Saravanan666/pypi-org huggingface dataset:
Package: alvan-text-predictor
A5-Server-Text-Prediction-ModuleModule to predict which action a query best fitsTo Installpipinstallalvan-text-predictorTo Usefrompredictionimportpredictoraspdpredictor=pd.Predictor('intents.json')predictor.query('turn the lights off')Predictor(intents_filename: str, pickle_file: str="data.pickle", intents_cache_file: str="intents_cache") -> PredictorThe constructor for the Predictor class takes in 1 required and 2 optional parameters: intents_filename, pickle_file, and intents_cache_fileParameterTypeRequiredDefaultDescriptionintents_filenamestr☑""Filename for the intents file, used to train the model.pickle_filestr☐"data.pickle"Filename for the file that contains/will contain the serialized model-training outputintents_cache_filestr☐"intents_cache"Filename for the intents cache. This is used to detect if any changes have been made to the original intents file. If the contents of this file differ from the file passed into theintents_filename, the model will be retrained and the contents of this file and thepickle_filewill be overwritten with the contents of theintents_filenamefile, and the serialized output of the training.query(query)The query method takes in a query, compares it to the trained intents, and replies with an object indicating which intent it most closely matches.The output has the following shape:{intenttag,intentcontext_set,intentresponses,intentflags}Examplepredictor.query('Turn on the lights')->{'lights_on',None,4,{"followup":0}}based on example belowIntents FileThe intents file is a json file with the following shape:{"intents":[<ListofObjectsrepresentingintentswiththefollowingshape:{"tag":<String-human-readabledescriptor>,"patterns":[<ListofStrings-variouswaysausercouldphrasetheirquery],"responses":<Int-uniqueidoftheintent>,"context_set":<Object-currentlyunusedmetadata>,"flags":<Objectwithbooleanvaluesforflagsusedbytheconsumer>}>]}Example:{"intents":[{"tag":"lights_on","patterns":["turn on the lights","it is too dark in here","I want the lights on","I want it brighter in here"],"responses":4,"context_set":None,"flags":{"followup":0}},{"tag":"lights_off","patterns":["turn off the lights","it is too bright in here","I want the lights off","I want it darker in here"],"responses":5,"context_set":None,"flags":{"followup":0}},]}Corequisitesnltk must be installed alongside.pipinstallnltk
Package: alveolus
Alveolus Documentation PackageAlveolus is a small package that makes your C++ Documentation process easier. Using Breathe and Exhale you can combine
your C++ Code Documentation with your Custom API doxumentation written in simple .rst files .
This projects relies heavily on Doxygen+Sphinx+Breathe+Exhale but makes the process of combining them nad using them
easier. Run alveolus-build in any c++ project and an index html with the ReadTheDocs theme will be created instantlyUsing AlveolusThere are 4 commands added to your pathalveolus-buildalveolus-createalveolus-cleanalveolus-cliInstallation InstructionsDependenciesCreditLicense
Package: alvin-cli
Alvin CLIThe Alvin CLI package is developed for the users to access all the UI features in their terminal. The Alvin CLI is easy
to use with descriptive commands and information on requirements for all the commands.Thealvin_clipackage depends on thealvin_api_clientpackage which is installed as a dependency during installation.Find the Alvin CLI documentationhere.Other resourcesAlvindocsRequest Invitehere
Package: alvonCV
Computer Vision Helper PackagesFace DetectionFace MeshInstall the packagepipinstallalvonCVDemo Code for Face DetectionimportalvonCVimportcv2importtimecap=cv2.VideoCapture(0)pTime=0# here you use the alvonCV packagefaceDetectorObj=alvonCV.FaceDetector()whileTrue:success,img=cap.read()img,bboxs=faceDetectorObj.findFaces(img,draw=True)cTime=time.time()fps=1/(cTime-pTime)pTime=cTimecv2.putText(img,f'FPS:{int(fps)}',(20,70),cv2.FONT_HERSHEY_PLAIN,3,(0,255,0),2)cv2.imshow("Image",img)cv2.waitKey(1)Demo Code for Face MeshimportalvonCVimportcv2importtimecap=cv2.VideoCapture(0)pTime=0faceDetectorObj=alvonCV.FaceMeshDetector()whileTrue:success,img=cap.read()img=faceDetectorObj.findFaceMesh(img,draw=True)cTime=time.time()fps=1/(cTime-pTime)pTime=cTimecv2.putText(img,f'FPS:{int(fps)}',(20,70),cv2.FONT_HERSHEY_PLAIN,3,(0,255,0),2)cv2.imshow("Image",img)cv2.waitKey(1)Demo Code for Hand DetectorimportalvonCVimportcv2importtimecap=cv2.VideoCapture(0)detector=alvonCV.HandDetector(detectionCon=0.8,maxHands=1)whileTrue:# Get image framesuccess,img=cap.read()# Find the hand and its landmarksimg=detector.findHands(img)lmList,bbox=detector.findPosition(img)fingersUp=detector.fingersUp()print(fingersUp)# Displaycv2.imshow("Image",img)cv2.waitKey(1)
Package: alwakeupword
alwakeupwordalwakeupword explicitly request the attention of a computer using a wake up word and also allows user to train model of their own wake up word.InstallationYou can install alwakeupword fromPyPI:pip install alwakeupword.The alwakeupword supports Python 3.6 and above.UsageStep 1:AlWakeUpWordDataPreparation.pyFollowing query on command line will help you to create a dataset for your own wake up word:For recording wake up wordalwakeupword -c makeData -r recordAudio -rad "./audio" -n 200Note: Here, -rad and -n are set to default values of ./audio in alwakeupword package path and 200 respectively.For recording background audioalwakeupword -c makeData -r recordBackgroundAudio -rbad "./backgroundAudio" -n 200Note: Here, -rbad and -n are set to default values of ./backgroundAudio in alwakeupword package path and 200 respectively.Step 2:AlWakeUpWordDataPreprocessing.pyFollowing query on command line will help you to preprocess the dataset you have created:alwakeupword -c processData -rad "./audio" -rbad "./backgroundAudio"Note: Here, -rad and -rbad are set to default values of ./audio and ./backgroundAudio in alwakeupword package path respectively.Step 3:AlWakeUpWordTrainer.pyFollowing query on command line will help you to train the preprocess data and to create a model:alwakeupword -c trainData -mp "./savedModel/model.h5"Note: Here, -mp is set to default value of ./savedModel/model.h5 in alwakeupword package path.Step 4:AlWakeUpWordPrediction.pyFollowing query on command line will help you to predict the accuracy of model and to detect if word is wake up word or not:alwakeupword -c predictWord -mp "./savedModel/model.h5"Note: Here, -mp is set to default value of ./savedModel/model.h5 in alwakeupword package path.Step 5:AlWakeUpWord.pyFollowing example.py will show you how to use wakeUpWord() function from AlWakeUpWord.py file to your scripts:"""
# example.py
from alwakeupword.AlWakeUpWord import wakeUpWord

modelPath = '{Path of your wake up  word model}'
while True:
    wakeUpWord(modelPath)
    print('Wake up word detected')
"""
"""
Output: When you run the file it will wait for your response. When you utter the wake up word then only 'Wake up word detected' will be printed in the console. 
"""Note: Here, if you saved your model in the default path while training data then you don't have to give modelPath parameter to wakeUpWord() function as it is already set to default path.License© 2022 Alankar SinghThis repository is licensed under the MIT license. See LICENSE for details.
Package: alwaysdata-api
alwaysdata_apiA dead simple Python interface to theAlwaysdataAPIInstallationpipinstallalwaysdata_api(Use flag--userif you can't install globally.)UsageIf you store your API key and account name in theALWAYSDATA_API_KEYandALWAYSDATA_ACCOUNTenvironment variables, the following example
works out of the box:fromalwaysdata_apiimportDomainDomain.list(name='paul')[0].name# 'paulkoppen.com'Alternatively, you can provide the authentication via code. The above
example then needs to be expanded to include the extra configuration:fromalwaysdata_apiimportAuth,Domainauth=Auth('account_name','api_key')Domain.list(name='paul',auth=auth)[0].name# 'paulkoppen.com'Seedemo.pyfor more examples.Every resources in theAPI docshas a class in this package (such
asDomain,Record,Mailbox, etc) and all resources share the same
set of methods. To give you a quick idea:Resource.get(id)retrieves the instance.Resource.list(**query_kwargs)finds and returns the list of resources.res.post()submits the resource instance to the server to be created.res.put()updates the instance on the server.Resource.delta(res1, res2).patch()sends the changes fromres1tores2to the server.res.delete()deletes it.DevelopmentAlthough it is a simple bit of code, surely it can be improved.
Contributions are very welcome. Just send a PR (please include tests). It
would be equally great to have some instructions in this repo Wiki (I
would have to find out how that works though :-))Code style follows PEP accurately and uses Google style docstrings.For testing:python-munittestLicenceMIT
Package: always-list-field
marshmallow-always-list-fieldThis is a small package that will ensure that your marshmallow will alway contain list.Some times you want to ensure that your marshmallow schema will always return a list, even if the input is a single item. Just to make an API response consistent.Installationpipinstallmarshmallow-always-list-fieldUsagefrommarshmallow_always_list_fieldimportAlwaysListFieldclassMySchema(Schema):my_list=AlwaysListField(fields.String())If input is:{"my_list":"foo"}it will result with:{"my_list":["foo"]}This will work with nested fields as well.If nested field is:classNestedSchema(Schema):my_list=AlwaysListField(fields.String())classMySchema(Schema):nested=fields.Nested(NestedSchema)and input is:{"nested":{"my_list":"foo"}}result will be:{"nested":{"my_list":["foo"]}}Additionally you can do something like this:classNestedSchema(Schema):data=fields.String()classSampleSchema(Schema):nested=AlwaysListField(fields.Nested(NestedSchema))assertresult=={"nested":[{"data":"hello"}]}and input is:{"nested":{"data":"hello"}}result will be:{"nested":[{"data":"hello"}]}Developmentpipinstall-rrequirements.txtTestingpytestLicenseMITAuthorDominik Szymanski
Package: alx
# alxSwiss army knife for Shell, Cloud and DevOps in Python.## How ToCheck out[Cheatsheet](https://github.com/gomes-/alx/blob/master/CHEATSHEET.md)[Simple Example](https://github.com/gomes-/alx/blob/master/examples/simple.md)## InstallMake sure path to your environment variable is set to alx, alx-server directory##### Linux$ sudo pip3 install alx##### Windows$ pip install alx##### Developer$ git clone git://github.com/gomes-/alx.git## Run$ alx arg1 [arg2] [options]
$ alx-server [shell]## Dependencies(required) Python3##### For Windows(optional) Unix Tools for Windows# alx-server (optional)## Setup & RunDownload & Edithttps://github.com/gomes-/alx/blob/master/alxkey.pyRun$ alx keydir /path/to/key/dir
$ alx-server## Dependencies(required) Azure: storage account(required)https://github.com/gomes-/alx/blob/master/alxkey.py##### Linux(required) sudohttps://github.com/gomes-/alx/
Package: alxcheck
alxcheckALX test Suite. Shell Utility that checks for ALX Project RequirementsDependenciesPython3bettypycodestylesemistandardFeaturesalxcheckchecks for the following:GeneralREADME.mdfile present.README.mdfile is not empty.All files in the current folder and sub-folders end with a new line.Crunsbettycheck.Note: You would have to make sure betty is installed. Check outHow To Install BettyPythonPython file is executable.shebangis present and at the top of the file (#!/usr/bin/python3or#!/usr/bin/env python3)Module documentation (docstrings)Function documentation (docstrings)Class documentation (docstrings)Parse and check for syntax error.JavaScriptJavascript file is executableNote: enabled with-jsor--nodejs-projectcommand line switch. SeeUsagebelowshebangis present and at the top of the file (#!/usr/bin/nodeor#!/usr/bin/env node)Note: enabled with-jsor--nodejs-projectcommand line switch. SeeUsagebelowsemistandardcheckNote: you would have to install semistandardnpm install semistandard -gvaris not used.Installationpipinstallalxcheckorpython3-mpipinstallalxcheckUsageAfter installation, to use this package, just run it as a shell command. This starts the checks with the current working directory as the root of the project.alxcheckIf the project is a JavaScript project with node.js scripts, a command line switch can be used to enable the first two checkslisted above.alxcheck-js#shorthand versionoralxcheck--nodejs-project#long versionContributingFeel free to contribute to the project by openingissuesor submittingpull requests. Your feedback is valuable!LicenseThis project is licensed under theMIT License.
Package: alx-utils
ALX UtilsDescriptionALX Utils is a collection of utility tools for ALX. It includes ainit-task: automate daily tasks by creating directories and files with specified names, getting prototypes of functions. for scripts setting the shebang, and permissions to executabletest-suite: set up a test-driven development environment for your project by creating a test directory and downloading test scripts, and providing a cli for controlling testssoon: betty pre-commit, devcontainer...InstallationYou can install ALX Utils usingpipby running the commandpipinstallalx-utilsUsageinit-Task Tool:alx-utils-init[HTML_FILE]test-suite Tool:alx-utils-checkershellDependenciesALX Utils requires Python >3.6 and depends on the following packages:beautifulsoup4ContributingContributions to ALX Utils are welcome and appreciated! If you would like to contribute, please follow these steps:Fork the repository on GitHub.Clone your forked repository to your local machine.Create a new branch for your changes.Make your changes and commit them to your branch.Push your changes to your forked repository on GitHub.Open a pull request from your forked repository to the original repository.Please make sure to follow the code style and guidelines of the project. Thank you for considering contributing to ALX Utils!LicenseThis package is released under the MIT License. See theLICENSEfile for more information.
Package: alyeska
Alyeska is a data engineering toolkit to simplify the nuts & bolts of data engineering tasks.More concretely, alyeska bridges the gap between common Python modules and common data engineering technologies. i.e. pandas, psycopg2, AWS Redshift, AWS secretsmanager, and more. Alyeska offers simple functions and/or syntactic sugar to common tasks:Safely executing many SQL statements against a database (sqlagent)Loading a pandas dataframe into a database (redpandas)Assuming an AWS IAM user with multi-factor authorization (locksmith.authmfa)Creating psycopg2 connections to Redshift (locksmith)Generate shell scripts that respect workflow dependencies (compose.compose_sh)While Alyeska mimics some functionalities, it is not a replacement for Airflow, AWS Glue, or other purpose-built data engineering technologies. That is, metaphorically, Alyeska is your parents’ toolbox. While terrific for fixing a leaky faucet, but it is no replacement for a plumber.Sample UsageAssume an AWS IAM user with multi-factor authorizationAlyeska’sauthmfacommand line utility is useful for quickly assuming an AWS IAM user with MFA.$authmfaMyAwsUserexportAWS_ACCESS_KEY_ID=ABCDEFGHIJKLMNOPQRSTUVWXYZexportAWS_SECRET_ACCESS_KEY=abcdefg1234567!@#$%^&exportAWS_SESSION_TOKEN=notarealsessiontoken///////5AVHwuGc*hYLp%$vr51*XTEHJjRD2JxavaD8wlJqi!aCZVhvp7nzt!U5elvoPZ@GlG%a9sT^HBrgKzQ8xZrpAADp65RYQzqvawF$eval`authmfaMyAwsUser`# export to environmentLearn more about how to config this utility withauthmfa-h.Load a pandas dataframe into AWS RedshiftLarge tables can be frustrating to load into Redshift. Alyeska reduces the process to a short one-line statement:>>>aly.redpandas.insert_pandas_into(cnxn,target_table,payload_df)In practice, it may function as>>>importalyeskaasaly>>>importalyeska.locksmith.redshiftasrs>>>importpandasaspd>>>cnxn=rs.connect_with_environment("my-user")>>>target_table="db.natural_numbers">>>sql=f"CREATE TABLE{target_table}(n INT NOT NULL)">>>aly.sqlagent.execute_sql(cnxn,sql)# create table>>>natural_numbers_df=pd.DataFrame({"n":range(1,1_000_001)})>>>aly.redpandas.insert_pandas_into(cnxn,target_table,natural_numbers_df)ComponentsTools are broken out into modules with niche purposes:composeis a workflow dependency management toollocksmithhelps authorize AWS sessions and Redshift connectionsloggingis another thin module that standardizes logging practicesredpandassupports less verbose pandas/redshift functionalitysqlagentsupports SQL executation and runtime configurationLicenseThis project is licensed under the Apache v2.0 License - see theLICENSEfile for details.ContributeBegin by reading ourCode of Conduct.There are some devtools required to contribute to the repo. Create a development environment and install pre-commit to run hooks on your code.$python3-mvenv.venv$source.venv/bin/activate$pipinstall-rrequirements.txt$pipinstall-rrequirements.dev.txt$pre-commitinstall$pre-commitautoupdateNamesakeThe Alyeska Pipeline Service company maintains the Alaska pipeline; a 1200 km long pipeline connecting the oil-rich, subterranean earth in Alaska to port on the north pacific ocean.
Package: alyssum
Alyssumthis package contains my personal helpers if you want to use below the usage examplesTitlefromalyssum.seperatorsimportTitletitle=Title(label='Default Text',length=80,color='yellow',design_char='*')title.configure(label='Default Text',length=80,color='blue',design_char='*')title.write()# writes "Default Text"title.write("any title")# writes "any title"title.configure(color=blue).write("any title")# can be usablelabel length can not be greater than (length - 2)color can be 'grey', 'red', 'green', 'yellow', 'blue', 'magenta', 'cyan' or 'white'length must be greater than 1 and smaller than 81Cleanerfromalyssum.cleanersimportCleanerCleaner.clean("Hello World!")Decoratorsfromalyssum.decoratorsimportrun_once,run_only@run_oncedefhello():print("hello")@run_only(2)defhello():print("hello")@run_once_message("It's come to end")...@run_only(3,"It's come to end"):...@run_only()...
Package: alyvix
Visual MonitoringAlyvix is anopen source APM software toolfor visual monitoring.Build end-user botsvisually interacting with any Windows application like ERPs or your favourite browser. Run and measure business-critical workflows like a human would, but continuously.Measure end-user experiences: Alyvix records the click-to-appearance responsiveness of each transaction. Report IT service quality to support technical and business actions.Get started with AlyvixWatch Alyvix Tutorials
Package: alyx-connector
Open Neurophysiology Environment - HaissLab flavourThe Open Neurophysiology Environment is a scheme for accessing neurophysiology data from an alyx database server, in a standardized manner. For information on how to manage file names when registering data with ONE to an alyx server, pleaseclick here. This github page contains an API for searching and loading ONE-standardized data, stored either on a user’s local machine or on a remote server. PleaseClick herefor the main documentation page.NB: The API and backend database are still under active development, for the best experience please regularly update the package by running :pip install --force-reinstall --no-deps git+https://gitlab.pasteur.fr/haisslab/data-management/ONE.git.This  will force the reinstallation of the package, without the need to do apip uninstall ONE-apifirst, and without reinstalling the dependancies like numpy etc (hence faster).RequirementsONE runs on Python 3.7 or later, and is tested on the latest Ubuntu and Windows (3.7 and 3.8 only).InstallingInstalling the package via pip typically takes a few seconds.  To install, activate your developpement environment :conda activate <myenvironment>Then run the One-api install using :pip install git+https://gitlab.pasteur.fr/haisslab/data-management/ONE.gitSet upFor setting up ONE for a given database e.g. our local version of Alyx at HaissLab:fromoneimportONEone=ONE(base_url='http://157.99.138.172:8080')Once you've setup the server, subsequent calls will use the same parameters:fromoneimportONEone=ONE()#uses the same parameters entered the first time and stored by default in C:\Users\<myusername>\AppData\Roaming\.one\.157.99.138.172_8080:For using ONE with a local cache directory (not recommanded for now):fromoneimportOneone=One(cache_dir='/home/user/downlaods/ONE/behavior_paper')Using ONETo search for sessions:fromoneimportONEone=ONE()print(one.search_terms())# A list of search keyword arguments# Search session with wheel timestamps from January 2021 onwardeids=one.search(date_range=['2021-01-01',],dataset='wheel.timestamps')['d3372b15-f696-4279-9be5-98f15783b5bb']# this is a list of unique ids of sessions returned. Here only one has been found with given parameters# Search for project sessions with two probeseids=one.search(data=['probe00','probe01'],project='brainwide')Further examples and tutorials can be found in the main IBL documentationdocumentation.(Not currentely supported :)To load data:fromone.apiimportONEone=ONE()# Load an ALF objecteid='a7540211-2c60-40b7-88c6-b081b2213b21'wheel=one.load_object(eid,'wheel')# Load a specific dataseteid='a7540211-2c60-40b7-88c6-b081b2213b21'ts=one.load_dataset(eid,'wheel.timestamps',collection='alf')# Download, but not load, a datasetfilename=one.load_dataset(eid,'wheel.timestamps',download_only=True)
Package: am2
Stuff made on the machine learning course at my universitySimple neuronInstantiating a neuron with a sigmoid transfer function:from am2 import Neuron, SigmoidTransferFunction

neuron = Neuron(SigmoidTransferFunction, weights=[0, 1, 1])
print neuron.run([3, 2, 1])Or with a staircase transfer function:from am2 import Neuron, StaircaseTransferFunction

neuron = Neuron(StaircaseTransferFunction, function=lambda y: y > 2, weights=[0, 1, 1])
print neuron.run([1, 1, 1])functionis optional (default islambda y: y >= 1)PerceptronA simplePerceptronis implemented:from am2 import Perceptron

train_dataset = [
    ((1, 0, 0), 1),
    ((1, 0, 1), 1),
    ((1, 1, 0), 1),
    ((1, 1, 1), 0),
]

perceptron = Perceptron(function=lambda y: y >= 1)
perceptron.train(train_dataset)  # executa o algoritmo de treinamento

print perceptron.run((1, 0, 0))
Package: am2r-yams
Yet Another Metroid2 Shuffler (YAMS)A patcher for providing a different randomization experience to AM2R different to what the game has built-in. It was primarily designed forRandovania, but it's also usable as a standalone patcher.Usage:./YAMS-CLI [path-to-original-data-file] [path-to-output-data-file] [path-to-json-file]The API/Schema for the input json file will soon be documented.CompilationThis project uses git submodules. So before compiling, please ensure you have cloned them (either by doinggit clone --recursive https://github.com/randovania/YAMS, or if you have already cloned the repo,git submodule update --init).After that, you can use the standard dotnet compilation step:dotnet build YAMS-CLI.LicenseAll code is licensed under the GNU Public License version 3. See theLICENSE-CODEfile for full details.Art assets are licensed underCC-BY-SA 4.0. For the full list of authors and more details, please read theAttribution.mdfile located inYAMS-LIB/sprites/.
Package: am4894airflowutils
am4894airflowutilshttps://packaging.python.org/en/latest/tutorials/packaging-projects/# build distpy-mbuild# upload to testpypipy-mtwineupload--repositorytestpypidist/*# upload to pypipy-mtwineupload--repositorypypidist/*# install dev requirementspipinstall-r.\requirements-dev.txt# run pytestpytest
Package: am4894bq
am4894bqA helper library for some BigQuery related stuff.Installpip install am4894bqQuickstartfromam4894bq.schemaimportget_schema# get a schema for a sample tableschema=get_schema('bigquery-public-data.samples.shakespeare')schema[SchemaField('word', 'STRING', 'REQUIRED', 'A single unique word (where whitespace is the delimiter) extracted from a corpus.', ()),
 SchemaField('word_count', 'INTEGER', 'REQUIRED', 'The number of times this word appears in this corpus.', ()),
 SchemaField('corpus', 'STRING', 'REQUIRED', 'The work from which this word was extracted.', ()),
 SchemaField('corpus_date', 'INTEGER', 'REQUIRED', 'The year in which this corpus was published.', ())]ExamplesYou can see some example notebooks in theexamples folderof the repository.