Saravanan666/pypi-org huggingface dataset:
Package: tclab-cae
# TCLAB_PYTHON_CAETCLab_CAE: Temperature Control Laboratory Control Automático EducaciónTCLab_CAEproporciona una interfaz de Python modificada para elTemperature Control Labimplementado en un microcontrolador Arduino a través de una interfaz USB.TCLab_CAEse implementa como una clase de Python dentro detclab_cae.  El paquetetclab_caeincluye:clockUn generador de Python para la implementación suave en tiempo real de    algoritmos de control de procesos.HistorianUna clase de Python para registrar los resultados de un control de proceso experimento.PlotterProporciona a un historiador trazado en tiempo real dentro de un cuaderno jupyter.TCLabModelUn modelo integrado del laboratorio de control de temperatura para simulación fuera de línea y más rápida que en tiempo real del control de procesos experimentos No es necesario conectar hardware para usarTCLabModel.Cursos de Control en Sistemas EmbebidosSi esta interesado en realizar cursos de control sin necesidad de usar Python o Matlab, sino que desea saber como programar los controladores directamente en el microcontrolador Arduino o en un microcontrolador PIC de Microchip empleando el PIC C Compiler, puede acceder a los siguientes cursos con un Cupon de Descuento en el sitio web deControl Automático EducaciónImplementación de Controladores en Dispositivos Embebidos usando el TCLAB_CAEInstalación ArduinoEl arduino debe tener instalado el programa para poder leer los nuevos sensores, cuyo repositorio esta disponible en<https://github.com/sergioacg/TCLAB_CAE>Instalación de la Bilioteca TCLab_CAELa instalación del TCLAB se hace en el terminal usando el manejador de paquetespipsi tienes Python o usandocondasi tienes anaconda (en el caso de Anaconda abra el programa como administrador en Windows para una correcta instalación de los paquetes):pip installtclab-caeconda installtclab-caeSi hay problemas con los permisos se puede intentar el comando:pip installtclab-cae--userPosteriormente, deberemos instalar la biblioteca de comunicación serial pyserial:pip install pyserialconda install pyserialConfiguración de hardwareConecte un dispositivo Arduino compatible (UNO, Leonardo, MEGA, NHduino) con el TCLab conectado a su computadora a través de la conexión USB y enchufe el adaptador de CC a un toma corriente en la pared.Comprobando que todo funcionaEjecuta el siguiente código :import tclab_cae.tclab_cae as tclabwith tclab.TCLab_CAE() as lab:print(lab.T1)Si todo ha funcionado, debería ver el siguiente mensaje de salida:Connecting to TCLab

TCLab Firmware Version X.X.X on NHduino connected to port XXXX

XX.XX

TCLab disconnected successfully.El número devuelto es la temperatura del sensor T1 en °C.
Package: tclambda
DO NOT USE, THIS IS PRE-ALPHA,HAS NO TESTS, NO MONITORING, BAD LOGGINGtclambdaAWS Lambda calling libraryWorks together with functions defined intc-sam-cliConfiguration of AWS Lambda# app.pyimporttclambdafromnumpy.polynomial.polynomialimportpolyfitasnp_polyfithandler=tclambda.LambdaHandler()@handler.register()defpolyfit(*args,**kwargs):returnlist(np_polyfit(*args,**kwargs))The registered handler must return something that is json serializable.Usage in other projectsConfiguration is primarely done by environmental variables.TC_NUMPY_QUEUE="https://sqs.eu-west-1.amazonaws.com/12345/NumpySqs"TC_NUMPY_BUCKET="s3-result-bucket"fromtclambda.auto_functionsimportnumpylambda_result=numpy.polyfit([1,2],[2,1],1)print(lambda_result.result())# Output: [2.999999999999998, -0.9999999999999992]Retry lambdaimporttclambdaimportrequestshandler=tclambda.LambdaHandler()@handler.register()defweb_data(*args,**kwargs):try:response=requests.get("https://example.com")response.raise_for_status()exceptExceptionase:raisetclambda.RetryException(e)returnresponse.textError handlingSentryInstall tclambda together with sentrypip install tclambda[sentry]Setting up sentry is done automatically through environmental variables.SENTRY_DSNSENTRY_RELEASESENTRY_ENVIRONMENTTestsCreate an S3 bucket to test live againstAddTC_THIS_BUCKET=my-test-s3-bucketto.envRunpipenv run test
Package: tcli-bar
Another CLI loading bar...This is a simple classic CLI loading bar animation for all your CLI loading bar
animation needs, whatever they might be! with lots of customisation options such as displaying percentages, and colour support. This animation can be easily incorporated into any function you want and also makes your CLI look way more professional! - literally from 0 to 100 JUST LIKE THAT!
enjoy ;)Also note that the install requires coloramaExample Codeimportmathnumbers=[x*5forxinrange(1000,3000)]list=[]forx,iinenumerate(numbers):list.append(math.factorial(x))tcli_bar.bar(x+1,len(numbers))#Hereprint('\nDone')Another example but with colour and moreimportmathimporttcli_barfromcoloramaimport*numbers=[x*5forxinrange(100,1500)]list=[]fori,ainenumerate(numbers):list.append(math.factorial(i))tcli_bar.bar(i+1,len(numbers),colour=Fore.GREEN,icon='#',description='Uploading...',show_percent=False)print('\nHello')A 'Free' exampleimporttcli_barfree_bar(set=100,icon='+',colour=Fore.RED,description='Scanning...')Results
Package: tclient
tclient: HTTP client for parallel http requests=========================TClient is an http client for doing http requests in parallel. This is really justa light wrapper around Tornado and uses Tornado's ioloop and httpclient.Features--------* Create multiple HTTP requests, have them run in parallel.* Convinient API for building requests with query params, forms, or JSON.* Integrates with BlueOx for logging and metrics.* Testing hooksUse---`tclient.Request` is based on `tornado.httpclient.HTTPRequest`, so you should checkoutit's [documentation](http://www.tornadoweb.org/en/stable/httpclient.html#request-objects)In summary:req1 = tclient.Request('http://localhost:8888')req1.params['search'] = 'turtles in a half-shell'req2 = tclient.Request('http://localhost:8888')req2.method = "POST"req2.body = {'name': 'Raphael'}req3 = tclient.Request('http://localhost:8888', method="POST")req3.form['name'] = 'Raphael'req3.form.add_file("tmnt.jpg", picture_fp)resp1, resp2, resp3 = tclient.fetch_all([req1, req2, req3], timeout=30)for r in (resp1, resp2, resp3):r.rethrow()print resp1.json['results']### Dependencies* tornado* urllib3 (just for form encoding)* pycurl (recommended)* blueox (optional)
Package: tclint
tclinttclintis a lint tool for Tcl. It analyzes Tcl source files and reports stylistic and functional errors that may inhibit readability or correctness.FeaturesConfigurable style checksUsage checks for built-in commandsMore coming soon!Getting StartedInstalltclintfrom PyPI usingpipx(recommended):pipxinstalltclintOr withpip:pipinstalltclintRuntclinton a Tcl source file by providing its path as a positional argument:tclintexample.tclIf the file contains any lint violations, they will be printed andtclintwill return a non-zero exit code. Otherwise, the output will be empty andtclintwill exit successfully.Example$catexample.tclputs too many arguments !puts "unexpected indent"puts   "too many spaces after command"$tclintexample.tclexample.tcl:1:1: too many args to puts: got 4, expected no more than 3 [command-args]example.tcl:2:1: expected indent of 0 spaces, got 2 [indent]example.tcl:3:5: expected 1 space between words, got 3 [spacing]Usagetclintis a command-line utility. It takes a list of paths as positional arguments, which may either be direct paths to source files, or directories which will be recursively searched for files ending in.tcl,.sdc, or.xdc.Collected files will be checked for lint violations.  See theViolationsdocumentation page for a description of all
lint violationstclintmay report.Aspects oftclint's behavior can be controlled by a configuration file. By default,tclintwill look for a file namedtclint.tomlor.tclintin the current working directory (in that order), but a path to an alternate configuration file can be provided using the-cor--configflag. SeeConfigurationfor documentation on the configuration file.Contributingtclintwelcomes community contributions. The best way to help the project is toopen an issueif you find a bug or have a feature request.PRs are also welcome, but for non-trivial changes please open an issue first to solicit feedback. This helps avoid wasted effort.Use the following steps to set uptclintfor local development:$gitclonehttps://github.com/nmoroze/tclint.git# or URL to fork$cdtclint
$pipinstall-e.[dev]Please format, lint, and run tests before submitting changes:$black--preview.
$flake8.
$pytestLicenseThis project is copyright 2023 Noah Moroze, released under theMIT license.
Package: tclite
Tokyo cabinet and tokyo tyrant object wrapper.Installation$  pip install tcliteUsage>>> import tclite as tl
>>> from datetime import datetime, date>>> class User(tl.Model):
...     username    = tl.UnicodeProperty(key='u')  # realy stored by short 'u' key
...     birthday    = tl.DateProperty()
...     rating      = tl.IntegerProperty(default=0)
...     reply_rate  = tl.FloatProperty(default=0)
...     about_me    = tl.CompressedUnicodeProperty(internal_encoding='cp1251')
...     friends     = tl.DumpProperty(default={})
...     messages    = tl.CompressedDumpProperty(default=[])
...     is_active   = tl.BooleanProperty(default=True)
...     created     = tl.DateTimeProperty(index=True) # indexed field
...     choice      = tl.ChoiceProperty([('1', 'first'), ('2', 'second')])
...
...     __file__ = 'var/readme_test.tclite'
...     #__host__ = 'localhost:1978'>>> User.db.clear()
>>> for i in xrange(100):
...     User(
...         username='user-%s' % i,
...         birthday=date(1900 + i, 1, 1),
...         created=datetime.now()
...     ).save()>>> users = User.filter('birthday >=', date(1950, 1, 1))
>>> users.count()
50>>> for user in users.filter('username endswith', '0').order('-created'):
...     user.username
u'user-90'
u'user-80'
u'user-70'
u'user-60'
u'user-50'>>> users.get().username
u'user-50'>>> users.delete()
>>> users.count()
0
>>> User.filter().count()
50>>> User(username='Foo', key='user-foo').save()  # set custom key name
>>> User.get('user-foo').username  # get by keyname
u'Foo'
Package: tcl_kernel
This uses the version of Tcl that comes with Python, through Tkinter, so you do not need to install anything else.Note: Requires Python 3!Install via:pip3 install tcl_kernelpython3 -m tcl_kernel.installTo use try one of these:jupyter notebookThen select the Tcl option in the ‘New’ sectionjupyter qtconsole –kernel tcljupyter console –kernel tclFor details of how this works, see the Jupyter docs onwrapper kernels.
Copyright 2015-2016 Ryan Pepper, Thomas Kluyver, Hans Fangohr, University of Southampton.This work was supported by an EPSRC Doctoral Training Centre grant (EP/L015382/1).I do not have time to add functionality to this project anymore but will happily review and merge pull requests to add new features/fix bugs.
Package: tcl-magic
Tcl magicIPythonextension which addsTclmagics.InstallationInstall or upgrade withpip:pip install -U tcl-magicUsageLoad the extension:In[1]: %load_ext tclmagicUse the extension:In[2]: %%tcl
       set tcl_precision 17
       expr acos(-1)

Out[2] 3.141592653589793
Package: tclwrapper
Python wrapper to interact with TCL command line interfaces. In most cases,
tkinter (with the send command) is enough but sometimes it is convenient to
interact directly with the tcl cli provided by applications.Installing Non-Development VersionIf you want to just install thetclwrapperpackage, you should be able to using the following command:$ pip3 install tclwrapperUsageAs an example, here is some code that interfaces with GTKWave’s TCL interface:fromtclwrapperimportTCLWrapperwithTCLWrapper('gtkwave','-W')astcl:tcl.eval('gtkwave::loadFile%s'%filename)tcl.eval('set num_facs [gtkwave::getNumFacs]')signals=tcl.eval('''
        set all_facs [list]
        for {set i 0} {$i < $num_facs} {incr i} {
            lappend all_facs [gtkwave::getFacName $i]
        }
        return $all_facs''',to_list=True)Installing for DevelopmentTo install this package for development, you should use a virtual environment, and install the package in editable mode using pip.To create a virtual environment for this project, run the command below.$ python3 -m venv path/to/new-venv-folderTo start using your new virtual environment, run the command below.
This needs to be run each time you open a new terminal.$ source path/to/new-venv-folder/bin/activateAt this point you are now using your new virtual environment.
Python packages you install in this environment will not be available outside your virtual environment.
If you want to stop using the virtual environment, just rundeactivate.To install thetclwrapperpackage in editable mode, inside thetclwrappertop git repository folder, run the command below.$ pip3 install -e .
Package: tcm
tcm (Test Case Meta)This is primarily an excercise in Python metaprogramming which also lets me see GitHub, CI tools, and PyPI in action.Things to develop:a class method decorator to hold a table of argumentsa metaclass to automatically generate multiple test methods out of each decorated sample methodTools to use:toxto test the code with different Python versionspylintandflake8to keep individual commits cleancoverallto ensure 100% code coverage
Package: tcman
Tor Circuits ManagerThis package provides a set of classes to construct circuit manager
that matches your taste.Use it in conjunction withmintc.How to:importasynciofrommintcimportTorControllerfromtcmanimport*classMyCircuitsManager(CircuitsManagerBase,RoundRobinManager,PromiscuousPathBuilder,Logger):passasyncdefmain():asyncwithTorController('127.0.0.1:9051')astc:awaittc.authenticate('password')asyncwithMyCircuitsManager(tc,num_hops=2,max_circuits=500)astcm:awaittcm.run()asyncio.run(main())Mind exceptions and re-run when shit happens.
Package: tcmd
TcmdAboutTcmd stands for top commands. This tool can be used to see the most used commands. The tool was written in python3 and is pretty simple. It can be downloaded from pypi.Requirementsworks on windows , linux and macOS (tested onDebian)python3 (Tcmd written in python3)pip (Installation is done via pip)python modulerich, is installed automatically (if it wasnt found usepip install rich)InstallationpipinstalltcmdOrwith pip3pip3installtcmdUse / DemoCommand :tcmdOutput :
Package: tc-messageBroker
tc-messageBrokerPythonRabbitMQ library written in Python. Responsible for brokering messages between different services.InstallationTo install the library first clone the code and then follow the instruction belowpip install .pip install -r requirements.txtUsageIn your project, you could use this library by importingtc_messageBrokerand to load sub-modules just separate it by dot. For examplefrom tc_messageBroker import RabbitMQfrom tc_messageBroker.rabbit_mq.queue import Queuefrom tc_messageBroker.rabbit_mq.event import EventYou can find examples underexampledirectory.
Package: tcmetatraderlinux
MetaTrader 5 for linux systemA simple package that useswine,rpycand a Python Windows version to allow usingMetaTrader5on Linux.InstallInstallWine.InstallPython for Windowson Linux with the help of Wine.Find the path topython.exe.Mine is installed on/home/user/.wine/drive_c/users/user/Local Settings/Application Data/Programs/Python/Python39.Installmt5library on yourWindowsPython version.pip install MetaTrader5
pip install --upgrade MetaTrader5Install this package on bothWindowsandLinuxPython versions:pip install mt5linuxHow To UseFollow the steps:Open MetaTrader5.OnWindowsside, start the server on a terminal:python -m mt5linux <path/to/python.exe>OnLinuxside, make your scripts/notebooks as you did with MetaTrader5:# import the packagefrommt5linuximportMetaTrader5# connecto to the servermt5=MetaTrader5(# host = 'localhost' (default)# port = 18812       (default))# use as you learned from: https://www.mql5.com/en/docs/integration/python_metatrader5/mt5.initialize()mt5.terminal_info()mt5.copy_rates_from_pos('GOOG',mt5.TIMEFRAME_M1,0,1000)# ...# don't forget to shutdownmt5.shutdown()Be happy!On step 2 you can provide the port, host, executable, etc... just typepython -m mt5linux --help.
Package: tcmpr
Program to text compression and decompression which has
implemented a few of the most popular lossless algorithms.DescriptionProgram compress and decompress text files, using chosen
algorithm. It can run on files as well as
in recursive way on directory with text files. Solid compression
mode can be set with proper flag, default if directory is given
it runs with non-solid mode and compress each file in that directory
creating new compressed file. Implemented algorithms are:Huffmann (Huffman coding)LZSS (Lempel–Ziv–Storer–Szymanski)LZW (Lempel–Ziv–Welch)Shannon (Shannon coding)UsageTo installtcmpruse following command (assuming that you havepipinstalled):$pipinstalltcmprCompress filefilename.txtwith default algorithm (huffman coding):$tcmprfilename.txtDecompress filefilename.txt.huffmanwith appropriate algorithm based on extension (here.huffman):$tcmpr-dfilename.txt.huffmanOther commands are described in official documentationDocsDocumentation to project is hosted onreadthedocs.orgunder followinglink hereAuthorsKonrad Porebakonrad@porebasoftware.pl
Package: tc-neo4j-lib
# neo4j-lib-py[![Maintainability](https://api.codeclimate.com/v1/badges/9d2ca104782798b9ed1d/maintainability)](https://codeclimate.com/github/TogetherCrew/neo4j-lib-py/maintainability)
[![Test Coverage](https://api.codeclimate.com/v1/badges/9d2ca104782798b9ed1d/test_coverage)](https://codeclimate.com/github/TogetherCrew/neo4j-lib-py/test_coverage)This repository contains a shared python library for accessing neo4j database. We’re using it across different TogetherCrew python projects.To install the library:`bash pip installtc-neo4j-lib-py`Usage:`Python from tc_neo4j_lib.neo4j_ops import Neo4jOps `
Package: tcn-sequence-models
TCN_Seq2SeqImplementation of different TCN based Sequence-to-Sequence models for timeseries
forecasting. Currently, only works for a numerical class label.The project includes wrapper classes for the models that add some additional
features like simple saving and loading of the trained models and automated
hyperparameter tuning. Also, there is functionality for basic data preprocessing
as well as saving and loading the preprocessing configurations.The project is written in Python 3.8 using Tensorflow 2.5.0TCN_Seq2Seq is on PyPI, so you can usepipto install it.pipinstall--upgradepip
pipinstalltcn-sequence-modelsModelsThree models exist:1. TCN-TCN model with attention.The structure of the TCN-TCN model with attention can be seen below.2. "Normal" TCN-TCN model without attention.The structure of the "normal" TCN-TCN model without attention can be seen below.There are two versions of the TCN-TCN models, an autoregressive model and a
none-autoregressive model.Autoregressive model:
For training, teacher-forcing is used for the decoder. In addition to other decoder
input data that is defined by the user, always the ground truth of the previous time
step is added as an additional feature. For example at position x(n+1) the ground
truth at position x(n) is added as an additional input.
During inference, the predictions of the previous time step is used instead.
The autoregressive model can be trained fast in a parallel way. However, inference
is slow because the decoder needs to be passed m times when the output length is m.None-regressive model:
For the none-regressive model only the input features are used for the decoder that
the user provides. If the user does not provide any features for the decoder, a
dummy feature is created with all 1s so that the decoder has some input. Without any
input the model would not work. This model is fast during training and inference.3. TCN-RNN model.The architecture of the TCN-RNN model can be seen in the following image:TCN blocksThe TCN blocks use as many layers as needed to get a connection from first timestep's
input to last timestep's output. The padding mode in the models can be set by the user.
If padding is set to 'causal', a timestep only gets information from the previous
timesteps. If set to 'same' or 'valid', also information from
future timesteps are used which might improve the predictions.The encoder of the "normal" TCN-TCN model will always have "causal" padding since
only the last output of the encoder is used.
Also, for the decoder of the TCN-TCN models in the autoregressive mode, padding will
always be causal. The picture below shows the TCN block with 'causal' padding.One TCN cell (residual block) is structured like this (when choosing ReLu as
activation function):InputsThe models expect input sequences for the encoder and optionally for the decoder.ExamplesFor examples, please refer to the notebooks.
Package: tcod
ContentsStatusAboutUsageInstallationRequirementsChangelogLicenseStatusAboutThis is a Pythoncffiport oflibtcod.This library ishosted on GitHub.Any issues you have with this module can be reported at theGitHub issue tracker.UsageThis module was designed to be backward compatible with the original libtcodpy
module distributed with libtcod.
If you had code that runs on libtcodpy then you can use this library as a
drop-in replacement.  This installs a libtcodpy module so you’ll only need to
delete the libtcodpy/ folder that’s usually bundled in an older libtcodpy
project.Guides and Tutorials for libtcodpy should work with the tcod module.The latest documentation can be found here:https://python-tcod.readthedocs.io/en/latest/InstallationDetailed installation instructions are here:https://python-tcod.readthedocs.io/en/latest/installation.htmlFor the most part it’s just:pip3 install tcodRequirementsPython 3.8+Windows, Linux, or MacOS X 10.9+.On Linux, requires libsdl2 (2.0.10+).ChangelogYou can find the most recent changeloghere.Licensepython-tcod is distributed under theSimplified 2-clause FreeBSD license.
Package: tcod-clock
AboutLibtcod used to include a global framerate limiter which was eventually deprecated.
This module was created as a replacement for that feature.importtimeimporttcod.clockFPS=30end_time=time.time()+3# Loop for 3 seconds.clock=tcod.clock.Clock()whiletime.time()<end_time:clock.sync(1/FPS)# This loop will run at 30 FPS until interrupted.# Timing information can be checked.  Check the docs for more info.print(f"{clock.last_fps=}")print(f"{clock.min_fps=}")print(f"{clock.max_fps=}")print(f"{clock.mean_fps=}")print(f"{clock.median_fps=}")
Package: tColorP
TColorPprint with color.fromTColorPimportTColorP,TColortcp=TColorP()tcp.success('success')tcp.error('error')tcp.warning('warning')tcp.normal('normal')tcp.formatColor([TColor.method_normal,TColor.foreground_cyan])print('hello')tcp.formatColor([TColor.method_normal])print('TColorP')how to usefromTColorPimportTColorP,TColortcp=TColorP()# print success infotcp.success('success')# print error infotcp.error('error')# print warning infotcp.warning('warning')# print normal infotcp.normal('normal')# print normal Styleprint(tcp.normalStyle)# change normal styletcp.normalStyle=[TColor.method_highlight,TColor.foreground_red]tcp.normal("normal")# change normal style oncetcp.normal('normal once',style=[TColor.method_highlight,TColor.foreground_red])# format colortcp.formatColor([TColor.method_normal,TColor.foreground_red])print('this will change color with red')# print suppos method and colorprint(TColor())# method_normal=0,method_highlight=1,method_underline=4,method_reverse=7# foreground_black=30,foreground_red=31,foreground_green=32,foreground_yellow=33,foreground_blue=34,foreground_fuchsia=35,foreground_cyan=36,foreground_white=37# background_black=40,background_red=41,background_green=42,background_yellow=43,background_blue=44,background_fuchsia=45,background_cyan=46,background_white=47LicenseMIT License,Thanks!