Saravanan666/pypi-org huggingface dataset:
Package: aquiladb
AquilaDB-PythonPython client library for AquilaDBinstallpip install aquiladbusage# import AquilaDB client
from aquiladb import AquilaClient as acl

# create DB instance
db = acl('localhost', 50051)

# convert a sample document
# convertDocument
sample = db.convertDocument([0.1,0.2,0.3,0.4], {"hello": "world"})

# add document to AquilaDB
db.addDocuments([sample])

# create a k-NN search vector
vector = db.convertMatrix([0.1,0.2,0.3,0.4])

# perform k-NN from AquilaDB
k = 10
result = db.getNearest(vector, k)AquilaDBAquilaDBis aResillient,Replicated,Decentralized,Host neutralstorage forFeature Vectorsalong withDocument Metadata. Dok-NNretrieval from anywhere, even from the darkest rifts of Aquila (in progress). It is easy to setup and scales as the universe expands.Github:https://github.com/a-mma/AquilaDBDocker Hub:https://hub.docker.com/r/ammaorg/aquiladbDocumentation (dedicated Wiki page):https://github.com/a-mma/AquilaDB/wikiResillientMake sure your data is always available anywhere through any network. It is not necessory to be always online. Work offline, sync later.ReplicatedYour data is replicated over nodes to attain eventual consistency.DecentralizedThere is no single point of failure.Host NeutralWant to use AWS, Azure, G-cloud or whatever? Got a legion of laptops? Connect them together? No worries as long as they can talk each other.Who is this forIf you are working on a data science project and need to store a hell lot of data and retrieve similar data based on some feature vector, this will be a useful tool to you, with extra benefits a real world web application needs.Are you dealing with a lot of images and related metadata? Want to find the similar ones? You are at the right place.If you are looking for a document database, this is not the right place for you.TechnologyAquilaDB is not built from scratch. Thanks to OSS community, it is based on a couple of cool open source projects out there. We took a couch and added some wheels and jetpacks to make it a super cool butt rest for Data Science Engineers. WhileCouchDBprovides us network and scalability benefits,FAISSprovides superfast similarity search. Along with our peer management service, AquilaDB provides a unique solution.created with ❤️ a-mma.indic (a_മ്മ)
Package: aquilapy
AquilaDB-PythonPython client library for Aquila Networkinstallpip install aquilapyTutorialfromaquilapyimportWallet,DB,Hubimportnumpyasnpimporttime# Create a wallet instance from private keywallet=Wallet("private_unencrypted.pem")host="http://127.0.0.1"# Connect to Aquila DB instancedb=DB(host,"5001",wallet)# Connect to Aquila Hub instancehub=Hub(host,"5002",wallet)# Schema definition to be usedschema_def={"description":"this is my database","unique":"r8and0mseEd901","encoder":"ftxt:https://ftxt-models.s3.us-east-2.amazonaws.com/ftxt_base_min.bin","codelen":25,"metadata":{"name":"string","age":"number"}}# Craete a database with the schema definition provideddb_name=db.create_database(schema_def)# Craete a database with the schema definition provideddb_name_=hub.create_database(schema_def)print(db_name,db_name_)# Generate encodingstexts=["Amazon","Google"]compression=hub.compress_documents(db_name,texts)print(compression)# Prepare documents to be inserteddocs=[{"metadata":{"name":"name1","age":20},"code":compression[0]},{"metadata":{"name":"name2","age":30},"code":compression[1]}]# Insert documentsdids=db.insert_documents(db_name,docs)print(dids)# Delete some documentsdids=db.delete_documents(db_name,dids)print(dids)# Perform a similarity search operationmatrix=np.random.rand(1,25).tolist()time.sleep(5)docs,dists=db.search_k_documents(db_name,matrix,10)print(len(docs[0]),len(dists[0]))created with ❤️ a-mma.indic (a_മ്മ)
Package: aquilify
Documentation:http://www.aquilify.vvfin.in/AQUILIFYAquilify is an ASGI (Asynchronous Server Gateway Interface) framework designed to facilitate the development of web applications with Python. It enables efficient handling of HTTP requests, WebSocket connections, middleware processing, and exception handling in an asynchronous environment.Installation$pipinstallaquilifyOr you can install theaquilify[full]$pipinstallaquilify[full]Basic SetupCreate a new aquilify app using the command show below :$aquilifycreate-appmyappNow, move inside themyappand run :$aquilifyrunserverTo make changes in ASGI server configuration openmyapp/config.cfg:Default Configuration :[ASGI_SERVER]server=NETIXhost=127.0.0.1port=8000debug=Truereload=Falseinstance=asgi:applicationvisit:http://localhost:8000output:Creating viewsmyapp/views.pyasyncdefmyview()->dict:return{"message":"Welcome to aquilify"},200myapp/routing.pyfromaquilify.core.routingimportruleimportviewsROUTER=[rule('/',view.myview)]run server$aquilifyrunserverStartingNetixv1.12(cpython3.12.1,win32)------------------------------------------------------------------------Options:run(host=127.0.0.1,port=8000,reuse_port=True,worker_num=1,ssl={},debug=True,app=asgi:application,log_level=DEBUG)------------------------------------------------------------------------[2024-01-0815:48:19]NetixdetectedAquilifystarting..:Aquilify[2024-01-0815:48:19,421]INFO:lifespan:startup[2024-01-0815:48:19,423]INFO:lifespan.startup.complete[2024-01-0815:48:20]Netix(ASGI)(pid17892)isstartedat127.0.0.1port8000output:http://localhost:8000/:{"message":"Welcome to aquilify"}CreditsThanks tostarlette.This project uses code adapted from the Starlette framework.DependenciesAquilify only requiresanyio, and the following are optional:[aiofiles][aiofile] - Required if you want to use theStaticMIddlewareorFile based Opertation.[jinja2][jinja2] - Required if you want to useTemplateResponse.[python-multipart][python-multipart] - Required if you want to support form parsing, withrequest.form().[itsdangerous][itsdangerous] - Required forSessionMiddlewareandCSRFsupport.[markupsafe][markupsafe] - Required forJinja2andCSRFsupport.
Package: aquirdturtle-collapsible-headings
aquirdturtle_collapsible_headingsMake headings collapsible like the old Jupyter notebook extension and like Mathematica notebooks.A selected header cell (i.e. markdown cell starting with some number of "#") can be collapsed / uncollapsed by clicking on the caret icon created to the left of header cells or by using a shortcut.In emulating the original extension, this extension now supports the following shortcuts:"T" or "Accel Q" shortcuts to toggle Collapse"Left-Arrow" to collapse the selected current header section"Right-Arrow" touncollapsethe selected current header section"Shift-A" to add a header above the current cell"Shift-B" to add a header below the current header sectionAll shortcuts only work in command mode and are editable by the user by going to Settings -> Advanced Settings Editor -> Keyboard Shortcuts and editing the shortcuts there.RequirementsJupyterLab >= 3.0InstallYou should be able to install the extension either using the jupyterlab extension manager (drawing from my npm repository) or by using pip, which draws from pypi. Both should be update to date. Conda not yet supported.pipinstallaquirdturtle_collapsible_headingsContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the aquirdturtle_collapsible_headings directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallaquirdturtle_collapsible_headings
Package: aquosRemote
Sharp AQUOS Remote PythonPython module for sending Remote Control Codes to your Sharp AQUOS Smart TVInstallpipinstallaquosRemoteSetupEnabled AQUOS Remote ControlGo to MENUGo to the Inital Setup TabGo to AQUOS Remote ControlEnable AQUOS RemoteFind your IPGo to MENUGo to the Inital Setup TabGo to Network SetupManual Setup (Click Yes)Go down to IP SetupUsage# Import ClientfromaquosRemote.aquosimportAquosTV# Connectaquos=AquosTV('IP_ADDRESS')aquos.on()# Turns on TVaquos.off()# Puts TV into standby modeaqous.set_standbymode()# Enables standby modeaquos.toggle_power()# Toggle poweraquos.toggle_power_source()# Toggle power sourceaqous.delay()# Waits a certian timeaquos.play()# Play button (⏯)aquos.pause()# Pause button (⏯)aqous.stop()# Stop buttonaquos.rewind()# Rewind button (⏪)aqous.fast_forward()# Fast forward button (⏩)aquos.rewind()# Skip forward button (⏭)aquos.rewind()# Skip back button (⏮)aqous.up()# Up button (▲)aqous.down()# Down button (▼)aqous.left()# Left button (◄)aqous.right()# Right button (►)aqous.enter()# Enter buttonaqous.remote_return()# Stop buttonaqous.exit()# Exit buttonaqous.volume_up()# Turns volume upaqous.volume_down()# Turns volume downaquos.volume_repeat(x)# Turns volume up x times up or downaquos.set_volume(xx)# Sets TV volume 0-100aquos.toggle_mute()# Toggles muteaqous.set_mute(boolean)# Sets mute to booleanaquos.mute_on()# Turns mute onaquos.mute_off()# Turns mute offaquos.set_input(x)# Sets TV input to input xaquos.toggle_3d()# Toggles 3Daquos.netflix()# Opens Netflixaquos.smart_central()# Opens Smart Centralaquos.get_device_name()# Gets TV device nameaquos.get_model_name()# Gets TV model nameaquos.get_software_version()# Gets TV software versionaquos.get_ip_protocol_version()# Gets TV ip protocolaquos.get_info()# Returns all above info into a pretty stringNote:aquos.on()function will work only after you use the setup=True argument or after you turn off tv using this function, then un-plug and re-plug-in the tv.License
Package: aqwebengine
aqwebengineQt QWebEngine onasyncsteroids:asyncversions of methods to load URL and set page htmlasyncversions of methods to get page content as HTML and plain textasyncmethod to call python from JSasyncmethod to call JS from PythonUsesqasyncas an engine to run PyQt/PySide2 asyncronously.Examplesw = AQWebEngine()
...
await w.set_html_async('<b>Hello</b>, world!')
...
await w.load('https://www.example.com')
...
html = await w.to_html_async()  # get current page as HTML
...
text = await w.to_plain_text()  # get current page as plain text
...
await w.eval_js('function inc(x) { return x + 1; }')
result = await w.eval_js('inc(41)')
assert result == 42
...
def do_something(x):
    return x + 1
w.register_python_function('do_something', do_something)
result = await w.eval_js('qt.callPython("do_something", 41)')
assert result == 42Python APIeval_jsAsynchronously evaluates the string argument as a JS expression.
Returns result to the Python caller. JSError is raised if evaluation of JS expression throws an exception.register_python_functionRegisteres a python (sync or async) function to be callable from JS (see qt.callPython JS API below).Python function should take a single JSON-serializable parameter.Arguments:function_name- the name of the function, as seen by JSfunction- python function. Can be sync or async, should take a single JSON-serializable argument.JS APIqt.callPythonCalls python host.Arguments:function_name- the name of the registered python function(optional)args- any JSON-serializable valuePython function may return JSON-serializable value, that will be returned to the JS caller.
Package: ar
arPython package for parsing ar archive file.Installationpip install arUsageList files insidefile.afromarimportArchivewithopen('file.a','rb')asf:archive=Archive(f)forentryinarchive:print(entry.name)Read content offile.txtcontained withinfile.a.fromarimportArchivewithopen('file.a','rb')asf:archive=Archive(f)print(archive.open('file.txt').read())Extract all files:fromarimportArchivewithopen('file.a','rb')asf:archive=Archive(f)forentryinarchive:withopen(entry.name)asoutput:content=entry.get_stream().read()output.write(content)DevelopingCreate a virtual environment using python version of likingpython3.10 -m venv venvActivate itsource venv/bin/activateInstall package editable together with relevant optional dependenciespip install -e '.[test,dev]'AuthorSamuel Carlsson
Package: araali
Araali APIThis is a python library to expose access to Araali APIsHelp# setup a python3 virtualenv to run araali (one time)
cd ~/
python3 -m venv araaliapienv
source araaliapienv/bin/activate
pip install araali

# every other time (enter the environment you setup)
cd ~/
source araaliapienv/bin/activate
pip install --upgrade araali

# export your api token
export ARAALI_API_TOKEN="<your-token-here>"

# usage help - command line
python -m pydoc araali
python -m araali -h

# from your code
>>> import araali
>>> help(araali)
Package: arab-bot-list
Welcome to arab_bot_listthis PyPi package helps you get info of bots , users and post data.
TODO : users and post data
/ server count with your token onArab Bot ListGetting startedYou need to import the module firstfromarab-bot-listimportbots# or usersAn example about scraping info for bots (Same can be done with users)bots.get_info('your_id')# returns a usable info objectBot Info ObjectTODO
Package: arabic2latin
arabic2latinConvert Arabic characters to their Latin (English) homophone.InstallationInstall from PyPI:pipinstallarabic2latinAlternatively, install from source:pipinstallgit+https://github.com/rexa222/arabic2latinUsage/Examplesfromarabic2latinimportarabic_to_latinarabic_text="السَّلَامُ عَلَيْكَ"converted_text=arabic_to_latin(arabic_text)print(converted_text)Output:alsaalaamo aalaykaDebug ModeIf you came across a character that was not converted, you can find it using debug feature and report it as guided in the contributing section.converted_text = arabic_to_latin(arabic_text, debug=True)Output:unknown character: ?ContributingIf you find a bug 🐛, please open abug report. If you have an idea for an improvement or new feature 🚀, please open afeature request.
Package: arabic-pronounce
Arabic PronounceThis project is based on the workings ofDr. Nawar Halabihttps://github.com/nawarhalabi/Arabic-Phonetiser* I am almost exaclty using the same code but just removing unneeded stuff and changing the format of the final output.Usagefromarabic_pronounceimportphonetisephonetise("كلمةٌ")>>>['k l m t u1 n']Testpython -m pytest
Package: arabic-pronunciation
Arabic PronunciationThis project is based on the workings ofDr. Nawar Halabihttps://github.com/nawarhalabi/Arabic-PhonetiserDr. Motaz Saadhttps://github.com/motazsaad/ara-pronunciation-toolDr. Taha ZerroukiI just refactored the code and added an api wrapper for it to be used easily through installing a packageUsagefromarabic_pronunciationimportphonetise_Arabicphonetise_Arabic.phonetise_word("بِمُسْتَطِيل")>>>['b i0 m u0 s t A T ii0 l']phonetise_Arabic.phonetise_word("نُتَابِعُهَا")>>>['n u0 t aa b i0 E u0 h aa','n u0 t aa b i0 E u0 h a']Testspython -m pytestStatic Dictionary ProductionAdd your corpus to the root of this projectpython arabic_pronunciation/corpus2cmudict.py -i {corpus_name}.txt -p {corpus_name}It might take long time to for large corpuses and the command line might not show that the process is going on but it is.NotesTravis auto deployment to Pypi is currenlty not working
Package: arabicscript
# arabicscript**arabicscript is a Python library of tools for Arabic script based on the Unicode standard.**This version works for Unicode 9.0.It is independant of font technology, and is intended for the script in general and not for any particular language. It provides utilities for querying properties of Arabic characters and manipulating Arabic-script strings. Some script-independent Unicode tools are also provided.## Main classes#### CharDefines some general attributes of a Unicode character#### ArabCharDefines attributes of a Arabic-script character*  joining_type(): returns the type of joining, e.g., Right_Joining, Left_Joining, Dual_Joining*  joining_group(): return the character group of joining, e.g., Beh, Dal, Reh, Heh, Waw, Yeh*  base_form(): returns the base form of the letter, that excludes any dots or other marks. E.g., base form of Beh is Dotless beh.*  is_combining(): checks if the character is a combining character (only right combining)#### ArabicCharDefines additional attributes of a character used in the Arabic language*  is_hamza(): check if character is any form of hamza (hamza, alef hamza, waw hamza, yeh hamza, etc.)*  is_islaq_letter(): check if character has the property of Idhlāq (Quranic Arabic), i.e., Beh, Reh, Feh, Lam, Meem, Noon*  is_qalqalah_letter(): check if letter is one of huruf qalqalah (jeem, dal, etc.)#### ArabStrRepresents strings consisting of Arabic-script characters*  base_form(): convert all letters in the string to their base forms*  joining_form(index): returns the joining form of the character in its context: isolated, initial, medial, final
Package: arabic-to-roman
Arabic number to Roman numeral converterA simple converter that converts an Arabic number (e.g.42) to a Roman numeral (e.g.XLII).RequiresPoetryto install:poetryinstall--no-devRunning the testsThe tests consist of:Unit tests usingpytestStatic type checking usingmypyTo run the tests, first install the required packages usingpoetryinstallThen run the tests withpoetryruntest
Package: arab-transliterator
Phonetic TranscriptionThe phonetic transcription below is based on the scheme found inArabic Through the Quranby Alan Jones (Islamic Texts Society, 2008). The correct form of pronunciation will depend on context, especially for the lettershamzaandalif maksūra. The basic form of transcription is shown below.Phonetic transcription for lettersLetterTranscription0alifā1bāb2tāt3thāth4jīmj5ḥāḥ6khākh7dāld8dhāldh9rār10zāyz11sīns12shīnsh13ṣādṣ14ḍādḍ15ṭāṭ16ẓāẓ17ʿaynʿ18ghayngh19fāf20qāfq21kāfk22lāml23mīmm24nūnn25hāh26wāww27yāy28hamza'29alif maksuraā30ta marbūtatPhonetic transcription for diactricsDiacriticTranscription0fathatanan1dammatanun2kasratanin3fathaa4dammau5kasrai6shadda(double)7sukūn'The long vowels are indicated byā,īandū, and themaddahmay also be used to lengthen a vowel. Theshaddais indicated by the doubling of a letterUsagePython 3.9 or later requiredpipinstallarab-transliteratorpython-marab_transliterator.transliterator[-tarab_text][-farab_file]Ex1.python-marab_transliterator.transliterator-t"وَلَقَدْ آتَيْنَا مُوسَى الْكِتَابَ وَقَفَّيْنَا مِن بَعْدِهِ بِالرُّسُلِ ۖ وَآتَيْنَا عِيسَى ابْنَ مَرْيَمَ الْبَيِّنَاتِ وَأَيَّدْنَاهُ بِرُوحِ الْقُدُسِ ۗ أَفَكُلَّمَا جَاءَكُمْ رَسُولٌ بِمَا لَا تَهْوَىٰ أَنفُسُكُمُ اسْتَكْبَرْتُمْ فَفَرِيقًا كَذَّبْتُمْ وَفَرِيقًا تَقْتُلُونَ"outputwalaqad ’ātaynā mūsá l-kitāba waqaffaynā min baʿdihi birrusuli  wa’ātaynā ʿīsá b-na maryama l-bayyināti wa'ayyadnāhu birūḥi l-qudusi  afakullamā jā'akum rasūlun bimā lā tahwá anfusukumu s-takbartum fafarīqan kadhdhabtum wafarīqan taqtulūnaEx2>>>fromarab_transliterator.transliteratorimportArabTransliterator
>>>Trans=ArabTransliterator()>>>Trans.translate("وَفَرِيقًا تَقْتُلُونَ")'wafarīqan taqtulūna'>>>ContributorsFeel free to contribute by making pull-requests or writing issues. Thanks
Package: arachnado
ArachnadoArachnado is a tool to crawl a specific website.
It provides aTornado-based HTTP API and a web UI for aScrapy-based
crawler.License is MIT.InstallArachnado requires Python 2.7.
To install Arachnado use pip:pip install arachnadoTo install Arachnado with MongoDB support use this command:pip install arachnado[mongo]RunTo start Arachnado executearachnadocommand:arachnadoand then visithttp://0.0.0.0:8888(or whatever URL is configured).To see available command-line options usearachnado –helpArachnado can be configured using a config file. Put it to one of the common
locations (‘/etc/arachnado.conf’, ‘~/.config/arachnado.conf’
or ‘~/.arachnado.conf’) or pass the file name as an argument when starting
the server:arachnado --config ./my-config.confFor available options checkhttps://github.com/TeamHG-Memex/arachnado/blob/master/arachnado/settings/defaults.conf.DevelopmentSource code:https://github.com/TeamHG-Memex/arachnadoIssue tracker:https://github.com/TeamHG-Memex/arachnado/issuesTo build Arachnado static assets node.js + npm are required.
Install all JavaScript requirements using npm - run the following command
from the repo root:npm installthen rebuild static files (we useWebpack):npm run buildor auto-build static files on each change during development:npm run watchChanges0.2 (2015-08-07)Initial release.
Package: arachne-runtime
Arachne RuntimeArachne Runtime is a thin Python library for executing different types of DNN models from a common Python API.
It wraps original DNN library runtime and absorbs the differences among DNN libraries.
Now, we support three types of DNN models as its inputs (e.g., tflite, onnx, and tvm).
It also supports RPC feature to help testing DNN models on remote edge devices such as Jetson devices.Installationpipinstallarachne-runtimeIn addition to the above command, you need to install the DNN library runtimes.TFLitepipinstalltensorflowONNX RuntimepipinstallonnxruntimeTVMTVM requires you to build its library.
Please followthe official documentUsageLocal ExecutionTo execute a DNN model via Arachne Runtime, first init a runtime module byarachne_runtime.init.
Then, you can setnumpy.ndarrayas inputs by aset_inputmethod.
After setting all inputs, arunmethod executes the inference.
The outputs of inference results can be retrieved by aget_outputmethod.importarachne_runtime# TFLitetflite_interpreter_opts={"num_threads":4}runtime_module=arachne_runtime.init(runtime="tflite",model_file="/path/to/model.tflite",**tflite_interpreter_opts)runtime_module.set_input(0,input_data)runtime_module.run()out=runtime_module.get_output(0)# ONNX Runtimeort_opts={"providers":["CPUExecutionProvider"]}runtime_module=arachne_runtime.init(runtime="onnx",model_file="/path/to/model.onnx",**ort_opts)runtime_module.set_input(0,input_data)runtime_module.run()out=runtime_module.get_output(0)# TVM Graph Executorruntime_module=arachne_runtime.init(runtime="tvm",model_file="/path/to/tvm_model.tar",env_file="/path/to/env.yaml")runtime_module.set_input(0,input_data)runtime_module.run()aout=runtime_module.get_output(0)Note that, in the case of TVM, users have to pass an additional YAML file (env.yaml) to the API.
This is because models compiled by TVM does not containsthe model signaturewhich is required by Arachne Runtime.
The type oftvm.runtime.devicewhich is needed by the TVM Graph Executor has to be specified by users as well.
Typically, the YAML file looks like below.model_spec:inputs:-dtype:float32name:input_1shape:-1-224-224-3outputs:-dtype:float32name:predictions/Softmax:0shape:-1-1000tvm_device:cpuRemote ExecutionWith RPC, you can train and build a DNN model on your local machine then run it on the remote device.
It is useful when the remote device resource are limited.To try the RPC feature, first you have to follow the installation step and start a RPC server on the remote device.# Remote devicepython-marachne_runtime.rpc.server--port5051Then, you can init a RPC runtime module byarachne_runtime.rpc.initon the local machine.
The rest of APIs is similar to the local execution.importarachne_runtime# TFLitetflite_interpreter_opts={"num_threads":4}runtime_module=arachne_runtime.init(runtime="tflite",model_file="/path/to/model.tflite",rpc_info={"host":"hostname","port":5051},**tflite_interpreter_opts)# To close rpc connection, call done()runtime_module.done()Runtime PluginPlease refer theplugin_examplesfor more details.LicenseArachne Runtime is licensed under theMITlicense.