Saravanan666/pypi-org huggingface dataset:
Package: claraterm
ClaraTerm 🚀ClaraTerm: Leading the AI-Driven Evolution in Terminal TechnologyTransforming terminal automation with advanced AI, paving the way for an AI-operated OS.IntroductionClaraTerm is an advanced terminal automation tool, integrating cutting-edge AI technology, specifically GPT, setting the stage for the future of AI-powered Operating Systems. It's a tool crafted for developers who are keen on leveraging AI for elevated efficiency in scripting and system operations.Key FeaturesGPT-Enhanced Script Automation: Utilizing GPT for superior scripting efficiency and intelligence.Multilingual Support: Offering Python compatibility, with extensions for Go and JavaScript underway.Automated Script Correction: Features robust error handling and automated correction mechanisms.Optimized Dependency Management: Streamlining the management of project dependencies with AI assistance.Foundation for AI-Based OS: Establishing a technical base for evolving into a comprehensive AI Operating System.Future AI Integration Plans: Preparing for compatibility with GUVI-GPT and Llama2 for extended AI capabilities.InstallationInitiate your journey with ClaraTerm, the stepping stone towards an AI-empowered Operating System:python-mvenvenvsourceenv/bin/activate
pipinstall--editable.UsageDeploy ClaraTerm for advanced terminal operations and be part of the transition towards AI-operated system environments:clara-i"[Your Query]"ContributingWe invite you to contribute to ClaraTerm, playing a critical role in shaping the next generation of AI-driven operating systems.SupportSupport our mission by starring ClaraTerm on GitHub, joining us in the pursuit of an AI-enhanced computing future!LicenseThis project is licensed under the MIT License.AcknowledgmentsG4F (https://github.com/xtekky/gpt4free) for their role in offline AI support.GUVI Geek Network Pvt. Ltd. (https://www.guvi.in/) for their vital collaboration.
Package: claraw10
This package allows you to:Create a scatterplotPlot the distribution of your dataGroup or cluster samples in your data1. Creating a scatterplot:To create a scatterplot, you have to run thescattergraphfunction from theplotting.pyfile, which takes the following arguments:x: this is the column that you want to plot on your x-axis. This should be a numpy array.y: this is the column that you want to plot on your y-axis. This should be a numpy array.xtitle: this will be the title of your x-axis. This should be a string.ytitle: this will be the title of your y-axis. This should be a string.graphtitle: this will be the title of your graph. This should be a string.outlier_treatment: this tells the graph how to visually differentiate outliers on your plot. You can choose one of the below options. This should be a string."color": plots the outliers in a different color"shape": plots the outliers with a different marker"size": plots the outliers with a different sizeNote: this argument will default to size if any other string is passedoutlier_sensitivity: this is a multiplier in a customized IQR calculation, which ultiamately generates a sub-array of outliers. This should be a float (recommended between 0 and 2). If outlier_sensitivity is zero, then your outliers are in the 1st and 4th quartile of your data. The higher the outlier_sensitivity, the fewer the outliers.2. Plotting your distributions:To plot your distribution, you have to run theplotdistributionfunction from theplotting.pyfile, which takes the following arguments:y: this is the column that has your target data. This should be a numpy array.numberofbins: choose the number of bins for the histogram. The larger the data set, the more likely youï¿½ll want a large number of bins. This should be an int.plottitle: this will be the title of your graph. This should be a string.3. Grouping/creating clusters:To create clusters, you have to run thecreate_clustersfunction from theclustering.pyfile, which takes the following arguments:x: the columns that you want to use as a basis for clustering. This should be a numpy array.y: this is the column that has your target data. This should be a numpy array.numberofclusters: the number of clusters to form as well as the number of centroids to generate. This should be an int.
Package: clarc
Clarc (CommandLine Archiva)clarc is a simple CLI tool to archive data like day-to-day commands, notes, text, passcodes etc.
The data is stored in a shared sqlite in-memory connection and can be archived & retrieved with helpful commands.
The output response is shown in the terminal as json.InstallationUsing Pip$pipinstallclarcManual$gitclonehttps://github.com/sandiemann/clarc-cli
$cdclarc
$pythonsetup.pyinstallInstall the dependencies:$python-mpipinstall-rrequirements.txtInitialize the application (database initialize):$python-mclarcinitUsage$python-mclarc--help
Usage:clarc[OPTIONS]COMMAND[ARGS]...

Options:-v,--versionShowtheapplication'sversionandexit.--install-completionInstallcompletionforthecurrentshell.--show-completionShowcompletionforthecurrentshell,tocopyitorcustomizetheinstallation.--helpShowthismessageandexit.

Commands:fetchfetchthearchivedentries--allorby--key${key}initinitializethedatabase.removeremoveanarchivedentrybyspecefic--key${key}upsertupdateonduplicatekeyelseinsertanewentrytodbFeaturesclarchas the following features:CommandDescriptioninitInitializes the application's sqlite database.upsertinsert or update a new entry to the database with akey:valuepair.fetchfetch all or a specific entry by key from database.removeremoves an entry from db bykeyfrom database.Release History0.1.0
Package: clarifai-datautils
Clarifai Python Data UtilsThis is a collection of utilities for handling various types of multimedia data. Enhance your experience by seamlessly integrating these utilities with the Clarifai Python SDK. This powerful combination empowers you to address both visual and textual use cases effortlessly through the capabilities of Artificial Intelligence. Unlock new possibilities and elevate your projects with the synergy of versatile data utilities and the robust features offered by theClarifai Python SDK. Explore the fusion of these tools to amplify the intelligence in your applications! 🌐🚀Website|Schedule Demo|Signup for a Free Account|API Docs|Clarifai Community|Python SDK Docs|Examples|Colab Notebooks|DiscordTable Of ContentsInstallationGetting StartedFeaturesImage UtilsUsageExamplesInstallationInstall from PyPi:pipinstallclarifai-datautilsInstall from Source:gitclonehttps://github.com/Clarifai/clarifai-python-datautilscdclarifai-python-datautils
python3-mvenvenvsourceenv/bin/activate
pip3install-rrequirements.txtGetting startedQuick intro to Image Annotation Conversion featurefromclarifai_datautilsimportImageAnnotationsannotated_dataset=ImageAnnotations.import_from(path='folder_path',format='annotation_format')FeaturesImage UtilsAnnotation LoaderLoad various annotated image datasets and export to clarifai PlatformConvert from one annotation format to other supported annotation formatsUsageImage Annotation Loaderfromclarifai_datautilsimportImageAnnotations#import from foldercoco_dataset=ImageAnnotations.import_from(path='folder_path',format='coco_detection')#clarifai dataset loader objectcoco_dataset.dataloader#info about loaded datasetcoco_dataset.get_info()#exporting to other formatscoco_dataset.export_to('voc_detection')More ExamplesSee many more code examples in thisrepo.
Package: clarifai-grpc
Clarifai Python gRPC ClientThis is the official Clarifai gRPC Python client for interacting with our powerful recognitionAPI.
Clarifai provides a platform for data scientists, developers, researchers and enterprises to master the entire
artificial intelligence lifecycle. Gather valuable business insights from images, video and text using computer vision
and natural language processing.Try the Clarifai demo at:https://clarifai.com/demoSign up for a free account at:https://portal.clarifai.com/signupRead the documentation at:https://docs.clarifai.com/Installationpython -m pip install clarifai-grpcVersioningThis library doesn't use semantic versioning. The first two version numbers (X.Yout ofX.Y.Z) follow the API (backend) versioning, and
whenever the API gets updated, this library follows it.The third version number (Zout ofX.Y.Z) is used by this library for any independent releases of library-specific improvements and bug fixes.Getting startedConstruct theV2Stubobject using which you'll access all the Clarifai API functionality:fromclarifai_grpc.channel.clarifai_channelimportClarifaiChannelfromclarifai_grpc.grpc.apiimportservice_pb2_grpcstub=service_pb2_grpc.V2Stub(ClarifaiChannel.get_grpc_channel())Alternatives to the encrypted gRPC channel (ClarifaiChannel.get_grpc_channel()) are:the HTTPS+JSON channel (ClarifaiChannel.get_json_channel()), andthe unencrypted gRPC channel (ClarifaiChannel.get_insecure_grpc_channel()).We only recommend them in special cases.Predict concepts in an image:fromclarifai_grpc.grpc.apiimportservice_pb2,resources_pb2fromclarifai_grpc.grpc.api.statusimportstatus_code_pb2YOUR_CLARIFAI_API_KEY="???"YOUR_APPLICATION_ID="???"SAMPLE_URL="https://samples.clarifai.com/metro-north.jpg"# This is how you authenticate.metadata=(("authorization",f"Key{YOUR_CLARIFAI_API_KEY}"),)request=service_pb2.PostModelOutputsRequest(# This is the model ID of a publicly available General model. You may use any other public or custom model ID.model_id="general-image-recognition",user_app_id=resources_pb2.UserAppIDSet(app_id=YOUR_APPLICATION_ID),inputs=[resources_pb2.Input(data=resources_pb2.Data(image=resources_pb2.Image(url=SAMPLE_URL)))],)response=stub.PostModelOutputs(request,metadata=metadata)ifresponse.status.code!=status_code_pb2.SUCCESS:print(response)raiseException(f"Request failed, status code:{response.status}")forconceptinresponse.outputs[0].data.concepts:print("%12s:%.2f"%(concept.name,concept.value))Seethe Clarifai API documentationfor all available functionality.TroubleshootingI get the following error when installing the library:Failed building wheel for grpcioTry upgradingsetuptoolsto a version40.7.1or higher.pip install --upgrade setuptoolsSource:https://github.com/grpc/grpc/issues/17829
Package: clarifai-pyspark
ClarifaiPySparkIntroductionThis readme provides overview of the Software Development Kit (SDK) under development for integrating Clarifai with Databricks. The primary use case for this SDK is to facilitate the interaction between Databricks and Clarifai for tasks related to uploading client datasets, annotating data, and exporting and storing annotations in Spark DataFrames or Delta tables.The initial use case for this SDK revolves around three main objectives:Uploading Client Datasets into Clarifai App:The SDK should enable the seamless upload of datasets into the Clarifai application, simplifying the process of data transfer from Databricks to Clarifai.Annotate the Data:It should provide features for data annotation, making it easier for users to add labels and metadata to their datasets within the Clarifai platform.Export Annotations to Spark DataFrames/Delta Tables:The SDK should offer functionality to export annotations and store them in Spark DataFrames or Delta tables, facilitating further data analysis within Databricks.Requirements:Databricks : Runtime 13.3 or laterClarifai :pip install clarifaiCreate yourClarifai accountFollow the instructions to get your ownClarifai PATProtocol Buffers : version 4.24.2pip install protobuf==4.24.2Setup:Install the package and initialize the clarifaipyspark class to begin.pipinstallclarifai-pysparkGetting Started:fromclarifaipyspark.clientimportClarifaiPySparkCreate a Clarifai-PySpark client object to connect to your app on Clarifai. You can also choose the dataset or create one in your clarifai app to upload the data.claps_obj=ClarifaiPySpark(user_id=USER_ID,app_id=APP_ID,pat=CLARIFAI_PAT)dataset_obj=claps_obj.dataset(dataset_id=DATASET_ID)Examples:Checkout these notebooks for various operations you can perform using clarifai-pyspark SDK.NotebookDescriptionGitHubClarifaiPyspark_Example_NBAn extensive notebook which walks through the journey from data ingestion to exporting annotationsexport_to_df_demoExplains the process of exporting annotations from clarifai app and storing it as dataframe in databricksIf you want to enhance your AI journey with workflows and leveraging custom models (programmatically) ourClarifai SDKmight be good place to start with.
Please refer below resources for further references.Docs -Clarifai DocsExplore our community page -Clarifai CommunityFork and contribute to our SDK here !Reach out to us on socials
Package: clarifai-utils
clarifai-python-utilsThis is the official Clarifai Python utilities project. This repo includes higher level convenience classes, functions, and scripts to make using ourAPIeasier. This is built on top of theClarifai Python gRPC Client.Try the Clarifai demo at:https://clarifai.com/demoSign up for a free account at:https://clarifai.com/signupRead the documentation at:https://docs.clarifai.com/Installationpip install -U clarifai-utilsInstallation from source (for development)python -m venv ~/virtualenv/clarifai-python-utils
source ~/virtualenv/clarifai-python-utils/bin/activate
cd clarifai-python-utils
python setup.py developVersioningThis library doesn't use semantic versioning. The first two version numbers (X.Yout ofX.Y.Z) follow the API (backend) versioning, and
whenever the API gets updated, this library follows it.The third version number (Zout ofX.Y.Z) is used by this library for any independent releases of library-specific improvements and bug fixes.Getting startedHere is a quick example of listing all the concepts in an application.Set some env vars firstexport CLARIFAI_USER_ID={the user_id of the app_id of the app you want to access resources in}
export CLARIFAI_APP_ID={the app_id of the app you want to access resources in}
export CLARIFAI_PAT={your personal access token}fromclarifai_utils.clientimportcreate_stubfromclarifai_utils.listing.listerimportClarifaiResourceLister# Create a client with auth information from those env vars.stub=create_stub()# Create the resource lister.lister=ClarifaiResourceLister(stub,auth.user_id,auth.app_id,page_size=16)# List all the concepts in the app:concepts=[]forcinlister.concepts_generator():concepts.append(c)Testingpipinstalltests/requirements.txt
pytesttests/LintingThe repo will be linted when changed in a github workflow.
To speed up development it's recommended to install pre-commit and toolspipinstall-rrequirements-dev.txt
pre-commitinstallYou could run all checks bypre-commitrun--all-files
Package: clarifyquestgen
ClarifyingQuestionsGeneratorClarifyingQuestionsGeneratoris a Python package designed to refine task descriptions through an iterative process of asking clarifying questions. It uses GPT models from OpenAI to generate questions and refine tasks, streamlining the process of task clarification and ensuring tasks are well-understood and actionable.InstallationTo installClarifyingQuestionsGenerator, you can use pip:pipinstallclarifyquestgenUsageAs a Python ModuleYou can useClarifyingQuestionsGeneratoras a module in your Python scripts.Example:fromclarifyquestgenimportClarifyingQuestionsGenerator# Initialize the generator with a task, instruction, and your OpenAI API keyclarifier=ClarifyingQuestionsGenerator(task="Describe the task here",instruction="Additional instructions here",gpt_api_key='your-openai-api-key')# Run the generator to refine your taskrefined_task=clarifier.run()print("Refined Task Description:")print(refined_task)Customizing Your GeneratorYou can customize the behavior ofClarifyingQuestionsGeneratorby adjusting the initialization parameters, such as the model, temperature, max tokens, etc., to fit the specific needs of your application or to tweak the behavior of the GPT model.Output ExampleWhen you runClarifyingQuestionsGenerator, it iteratively asks questions, refines the task, and analyzes its feasibility until a satisfactory level of clarity and feasibility is reached. Here is an example interaction:Question 1: What specific parts of the database schema need to be refactored?
...user chooses an answer...
Question 2: What is the expected format or style of the comments?
...user chooses an answer...
...
Refined Task Description:
Refined task: Add comments to specific sections or functions within two code files. Follow the expected format for comments and adhere to specific guidelines or conventions provided.ContributingContributions, issues, and feature requests are welcome! Feel free to checkissues page.LicenseMIT
Package: claripy
claripyClaripy is an abstracted constraint-solving wrapper.Project LinksProject repository:https://github.com/angr/claripyDocumentation:https://api.angr.io/projects/claripy/en/latest/UsageIt is usable!General usage is similar to Z3:>>>importclaripy>>>a=claripy.BVV(3,32)>>>b=claripy.BVS('var_b',32)>>>s=claripy.Solver()>>>s.add(b>a)>>>print(s.eval(b,1)[0])
Package: claripy-sdk
Clarity.io Python SDKSDK to access Clarity.io API resourcesInstallationThe package is available onPyPiso simple install withpip:pipinstallclaripy-sdkUsagePlease see theSnippetsdirectory to see sample use cases. Below is the general use case:importosfromclaripy.clarityimportClarityconnection=Clarity(os.getenv("API_KEY"))latest_data_by_min=connection.__measurements__.get()devices=connection.__devices__.get()sources=connection.__datasources__.get()To create aClarityobject, simply pass the API key as a parameter. You then have access to data from the three Clarity endpoints: measurements, devices, and datasources.ResourcesClarity.ioClarity.io DashboardClarity.io API DocumentationLicenseThis repository is licensed under theApache LicenseContributing
Package: clarite
CLeaning to Analysis: Reproducibility-based Interface for Traits and ExposuresFree software: 3-clause BSD licenseDocumentation:https://www.hall-lab.org/clarite-python/.ExamplesRun an EWAS in a few lines of codeMore realistically, perform some QC first:Genotype data is supported via Pandas-GenomicsInstallationIn order to use ther_surveyregression_kind in theewasfunction, R must be installed along with thesurveylibrary.Install R and ensure it is accessible from the command line.  You may need to add its location to the PATH environmental variable.Useinstall.packagesin R to install thesurveylibrary.QuestionsIf you have any questions not answered by thedocumentation,
feel free to open anIssue.Citing CLARITE1.
Lucas AM, et al (2019)CLARITE facilitates the quality control and analysis process for EWAS of metabolic-related traits.Frontiers in Genetics: 10, 12402.
Passero K, et al (2020)Phenome-wide association studies on cardiovascular health and fatty acids considering phenotype quality control practices for epidemiological data.Pacific Symposium on Biocomputing: 25, 659
Package: claritygov
# ClarityGov Python API Client[![PyPI version](https://badge.fury.io/py/claritygov.svg)](https://badge.fury.io/py/claritygov)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)Python API Client for ClarityGov, a free public developer API for accessing government legislative data in a standardized format.## InstallationYou can install theclaritygovpackage using pip:` pip install claritygov `## UsageFor more information on the ClarityGov API and the available endpoints, see the [ClarityGov API documentation](https://api.claritygov.com/docs).## LicenseThis project is licensed under the Creative Commons Attribution 4.0 International Public License. See the [LICENSE](LICENSE) file for details.## NoticeClarityGov is actively under development, and as such documentation may be incomplete and package may not be fully functional or stable. Please report any issues to the package’s [GitHub Issues](https://github.com/ianmcvann/ClarityGovClient/issues)
Package: clarityviz
# clviz[![](https://img.shields.io/pypi/v/clarityviz.svg)](https://pypi.python.org/pypi/clarityviz/0.0.1)
[![Build Status](https://travis-ci.org/alee156/clviz.svg?branch=master)](https://travis-ci.org/alee156/clviz)clvizis a Python 2 package for Clarity brain analysis. It supports  ANALYZE (plain, SPM99, SPM2 and later), GIFTI, NIfTI1, NIfTI2, MINC1, MINC2, MGH and ECAT as well as Philips PAR/REC.## InstallationTo install the prerequisite packages, clone the directory using:` git clonehttps://github.com/alee156/clviz.gitcd clviz pip install-rrequirements.txt `Afterwards install opencv. This is easily accomplishable if you have brew or conda by using either` brew install opencv `or` conda install opencv `If not, install opencv by following their build instruction here:http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_setup/py_table_of_contents_setup/py_table_of_contents_setup.html#py-table-of-content-setupAfter installing the prerequisites there’s two options for using clviz. You can use clviz as a standalone package and
perform basic analysis on your own files, or you can install ndreg and ndio and use clviz as a powerful integrating tool
for graph-based analysis.` pip install clarityviz `## Docker Installation` docker pull lkzhu1/ubuntu:prototype1 docker run-t-ilkzhu1/ubuntu:prototype1 `## Getting StartedIn development but tutorials will be uploaded shortly!## DocumentationComplete documentation is located athttps://neurodatadesign.github.io/seelviz//reveal/clarityviz.m.html.## CreditsCredit to installation script in .travis.yml goes tohttps://github.com/milq/scripts-ubuntu-debian.
Package: clark
pipinstallclarkTestingRun tests by executingpython setup.py test.ClassesSuper ArgParserA nicer way to deal with config files and argparser:config_file.cnf:test = 1
debugYour app:fromclarkimportSuperArgParser,ConfigFileActionparser=SuperArgParser()parser.add_argument('-c',action=ConfigFileAction)parser.add_argument('--debug',action='store_true')parser.add_argument('--test',type=int)parser.add_argument('--engage')options=parser.parse_args(['--engage','foo','-c','config_file.cnf'])assertoptions.test==1assertoptions.engage=='foo'assertoptions.debugisTrueSuper EnumA simple Enum class with lots of nice properties.fromclarkimportSuperEnumclassColors(SuperEnum):red=SuperEnum.Eblue=SuperEnum.Egreen=SuperEnum.EclassOtherColors(SuperEnum):yellow=SuperEnum.Ered=SuperEnum.Ered=Colors['red']red=Colors.redassertredinColorsassertstr(red)=='red'assertred==Colors.redassertColors.red!=OtherColors.redclassFoo(object):classColors(SuperEnum):blue=SuperEnum.ElementassertFoo.Colors.blue!=Colors.blueSuper JSONMake simplejson slightly better (don’t crash on datetime objects)fromclarkimportsuper_jsonnow=datetime.datetime.now()x=super_json.loads(super_json.dumps({'a':now}))assertx['a']==now.isoformat()Super PidFileA simple PidFile class.  Instantiate when you boot up, and close it when you exit.fromclarkimportSuperPidFilepidfile=SuperPidFile()try:...runyourapp...finally:pidfile.close()Super ThreadThreads that terminate nicely and are awesome.fromclarkimportSuperThreadclassT(SuperThread):defbootstrap(self):self.i=0defsleep(self):time.sleep(0.01)defwork(self):self.i+=1defcleanup(self):assertself.i>0t=T()t.start()time.sleep(1)t.terminate()t.join()assertnott.is_alive()