STREAM',
            'node': 'EXAMPLE_NODE',
            'host': 'EXAMPLE_HOST',
            'port': 28777,
        }

    logstream: name of log.io stream default (PythonStream)
    node: name of log.io node default (PythonNode)
    host: log.io server domain or ip address  (default localhost)
    port: log.io api port (default 28777)
Package: log-ip
log_ippython script that dumps your IP into a log filehttps://github.com/FrenchCommando/log_ipFormatfilename is 'log-ip.txt' in current working directory - file is created if not exists, results are appended to the end of the file every time the script is run.One entry for Public IP - one for Private IP.Each entry is a line with timestamp - hostname - ip value.Dependencypython 3 - currently working with 3.7Usageusing pippip install log-iprun in cmd : log-ipContributionAnything is always welcomeGithub
Package: logiq
Copyright (c) 2019 - 2020 Bnz-0Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Description: Github repo:https://github.com/Bnz-0/logiqPlatform: UNKNOWN
Requires-Python: >=3.6.0
Package: logiqaidstsyslogng
Syslog-ng python destination for LOGIQ.AIDescriptionA python destination to push data via syslog-ng to LOGIQ.AIHelpPlease reach out to us onhttps://www.logiq.ai/contact/AuthorsContributors names and contact infoRanjan Parthasarathy (support@logiq.ai)Version History1.0.5Configurable timeout for destinations1.0.4Support for python2/3 as a universal wheel1.0.3Publish periodic stats to log fileSkip SSL verification for publish by defaultUse filename vs pathname for logger1.0.2Initial ReleaseLicenseThis project is licensed under the MIT License - see the LICENSE.md file for details
Package: logiskipper
LogiSkipperLogiskypperis a Python library for generating and parsing files in SKIPPER format.UsageYou can use this module toCreate or write file, which generates filesfor expectedfor headers of shipping ordersfor shipping delivery addressesfor shipping billing addressesfor shipping control linesGenerator example::>>> from skipper.writer import generator as gen
>>> from datetime import date
>>> d = date(2015,01,12)
>>> g = gen.Generator()
>>> ex_data = g.setExpectedData(segment_id='ATT', depositor_code='353', store_code='002', expected_number='PC150059', supplier_code='0AURAY', supplier_name='Found Yes', station_number='1000', product_code='1002EU', quantity=21, product_name='Lilypad Lid 23cm', received_date=d)
>>> filename = g.renderExpected(directory='out', filename='test_file', datas=[ex_data])
>>> print filename
test_fileRead filesfor receptionsfor control shipments (detail)for stocksKnown issues / RoadmapBug TrackerBugs are tracked onBitbucket Issues.
In case of trouble, please check there if your issue has already been reported.CreditsContributorsClovis Nzouendjou <clovis@anybox.fr>MaintainerThis module is maintained by theAnyboxteams.
Package: logistics
Logistics Python PackagelogisticsVandal framework logistics package.vandalData science, Data manipulation and Machine learning library.
Use permitted according to the terms of use and conditions set by the attached license.Example of useVisit my Data Library athttps://github.com/dkundih/DataLibraryin order to see the demonstration of use in practical examples.Installation# using pip.pipinstallvandalDependencies# using pip.pipinstallvandal-rrequirements.txtCHANGELOG0.0.2 (20/12/2021)initial release.0.0.2 (20/12/2021)initial change.0.0.3 (07/06/2022)transformed into Vandal framework logistics package.0.0.4 (07/06/2022)bug fix.0.0.5 (11/06/2022)added FilePathType and SpecialType.added multiple folders to aid testing and development.0.0.6 (13/06/2022)Manifesto added.special methods redefined.PEP8 alignments.0.0.7 (13/06/2022)added meta data.0.0.8 (28/07/2022)redefined the package structure.added VandalTypes function to print all the types.0.0.9 (28/07/2022)quick bug fix.0.0.10 (28/07/2022)resolved VandalTypes issues.0.0.11 (28/07/2022)VandalTypes now assigned in the types module instead of init.0.0.12 (13/08/2022)migrated paint_text from duality and vandal to logistics.0.0.15 (21/08/2022)withdrawal of failed changes.0.0.16 (21/08/2022)init changes.0.0.17 (21/08/2022)init changes.
Package: logistigate
# logistigatepython implementation of logistigateOverview of logistigate----------------Generally speaking, the logistigate methods infer aberration likelihoods atentities within a two-echelon supply chain, only using testing data from samplepoints taken from entities of the lower echelon. It is assumed that productsoriginate within the system at one entity of the upper echelon, and areprocured by one entity of the lower echelon. The likelihood of a lower-echelonentity obtaining product from each of the upper-echelon entities is stored inwhat is deemed the "transition matrix" for that system. Testing of products atthe lower echelon yields aberrational (recorded as "1") or acceptable ("0")results. We then distinguish possible information-availability settings intotwo categories, Tracked and Untracked:- In the Tracked case, both the upper-echelon and lower-echelon entitiestraversed by the tested product are known upon testing.- In the Untracked case, only the lower-echelon entity is entirely known, inaddition to the system's transition matrix.It is further assumed that products are aberrational at their origin in theupper echelon with some entity-specific fixed probability, and that productsacceptable at the upper echelon become aberrational at the destination in thelower echelon with some other entity-specific fixed probabiltiy. It is thesefixed probabilities that the logistigate methods attempt to infer.More specifically, the logistigate methods were developed with the intent ofinferring sources of substandard or falsified products within a pharmaceuticalsupply chain. Entities of the upper echelon are referred to as importers, andentities of the lower echelon are referred to as outlets. This terminology isused interchangeably throughout the logistigate package.
Package: logitboost
This is a Python implementation of the LogitBoost classification algorithm[1]built on top ofscikit-learn.
It supports both binary and multiclass classification; see theexamples.This package provides a single class,LogitBoost, which can be used
out-of-the-box like any sciki-learn estimator.Documentation website:https://logitboost.readthedocs.ioInstallationThe latest version of LogitBoost can be installed directly after cloning from
GitHub.gitclonehttps://github.com/artemmavrin/logitboost.gitcdlogitboostmakeinstallMoreover, LogitBoost is on thePython Package Index (PyPI), so a
recent version of it can be installed with thepiptool.python-mpipinstalllogitboostThis project was developed in Python 3.7, and it is tested to also work with
Python 3.6.References[1]Jerome Friedman, Trevor Hastie, and Robert Tibshirani. “Additive Logistic
Regression: A Statistical View of Boosting”. The Annals of Statistics.
Volume 28, Number 2 (2000), pp. 337–374.JSTOR.Project Euclid.
Package: logitol
logitollogitol is a small library aimed to encapsulate various different logging approaches into a single place. And to be loaded/instantiated through a common interface.ConceptLogging is a rather universal need across all applications. However, there are times where the builtin python loggers are sufficient, and those where they are
not. And so, in many cases, it is desirable to encapsulate in such a way that you can interchange loggers as needed - and to include all 'loggers' in a single
library for consistency.DesignThere is a single entry point:get_logger(logger_type, **kwargs)Examplesfromlogitolimportget_loggerlogger=get_logger()
Package: logitorch
LogiTorchLogiTorch is a PyTorch-based library for logical reasoning in natural language, it consists of:Textual logical reasoning datasetsImplementations of different logical reasoning neural architecturesA simple and clean API that can be used with PyTorch Lightning📦 Installation📖 Documentation🖥️ Features📋 DatasetsDatasets implemented in LogiTorch:AR-LSATConTRoLLogiQAReClorRuleTakerProofWriterSNLIMultiNLIRTENegated SNLINegated MultiNLINegated RTEPARARULES PlusAbductionRules🤖 ModelsModels implemented in LogiTorch:RuleTakerProofWriterBERTNOTPRoverFaiRRLReasonerDAGNFocal ReasonerAdaLoGNLogiformerLogiGAN🧪 Example UsageTraining Exampleimportpytorch_lightningasplfromlogitorch.data_collators.ruletaker_collatorimportRuleTakerCollatorfromlogitorch.datasets.qa.ruletaker_datasetimportRuleTakerDatasetfromlogitorch.pl_models.ruletakerimportPLRuleTakerfrompytorch_lightning.callbacksimportModelCheckpointfromtorch.utils.data.dataloaderimportDataLoadertrain_dataset=RuleTakerDataset("depth-5","train")val_dataset=RuleTakerDataset("depth-5","val")ruletaker_collate_fn=RuleTakerCollator()train_dataloader=DataLoader(train_dataset,batch_size=32,collate_fn=ruletaker_collate_fn)val_dataloader=DataLoader(train_dataset,batch_size=32,collate_fn=ruletaker_collate_fn)model=PLRuleTaker(learning_rate=1e-5,weight_decay=0.1)checkpoint_callback=ModelCheckpoint(save_top_k=1,monitor="val_loss",mode="min",dirpath="models/",filename="best_ruletaker.ckpt",)trainer=pl.Trainer(accelerator="gpu",gpus=1)trainer.fit(model,train_dataloader,val_dataloader)Testing Examplefromlogitorch.pl_models.ruletakerimportPLRuleTakerfromlogitorch.datasets.qa.ruletaker_datasetimportRULETAKER_ID_TO_LABELimportpytorch_lightningasplmodel=PLRuleTaker.load_from_checkpoint("best_ruletaker.ckpt")context="Bob is smart. If someone is smart then he is kind"question="Bob is kind"pred=model.predict(context,question)print(RULETAKER_ID_TO_LABEL[pred])
Package: logix-trend-converter
Logix Trend ConverterIf you use Rockwell/Allen-Bradley's RSTrendX tool (the trending tool bundled with RSLogix 5, RSLogix 500, and RSLogix 5000/Studio 5000) and want to export the data you've trended, you'll find the "Create Snapshot" option gives you two files: a*.DBFfile and a*.IDXfile. These files contain your data and tag (trend pen) names, but they're a binary-encoded file using a defunct database format ("dBASE").This package allows you to quickly convert those files into a more accessible format (CSV).It's important to give credit where it's due: much of the heavy lifting of this project is performed by two other packages:simpledbf(PyPI.org)- the relevant code from thesimpledbfpackage is bundled within this package, so it is not necessary to install thesimpledbfpackage to your environment.pandas(PyData.org)- the indispensable.InstallationLogix Trend Converter can be installed withpipusing the following command:pipinstalllogix-trend-converterSuggested Usagefromlogix_trend_converterimportconverterasltcfrompathlibimportPathdbf_file=Path("data/my_data.dbf")idx_file=Path("data/my_data.idx")df=ltc.convert_file_to_pd_dataframe(dbf_file,idx_file)df.head()ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: logjson
logjsonGoal: easily generate structured JSON logging.logstashmode is optional.importloggingimportlogjsonlogger=logging.getLogger('blah')handler=logging.StreamHandler()handler.setFormatter(logjson.JSONFormatter(pretty=True))logger.addHandler(handler)logger.info('hi%s%s!','you','there')Output:{"name":"blah","msg":"hi %s %s!","args":["you","there"],"levelname":"INFO","levelno":20,"pathname":"<snip>","filename":"test_main.py","module":"test_main","exc_text":null,"stack_info":null,"lineno":17,"funcName":"test_main","created":1511750128.6285746,"msecs":628.5746097564697,"relativeCreated":23.08201789855957,"thread":139929130264384,"threadName":"MainThread","processName":"MainProcess","process":18460,"message":"hi you there!","created_iso":"2017-11-27T02:35:28.628575+00:00"}Logstash mode is only one param away:logger=logging.getLogger('ls')handler=logging.StreamHandler()handler.setFormatter(logjson.JSONFormatter(pretty=True,logstash_mode=True))logger.addHandler(handler)logger.info('logstash test')Output:{"@message":"logstash test","@source_host":"localhost.localdomain","@timestamp":"2017-11-27T02:35:28.631275+00:00","@fields":{"name":"ls","msg":"logstash test","args":[],"levelname":"INFO","levelno":20,"pathname":"<snip>","filename":"test_main.py","module":"test_main","exc_text":null,"stack_info":null,"lineno":42,"funcName":"test_logstash","created":1511750128.631275,"msecs":631.274938583374,"relativeCreated":25.782346725463867,"thread":139929130264384,"threadName":"MainThread","processName":"MainProcess","process":18460}}
Package: logjsonfmt
log-json-fmtpython logging json formatter.Installation$pipinstalllogjsonfmt---> 100%ExampleCreate itCreate a fileexample.pywith:importloggingimportlogjsonfmtlogger=logging.getLogger()logHandler=logging.StreamHandler()formatter=logjsonfmt.JSONFormatter(hostname="app.example.com",indent=True)logHandler.setFormatter(formatter)logger.addHandler(logHandler)logger.error('hello world!',extra={"tags":["app=helloworld"]})Out:{"@timestamp":"2021-05-27 03:03:01.745983 +00:00","app_host_name":"app.example.com","logger_name":"root","level":"ERROR","pathname":".\\example.py","lineno":17,"func_name":"<module>","thread_id":30612,"thread_name":"MainThread","process_id":7356,"process_name":"MainProcess","message":"hello world!","tags":["app=helloworld"]}
Package: log-kbots
log_kbotsA biblioteca fornece uma função que aceita dados de robôs como entrada, os valida e cria um arquivo json pronto para upload.Instalação:pip install log-kbotsFunções:make_log( robot_name: str, schedule_value: str, start_date: datetime, status: str, error_value: str = None, detail_value: str = None):Gera o arquivo json.save_log(PATH_LOG: str, robot_name: str, schedule_value: str, start_date: datetime, status: str, error_value: str = None, detail_value: str = None):Gera o arquivo json e o salva noPATHindicado.post_log(URL: str, robot_name: str, schedule_value: str, start_date: datetime, status: str, error_value: str = None, detail_value: str = None):Gera o arquivo json e o envia para aURLindicada.Parâmetros:PATH_LOG (str):Caminho para o diretório para fazer o salvamento do arquivo Json.URL (str):Caminho para o diretório para fazer o envio do arquivo Json.robot_name (str):Código do robô (ex: DGA-100).schedule_value (str):Agendamento do robô em formato padrãocron.start_date (datetime):Datahora do início do processamento do dado.status (str):Status final do processamento do dado, necessariamente deve conter um dos seguintes status: 'WARNING', 'FAIL' e 'SUCCESS'.error_value (str, optional):Mensagem de erro quando existente. Padrão é None.detail_value (str, optional):Informações adicionais ao dado quando necessário. Padrão é None.Requirements:requests >= 2.25.1Desinstalação:pip uninstall log-kbots
Package: logkk
Logkk易于使用且线程安全的日志模块安装pipinstalllogkk基础使用：fromlogkkimportLogManagerlog_manager=LogManager()log_manager.info("this is a info log")log_manager.warn("this is a warn log")logger=log_manager.get_logger(name="hello")logger.info("this is a info log")logger.warn("this is a warn log")使用文件记录日志：fromlogkkimportLogManager,Levelfromlogkk.handlersimportFileHandlerfmt="[{datetime}] [{level}] [{name}]{message}"handler=FileHandler(filepath="/var/log/demo.log")log_manager=LogManager(level=Level.INFO,fmt=fmt,handlers=[handler])logger=log_manager.get_logger(name="hello")logger.info("this is a info log")logger.warn("this is a warn log")作为函数装饰器使用给每个函数添加装饰器，打印函数的参数和返回值，在程序调试阶段是非常好用的。deflog_required(f):deffunc(f2):@wraps(f2)defdecorated(request,*args,**kwargs):name=f"{f.__module__}:{f.__name__}"logger=log.get_logger(name=name)logger.info(f"{request.method}{request.path}")logger.info("params=",request.body)setattr(request,"logger",logger)r=f(request,*args,**kwargs)ifisinstance(r,BaseResponse):logger.info("response=",r.result)else:logger.info("response=",r)returnrreturndecoratedreturnfunc(f)@log_requireddefUpdateMyInfoHandler(request:HttpRequest):# ...return{"code":0,"message":"OK"}技术方案说明写日志文件主要有两种方式：每次写日志时打开文件，写完后关闭文件；保持一个文件句柄，每次写日志时直接写入文件；很明显，第二种方式效率更高，但是一直保持一个打开的文件对象，会不会有问题呢？查询了一些资料后发现，并没有什么问题，因为：当进程退出时，不管是正常退出还是异常退出，亦或是进程被强制杀掉，操作系统都会回收给进程分配的资源，
也就是说系统会关闭这个文件句柄。当进程异常退出时，为了保证我们写的日志不丢失，每次写日志时需要调用flush将日志落盘。因为write只是将内容写到系统缓冲区，并没有写到磁盘上，
当进程异常退出时，系统缓冲区的数据会丢失，而flush就是将系统缓冲区的内容写到磁盘上，并清空系统缓冲区。另外，如果多线程写日志，会出现写入混乱的情况，所以需要加锁，虽然这样会影响效率，但是更加安全可靠。
Package: loglet
Logletis a tiny tool for keeping tabs on long-running processes.
Send log messages to Loglet using a simplePOSTrequest and then view
them in your browser or subscribe to an Atom feed.This Python package provides a small client library of Loglet.  You can
creates a new loglet simply by this and send messages by using standardlogginginterface.  For example:import logging
from loglet import LogletHandler

logger = logging.getLogger(__name__)
loglet = LogletHandler(mode='threading')
logger.addHandler(loglet)
logger.setLevel(logging.DEBUG)

logger.info('hello')
logger.error('something horrible has happened')If you have a loglet already, you can specify logid explicitly:loglet = LogletHandler('2LNbYgNEAaezJduj')There are 4 types of sync/async modes:'sync'(default)Simply sends all logs synchronously.  It can affect serious inefficiency
to your application.'threading'Sends all logs asynchronously by using standardthreadingmodule.
Threads are rich and heavy to use for just input/output.'multiprocessing'Sends all logs asynchronously by using standardmultiprocessingmodule.
It requires to use Python 2.6 or higher.  It forks for every message
internally.'gevent'Sends all logs asynchronously by greenlet (coroutine).  It requires
to installgevent.  Most efficient way though additional dependency is
required.
Package: loglevel
loglevelUsageimportloglevelloglevel.set_loglevel('loglevel.yml')yaml datakeyring:WARNINGkeyring.backend:WARNINGwebsocket:WARNINGrequests:WARNINGselenium:WARNINGseleniumrequests:WARNINGurllib3:WARNINGnose:WARNINGInstallationRequirementsCompatibilityLicenceAuthorspackage_name was written byfx-kirin.