Saravanan666/pypi-org huggingface dataset:
Package: airwater
.Airwateris a Python library that offers a sensible and human-friendly approach to Simply write spider, it helps you work with write spider with fewer imports and a lot less code.Why use Arrow over built-in modules?Python’s standard library and some other low-level modules have near-complete date, time and timezone functionality, but don’t work very well from a usability perspective:Too many modules: datetime, time, calendar, dateutil, pytz and moreToo many types: date, time, datetime, tzinfo, timedelta, relativedelta, etc.Timezones and timestamp conversions are verbose and unpleasantTimezone naivety is the normGaps in functionality: ISO-8601 parsing, timespans, humanizationFeaturesQuick StartInstallation$pipinstall-UairwaterExample Usage.
Documentation
————-For full documentation, please visitContributingContributions are welcome for both code and localizations (adding and updating locales). Begin by gaining familiarity with the Arrow library and its features. Then, jump into contributing:
Package: airwaveapiclient
Airwaveapiclient is a utility tool for Aruba Networks AirWave users.
This module connects to AirWave and gets the information such as the access point list,
detail, client, etc.RequirementsPython2.7, 3.5, 3.6, 3.7, PyPy.InstallationPyPI or Github$ pip install airwaveapiclient

or

$ git clone https://github.com/mtoshi/airwaveapiclient
$ cd airwaveapiclient
$ sudo python setup.py installUsing exampleDocumentation:ReadthedocsSample code:GithubLogin>>> airwave = AirWaveAPIClient(username='admin',
...                            password='*****',
...                            url='https://192.168.1.1')
>>> airwave.login()Get Access Point List>>> res = airwave.ap_list()
>>> res.status_code
200
>>> res.text # xml output
'<?xml version="1.0" encoding="utf-8" ...'Get Access Point Detail>>> ap_id = 1
>>> res = airwave.ap_detail(ap_id)
>>> res.status_code
200
>>> res.text # xml output
'<?xml version="1.0" encoding="utf-8" ...'Logout>>> airwave.logout()See alsohttp://www.arubanetworks.com/products/networking/network-management/
Package: airwaves
airwaves-cliis a small, single purpose command line utility for uploading data to the Internet Archive for theUnlocking the Airwavesproject. Page scans of digitized archival folders are packaged into a zip file and then uploaded to theMedia Historycollection at the Internet Archive using their API. The uploaded data includes metadata for the folder which is being curated in an Airtable database.InstallFor convenience the airwaves-cli is distributed on the Python Package Index. Once you have Python installed you can:pip install airwavesRunOnce the airwaves package is installed you can publish an item that has been cataloged in
our AirTable database by giving theuploadsubcommand a folder identifier and the name of the zip file containing the page scans for that folder.airwaves upload naeb-b110-f04-03 naeb-b110-f04-03.zipThe first time you run airwaves it will prompt you for the Airtable and Internet
Archive API keys since these are private to the project.If you want to see all the NAEB items by searching theAAPBwebsite you can:airwaves items
Package: airy
Airy is a new Web application development framework.Contrast to most currently available frameworks, Airy
doesn’t use the standard notion of HTTP requests and pages.Instead, it makes use of WebSockets (via Socket.io) and
provides a set of tools to let you focus on the interface,
not content delivery.Currently Airy supports MongoDB only. We will support other
NoSQL databases, but we have no plans for supporting SQL.RequirementsAiry will install most required modules itself when you create a new
project, so all you need is:Python 2.6+MongoDBInstallationpip install airyThis will install Airy itself and theairy-admin.pyscript.UsageOnce you have it installed, you should be able to useairy-admin.pyTo create a new project, open a terminal and do:airy-admin.py startproject project_name
cd project_name/
python manage.py update_ve
python manage.py runserverYou should have it running locally on port 8000. Open your browser
and navigate tohttp://localhost:8000Note: if it complains about a “Connection Error” it means you have
no MongoDB running, or it’s dead.AboutAiry is created by Leto, a startup agency based in London, UK.
Check out Leto website for help and support:http://letolab.com
Package: airyconf
airyconfLightweight configuration managerInspired byzifeo/dataconf,
implemented with no required dependencies for Python 3.6+
(except Python 3.6 requires dataclasses package to support the corresponding functionality). Supports typing, dataclasses and custom classes:fromtypingimportListfromdataclassesimportdataclassfromairyconfimportparseclassCustomObject(object):def__init__(self,a:float):self.a=adef__eq__(self,other):returnisinstance(other,CustomObject)andother.a==self.a@dataclassclassSomeDataclass:b:strc:List[CustomObject]json_config='{"SomeDataclass": {"b": "str", "c": [{"a": 1.0}, {"a": 2.0}]}}'expected=SomeDataclass('str',[CustomObject(1.),CustomObject(2.)])assertparse(json_config,SomeDataclass)==expectedOptionally, supports yaml if installed:fromairyconfimportparse_configparse_config('file/to/config.yaml',SomeDataclass)Thanks torossmacarthur/typing-compatby Ross MacArthur,
typing is supported for Python 3.6+ (the single-file package is copied to avoid dependencies).Package is under development. TODO:Add documentationGithub actions: tests & coverage & package updateMultiple typing arguments (Tuple[int, CustomClass, str])Nested typing arguments (List[Dict[str, List[int]]])Specify classes in config (a: SomeCustomClass({param: 1}))
Package: aisa
Auto-Information State AggregationThis is a python module aimed at partitioning networks through the maximization of Auto-Information.
If you use this code, please cite the following paper:State aggregations in Markov chains and block models of networks,Faccin, Schaub and Delvenne,ArXiv 2005.00337The module provides also a function to compute the Entrogram of a network with a suitable partition.
The Entrogram provides a concise, visual characterization of the Markovianity of the dynamics projected to the partition space.
In case you use this, please cite the following paper:Entrograms and coarse graining of dynamics on complex networks,Faccin, Schaub and Delvenne,Journal of Complex Networks, 6(5) p. 661-678 (2018),ArXiv 1711.01987Getting the codeRequirementsThe following modules are required toaisato work properly:numpyandscipynetworkxtqdm(optional)InstallDownload the codehereand unzip locally or clone thegitrepository fromGithub.On the terminl run:pip install --user path/to/moduleUninstallOn the terminl run:$ pip uninstall aisaUsageRead theonline documentationthat describes all classes and functions of the module.Some simple notebook examples on module usage are provided in theexamplessubfolder:a simple example of computing and drawing theentrogramand detecting the partition that maximize the auto-information in a well know small social network, see innbvieweran example on how to build arange dependent networkand find the partition that maximize auto-nformation, see innbviewerLicenseCopyright: Mauro Faccin (2020)AISA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.AISA is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.Check LICENSE.txt for details.
Package: aisapi
AIS-apiPython wrapper package for the AIS API.InstallpipinstallaisapiLook at the fileexample.pyfor a usage example.Developgitclonehttps://github.com/sviete/AIS-api.gitPublish the new version to piprm-rfdist
python3setup.pysdistbdist_wheel
twineuploaddist/*
Package: aisaratuners
aisaratuners is an open-source optimization library to automate and expedite hyperparameter search. aisaratuners has two modules, aisara_general_tuner.py which is a framework agnostic, and aisara_keras_tuner.py which is tailored for Karas API.aisaratuners leverages AiSara algorithm, Latin hypercube sampling, and the concept of search space reduction for fast reach of the optimum hyperparameters.Change Log1.4.9 (19/11/2020)1.5.2 (20/1/2022)
Package: aisastack
AisaStackGenerator tools for Full-Stack Developers work with the Django Rest Framework and Vue JS.MotivationMotivation 01:Build modular programs, Eric Raymond's 17 Unix RulesMotivation 02:Small is beautiful, Make each program do one thing well, Mike Gancarz The UNIX PhilosophyIn DRF we actually use ViewSet, in our opinion it is something that approaches the motivation above.
You can see how this workshere.Include Frontend FrameworkVue JSInstallationWith pip:$ pip install aisastackor manual install with clone this project:$ cd aisastack
$ python setup installStack Name AvailableThere are several UI stack available:blight: Bootstrap 4 Admin Light (vue) (you can see the package.json for detail dependencies).nativescript: NativeSacript Vue (coming soon)How ToCommand to do a clone:$ aisvue --clone <stack-name>
$ cd <stack-name>Creat app:$ aisvue --build <stack-name>A prompt will appear that you can fill in with the model name in the Django Rest backend.
Package: aiscan
AI ScanThis is a scanner that will scan your AI models for problems. Currently it focuses on bias testing. It is currently pre-alpha.How to UseFirst install it:pip install aiscanRun it in this manner (currently supports models from HuggingFace’s repository):aiscan --huggingface roberta-base --task fill-maskHere is another example with a different bias task.aiscan --huggingface cross-encoder/nli-distilroberta-base --task zero-shot-classificationThat’s it for now. More will come.Future WorkMore bias tests. More metrics for bias testing based on the research in the field.Integration with other types of testing (eg. adversarial robustness)More kinds of models besides HuggingFace models. We are especially interested in MLFlow integration.Documentation.Please contribute if you can. Help is always helpful.LicenseApacheCreditA project of IQT Labs.
Package: ais-cli
- AIS -Ais (ai shell) is interactive command line ai tool powered by ChatGPT (GPT-3.5). Ais can translate your query into a bash command and explain it to you if you want. In this way, you can get rid of hours of searching for small tasks and increase your learning spectrum.Getting startedInstallation instructions(require python3.6+)pipinstallais-cliSetupSetup open ai access keyaissetACCESS_KEY<KEY>Running queryiesTranslate to commandYou can just write the what do you want. It will translate a bash commandais•openrtsp://113.76.151.33/1withffplaywithoutsound
──────────────Command───────────────────

ffplay-anrtsp://113.76.151.33/1

?Selectaction(Usearrowkeys)◌✅Runthiscommand❔Explainthiscommand❌CancelExplain commandsIf you select "Explain this command" action ChatCPT will explain this command for you.?Selectaction(Usearrowkeys)◌✅Runthiscommand❔Explainthiscommand❌CancelRegular questionsRunais askfor asking normal questions.ais•aisaskWhoisAtaturk?
───────────Result─────────────

AtaturkisthefounderofmodernTurkeyanditsfirstpresident.Run system command in interactive modeUse the!character as the first characterais•!whoami

knidRun without interactive modeCreate bash command$'ais-c"create port scanner with bash"Ask regular questions$ais-c"ais ask Suggest me a horror movie"
Package: aisdb
DescriptionPackage features:SQL database for storing AIS position reports and vessel metadataVessel position cleaning and trajectory modelingUtilities for streaming and decoding AIS data in the NMEA binary string format (SeeBase Station Deployment)Integration with external datasources including depth charts, distances from shore, vessel geometry, etc.Network graph analysis, MMSI deduplication, interpolation, and other processing utilitiesData visualizationWeb Interface:https://aisdb.meridian.cs.dal.ca/Docs:https://aisdb.meridian.cs.dal.ca/doc/readme.htmlSource Code:https://git-dev.cs.dal.ca/meridian/aisdbWhat is AIS?Wikipedia:https://en.wikipedia.org/wiki/Automatic_identification_systemDescription of message types:https://arundaleais.github.io/docs/ais/ais_message_types.htmlInstallRequires Python version 3.8 or newer.
Optionally requires SQLite (included in Python) or PostgresQL server (installed separately).
The AISDB Python package can be installed using pip.
It is recommended to install the package in a virtual Python environment such asvenv.python-mvenvenv_aissource./env_ais/*/activatepipinstallaisdbFor information on installing AISDB from source code, seeInstalling from SourceDocumentationAn introduction to AISDB can be found here:Introduction.Additional API documentation:API Docs.Code examplesParsing raw format messages into a
databaseAutomatically generate SQL database
queriesCompute trajectories from database rowsVessel trajectory cleaning and MMSI deduplicationCompute network graph of vessel movements between
polygonsIntegrating data from web sources, such as depth charts, shore distance, etc.
Package: aisdc
AI-SDCA collection of tools and resources for managing the statistical disclosure control of trained machine learning models. For a brief introduction, seeSmith et al. (2022).User GuidesA collection of user guides can be found in the 'user_stories' folder of this repository. These guides include configurable examples from the perspective of both a researcher and a TRE, with separate scripts for each. Instructions on how to use each of these scripts and which scripts to use are included in the README of the'user_stories'folder.ContentaisdcattacksContains a variety of privacy attacks on machine learning models, including membership and attribute inference.preprocessingContains preprocessing modules for test datasets.safemodelThe safemodel package is an open source wrapper for common machine learning models. It is designed for use by researchers in Trusted Research Environments (TREs) where disclosure control methods must be implemented. Safemodel aims to give researchers greater confidence that their models are more compliant with disclosure control.docsContains Sphinx documentation files.example_notebooksContains short tutorials on the basic concept of "safe_XX" versions of machine learning algorithms, and examples of some specific algorithms.examplesContains examples of how to run the code contained in this repository:How to simulate attribute inference attacksattribute_inference_example.py.How to simulate membership inference attacks:Worst case scenario attackworst_case_attack_example.py.LIRA scenario attacklira_attack_example.py.Integration of attacks into safemodel classessafemodel_attack_integration_bothcalls.py.risk_examplesContains hypothetical examples of data leakage through machine learning models as described in theGreen Paper.testsContains unit tests.DocumentationDocumentation is hosted here:https://ai-sdc.github.io/AI-SDC/Quick StartDevelopmentClone the repository and install the dependencies (safest in a virtual env):$ git clone https://github.com/AI-SDC/AI-SDC.git
$ cd AI-SDC
$ pip install -r requirements.txtThen run the tests:$ pip install pytest
$ pytest .Or run an example:$ python -m examples.lira_attack_exampleInstallation / End-userInstallaisdc(safest in a virtual env) and manually copy theexamplesandexample_notebooks.$ pip install aisdcThen to run an example:$ python attribute_inference_example.pyOr start upjupyter notebookand run an example.Alternatively, you can clone the repo and install:$ git clone https://github.com/AI-SDC/AI-SDC.git
$ cd AI-SDC
$ pip install .This work was funded by UK Research and Innovation Grant Number MC_PC_21033 as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme (https://dareuk.org.uk/), delivered in partnership with HDR UK and ADRUK. The specific project was Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMATTER).­ This project has also been supported by MRC and EPSRC [grant number MR/S010351/1]: PICTURES.
Package: aisee
An open-source library for computer vision built on top of PyTorch and Timm libraries. It provides an easy-to-use interface for training and predicting with State-of-the-Art neural networks.AISee key features🤗 Simple interface for training and predicting using timm library models.📁 Easily load images from a folder, a pandasDataFrameor a single image path.🏋🏽‍♂️ Train SOTA neural networks from pre-trained weights or from scratch in very few lines of code.🖼️ Supports multiclass and multilabel image classification tasks.🔨 We take care ofDataLoaders, image transformations and training and inference loops.InstallationInstall AISee using pip.pipinstallaiseeQuick tourHere's an example of how to quickly train a model using AISee. We just have to initialize aVisionClassifiermodel and create aTrainer. As easy as it gets!fromaiseeimportTrainer,VisionClassifier# Initialize a VisionClassifier modelmodel=VisionClassifier(model_name="vgg11_bn",num_classes=4,)# Create a Trainertrainer=Trainer(base_model=model,data=f"animals/",output_dir="trained_weights.pt",)# Traintrainer.train()To predict callpredictmethod, we take care of the rest:# Predicttrainer.base_model.predict("animals/without_label")Try it on Google ColabGetting startedVisitAISee Getting startedto get an overview of how AISee works.ExploreAISee Documentationpage for a detailed guide and comprehensive API reference.Check outAISee Examplesfor Jupyter Notebook examples and tutorials, showcasing how to use AISee effectively in various scenarios.ContributingWe value community contributions, and we encourage you to get involved in the continuous development of AISee. Please refer toAISee Developmentpage for guidelines on how to contribute effectively.We also appreciate your feedback which helps us develop a robust and efficient solution for computer vision tasks. Together, let's make AISee the go-to library for AI practitioners and enthusiasts alike.Contact UsFor any questions or inquiries regarding the AISee library or potential collaborations, please feel free to contact us inmarketing@iic.uam.es.Instituto de Ingeniería del Conocimiento (IIC)IICis a non-profit R&D centre founded in 1989 that has been working on Big Data analysis and Artificial Intelligence for more than 30 years. Its value proposition is the development of algorithms and analytical solutions based on applied research tailored to different businesses in different sectors such as energy, health, insurance and talent analytics.
Package: ai-service-wrapper
BE-Base-PythonImplementfromflaskimportFlaskfromconfigimportconfigfromai_service_wrapperimportAiServiceWrapperdefcreate_app():app=Flask(__name__)app.config.update(config)AiServiceWrapper(app)returnappif__name__=="__main__":app=create_app()app.run("0.0.0.0",8000,debug=True)
Package: aisfx
aiSFXRepresentation Learning for the Automatic Indexing of Sound Effects Libraries (ISMIR 2022): Deep audio embeddings pre-trained on UCS & Non-UCS-compliant datasets.This work was inspired by the creation of theUniversal Category System (UCS), an industry-proposed public domain initiative initialized byTim Nielsen,Justin Drury,Kai Paquin, and others. First launching in the fall of 2020, UCS offers a standardized framework for sound effects library metadata designed by and for sound designers and editors.How To UsePlease refer to this package'sdocumentationforInstallation InstructionsandTutorialsof how to extract embeddings.Visualizations of UCS ClassesClick the above to visualize coarse-level "Category" UCS classes in Pro Sound Effects (PSE), Soundly (SDLY), and UCS Mixed (UMIX).Cite This WorkPlease cite the paper below if you use it in your work.This paper has been accepted at the 23rd International Society for Music Information Retrieval Conference (ISMIR) in Bengaluru, India (December 04-08, 2022). To cite our work, please refer to the following.[1]Representation Learning for the Automatic Indexing of Sound Effects Libraries@inproceedings{ismir_aisfx,
    title={Representation Learning for the Automatic Indexing of Sound Effects Libraries},
    author={Ma, Alison Bernice and Lerch, Alexander},
    booktitle={Proceedings of the 23rd International Society for Music Information Retrieval Conference (ISMIR)},
    year={2022},
    pages={866--875}
  }AcknowledgementsWe would like to thank those who provided the data required to conduct this research as well as those who took the time to share their insights and software licenses for tools regarding sound search, query, and retrieval.Universal Category System (UCS)•Alex Lane•All You Can Eat Audio•Articulated Sounds•Audio Shade•aXLSound•Big Sound Bank•BaseHead•Bonson•BOOM Library•Frick & Traa•Hzandbits•InspectorJ•Kai Paquin•KEDR Audio•Krotos Audio•Nikola Simikic•Penguin Grenade•Pro Sound Effects•Rick Allen Creative•Sononym•Sound Ideas•Soundly•Soundminer•Storyblocks•Tim Nielsen•Thomas Rex Beverly•ZapSplatLicense: Pre-trained Model & PaperThis pre-trained model and paper [1] is made available under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Package: aish
Aish - ChatGPT CLIThis command-line interface (CLI) application is used to interact with the OpenAI through the OpenAI API. The chatbot takes an input prompt and returns a response from the selected model.InstallationEnsure you have Python 3.7+ installed. To install the required libraries, use:pipinstallaishUsageTo use the application, you need to set the environment variableOPENAI_API_KEYwith your OpenAI API key. Then, you can run the script from the terminal using the command:aishHowcanilistallfilesolderthan30days?aish-sHowcanilistallfilesolderthan30days?aish-ccodeWriteahelloworldappinpythonOptional parameters will take default values if not provided:ModelVersion: The model version that you want to use. Default is "gpt-3.5-turbo".TemperatureValue: The randomness of the AI’s responses. A lower value makes the output more focused and deterministic, while higher values produce more diverse and random outputs. Default is 0.5.TopPValue: A parameter for controlling randomness. A higher value generates more random responses, and a lower value generates more deterministic responses. Default is 0.5.TimeoutValue: The maximum time in seconds that the request will wait for a response from the API. Default is 60.HelpYou can display the help message which provides details about the command usage and the different parameters by running:aish--help