Saravanan666/pypi-org huggingface dataset:
Package: cognite-pygen
Cognite Python SDK GeneratorThis is the Cognite Python SDK Generator,pygen. The purpose of this package is to help developers to
work with Cognite Data Fusion's (CDF) Data Models (DM) in Python.Beta NoteThis package,pygen, is currently in beta, which is reflected in the version number0.99.x. Beta
indicates that a product or functionality is in a mature testing phase, but is still subject to change or retirement.
We will provide a deprecation notice a minimum of two months before retiring the product or functionality. We recommend
not using the product or functionality in production systems.The core functionality is to provide a Python client that matches a data model. This enables the developer for the following
benefitsClient-side validation of the data before writing it to CDF.Autocompletion is matching the data model in the integrated developer environment (IDE). This is important as it enables:Discoverability of a data model through Python.Reduced typing errors in development.Keeping the language domain specific for the developer. Instead of working with generic concepts such as instances,
nodes and edges, the developer can work with the concepts in the data model.DocumentationSee thedocumentationfor more information.InstallationWithout any optional dependenciesTo install this package without CLI support:pipinstallcognite-pygenWith optional dependenciescliThis includes CLI support such that you can run the package from the command line.pipinstallcognite-pygen[cli]If using zsh:pipinstall'cognite-pygen[cli]'UsageThe goal of the package is to have representations of all the types in a given data model with API calls to.list(),.apply(),.delete(), and.retrieve()individuals for each type.Creating a Python SDK from a Data ModelGiven a Data Model with external idMoviein the spacemoviesin CDF, the following command will generate a Python SDKpygengenerate--spacemovies\--external-idMovie\--version1\--tenant-id<tenant-id>\--client-id<client-id>\--client-secret<client-secret>\--cdf-cluster<cdf-cluster>\--cdf-project<cdf-project>If you are not using Microsoft Entra ID (Azure AD) you need to specify the parameter --token-url, --scopes and --audience instead of --tenant-id.DependenciesDependencies for the generated SDKcognite-sdkThis is the basis for all requests to the Cognite Data Fusion API.pydanticThis is used for all data classes in the generated SDK.pandasThis is used for.to_pandas()methods in the generated SDK.Dependencies for thepygenjinja2This is used for the templating of the generated SDK.inflectThis is used for the singularization/pluralization of words in the generated SDK.typer(Optional) This is used for the CLI of thepygenpackage.black(Optional) This is used to format the code generated by thepygenpackage.ChangelogWondering about previous changes to the SDK? Take a look at theCHANGELOG.ContributingWant to contribute? Check outCONTRIBUTING.
Package: cognite-robotics-sdk
Cognite Robotics SDKThis is the Cognite Robotics SDK for Python. It is a Python package that provides a client library for interacting with the Cognite Robotics APIs of Cognite Data Fusions.InstallationThe SDK is available on PyPI and can be installed usingpip:pipinstallcognite-robotics-sdkEnvironment VariablesSet the following environment variables in a .env file:COGNITE_PROJECTCOGNITE_CLUSTERCOGNITE_TENANT_IDCOGNITE_CLIENT_IDCOGNITE_CLIENT_SECRETReference documentationThe reference documentation will be added soon. Please reach out to the Cognite Robotics team for documentation and example code.Getting startedPlease reach out to the Cognite Robotics team for documentation and example code.ChangelogWondering about upcoming or previous changes to the SDK? Check out thechangelog.
Package: cognite-sdk
Cognite Python SDKThis is the Cognite Python SDK for developers and data scientists working with Cognite Data Fusion (CDF).
The package is tightly integrated with pandas, and helps you work easily and efficiently with data in Cognite Data Fusion (CDF).Reference documentationSDK DocumentationCDF API DocumentationCognite Developer DocumentationInstallationWithout any optional dependenciesTo install the core version of this package:$pipinstallcognite-sdkWith optional dependenciesA number of optional dependencies may be specified in order to support a wider set of features.
The available extras (along with the libraries they include) are:numpy[numpy]pandas[pandas]geo[geopandas, shapely]sympy[sympy]functions[pip]yaml[PyYAML]all[numpy, pandas, geopandas, shapely, sympy, pip, PyYAML]To include optional dependencies, specify them like this with pip:$pipinstall"cognite-sdk[pandas, geo]"or like this if you are using poetry:$poetryaddcognite-sdk-Epandas-EgeoPerformance notesIf you regularly need to fetch large amounts of datapoints, consider installing withnumpy(or withpandas, as it depends onnumpy) for best performance, then use theretrieve_arrays(orretrieve_dataframe) endpoint(s). This avoids building large pure Python data structures, and instead reads data directly into memory-efficientnumpy.ndarrays.Windows specificOn Windows, it is recommended to installgeopandasand its dependencies usingcondapackage manager, seegeopandas installation page.
The following commands create a new environment, installgeopandasandcognite-sdk.condacreate-ngeo_env
condaactivategeo_env
condainstall--channelconda-forgegeopandas
pipinstallcognite-sdkChangelogWondering about upcoming or previous changes to the SDK? Take a look at theCHANGELOG.Migration GuideTo help you upgrade your code(base) quickly and safely to a newer major version of the SDK, check out our migration guide. It is a more focused guide based on the detailed change log.MIGRATION GUIDE.ContributingWant to contribute? Check outCONTRIBUTING.
Package: cognite-sdk-core
Cognite Python SDKThis is the Cognite Python SDK for developers and data scientists working with Cognite Data Fusion (CDF).
The package is tightly integrated with pandas, and helps you work easily and efficiently with data in Cognite Data
Fusion (CDF).DocumentationSDK DocumentationAPI DocumentationCognite Developer DocumentationPrerequisitesIn order to start using the Python SDK, you needPython3 (>= 3.5) and pipAn API key. Never include the API key directly in the code or upload the key to github. Instead, set the API key as an environment variable. See the usage example for how to authenticate with the API key.This is how you set the API key as an environment variable on Mac OS and Linux:$exportCOGNITE_API_KEY=<yourAPIkey>On Windows, you can followsthese instructionsto set the API key as an environment variable.InstallationTo install this package:$pipinstallcognite-sdkTo install this package without the pandas and NumPy support:$pipinstallcognite-sdk-coreTo install with pandas, geopandas and shapely support (equivalent to installingcognite-sdk).
However, this gives you the option to only have pandas (and NumPy) support without geopandas.$pipinstallcognite-sdk-core[pandas,geo]On Windows, it is recommended to installgeopandasand its dependencies usingcondapackage manager, seegeopandas installation page.
The following commands create a new environment, installgeopandasandcognite-sdk.condacreate-ngeo_env
condaactivategeo_env
condainstall--channelconda-forgegeopandas
pipinstallcognite-sdkExamplesFor a collection of scripts and Jupyter Notebooks that explain how to perform various tasks in Cognite Data Fusion (CDF)
using Python, see the GitHub repositoryhereChangelogWondering about upcoming or previous changes to the SDK? Take a look at theCHANGELOG.ContributingWant to contribute? Check outCONTRIBUTING.
Package: cognite-toolkit
Official Cognite Data Fusion project templatesThe CDF Toolkit is a command-line interface (cdf-tk) used for configuring and administrating Cognite Data
Fusion (CDF) projects.
It supports three different modes of operation:As aninteractive command-line toolused alongside the Cognite Data Fusion web application to retrieve and
push configuration of the different Cognite Data Fusion services like data sets, data models, transformations,
and more. This mode also supports configuration of new Cognite Data Fusion projects to quickly get started.As tool to support theproject life-cyle by scripting and automatingconfiguration and management of Cognite Data
Fusion projects where CDF configurations are kept as yaml-files that can be checked into version
control. This mode also supports DevOps workflows with development, staging, and production projects.As atool to deploy official Cognite project templatesto your Cognite Data Fusion project. The tool comes
bundled with templates useful for getting started with Cognite Data Fusion, as well as for specific use cases
delivered by Cognite or its partners. You can also create your own templates and share them.For more informationMore details about the tool can be found atdeveloper.cognite.com.You can find an overview of the modules and packages in themodule and package documentation.See./CONTRIBUTING.mdfor information about how to contribute to thecdf-tktool or
templates.
Package: cognite-transformations-cli
Cognite Transformations CLITransformations CLIUse the Transformations command-line interface (Transformations CLI) to manage the lifecycle of your transformation jobs using the command line. With the Transformations CLI, you can process data from the CDF staging area (RAW) into the CDF data model. To learn more about how the Cognite Transformations CLI package works, see thedocumentationhereTheTransformations CLIis based on Python and replaces theJetfire CLI.GitHub ActionTheTransformations CLIprovides a GitHub Action to deploy transformations. You'll find the documentationhere.We've also created aCI/CD templatethat uses GitHub Workflows. You'll find the documentationhere.Using Transformations CLI in Azure PipelinesWe publishtransformations-clidocker images indockerhubfor every version released. The images tagged ascognite/transformations-cli:<version>-azurecan be used in Azure Pipelines (See an example taghere). We suggest you checkthe example Azure Pipeline workflow configurationandthe corresponding transformation manifest.Migrating from Jetfire CLITransformations CLIreplaces theJetfire CLI. If you've already used theJetfire CLIin a GitHub Action, we recommend migrating to theTransformations CLIGitHub Action. You'll find the migration guidehere.ContributingWe usepoetryto manage dependencies and to administrate virtual environments. To developTransformations CLI, follow these steps to set up your local environment:Install poetry: (add--userif desirable)$ pip install poetryClone repository:$ git clone git@github.com:cognitedata/transformations-cli.gitMove into the newly created local repository:$ cd transformations-cliCreate a virtual environment and install dependencies:$ poetry installAll the code must passblackandisortstyle
checks before it can be merged. We recommend installing pre-commit hooks to ensure this locally before you commit your code:$ poetry run pre-commit installTo run tests:$ poetry run pytest
$ poetry run pytest <test file path>
$ poetry run pytest <test file path>::<test function name>To publish a new version, change the version incognite/transformations_cli/__init__.pyandpyproject.toml. Make sure to update theCHANGELOG.This project adheres to theContributor Covenant v2.0as a code of conduct.
Package: cognite-turbulent-flux-sdk
Turbulent Flux SDKSDK for fetching structured data from the Turbulent Flux API.PrerequisitesPython 3 (>= 3.7)pip3SetupThe package can be installed using pip.pip install cognite-turbulent-flux-apiExamplesExample usage is shown in examples in/notebooks/examples.ipynb.LicenseCognite Packages use the Apache 2.0 License. SeeLICENSE
Package: cognite-utils
Cognite Python UtilitiesThis is a package of utilities extending the use of Cognite products. Its aim is to assist
Cognite users with common/recurrent workflows and applications.Directory Structure├── cognite                         <- Source code
│   └── utils
│       ├── contextualization       <- Functionalities relating to contextualization
│       ├── infrastructure          <- Functionalities relating to infrastructure
│       ├── ...
│       └── __init__.pyGetting StartedTo install this package, run:$pipinstallcognite-utilsOr, if usingpoetry, run:$poetryaddcognite-utilsThe following shows how you can load functionalities in the package:>>>fromcognite.utils.contextualizationimportEntityMatchCategorizer>>>fromcognite.utils.infrastructureimportProjectArchiverDocumentationCognite Python Utilities(current project)Cognite Python SDKCognite Python Experimental SDKTo update project documentation, follow instructionshere.ContributingWant to contribute? Check outDevelopmentpage.
As an open source project, we abide by theContributor Covenant.
Package: cognitivecluster
Cognitive ClusterCognitive Cluster is a library for clustering written metacognitive exercises based on document embeddings.
This is useful for creating cross-cluster partnerships to increase the diversity of problem solving techniques in teams.InstallationTo instalcognitivecluster, runpip install cognitivecluster. The library currently supports Python 3.6+.UsageTo cluster people based on writing samples, pass either a Numpy array or a Pandas series tocognitivecluster.sentence_vector_clusters.Examples>>>importcognitivecluster>>>cognitivecluster.sentence_vector_clusters(np_sentence_array)array([0,0,1,1,2,2],dtype=int32)>>>df['cluster']=cognitivecluster.sentence_vector_clusters(df.metacognitive_exercises)>>>dfmetacognitive_exercisescluster0Ilikecarrots01Ilikepotatoes02Iamacat13Iamadog14Thisisatest25Thisisasequence2
Package: cognitive-complexity
congnitive-complexityLibrary to calculate Python functions cognitive complexity via code.Installationpipinstallcognitive_complexityUsage>>>importast>>>funcdef=ast.parse("""... def f(a):...     return a * f(a - 1)  # +1 for recursion... """).body[0]>>>fromcognitive_complexity.apiimportget_cognitive_complexity>>>get_cognitive_complexity(funcdef)1Flake8-Cognitive-Complexity ExtensionPerhaps the most common way to use this library (especially if you are
already using theFlake8 linter)
is to use theflake8-cognitive-complexity extension.
If you run Flake8 with this extension installed, Flake8 will let you know
if your code is too complex. For more details and documentation, visit theflake8-cognitive-complexity extension repository.What is cognitive complexityHere are some readings about cognitive complexity:Cognitive Complexity, Because Testability != Understandability;Cognitive Complexity: A new way of measuring understandability,
white paper by G. Ann Campbell;Cognitive Complexity: the New Guide to Refactoring for Maintainable Code;Cognitive Complexityfrom CodeClimate docs;Is Your Code Readable By Humans? Cognitive Complexity Tells You.Realization detailsThis is not precise realization of original algorithm
proposed byG. Ann Campbell,
but it gives rather similar results.
The algorithm gives complexity points for breaking control flow, nesting,
recursion, stacks logic operation etc.ContributingWe would love you to contribute to our project. It's simple:Create an issue with bug you found or proposal you have. Wait for
approve from maintainer.Create a pull request. Make sure all checks are green.Fix review comments if any.Be awesome.Here are useful tips:You can run all checks and tests withmake check. Please do it
before TravisCI does.We useBestDoctor python styleguide.
Sorry, styleguide is available only in Russian for now.We respectDjango CoC.
Make soft, not bullshit.
Package: cognitive-face
Microsoft Face API: Python SDK & SampleThis SDK has been deprecated and been replaced byMicrosoft.Azure.CognitiveServices.Vision.Facewhich is a part of Azure SDK.This repo contains the Python SDK for the Microsoft Face API, an offering withinMicrosoft Cognitive Services, formerly known as Project Oxford.Learn about the Face APIDocumentation & API Reference & SDKsGetting startedInstall the module usingpip:pipinstallcognitive_faceUse it:importcognitive_faceasCFKEY='subscription key'# Replace with a valid Subscription Key here.CF.Key.set(KEY)BASE_URL='https://westus.api.cognitive.microsoft.com/face/v1.0/'# Replace with your regional Base URLCF.BaseUrl.set(BASE_URL)img_url='https://raw.githubusercontent.com/Microsoft/Cognitive-Face-Windows/master/Data/detection1.jpg'result=CF.face.detect(img_url)printresultInstalling from the source codepythonsetup.pyinstallRunning the unit testsTo run the tests you will need a valid subscription. You can get onehere.Copycognitive_face/tests/config.sample.pyintocognitive_face/tests/config.py.Change theKEYandBASE_URLparameters to your own subscription's API key and endpoint.Run the following:pythonsetup.pytestRunning the sampleA sample desktop application is also provided.Currently it support the following combination of prerequisites:Python 3+wxPython 4[Recommended]Python 2+wxPython 4Python 2+wxPython 3P.S. WxPython 3 does not support Python 3 by design.Then run the following:gitclonehttps://github.com/Microsoft/Cognitive-Face-Python.gitcdCognitive-Face-Python
pipinstall-rrequirements.txt
pythonsampleContributingWe welcome contributions. Feel free to file issues and pull requests on the repo and we'll address them as we can. Learn more about how you can help on ourContribution Rules & Guidelines.You can reach out to us anytime with questions and suggestions using our communities below:Support questions:StackOverflowFeedback & feature requests:Cognitive Services UserVoice ForumThis project has adopted theMicrosoft Open Source Code of Conduct. For more information see theCode of Conduct FAQor contactopencode@microsoft.comwith any additional questions or comments.UpdatesFace API Release NotesLicenseAll Microsoft Cognitive Services SDKs and samples are licensed with the MIT License. For more details, seeLICENSE.Sample images are licensed separately, please refer toLICENSE-IMAGE.Developer Code of ConductDevelopers using Cognitive Services, including this sample, are expected to follow the “Developer Code of Conduct for Microsoft Cognitive Services”, found athttp://go.microsoft.com/fwlink/?LinkId=698895.
Package: cognitivegeo
CognitiveGeoCognitiveGeois a Python GUI for geoscientific data analysis (i.e., seismic).DependencyCognitiveGeouses a number of open source projects to work properly:PyQt5- GUIsVispy- VisualizationsInstallationpip install --upgrade cognitivegeoRunimport cognitivegeo as cgeocgeo.gui.start()DevelopersCognitiveGeois developed byGeoPy Team.SupportContact usif you have any problem with usingCognitiveGeoand/or want to be a developer.LicenseGNU Affero General Public License v3 or later (AGPLv3+)
Package: cognitivepersonalizationtaskfingerprinting
TaskFingerprinting Package For Cognitive Personalization of Task DesignSource code for the  framework built for our study on assessing crowd workers behaviors for task design personalization in crowdsourcing. Beside crowdsourcing, this package can be used to perform cognitive personalization seamlessly in web tasks.
If you use our work in your research, please cite the following paper published at Sensors 2023.Dennis Paulino, Diogo Guimarães, António Correia, José Ribeiro, João Barroso and Hugo Paredes. A Model for Cognitive Personalization of Microtask Design. Sensors 2023, 23, 3571.https://doi.org/10.3390/s23073571Example of installing and using the package:pip install cognitivepersonalizationtaskfingerprinting
import cptf.cog_personalization_micro_task_fingerprinting as taskfingerprinting
import cptf.cog_personalization_deep_learning_model as dl

#It is performed the task fingerprinting technique based on prompt user to enter the path of the interaction log files, and then it will be generated a csv file
taskfingerprinting.main()

#Based on the task fingerprintings' csv file, the user will be prompted to select the file and develop a deep learning model
#CAUTION: It is best to look at the source code and adapt the respective parameters to better ajust for each specific dataset
dl.main()ContactPlease feel free to contact Dennis Paulino (dpaulino@utad.pt) for further questions.
Package: cognitive-service-vision-model-customization-python-samples
Azure Cognitive Services Computer Vision - Python SDK SamplesModel CustomizationComputer Vision's Model Customization is a custom model training service that allows users like developers to easily train an image classification model (Multiclass only for now) or object detection model, with low-code experience and very little understanding of machine learning or computer vision required.This is a sample repository demonstrating how to train and predict a custom model with Cognitive Service for Vision, using Python. To get started, check outthis tutorial in Python notebook.Product RecognitionOne of the scenarios we would like to introudce as a model customization scenario is Product Recognition. Computer Vision's product recognition service has been designed to be used in retail scenarios, where users would like to detect products, such as Consumer Packaged Goods (CPG), on a shelf. It comes with a set of APIs, a pre-built AI model, and a custom AI model that can be trained following the model customization guide above. You can try these out by following tutorials in Python notebooks:Image Composition: for stitching together the segmented shelf images using Image Stitching API, as well as adjusting any slanted or squished shelf images to a correct orientation using Image Rectification APIProduct Recognition: for detecting products and gaps on a shelf image using a pre-built model, and individually classifying the detected products using customized model, both using Product Understanding APIPlanogram Compliance: for assessing the matchings between a planogram schema and the detected products on a shelf using Planogram Compliance APIExisting Custom Vision (customvision.ai) CustomersRefer toexport_cvs_data_to_blob_storage.ipynbfor instructions or directly runexport_cvs_data_to_blob_storage.pyto export Custom Vision images and annotations to your own blob storage, which can be later used for model customization training.Once data is exported, you can use it with Cognitive Service Vision Model Customization.RESTful API & SDKIf you would like to explore more functionalities offered in Cognitive Service Vision, you can refer tothis linkfor a quick start.FAQ & DocsFor frequently asked questions or quick troubleshooting, check outFAQ, including things like troubleshooting guides, quota information, etc.For more documentation, check out:API:HereSDK:Here(note that model customization training and product recognition are not supported in this SDK yet, while prediction is supported.)
Package: cognito
cognito : powerful data wrangling toolkitDeploymentBuild StatusMetricsGitHubLicenseCommunityCognito is an exclusive python data preprocessing library and command line utility that helps any developer to transform raw data into a machine-learning format. We at CleverInsight Open Ai Foundation took the initiative to build a better automated data preprocessing library and here it is.InstallationPrerequisitePython3.Install the extension by using pip.$pipinstallcognitoAuthorsCore LeadBastin Robins J<robin@cleverinsight.co>Development LeadsAnshul Saxena<anshul.saxena@christuniversity.in>Vandana Bhagat<vandana.bhagat@christuniversity.in>Data Science TeamAastik<aastikkharbanda.ak@gmail.com>Aishika<aishikabanik98@gmail.com>Nibir Paul<nibirpaul491@gmail.com>Rishika Kotta<rishikakotta21@gmail.com>Tanvi Rath<rath.t98@gmail.com>CollaboratorsCHRIST (Deemed to be University) Lavasa <https://lavasa.christuniversity.in/>`_ provides developer & mentor contribution via CleverInsight Open AI Foundation Incubation Center at Christ Univeristy Lavasa since 2019.
Package: cognito-client
Cognito ClientA client to smooth over process of common authentication scenarios
using AWS Cognito. Intended to be used within an interactive terminal
environment (e.g. Python Notebook, CLI).UsageInstallpip install cognito_clientUseCreate an.envfile in the format ofexamples/env.exampleand create a client in the same directory:client=CognitoClient()OR pass the Cognito identifiers as arguments to the function:client=CognitoClient(client_id="XXX",user_pool_id="us-west-XXX",identity_pool_id="us-west-2:XXX",)Seeexamples/temporary-credentials-example.ipynb.