Saravanan666/pypi-org huggingface dataset:
Package: basking-sdk
Basking.io SDKIntegrate your data with the Basking API, customize the reports & experience,
and join the community of developers building with workplace software apps together with Basking.Basking.io is a cloud based workplace occupancy platform.
More Information about Basking can be found here:https://basking.ioBasking uses a GraphQL API. The following Python SDK is a wrapper around the production API
designed for our customers that require to access to data programmatically.Requirementspython >=3.7 (current tested version: 3.9)pipenvAbasking.io accountGetting startedset the following environment variables:BASKING_USERNAME: Your username (usually your email)BASKING_USER_PASSWORD: Your password for Basking.Optional:BASKING_AWS_REGION: the aws region where your Basking.io instance is hosted. (Defaults toeu-central-1)Optional:BASKING_API_URL: the url of the Basking API you would like to query. (Defaults toapi.basking.io)import logging
from basking.basking_sdk import Basking

# set the default logging
logging.basicConfig()

# mofify the required level of logging for Basking
logging.getLogger('Basking').setLevel(logging.INFO)
logging.getLogger('botocore').setLevel(logging.INFO)

# initialize the SDK and set general query parameters
basking_client = Basking()

# list buildings the current user has access to
df_buildings = basking_client.location.get_user_buildings(pandify=True)

# get building meta data
building_meta_data = basking_client.location.get_building(
building_id=building_id
)
tz_str = building_meta_data['data']['getBuilding']['timeZone']


# get building daily occupancy statistics
df_daily = basking_client.occupancy.get_building_occupancy_stats_daily(
building_id=building_id,
start_obj_tz_unaware=start_date_obj,
end_obj_tz_unaware=end_date_obj,
pandify=True
)
df_daily.to_csv('./df_daily.csv')For more examples, seebasking.api_usage_example, orcontact us
Package: basler2
FeaturesTODORequirementsTODOInstallationYou can installBaslerTwoviapipfromPyPI:$pipinstallbasler2UsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,BaslerTwois free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: basqet-python
Basqet Python SDKThe Official Python library for the Basqet API.InstallationInstall Pythonpipinstallbasqet-pythonUsage/ExamplesFetch Available currencyfrombasqetimportBasqetClientbasqet=BasqetClient("pub_test_Y4oKBXgfTU1JbJiHeGFc66rc1FFivg-","sec_test_eOX9Tp7z-EgK6sw9ijghAWqC9xR_rP0",)# Fetch all fiat currencycurrencies=basqet.fetch_all_currency("FIAT")Initialize transactionfrombasqetimportBasqetClientbasqet=BasqetClient("pub_test_Y4oKBXgfTU1JbJiHeGFc66rc1FFivg-","sec_test_eOX9Tp7z-EgK6sw9ijghAWqC9xR_rP0",)payment_data={"customer":{"name":"tunde","email":"customer@example.com"},"amount":"1000","currency":"NGN","meta":{"reference":"bghggbbvv"}}transaction_obj=basqet.initialize_transaction(payment_data);Initiate transactionfrombasqetimportBasqetClientbasqet=BasqetClient("pub_test_Y4oKBXgfTU1JbJiHeGFc66rc1FFivg-","sec_test_eOX9Tp7z-EgK6sw9ijghAWqC9xR_rP0",)transaction_obj=basqet.initiate_transaction(<transactionId>,{'currency_id':<currency_id>})Verify transactionfrombasqetimportBasqetClientbasqet=BasqetClient("pub_test_Y4oKBXgfTU1JbJiHeGFc66rc1FFivg-","sec_test_eOX9Tp7z-EgK6sw9ijghAWqC9xR_rP0",)transaction_obj=basqet.verify_transaction(<transactionId>)Mock webhook eventsfrombasqetimportBasqetClientbasqet=BasqetClient("pub_test_Y4oKBXgfTU1JbJiHeGFc66rc1FFivg-","sec_test_eOX9Tp7z-EgK6sw9ijghAWqC9xR_rP0",)transaction_obj=basqet.mock_webhook_event(<transactionId>,{status:'SUCCESSFUL'})Documentation/API referenceDocumentation
Package: basque-events
Event data from the Basque CountryThis package has been developed using event data from the Open Data Euskadi api. Several functions have been created to facilitate users the interpretation of the data. The following variables have been used:Event typeName of the eventMunicipalityEstablishmentOpening/Closing hoursPriceURLFunctionsFunctionDescriptionInputsdata_apiUsed by other functions to obtain data from the apiApi URLinfo_eventsGets event data from an specific month inMonth, year & language (basque or spanish)year_dataDisplays two barplotsYear and boolean value (True or False)downloadDownloads the dataMonth, year and format (csv or json)File structureexamplesdatos_aÃ±o.JPGbasque_eventsbasque_events.py__init__.pyLICENSE.txtREADME.mdsetup.cfgsetup.pyDependenciesPandas- Tools for manipulating different data types.Matplotlib- Graph creation.Request- Obtain requested data from the api.Seaborn- Easy to use data visualization.Installationpipinstallbasque_eventsUse caseimportbasque_eventsasbe# Event info from 2021/01be.events_info(2021,1,'eus')# Download data from 2021/05 in csv formatbe.download(2021,5,'es','csv')# Display two barplots with event data from 2021be.year_data(2021,True)LicenseMITAuthorsMikel Madariaga & Naroa Barrutia
Package: bassextile-pkg
Example PackageCeci est un simple package de vÃ©rification et de calcul.
Il donne la table de multiplication selon une base donne par l'utlisateur.
Et il ne se limite pas lÃ , il vÃ©rifie si une annÃ©e donnÃ©e par l'utilisateur
est bissextile.Github-flavored Markdownto write your content.
Package: bassextilev2-pkg
Example PackageCeci est un simple package de vÃ©rification et de calcul.
Il donne la table de multiplication selon une base donne par l'utlisateur.
Et il ne se limite pas lÃ , il vÃ©rifie si une annÃ©e donnÃ©e par l'utilisateur
est bissextile.Github-flavored Markdownto write your content.
Package: bassextilev3-pkg
Example PackageCeci est un simple package de vÃ©rification et de calcul.
Il donne la table de multiplication selon une base donne par l'utlisateur.
Et il ne se limite pas lÃ , il vÃ©rifie si une annÃ©e donnÃ©e par l'utilisateur
est bissextile.Github-flavored Markdownto write your content.
Package: bassg
BASSGBASSG(Basic Agile Static Site Generator)is a simple and easy to use static site generator for quickly creating static websites usingJinja2templating andMarkdown.Features:Jinja2 templatingMarkdownCustom assets & site folder structureEasy to use conventions for creating pages, templates, and markdownInstalling$ pip install bassgI would recommend installingBASSGin avirtual environment. This allows you to accessBASSGusing the "bassg" entry point:$ bassg --helpOtherwise,BASSGcan be accessed with python directly:$ python -m bassg --helpUsageIf you need help with a command, run:$ bassg <command> --helpto see the help menu for that command.Create the project directory:$ bassg create <project_name>This will create the folder structure in the current directory (root):📦project_name
 ┣ 📂build
 ┃ ┣ 📂assets
 ┃ ┣ 📂config
 ┃ ┣ 📂markdown
 ┃ ┗ 📂templates
 ┗ 📂siteGenerating the site:$ bassg generate <project_name>This will compile all of the html files in thebuild/directory using Jinja2 and MarkdownCreating and using templates:Place Jinja2 templates in thetemplates/directory. Any html files in the basebuild/directory that include these templates will be rendered accordingly.Using markdown:If you want to use markdown in your site, create a file_name.md file with the same file name as the html page you want to insert it in. Place this file in thebuild/markdown/directory. The parsed markdown data will be passed into the Jinja2 parser as 'markdown'. If you wish to have multiple markdown files per page, you can use the naming convention:file_name0.mdfile_name1.md...These can then be accesed in thefile_name.htmlfile as markdown[0], markdown[1], etc.Per-page config (json) files:You can create (optional) configuration files for individual pages with a similar syntax to creating markdown files. Anyfile_name.jsonfiles with the same file name as an html page in thebuild/directory will be passed in through the Jinja2 parser.For example:file_name.json:{"title":"Documentation","date":"08/22/2021","language":"English","folder_struct":"docs"}All of these variables will be passed into the Jinja2 parser forfile_name.html.The special attributefolder_structtellsBASSGwhich folder to place the page in when it generates the site. In this example, the generatedfile_name.htmlfile will be placed in thesite/docs/folder. This can be used to easily organize the pages of your site.
Package: bassist
bassistCreate borg backups using a config fileInstallationInstall this tool usingpip:pip install bassistUsageFor help, run:bassist --helpYou can also use:python -m bassist --helpDevelopmentgit clone https://git.sr.ht/~rensoliemans/bassist
cd bassistIf usingdirenv:echo "layout python" > .envrc
direnv allowOtherwise, create avirtual
environmentand activate
it. Then, install the dependencies and dev dependencies:pip install -e '.[dev]'To run the tests:pytest
Package: bassmap
BassMapBassMap is an intuitive Python Package that allows the user to display a variety of features on an iPyLeaflet and Folium map display. In addition the Landsat 8 Tools allows the user to create a variety of multispectral composites using local GeoTIFFs from Landsat 8 spectral bandsBassMap on StreamlitFree software: MIT licenseDocumentation:https://bassneel.github.io/bassmapFeaturesLandsat 8 ToolsMultispectral CompositesCreate True Color CompositeCreate False Color (Buildings) CompositeCreate Color Infrared (Vegetation) CompositeCreate Healthy Vegetation CompositeSpectral IndiciesCreate Normalized Difference Moisture Index (NDMI) CompositeCreate Normalized Difference Vegetation Index (NDVI) CompositeWith IPyLeaflet (Mapomatic)Change basemapDisplay shapefilesDisplay GeoJSON filesDisplay vector filesDisplay Cloud Optimized GeoTIFFsWith Folium (Foliumatic)Change basemapDisplay shapefilesDisplay GeoJSON filesDisplay vector filesDemoCreditsThis package was created withCookiecutterand thegiswqs/pypackageproject template.
Package: basstatpl
BasstatPLBasstatPL is a package for performing different tabulations and calculations for descriptive statistics.
It provides:Frequency table constructors for simple and grouped dataCentralization measuresPosition measurementsMeasures of dispersionRequirements 📋The required packages are pandas, numpy, and matplotlib.python -m pip install pandas
python -m pip install numpy
python -m pip install matplotlibSome packages like numpy get installed when you install pandasSupported Python VersionsPython >= 3.6Installation 🔧Using pip you can install the necessary packages, if you don't have them installed, then basstatPL will be installedpython -m pip install basstatplLicenseMIT
Package: bastila-search
BastilaBastilais a tool for removing deprecated code. You define deprecated patterns using regex in the app and then prevent additional usages of those deprecated patterns from being used. The tool can also be used to track the removal of these patterns as well.Installation InstructionsInstall the packagepip install bastila-searchCreate a precommit filetouch .git/hooks/pre-commitAdd execute access to that filechmod +x .git/hooks/pre-commitAdd the script to your pre-commit file#!/bin/sh
bastila_runRun the setup command to create a config file with your env varsbastila_setup. This will create a config.json file that should be kept in the root of your repository.Support:hello@bastila.app
Package: bastio-agent
Bastio Agent============Bastio is an operating system user account provisioning tool to helppeople with a lot of servers and users create and delete system user accounts.With Bastio, you no longer need to keep track of your team's access toservers. For example, once a team member leaves, you can disable her access toall your server assets in one click.Bastio saves you time by not making you wait for your team members to generateand send you their public keys; team members can upload their existingpublic key. Crypto-savvy members of your team are still free togenerate their own.Install |project_name| on your servers to enable OS account provisioning and SSHaccess management. No daunting configurations needed; all you have to do is specifyyour API key and run the agent.Resources---------Extensive documentation is available with the distribution. Refer to the ``doc``directory after you have generated the Sphinx documentation.Website: https://bastio.comLatest Documentation: http://docs.bastio.comSupport Email: support@bastio.comGit Repository: http://github.com/Databracket/bastio-agentCopying-------Free use of this software is granted under the terms of the GNU General PublicLicense (GPLv3+). For details see the ``LICENSE`` file included with thisdistribution.
Package: bastionai
BastionAI ClientBastionAI Client is a python library to create client applications for BastionAI Server (Mithril Security's confidential training server).If you wish to know more about BastionAI, please have a look to the projectGithub repository.InstallationUsing pip$pipinstallbastionaiLocal installationNote: It is preferrable to install BastionAI package in a virtual environment.Execute the following command to install BastionAI locally.pipinstall-e.UsageUploading a model and datasets to BastionAIThe snippet below sets up avery simplelinear regression model and dataset to train the model with.importtorchfrombastionai.utilsimportTensorDatasetfromtorch.nnimportModulefrombastionai.psg.nnimportLinearfromtorch.utils.dataimportDataLoaderclassLReg(Module):def__init__(self)->None:super().__init__()self.fc1=Linear(1,1,2)defforward(self,x:Tensor)->Tensor:returnself.fc1(x)lreg_model=LReg()X=torch.tensor([[0.0],[1.0],[0.5],[0.2]])Y=torch.tensor([[0.0],[2.0],[1.0],[0.4]])train_dataset=TensorDataset([X],Y)train_dataloader=DataLoader(train_dataset,batch_size=2)X=torch.tensor([[0.1],[-1.0]])Y=torch.tensor([[0.2],[-2.0]])test_dataset=TensorDataset([X],Y)test_dataloader=DataLoader(test_dataset,batch_size=2)Training a model on BastionAIWith this snippet below, BastionAI is used to securely and remotely train the model.The model, along with the training and testing datasets are uploaded to BastionAI through the client API.frombastionai.clientimportConnection,SGDwithConnection("localhost",50051,default_secret=b"")asclient:remote_dataloader=client.RemoteDataLoader(train_dataloader,test_dataloader,"Dummy 1D Linear Regression Dataset (param is 2)",)remote_learner=client.RemoteLearner(lreg_model,remote_dataloader,metric="l2",optimizer=SGD(lr=0.1),model_description="1D Linear Regression Model",expand=False,)remote_learner.fit(nb_epochs=100,eps=100.0)lreg_model=remote_learner.get_model()# Gets trained model from BastionAI server.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseThis project is licensed underApache 2.0 License.
Package: bastion_ssh
Bastion SSH is designed for protecting,monitoring and accessing multiple SSH resources.Main FeaturesScreenShotsInstallationUsageMain FeaturesMonitor user’s inputScreenShotsInstallationTo install Bastion SSH, simply:$pipinstallbastion_sshUsageHello World:$bastion_ssh.py-H192.168.0.1-urootRelease History0.0.1 (2016-03-09)FrustrationConception0.0.2 (2016-03-15)Fix Bugs
Package: basxconnect
basxconnectA core implementation of a CRM system for non-profit organizations.Documentation atreadthedocsVisionProviding non-profit organizations with a code framework that helps to build tools for managing their data.MissionCreate and maintain a django-based framework which implements:A standardized UI concept based on IBM's carbon design systemCRM-like core data models which are open to extensionCode building blocks and shortcuts which allow easy customization and extensionCore valuesOpen source development (this, however doesnotinclude free support)Use of existing and proven technology where possible while minimizing external dependenciesFocus on workflows and tasks for Non-Profit organizations (User experience)Writing code and APIs for developers, not for website administrators (Developer experience)Development does not center around a finalproductbut around the tools to create aproduct(because everyproductis different)What we have so farIntegration of the core parts of carbon design in django ("like bootstrap for django")CRUD-framework ("Quick generation of create/read/update/delete views")HTML-layout framework which allows a more flexible hooking-in than the standard HTML-rendering mechanisms ("DOM on the server side")Core model to manage people, communication and relationshipsAdditional modules for:Generating reportsGenerating documents and PDFs from word templatesCustom add/edit formsData import from PDFs with form-fieldsCustomiable Database triggers to send notification emailsInvoicing moduleDemo project which implements a bare-bone version of aproduct(this repository)What we still need to doImproving the existing code base to have better support for extendabilityWrite documentation where necessary and add code-examples and how-to'sImplement revisions completely to allow going to a certain date and maybe display diffsOther TODOs:Explain more why basxConnect is necessary and the difference to alternative solutionsWe would like to thank the following projects for their work, they provide a valuable base for basxConnect:Translation:https://weblate.org/Design System:https://www.carbondesignsystem.com/
Package: basylic
basylic-python-clientModule to accessBasylicAPI.SummaryDeveloped by ETAONIS, Basylic is a SaaS solution performing document
fraud detection with "state-of-the-art" performances. The solution is
also used as a powerful tool to extract information from
documents. This module provides a Python interface for accessing
Basylic.InstallationThis module is available on PyPI. You can install it withpipcommand:pip install basylicAlternatively, you can access the module source code on GitHub:https://github.com/basylic-team/basylic-python-clientBasic usageTo access the API, refresh and access tokens should be provided. You
can access your Refresh token via the Portal interface. Then, we
recommend you store its value in the environment variableBASYLIC_REFRESH_TOKEN.frombasylicimportBasylicbasylic=Basylic()Alternatively, if your refresh token is stored in a non-standard
location, you can specify its value with argumentrefresh_tokenduring class instantiation:frombasylicimportBasylicbasylic=Basylic(refresh_token=...)At least two arguments should be set: one offile_path,file_objorftp_file, anddocument_type:basylic.send_document(file_path="corinne-berthier-recto-verso.pdf",document_type="french_ids")Thefile_pathargument is a string with the document path (e.g.: "~/FILE.pdf")document_typeis a string specifying which Basylic API to call (e.g.: "french_ids")Possible values fordocument_typeare:'french_ids','rib','ri','avis-imposition'...With those arguments specified,send_documentreturns a comprehensive JSON document.Data about applicants can also be joined to the API call:applicants_information={"applicant_0":{"identity":"BERTHIER CORINNE"}}basylic_result=basylic.send_document(file_path="corinne-berthier-recto-verso.pdf",document_type="french_ids",applicants_information=applicants_information)print(basylic_result)And various arguments could be passed as kwargs. For example:a.save_report=Truewill save the result of your request in your
user space on Basylic Portal;b.with_image=Truewill return a base64 image for each recognised document;For example, this code:applicants_information={"applicant_0":{"identity":"BERTHIER CORINNE"}}basylic_result=basylic.send_document(file_path="corinne-berthier-recto-verso.pdf",document_type="french_ids",applicants_information=applicants_information,with_image=True,save_report=True)print(basylic_result)will act in the following way:Uploads of document whose path isfile_pathto Basylic servicefrench_ids;Produces of a JSON documentbasylic_resultwith all relevant information;Comparesidentityprovided and identity extracted by Basylic OCR;A base64 encoded image will be returned in the approriate key ofbasylic_result.
Package: bat
Zeek Analysis Tools (ZAT)The ZAT Python package supports the processing and analysis of Zeek data
with Pandas, scikit-learn, and SparkRecent Improvements (Fall 2019):Renamed toZeekAnalysis Tools :)Better Docs (https://supercowpowers.github.io/zat/)Faster/Smaller Pandas Dataframes for large log files:Large DataframesBetter Panda Dataframe to Matrix (ndarray) support:Dataframe To MatrixScalable conversion from Zeek logs to Parquet:Zeek to ParquetVastly improved Spark Dataframe Class:Zeek to SparkUpdated/improved Notebooks:Analysis NotebooksBroCon 2017 PresentationData Analysis, Machine Learning, Bro, and You!
(Video)Why ZAT?Zeek already has a flexible, powerful scripting language why should I use
ZAT?Offloading:Running complex tasks like statistics, state machines,
machine learning, etc.. should be offloaded from Zeek so that Zeek can
focus on the efficient processing of high volume network traffic.Data Analysis:We have a large set of support classes that help
bridge from raw Zeek data to packages like Pandas, scikit-learn, and
Spark. We also have example notebooks that show step-by-step how to get
from here to there.Getting StartedExamples of Using ZATAnalysis NotebooksZeek to Scikit-LearnZeek to ParquetZeek to SparkSpark ClusteringZeek to KafkaZeek to Kafka to SparkClustering: Picking K (or not)Anomaly Detection ExplorationRisky Domains Stats and DeploymentZeek to MatplotlibInstall$ pip install zatDocumentationhttps://supercowpowers.github.io/zat/About SuperCowPowersThe company was formed so that its developers could follow their passion for Python, streaming data pipelines and having fun with data analysis. We also think cows are cool and should be superheros or at least carry around rayguns and burner phones.Visit SuperCowPowers
Package: batcat
😸😹😺😻😼😽😾😿🙀🐱BatCat is designed to help data scientists to practice machine learning operations (MLOps) on Amazon Web Services (AWS).Services of AWS covered:
- AWS Lambda: a serverless, event-driven compute service
- AWS S3 (Simple Storage Service): provides object storage service
- Amazon Athena: a serverless, interactive query service on S3
- Amazon Redshift: a data warehouse productPhilosophy of BatCat’s MLOpsBatCatis designed to make the data scientist’s life easier when practicingmachine learning operations (MLOps)onAmazon Web Services (AWS).Services of AWS included:AWS Lambda: a serverless, event-driven compute serviceAWS S3 (Simple Storage Service): provides object storage serviceAmazon Athena: a serverless, interactive query service on S3Amazon Redshift: a data warehouse productAmazon SageMaker: a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud.Elastic Container Registry (ECR): is a fully managed Docker container registry that makes it easy to store, share, and deploy container images.AWS Step Functions: a low-code, visual workflow service that developers use to build distributed applications, automate IT and business processes, and build data and machine learning pipelines using AWS services.Secrets Manager: is a secure and convenient storage system for API keys, passwords, certificates, and other sensitive data.InstallpipinstallbatcatStory of the BatCatThe package names after a cat of my friend, Clara.LicenseBatCat is licensed under the MIT License. © Contributors, 2024.