Saravanan666/pypi-org huggingface dataset:
Package: timewarpy
TimeWarPY - Time Series Pre and Post Processing MethodsBackground and ObjectiveTimeWarPy is a library I created because I kept running into time-series related pre and post processing that is discussed a lot in ML literature but not standardized in a popular ML library. Most industry related forecasting methods are not well suited for real-time deep learning architectures. TimeWarPy is a stab at making these operations both fast and convenient for real-time applications through an easy to use set of core processing objections.InstallationTimeWarPY can be installed directly with PyPi or directly from sourceherepip install timewarpyMotivationUnivariate DataTime series data sets for deep learning generally need to be put in the visual format below. There will be a sequence in time (vector) for training and a prediction sequence in time (another vector) that is normally shorter.This single example is then rolled in time to generate many examples of these training and predicting sequences as shown below.There are a million variations to the rolling mechanism shown above including changing window sizes, increasing the number of time increments to roll, dealing with non-continuous time series, adding contextual information, dealing with time dependent meta data storage, and much more. TimeWarPY is intended to standardize these transformations to be ready for deep learning methods out of the box.The example here:Preprocessing Univariate Data for Recurrent Neural Network Traininggives a good indication how quickly TimeWarPY can get you training models with TensorFlow.
Package: timewarrior-extensions
timewarrior-extensionsExtensions forTimewarriorInstallationInstall the extensionspipinstall--usertimewarrior-extensionsAnd install extensions you wantExtensionsPercentageTime report with %Installationtimewarrior_percentage_installUsagetimewpercentagetimewpercentage:daytimewpercentage:weektimewpercentage:lastweektimewpercentage:monthOutputTag             Duration  Portion
--------------  --------  -------
Multi word tag  3:00       44.9%
development     1:31       22.8%
meeting         1:00       15.0%
review          1:00       15.0%
S2              0:08        2.1%
S               0:01        0.2%
--------------  --------  -------
Total           6:40      100.0%Developmentgitclonehttps://github.com/ViliamV/timewarrior-extensions.gitcdtimewarrior-extensionspoetryinstallln-rspre-commit.git/hooks/
Package: timewatch
[![Version Status][v-image]][pypi-url]## timewatchTired of reporting work hours every day/month?Your boss trusts you with your time, but HR demands you fill timewatch's form?You're too preoccupied with work, and forget filling up timewatch.co.il?We've all been there, just set up a monthly timewatch cron and get back to work!### What is this?This script automatically sets default working hours for all work days using timewatch.co.il's web interface.It reads expected work hours for each day and automatically sets each day's work to that amount.It is therefor handling govt. off days and weekends, and is quite configurable.## UsageTo report required working hours for the current month, simply execute```./main <company id> <employee number> <password>```### Full usage and functionality```usage: main.py [-h] [-y YEAR] [-m MONTH] [-v] [-o {all,incomplete,regular}][-s STARTTIME] [-j JITTER]company user passwordAutomatic work hours reporting for timewatch.co.ilpositional arguments:company               Company IDuser                  user name/idpassword              user passwordoptional arguments:-h, --help            show this help message and exit-y YEAR, --year YEAR  Year number to fill-m MONTH, --month MONTHMonth number or name-v, --verbose         increase logging level-o {all,incomplete,regular}, --override {all,incomplete,regular}Control override behavior. all - override all workingdays, unsafe to vacation/sick days. incomplete = onlyoverride days with partial records. regular - overrideregular days (without absence reason) only-s STARTTIME, --starttime STARTTIMEpunch-in time-j JITTER, --jitter JITTERpunching time random range in minutes.```### Installation```git clone https://github.com/nirizr/timewatch.gitcd timewatchpip install -r requirements.txt```or```pip install timewatch --target './'cd timewatch./main.py <args>```### Known issues* Doesn't sign the doc (I suggest you do it manually after reviewing there are no bugs in the report).* no support for reporting vacation/sick days through the script yet (You can report using timewatch's web interface before/after running the script)[v-image]: https://img.shields.io/pypi/v/timewatch.svg[dm-image]: https://img.shields.io/pypi/dm/timewatch.svg[pypi-url]: https://pypi.python.org/pypi/timewatch/
Package: timewatcher
TimeWatcherAutomatically update hours card on TimeWatchPython CLI program to help you automate filling up Timewatch card.
Integrate withlaunchdto make this fully automated process every month.
NOTE: This program should only be used on MacOS currently.UsageFirst install the package to your machinepipinstalltimewatcherAnd execute the program when you want to fill the timetimewatcherThe first time you run it, you will go through the process of setting up the configuration file.After you set it up for the first time, it will be save to~/Library/Preferences/TimeWatcher/config.iniand it can be changed in any time.Furthermore, if you set theauto_executeparameter toTrueit will also add alaunchdagent to automatically execute the program at specific date and time.ConfigurationThis is the list of the user configuration options:company_number- Mandatory, your company number in Timewatchemployee_number- Mandatory, you employee number used in Timewatchemployee_password- Mandatory, password used to Timewatch, by default this is your ID numberauto_execute- Do you want to automatically execute this program at specific date and time? Default is 'True'day_to_execute- If you chose 'True' to the previous one, you can set the day of the month to run on. Input only digit. E.g:12will make the program execute on the 12th day of each month.hour_to_execute- If you chose 'True' to the previous one, you can set the time in the day you want to execute the program. Input only digit. E.g:11will make the program execute on 11 A.M.start_time- Optional, the time to set as your start time in Timewatch. Default to 0900 (9 A.M)end_time- Optional, the time to set as your end time in Timewatch. Default to 1800 (6 P.M)auto_end_time- Optional, if set toTrueit will calculate theend_timebased on the needed hours for each date in Timewatch. This mean the program can handle half days automatically using this option. The default isTrue.DevelopmentIf you want to run this program locally, clone the repo to your machinehttps://github.com/birkagal/timewatchercdtimewatcherInitialize environment usingpoetrypoetryinstall--no-root&&pre-commitinstallYou can make any changes you want.
To execute run thetimewatchercommand inside the directorytimewatcher
Package: timewave
Python librarytimewavea stochastic process evolution simulation engine in python.simulation enginetimewave consists of four building blocks.The Statewhich evolves over time during a simulation path. It is the nucleus or
node which marks a point of time in a path.The Produceris the objects that provides states to the simulation and does the
actual time evolution. Think of the producer building as the constructor
of a stochastic process like a Brownian motion or, less mathematical,
future stock prices or future rain intensities.The Consumeris an object that takes a state as a point in time provided by the
producer and consumes it, i.e. does something with it - the actual
calculation if you like.The Enginefinally, which organizes the creation of states by the producer and the
consumption by the consumer. The engine uses, if present,
multiprocessing, i.e. takes full advantage of multi cpu frameworks.
Therefore the engine splits the simulation into equal but distinct
chunks of path for the number of workers (by default the number of cpu)
and loops over the set of dedicated path in each worker. Each path is
evolved by the producer in states which are at each point in time
consumed directly by consumers. States are, due to limits of resources,
not stored during the simulation. If you like to, use the storage
consumer to save all states.main frame workflowsetup simulation byengine = Engine(Producer(), Consumer())
engine.run(range(20))then run loop starts byproducer/initialize()setup workers (by default by the number of cpu’s) on each worker start
loop byproducer/consumer.initialize_worker()and invoke loop over paths and start again withproducer/consumer.initialize_path()then do time evolution of a pathproducer.evolve() / consumer.consume()and finish with last consumer in consumer stackconsumer[-1].finalize_path()andconsumer[-1].finalize_worker()put results into queue and take them out byconsumer[-1].put()/get(result)finish simulation (kind of reduce method)consumer[-1].finalize()before returning results from run.Development VersionThe latest development version can be installed directly from GitHub:$pipinstall--upgradegit+https://github.com/sonntagsgesicht/timewave.gitContributionsIssuesandPull Requestsare always welcome.LicenseCode and documentation are available according to the Apache Software License (seeLICENSE).
Package: timeweather
TimeWeather 0.1.0安装方法(python-m)pipinstallpyweather使用方法frompyweatherimport*#QWFreeAPI 1-3三个和风天气开发版免费API#LocateMe_China myip.ipip.net中国IP查询#LocateMe和LocateMe_en myip.la国际IP查询addr=LocateMe_China()loc=QWFreeAPI1.locate(addr)print(QWFreeAPI1.weather(loc))函数#将字符串转为整数#获取字符串中第一个合法的整数#123d -> 123#asdf1234ghjk -> 1234parse_int(s)->int#如果使用热点，定位可能失败，返回空字符串#国内快速定位（https://www.ipip.net/myip.html）LocateMe_China()->str#国际中文定位，稍慢（https://www.myip.la/）LocateMe()->str#国际英文定位（https://www.myip.la/）LocateMe_en()->str类#天气PointWeather(time=None,temp=None,feelslike=None,wea=None,weaicon=None,winddir=None,winddeg=None,windscale=None,windspeed=None,humidity=None,vis=None,aqi=None)get_icon()->bytes#获取天气图标add(pw)->None#添加天气数据，pw为另一个PointWeather对象#可以使用str和repr#和风天气API（不支持签名）QWeatherAPI(key)locate(loc)->str#定位城市代码weather(cid)->PointWeather#根据城市代码查询天气#免费开发版API：QWFreeAPI 1-3#tianqiapi.com APITianqiAPI(appid,appsecret)weather(cid=None,city=None,ip=None)->PointWeather#查询天气，默认查询本地天气#免费API：TianqiFreeAPI#心知天气API（只支持温度和天气种类，不支持签名）SeniverseAPI(key)weather(loc)->PointWeather#根据城市查询天气#免费API：SeniverseFreeAPI 1-3
Package: timeweb-cloud
timeweb-cloudAPI Timeweb Cloud позволяет вам управлять ресурсами в облаке программным способом с использованием обычных HTTP-запросов.Множество функции, которые доступны в панели управления Timeweb Cloud, также доступны через API, что позволяет вам автоматизировать ваши собственные сценарии.Эта библиотека позволяет вам легко использовать API Timeweb Cloud в вашем приложении на Python.Документация APIКак определяется etag?Установкаpipinstalltimeweb-cloudПример использованияБиблиотека поддерживает синхронный и асинхронный варианты использования. В примерах ниже используется синхронный вариант, но вы можете использовать асинхронный вариант, заменивtw = Timeweb(...)наtw = AsyncTimeweb(...)fromtimewebimportTimewebtw=Timeweb('token')account_status=tw.account.get_status()# schemas.account.AccountStatusprint(account_status)Что доступно?Аккаунтtw.accountБазы данныхtw.dbsБалансировщикиtw.balancersВыделенные серверыtw.servers.dedicsДоменыtw.domainsОблачные серверыtw.servers.cloudОбразыtw.imagesПроектыtw.projectsТокены APItw.tokensKubernetestw.k8sS3-хранилищеtw.s3SSH-ключиtw.ssh_keysПочтаtw.mailEtagEtag - это уникальный идентификатор, который используется для проверки изменений в API. Он будет использоваться чтобы определять текущею версию Swagger API, т.к. сейчас Swagger API Timeweb Cloud не имеет версионности и/или changelog'а. Получается он из запроса к спецификации OpenAPI по ссылкеhttps://timeweb.cloud/api-docs-data/bundle.json. Сервер сам его возвращает и мы пока операемся на него. В будущем, когда/если Swagger API Timeweb Cloud будет иметь версионность и/или changelog, будем опираться на их версию Swagger API.
Package: time-weighted-average-for-water
Time weighted average for waterIt is a tool to calculate time weighted average for gauged water level, flow, or something similar.Table of contentsInstallation, update and uninstallationTo installTo updateTo uninstallUsageHow to cite?ChangelogTodoInstallation, update and uninstallationTo installQuick installation withpip:pipinstalltime-weighted-average-for-waterOr from github:pipinstallgit+https://github.com/longavailable/time-weighted-average-for-waterAlso, you can just copy related functions fromtwaw/twaw.pyto your work.To updatepipinstall--upgradetime-weighted-average-for-waterTo uninstallpipuninstalltime-weighted-average-for-waterUsageSeetests/001-daily-average.py.importpandasaspdfromtwawimportdailyAverage# load dataurl_demodata='https://raw.githubusercontent.com/longavailable/datarepo02/main/data/twaw/test-data-for-twaw.csv'data=pd.read_csv(url_demodata)data['time']=pd.to_datetime(data['time'])# usagesitems=['Z','Q']results1=dailyAverage(data,itemHeader=items,timeHeader='time')results2=dailyAverage(data,itemHeader=['Z'],timeHeader='time')results3=dailyAverage(data,itemHeader='Q',timeHeader='time')# exportnewdata=pd.DataFrame(data=results1)newdata2=newdata.dropna(subset=items,how='all').sort_values(by=['year','month','day'])iflen(newdata2)>0:filename='test-o.csv'newdata2.to_csv(filename,index=False)else:print('No data to export!')How to citeIf this tool is useful to your research,starand cite it as below:Xiaolong Liu. (2023, March 02). longavailable/time-weighted-average-for-water. Zenodo.
https://doi.org/10.5281/zenodo.7691849Easily, you can import it toMendeley.Changelogv0.1.0First release.TodoAdd a method description.
Package: timewheel-scheduler
IntroductionThis is an asynchronous job scheduler with crontab syntax.How to useHere is a basic example:# Should run as-isimportasynciofromtypingimportAnyfromdatetimeimportdatetimefromtimewheelimportTimeWheelfromtimewheel.scheduleimportScheduleasyncdefmy_job():print("hello from job!")awaitasyncio.sleep(3)asyncdefmy_another_job(some_value:Any):print(f"Hey! This is my some_value{some_value}")awaitasyncio.sleep(1)defmy_special_job():print("Doing the special job!!")asyncdefmain():timewheel=TimeWheel(schedules=[# Runs every 29 minutes using America/Sao_Paulo# as baseSchedule(name="my-schedule",expression="*/29 * * * *",timezone="America/Sao_Paulo",job=my_job),# Runs every 5th, 10th and 20th minute on wednesday# using America/Los_Angeles tz as baseSchedule(name="another-schedule",expression="5,10,20 * * * 2",timezone="America/Los_Angeles",job=my_another_job),# Runs the special-job every minute except on 2021-12-31Schedule(name="special-job",expression="* * * * *",timezone="America/Sao_Paulo",excluded_dates=[datetime.strptime('2021-12-31','%Y-%m-%d')],job=my_special_job),# Runs the jobs every minute between monday and friday# using the range notationSchedule(name='another-job',expression='* * * * 0-4',timezone='America/Sao_Paulo',job=my_special_job)])awaittimewheel.run()asyncio.get_event_loop().run_until_complete(main())The timezone information is based onIANA.
Package: timewinder
Timewinder is a Python 3 library to build and run temporal logic models.
The goal of the library is to bring formal methods, specifically Lamport'sTemporal Logic of Actions, to a broader audience.
While very much inspired byTLA+, Timewinder tries to be simpler, more readable, and more industry-focused.
That said, TLA+ is an impressive tool and Timewinder is not trying to cover the full spectrum of what TLA+ can do.The project intends to work toward the following goals:Introduce more developers into modeling and formal methodsIncrease the number of people working with temporal logicImprove design docs, with testable example models that non-experts can also readMake running models easy and automatable,even from the command line.Project StatusThis project is still alpha, so the API may change.
Please join in on theGithub Discussionsto talk about models, examples, and direction.
Help is definitely wanted and appreciated!ExamplesAccount TransfersfromPractical TLA+, Chapter 1Blocking QueuefromWeeks of Debugging Can Save You Hours of TLA+High-level UsageTimewinder starts with the@timewinder.objectand@timewinder.processdecorators.objectwraps classes and registers them as a Timewinder data structures.processwraps functions that we intend to check. Insideprocessfunctions, Python'syieldkeyword represents an atomicity boundary -- that is, at every yield point, the evaluator saves the state and is free to run any other available process in any order.
A function with no yield keyword always runs to completion in one step.Finally, there are predicates, which are properties about the objects to be checked at every stage.These are all combined in theEvaluator, where Timewinder will exhaustively generate (up to a limit number of steps) all the potential program states resulting from running the processes in any order.InstallationYou can simplypip install timewinder.DevelopingPre-requisitesYou only needtoxfor testing and documentation building:python3 -m pip install toxOr clone the repository:git clone https://github.com/timewinder-dev/timewinderRunning the test suiteYou can run the full test suite with:tox
Package: time-window
Time WindowTime Window is a small Python library that implements a representation for a
period of time, in the form of a half-open interval[since, until).InstallationInstall usingpipfrom PyPI.pipinstalltime-windowUsageInstantiate a newTimeWindowobject using twodatetime.datetimeobjects for
the boundaries.>>>fromdatetimeimportdatetime>>>fromtime_windowimportTimeWindow>>>since=datetime(2019,1,23)>>>until=datetime(2019,1,29)>>>tw=TimeWindow(since,until)>>>twTimeWindow(datetime.datetime(2019,1,23,0,0),datetime.datetime(2019,1,29,0,0))Alternatively, instantiate a newTimeWindowobject using adatetime.datetimeobject and onedatetime.timedeltafor the definition of the boundaries.>>>fromdatetimeimporttimedelta>>>delta=timedelta(days=1)>>>tw=TimeWindow.from_timedelta(since,delta)>>>twTimeWindow(datetime.datetime(2019,1,23,0,0),datetime.datetime(2019,1,24,0,0))Get the size of the window.>>>tw.deltadatetime.timedelta(1)Get the time that is in the middle of the window.>>>tw.middledatetime.datetime(2019,1,23,12,0)You can also check if twoTimeWindowobjects overlap.>>>tw=TimeWindow(datetime(2019,1,23),datetime(2019,1,29))>>>tw2=TimeWindow(datetime(2019,1,27),datetime(2019,1,30))>>>tw.overlaps(tw2)TrueComplementary to the above action, you can check if two time windows are
contiguous (i.e., adjacent, sharing one boundary).>>>tw=TimeWindow(datetime(2019,1,23),datetime(2019,1,29))>>>tw2=TimeWindow(datetime(2019,1,20),datetime(2019,1,23))>>>tw.contiguous(tw2)[TimeWindow(datetime.datetime(2019,1,20,0,0),datetime.datetime(2019,1,23,0,0)),TimeWindow(datetime.datetime(2019,1,23,0,0),datetime.datetime(2019,1,29,0,0))]>>>tw3=TimeWindow(datetime(2019,1,20),datetime(2019,1,21))>>>tw.contiguous(tw3)FalseAnyTimeWindowobject offers some of the standardset operations.>>>tw=TimeWindow(datetime(2019,1,23),datetime(2019,1,29))>>>tw2=TimeWindow(datetime(2019,1,27),datetime(2019,1,30))>>>tw.intersection(tw2)TimeWindow(datetime.datetime(2019,1,27,0,0),datetime.datetime(2019,1,29,0,0))>>>tw.union(tw2)TimeWindow(datetime.datetime(2019,1,23,0,0),datetime.datetime(2019,1,30,0,0))>>>tw.complement(tw2)TimeWindow(datetime.datetime(2019,1,23,0,0),datetime.datetime(2019,1,27,0,0))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.
Package: timework
timeworkmeasure / limit execution time using with-statements or decorators, cross-platformInstallpipinstalltimeworkUsageimporttimeworkastwStatementwithtimework.Stopwatchmeasure execution timeExamplewithtw.Stopwatch()ass:s.split()s.stop()s.restart()s.pause()s.resume()s.get_sec()s.get_hms()Functionsget_sec()  get_hms()  restart()  pause()
resume()   split()    stop()Decorator@timework.timermeasure execution timeExample@tw.timer(logging.warning)defyour_function():...Argumentsoutput: A function object that specifies where to log messages.
        For example: print. timework.nil does not log messages.
detail: A boolean value, whether to print start and end time.
        This argument must be passed using keywords.timework.limitlimit execution timeExample@tw.limit(3)defyour_function():...Argumentstimeout: This argument sets the timeout limit of the decorated
         function. Once the run time of the process reaches
         [timeout] seconds but not yet finishes, then raise
         TimeoutException and stop the inner function.LicenseMIT License ©bugstop
Package: timeworker
简介 Description一个中文时间标准化库。A chinese time string normalization package.功能绝对时间转换：'2020年2月' --> ['2020-02-01', '2020-02-29']'去年1月30日至今年三月二号' --> ['2021-01-30', '2022-03-02']相对时间转换'最近五天' --> ['2022-12-03', '2022-12-08']安装方法 installpip install timeworker使用方法 Usageimport timeworker
time_str = '最近五天'
timeworker.parse(time_str)
Package: timewsync
Timewarrior Synchronization ClientThis repository contains the client of the Timewarrior Sync project.InstallationUsing PIPTo installtimewsyncin your current Python environment:pip install timewsyncUsing NixTo installtimewsyncin your current Nix environment:nix-env -f default.nix -iUsageusage: timewsync [-h] [--version] [-v] [--data-dir DATA_DIR] {generate-key} ...

timewarrior synchronization client

positional arguments:
  {generate-key}
    generate-key       generates a new key pair.

optional arguments:
  -h, --help           show this help message and exit
  --version            print version information
  -v, --verbose        enable debug output
  --data-dir DATA_DIR  the path to the data directoryData directoryThe data directory contains all information required bytimewsyncfor storing configuration options and tracking changes.The path of the data directory defaults to~/.timewsync. This can be
overridden with the command line flag--data-dir.ConfigurationAn example configuration file is available underexample.conf. The
two available configuration options are the base URL of the server and
the client ID.timewsyncreads the configuration from$TIMEWSYNC/timewsync.confwhere$TIMEWSYNCrepresents the path of the data directory (i.e. if
the default data directory path is assumed, the configuration file is
at~/.timewsync/timewsync.conf).Authentification keysThe public-private key pair used for authentification is also stored
in the data directory under$TIMEWSYNC/private_key.pemand$TIMEWSYNC/public_key.pem. The key pair can be generated using thegenerate-keyssubcommand.HooksHooks are special files located in the data directory which will be
contextually executed on specific events. They reside in$TIMEWSYNC/hooks(e.g.$TIMEWSYNC/hooks/conflicts-occurred).Available hooks:conflicts-occurred: Triggered when the server responds with the
information that a conflict has been resolved by merging intervals.DevelopmentUsing a virtual environmentTo avoid conflicts between packages, you can use a virtual environment.
Make sure you havevirtualenvinstalled (first time only):pipinstallvirtualenvCreate a new virtual environment (first time only):virtualenv-ppython3venvTo activate your virtual environment run:# For bash users (usually default)sourcevenv/bin/activate# For fish userssourcevenv/bin/activate.fishOn NixOS, all of the above steps boil down to:nix-shellInstalling the projects dependenciesTo install the project dependencies:pipinstall-rrequirements.txtNow you should be good to go :)Running the clientOnce you have all dependencies installed, the client can be started:python-mtimewsyncAcknowledgementsThis project was developed during the so-called "Bachelorpraktikum" at TU Darmstadt. It was supervised by the Department of Biology,Computer-aided Synthetic Biology. For more information visitkabisch-lab.de.This work was supported by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) (031A532B, 031A533A, 031A533B, 031A534A, 031A535A, 031A537A, 031A537B, 031A537C, 031A537D, 031A538A).