Saravanan666/pypi-org huggingface dataset:
Package: quickumls-simstring
Orginal version bychokkan, availablehere. New version (compiles
under macOS) fromhere.Windows/Anaconda/VisualStudio specific steps:Make sure that iconv is available to your desired conda environment:conda install-cconda-forgelibiconvThen, open a Visual Studio Developer Prompt (e.g. “VS 2015 x64
Developer Command Prompt” or “VS 2015 x86 Developer Command Prompt”)Now you can any of these commands to build or install:pythonsetup.pybuildpythonsetup.pyinstallNOTE: If there is a failure that ‘rc.exe’ cannot be found, add
the appropriate WindowKits binary path to PATH. More info on thishere.
Package: quickunit
Given standard test setup, will determine which tests need to run against a given diff.For example, say you’re working in your branch called my-new-sexy-feature, which modifies the following files:src/foo/bar/__init__.py
src/foo/bar/baz.py
src/foo/biz.pyIf you’re using a traditional test layout, we’ll automatically add the following rule for you:tests/{path}/test_{filename}Otherwise you can add rules using regular expression syntax in combination with the path and filename formatters.Now if we run with the default options,nosetests--with-quickunit, it will look for tests (by default) in
the following base directories:tests/src/foo/bar/test_baz.py
tests/src/foo/test_biz.py(It does this by analyzing the diff against master, and determining which files you’ve changed
are tests, including them, and which files containing test coverage in a parallel directory.)ConfigIf you want to support multiple directories for searching (let’s say you break up unittests from integration tests)
you can do that as well:::–quickunit-rule=tests/{path}/test_{filename} –quickunit-rule=tests/{path}/{basename}/tests.pyOr, if you’d prefer, viasetup.cfg:quickunit-rule = tests/{path}/test_{filename}
                 tests/{path}/{basename}/tests.pyRulesRules are a combination of simple formatting a regular expressions.The following formatted variables are available within a rule:{path}The base path of the filename (e.g. foo/bar){filename}The filename excluding the path (e.g. baz.py){basename}The filename excluding the extension (e.g. baz)A rule is first formatted (using.format(params)) and then compiled into a regular expression on top of each changed file.
Package: quick-vcn
Set up an OCI VCN and components quicklyTo install:pip install -e .To use:quick-vcn --profile YOUR-PROFILE \
    --compartment YOUR-COMPARTMENT-NAME \
    --region uk-london-1 \
    [ --vcn-name "net" ] \
    [ --vcn-cidr 10.0.0.0/16 ] \
    [ --subnet-name "sub" ] \
    [ --subnet-cidr 10.0.0.0/24 ]
Package: quickvec
QuickVecQuickVec is a simple package to make it easy to work with word embeddings.
QuickVec supports instantaneous loading of word embeddings after converting
them to a native SQLite format. QuickVec is designed to do exactly one thing
well: allow you to quickly load word embeddings and look up the vectors for
words.Installationpip install quickvec(requires Python 3.6+)Design philosophyQuickVec was created to supportNERPy,
a named entity recognition framework that uses word embeddings for feature
generation. NERPy originally used gensim, but the time and memory required to
load a word embedding completely into memory was a large performance
bottleneck. NERPy then turned to Magnitude, but its conversion process is quite
slow, and its installation process caused problems for NERPy users.
The NERPy developers created QuickVec based on the design of Magnitude,
but with the goal of creating a package with minimal features and dependencies.FAQHow does QuickVec compare togensim'sKeyedVectorsfor loading word embeddings?QuickVec can load word embeddings instantaneously after conversion to its
native SQLite-based format, and does not load the whole embedding into memory,
making it more memory efficient. However, QuickVec only supports text-format
word embeddings files, and in general has far less functionality.How does QuickVec compare toMagnitudefor loading word embeddings?Like Magnitude, QuickVec can instantly load word embeddings after conversion
to its native SQLite-based format. QuickVec's conversion process is faster
than Magnitude's. However, QuickVec does not support many of Magnitude's
features, such as word similarity or generating vectors for out-of-vocabulary
words, and QuickVec does not provide pre-converted word embeddings and only
supports loading from text-format embeddings.
Package: quickvision
QuickvisionFaster Computer Vision.Install QuickvisionInstall from PyPi.Current stablerelease 0.1.1needsPyTorch 1.7.1andtorchvision 0.8.2.pip install quickvisionWhat is Quickvision?Quickvision makes Computer Vision tasks much faster and easier with PyTorch.It provides: -Easy to use PyTorch native API, forfit(),train_step(),val_step()of models.Easily customizable and configurable models with various backbones.A complete PyTorch native interface. All models arenn.Module, all the training APIs are optional and not binded to models.A lightning API which helps to accelerate training over multiple GPUs, TPUs.A datasets API to convert common data formats very easily and quickly to PyTorch formats.A minimal package, with very low dependencies.Train your models faster. Quickvision has already implemented the long learning in PyTorch.Quickvision is just PyTorch!!Quickvision does not make you learn a new library. If you know PyTorch, you are good to go!!!Quickvision does not abstract any code from PyTorch, nor implements any custom classes over it.It keeps the data format inTensorso that you don't need to convert it.Do you want just a model with some backbone configuration?Use model made by us. It's just ann.Modulewhich has Tensors only Input and Output format.Quickvision provides reference scripts too for training it!Do you want to train your model but not write lengthy loops?Just use our training methods such asfit(),train_step(),val_step().Do you want multi GPU training but worried about model configuration?Just subclass the PyTorch Lightning model!Implement thetrain_step(),val_step().
Package: quickviz
Widgets for quickly visualizing pandas dataframesIt is often necessary to plot data in order to understand it. Plotting
allows to quiclky spot glitches in the data: that person who is 180 meters
tall or this car which can speed thousands of kilometers per hour will
immediately stand out. In this situation, one wants a way to quickly
(rather than beautifully) plot their data. Quickviz provides a set of
widgets to do this in a few clicks.
Package: quickweb
QuickWebQuickWeb is a Python Web Application Server based on the production-provedCherryPyWeb Framework extended with developer friendly features.FeaturesDevelopmentCustom static content folders (any folder containing a.staticfile)Automatic path based routing for templates and controllers, e.g.:/about.html is available as /about/submit.py is available as /submitZero code Jinja2 template rendering for .html files, that can:Use data from YAML files (name.html < name.yaml)Use HTTP specific data e.g.{{session}}Zero code Markdown files renderingLow code Python controllersIntegrated HTTP functional TestingReload on code changesProductionSSLsupportMutual TLSsupportInstallingQuickWeb requires Python3.6+ and can run on Windows, Linux or Mac.Install quickweb using pip:pipinstallquickwebIf the installation is succesful thequickwebcommand will be available, it will allow you to manage quickweb applications from the command line.Getting StartedCreating your first applicationCreate your first quickweb app using a bootstrap based template:quickwebcreatemy-web-appbootstrap-navbar-fixed-topYou will get amy-web-appdirectory containing a quickweb sample app using theBootstraplibrary.Changing the application contentCheck the application directory using your preferred HTML/CSS/JavaScript editor/IDE, edit the the content from thewebrootdirectory as needed.Starting the applicationAfter making some changes and you can test the application executing:quickwebrunmy-web-appA web server is started using port 8080 on your local system. You can check your application by browsing tohttp://localhost:8080. If you later change some of the YAML/HTML/CSS/JS, you can check the changes by refreshing the corresponding page on your browser.Deploying to a cloud platformWhen your application is ready for public access you can deploy it to a cloud platform, it has been tested with the following providers:Heroku Cloud Application Platform (deploy with: git push heroku master)IBM Bluemix (deploy with: cf push appname)Other CloudFoundry based provider (deploy with: cf push appname)It should be able to run from otherCloud Foundrybased providers.NOTES:Check the cloud provider documentation for the web app detailed setup instructionsUse the instructions for python web applications setup/deploymentThe level of support for python based apps will depend on your provider, check it's documentation for detailsContributingCheck theContributing Guide.Maintained ByJoão Pinto for theOpen Pipeinitiative
Package: quick-xinput
Quick XinputDoes it drive you mad when you're living that keyboard-driven terminal lifestyle
and your wrist grazes the touchpad sending you off to a different window or
desktop?If so,quick_xinputis the Python module you need in your life. Bring up a
quick selector in the terminal,fzf, or with a GUI,rofiand select an
xinput device to toggle on/off.Usage# toggle a device using fzfpython-mquick_xinput# toggle a device using rofipython-mquick_xinput--rofi# disable a device with rofi (good for keybindings)python-mquick_xinputoff--rofiSource of TruthThis project is available onGitHubandGitLab. Each push tomasterautomatically goes to both so choose whichever platform you prefer.
Package: quickxorhash
This python library is a Cython wrapper for the [original C implementation](https://https://github.com/flowerysong/quickxorhash).
quickxorhash is a C library (libqxh) implementing Microsoft’s QuickXORHash algorithm.## AlgorithmQuickXORHash is a non-cryptographic hash function that XORs the input
bytes in a circular shift pattern, then XORs the least significant
bits of the hash with the input length. The canonical representation
of the resulting hash is a Base64 encoded string, because hexadecimal
is too plebeian.## Usage`python >>> import quickxorhash >>> h = quickxorhash.quickxorhash() >>> h.update(b'hello world') >>>print(h.digest())b'h(\x03\x1b\xd8\xf0\x06\x10\xdc\xe1\rrk\x03\x19\x00\x00\x00\x00\x00'>>> import base64 >>>print(base64.b64encode(h.digest()))b'aCgDG9jwBhDc4Q1yawMZAAAAAAA=' `
Package: quicky-repo
Fast Repo⚡ Initialize your projects as fast as you want. ⚡🎲 PrerequisitesTo run this project you will need to havePythonandNode.Node Version Managers:fnm,nvm,asdf...Python Version Managers:pyenv,virtualenv...⬇️ How to install and use itgitclonehttps://github.com/kauefraga/fast-repo.gitcdfast-repo

pipinstall-rrequirements.txt# Has nothing to try out now...You are welcome to open issues and pull requests!🛠 TechnologiesThe following tools have been used to build the project:🐍Python- A programming language that lets you work quickly
and integrate systems more effectively.🕶Click- Composable command line interface toolkit🎨Rich- Rich is a Python library for rich text and beautiful formatting in the terminal.📝 LicenseThis project is licensed under the MIT License - See theLICENSEfor more information.
Package: quick-zip
About the ProjectDocumentationWhat QuickZip IsQuickZip is a CLI utility I developed to solve a backup problem on my machines. I wanted a way to quickly backup up small sets of configuration files and data without deploying a massive, hard to maintain tool with too much front-end configuration. QuickZip uses a config.toml file to build tiny list of backups that are conducted when called (typically via cron).Key FeaturesCreate jobs with configuration file, including support for variables and defaultsBeautiful CLIBackup AuditsWebhook Support$quick-zip--helpUsage: qz [OPTIONS] COMMAND [ARGS]...Options:--verbose / --no-verbose        [default: False]--install-completion [bash|zsh|fish|powershell|pwsh]Install completion for the specified shell.--show-completion [bash|zsh|fish|powershell|pwsh]Show completion for the specified shell, tocopy it or customize the installation.--help                          Show this message and exit.Commands:audit   🧐 Performs ONLY the audits for configured jobsconfig  📄 displays the configuration filedocs    💬 Opens quickZip documentation in browserjobsrun     ✨ The main entrypoint for the application.What QuickZip Isn'tQuickZip is NOT a replacement for a robust backup or imaging software. It's primary use case is to collect configuration files on system other similar types of files, zip them up, and stick them somewhere else on your file system.Why notx???I can't comment on every backup utility but I can mention the few that I looked at.Borgwas a strong contender and I will likely use it for other things down the line, however I felt like there was too much upfront configuration before being able to use.Rsync/Rclonewere both great options but I felt there were too confusing/robust for what I was trying to do. On top of that, I was looking for a few features that I hadn't seen.Backup Audits: The ability to "audit" backups and specify how old the newest backup should beWebhook Support: Send backup data to Home Assistant for notifications and dashboards.Also, I just like building stuff. 👍
Package: quickzoom
quickzoomquickzoom is a python command line tool that enables you easy connecting to zoom roomsFree software: MIT licenseFeaturesAfter pip install you can use the following commands in the command linequickzoom <roomlabel>Will search your config file for the room label. If the room label exists. Tt willl grabd the meeting id and password and connect to zoom.quickzoom –newroom / -nWill create a new room in your config. Will prompt you for room label, meeting id, meeting password.All your rooms will be saved in your homedirectory/.quickzoom/rooms.json:windows: C:/Users/User/.quickzoom/rooms.jsonOf course you can also manually edit the rooms.json file and quickly paste in some zoom rooms.ToDoimplement connect.get_zoom_exe() for macOs and linuximplement connect.connect_to_meeting() for macOs and linuxCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.1 (2020-11-19)First release on PyPI.newroom flag: user will be prompted for room lable, meeting id, room password. Will be saved in config file.raise error if on macOs or linux
Package: quico
QuicoQuico ou quick-container est un raccourci pour docker. Il permet de compiler et lancer rapidement un conteneur.Environnement requispython 3Installation$./install.shUtilisation$quico[-h]-tTAG[-nNETWORK][-fFILE][-pPUBLISH][-vVOLUME]directory

Quicoouquick-containerpermetdecompilerpuislancerrapidementun
conteneurdocker.

positionalarguments:directoryDossieroucompilerl'imagedocker.

optionalarguments:-h,--helpshowthishelpmessageandexit-tTAG,--tagTAG-nNETWORK,--networkNETWORKRéseauoulancerleconteneurdocker-fFILE,--fileFILECheminversleDockerfileàcompiler-pPUBLISH,--publishPUBLISH-vVOLUME,--volumeVOLUME
Package: quico-pkg-johannhospice
QuicoQuico ou quick-container est un raccourci pour docker. Il permet de compiler et lancer rapidement un conteneur.Environnement requispython 3Installation$./install.shUtilisation$quico[-h]-tTAG[-nNETWORK][-fFILE][-pPUBLISH][-vVOLUME]directory

Quicoouquick-containerpermetdecompilerpuislancerrapidementun
conteneurdocker.

positionalarguments:directoryDossieroucompilerl'imagedocker.

optionalarguments:-h,--helpshowthishelpmessageandexit-tTAG,--tagTAG-nNETWORK,--networkNETWORKRéseauoulancerleconteneurdocker-fFILE,--fileFILECheminversleDockerfileàcompiler-pPUBLISH,--publishPUBLISH-vVOLUME,--volumeVOLUME
Package: quidax-python
quidax-pythonPython plugin forQuidaxView onpypi.python.orgInstallationpipinstallquidax-pythonInstantiate Quidaxfromquidaxapi.quidaximportQuidaxquidax_secret_key="5om3secretK3y"quidax=Quidax(secret_key=quidax_secret_key)# to use users classquidax.users.all_sub_account()# to list all marketsquidax.markets.list_all_markets()# to fetch user tradesquidax.trades.trades(user_id)# to fetch wallets from a user and a specific wallet.quidax.wallets.fetch_a_specific_currency_wallet(user_id,currency)DOC Reference:https://developer.quidax.com/Other methods can be found in the docs folderAvailable resourcesUsersOrdersInstantOrdersMarketsWalletsWithdrawalDepositsTradesQuotesPlease reference thedocsfolder for usage,This repo won't be possible without the hard work ofandela-sjames.
Package: quidditas
Installationpip install quidditasFrom sourceUse the setup.py to create a bdist or sdist you can install with pip.Directory layoutquidditas - Quidditas source file and tests
examples  - Contains small example games using pygame and quidditasUsageTo import and instantiate quidditas:from quidditas import Quidditas, Processor

q = Quidditas()Further informationSee the quidditas homepage for more help athttp://quidditas.googlecode.com/
Package: quietex
QuieTeXMake your LaTeX build output just show the important stuff (seeherefor before and after screen recordings):QuieTeX is a minimal command-line tool which filters and colourizes the output ofpdflatexin real-time.It is not a build tool, it does not do any clever summaries, it just makes it easier to read.FeaturesHides open/close file loggingColours errors redColours warnings yellowTeX input prompt works inerrorstopmodeandscrollmodelatexmkintegrationUsageTo install:pip3installquietexTo use:quietexpdflatextest.texTo use withlatexmk, add this to yourlatexmkrc:# Make output prettiereval`quietex --latexmkrc`;
Package: quiet.py
quiet.pyPython ctypes bindings for libquiet to transmit data with sound.UsageEncode a message, and then decode itfrom quiet import Encode, Decoder

def test():
    encoder = Encoder()
    decoder = Decoder()

    for chunk in encoder.encode('hello, world'):
        message = decoder.decode(chunk)
        if message is not None:
            print(message)


test()decode messages from recording in realtimeimport sys
import pyaudio
from quiet import Encode, Decoder

def decode():
    if sys.version_info[0] < 3:
        import Queue as queue
    else:
        import queue

    FORMAT = pyaudio.paFloat32
    CHANNELS = 1
    RATE = 44100
    CHUNK = 16384  # int(RATE / 100)

    p = pyaudio.PyAudio()
    q = queue.Queue()

    def callback(in_data, frame_count, time_info, status):
        q.put(in_data)
        return (None, pyaudio.paContinue)

    stream = p.open(format=FORMAT,
                    channels=CHANNELS,
                    rate=RATE,
                    input=True,
                    frames_per_buffer=CHUNK,
                    stream_callback=callback)

    count = 0
    with Decoder(profile_name='ultrasonic-experimental') as decoder:
        while True:
            try:
                audio = q.get()
                audio = numpy.fromstring(audio, dtype='float32')
                # audio = audio[::CHANNELS]
                code = decoder.decode(audio)
                if code is not None:
                    count += 1
                    print(code.tostring().decode('utf-8', 'ignore'))
            except KeyboardInterrupt:
                break


decode()
Package: quik
A fast and lightweight Python template engineFeaturesEasy to use.High performance.Autoescaping.Template inheritance.Supports native python expressions.NutshellHere a small example of a Quik template<ul>#for @user in @users:
        #if @user.age > 18:<li><ahref="@user.url">@user.username</a></li>#end
    #end</ul>Use ItRender via template:fromquikimportFileLoaderloader=FileLoader('html')template=loader.load_template('index.html')printtemplate.render({'author':'Thiago Avelino'},loader=loader).encode('utf-8')
Package: quikcsv
QuikCSVPython package for quickly creating temporary csv files to help with testing. The CSV file exists in memory only so you can create files on the fly without needing to cleanup or delete files later. No need to use with statements or close resources, that is taken care of.InstallationNot ready just yet.SampleTake a function that takes an open CSV file as its argument. Instead of creating and opening an actual CSV file on the disk, decorate the function.fromquikcsvimportQuikCSV@QuikCSV([dict(data=[['Column A','Column B','Column C'],['100','101','102']])])defcsv_func(csv_file):# Work with csv file here.## QuikCSV.one will map the data above to mimic a csv file with the# respective columns and rows, passing the file to the csv_file argument# (or by default, the first argument if there are multiple.)If you want, you can specify the argument that the CSV mock file will be passed to.@QuikCSV([dict(data=[['Column A','Column B','Column C'],['100','101','102']],arg='csv_file')])defcsv_func(first_arg,csv_file,third_arg):# Mock CSV will be accessible on the csv_file variable.Options can be passed via the opts argument to quickly generate additional rows of data from existing rows.@QuikCSV([dict(data=[['Column A','Column B','Column C'],['100','101','102']],opts=dict(add_rows=2,row_pattern='copy',base_row_index=1))])defcsv_func(csv_file):# Output csv file will look like this:## Column A, Column B, Column C# 100,      101,      102# 100,      101,      102# 100,      101,      102## 2 rows of data are added, copied from index 1 of the passed data.'copy' is a predefined row creation pattern to make things easy, but you can also pass a custom function@QuikCSV([dict(data=[['Column A','Column B','Column C'],['100','101','102']],opts=dict(add_rows=2,row_pattern=lambdax:[n+1forninx],base_row_index=1))])defcsv_func(csv_file):# Output csv file will look like this:## Column A, Column B, Column C# 100,      101,      102# 101,      102,      103# 101,      102,      103## The passed function should apply against the row of data, not the# individual element.The above example applies the same function to the same row again and again, but by setting the 'incremental' option to True, the function will apply the newly created row of data on the next iteration.@QuikCSV([dict(data=[['Column A','Column B','Column C'],['100','101','102']],opts=dict(add_rows=2,row_pattern=lambdax:[n+1forninx],base_row_index=1,increment=True))])defcsv_func(csv_file):# Output csv file will look like this:## Column A, Column B, Column C# 100,      101,      102# 101,      102,      103# 102,      103,      104#Features in the worksRandom data generation - completely random or pseudo-random via user defined options
Package: quikenv
QuikenvThis was wrapper around the python dot-env project athttps://github.com/theskumar/python-dotenvhttps://pypi.org/project/python-dotenv/Here is the pypi release I made:https://pypi.org/project/quikenv/I really liked the project but wanted something even more lazy. All credit goes to the original developers. I only added features I wanted on top of an already really great project.This wrapper has the following features:A ezload() classmethod that automatically looks for for your .env file in the current working directory and 2 dirs upimport quikenv

env = quikenv.ezload()
var = env.get("my_environment_var")
print(env.environment_variables)A proper_load() classmethod that will load this class with a given file pathimport quikenv

env_path = "C:/somedir/.env"
env = quikenv.proper_load(env_path)
var = env.get("my_environment_var")
print(env.environment_variables)A normal procedural start for the classimport quikenv

env_path = "C:/somedir/.env"
env = quickenv.Quikenv(env_path)
env.load()
var = env.get("my_environment_var")
print(env.environment_variables)A lot of safety features (I don't think speed is relevant here):Errors out when you give it an invalid file path to the .env fileWill NOT add values from your computer and will only use values from your .env fileErrors out when you try to get a value that doesn't exist or has am empty valueMy years of programming has taught me that I'm still stupid enough to do this. You probably are too.At least you get told now when something is null/empty instead of spending 2 hours debugging your idiocy.