Saravanan666/pypi-org huggingface dataset:
Package: auto-flutter
Auto FlutterIntroAuto Flutteris a python package to create structure based flutter projects.AboutStructuring projects by creating folders for all projects are boring. This python package will automate the job for you. It will create flutter application and generate structure for your project. For now it will followbloc pattern.GeneratedSample Git Repbyauto-flutterSupported PlatformsLinux (Dabian Based)Requirmentspython >=3.6InstructionInstallationLinux :pip install auto-flutter==1.1.0**Note : Please updated version**UpgradeLinux :pip install --upgrade auto-flutterUsageGenerate flutter app usingauto-flutterYou will be asked forEnter project name (project name)example :helloIt will create and generate a structured the flutter projectLicenseGNU GPL V3
Package: auto-flutter-dig
Auto FlutterAutomatic build tools for flutter build toolsReasonFlutter build tools does not allow to create or bind tasks, making some process tedious.
Auto Flutter came with that in mind. Tasks that can be bind with other, and some integrations, like Firebase AppDist out of box.LicenseCC BY-ND 4.0You can use and redist freely.You can also modify, but only for yourself.You can use it as a part of your project, but without modifications in this project.InstallationFrom PyPI (preferred):python-mpipinstallauto_flutter_digFrom github release:python-mpipinstall"https://github.com/DIG-/auto-flutter/releases/download/0.6.2/auto_flutter_dig-0.6.2-py3-none-any.whl"orpython-mpipinstall"https://github.com/DIG-/auto-flutter/releases/download/0.6.2/auto_flutter_dig.tar.gz"From github main branch:python-mpipinstall"git+https://github.com/DIG-/auto-flutter.git@main#egg=auto_flutter_dig"Usagepython-mauto_flutter_digoraflutterFirst steps# To show helpaflutterhelp# To show how to configure environmentafluttersetup-h# Check if everything is okafluttersetup--checkGo to your flutter project root. Aka. Where ispubspec.yamland:aflutterinit--name"Name to your project"# And let the magic happen
Package: autofns
AutoFNSIntroductionAutoFNS is a wrapper around OpenAI's Chat Completion API.It allows you to define functions as you normally would, and have them be called
automatically when a message containing tool calls is detected. This allows you to focus on
more important things, like the logic of your application.Note: 🧪 This is a work in progress and may not be ready for production use.Requirementspython >= 3.10openaiInstallationpipinstallautofnsUsagefromautofnsimportAutoFNSFUNCTION_DEFINITIONS=[{"type":"function","function":{"name":"get_temp_units","description":"Get a list of temperature units",}},{"type":"function","function":{"name":"get_current_temperature","description":"Get the current temperature in a city",},"parameters":{"type":"object","properties":{"city":{"type":"string","description":"The city to get the temperature of",},"unit":{"type":"string","description":"The unit to get the temperature in","enum":["Fahrenheit","Celsius","Kelvin"],}},"required":["city","unit"],}},]defget_temp_units():return["Fahrenheit","Celsius","Kelvin"]defget_current_temperature(city:str,unit:str):return"The current temperature in{}is{}degrees{}".format(city,72,unit)fns=AutoFNS("gpt-4-32k",fns_mapping={"get_temp_units":get_temp_units,"get_current_temperature":get_current_temperature},fns_definitions=FUNCTION_DEFINITIONS,)result=fns.create_completion(messages=[...])You can also use theAutoFNSAsyncclass to use async functions:fromautofnsimportAutoFNSAsyncfns=AutoFNSAsync(...)result=awaitfns.create_completion(messages=[...])LicenseThis project is licensed under the terms of the MIT license.ContributingContributions are welcome! Please open an issue or a pull request.
Package: autoforecast
Auto ForecastAutoML library for time series forecastingQuick startUpgrade pip$pipinstallpip--upgradeInstall autoforecast$pipinstallautoforecastTry it out with your own datasetPreprocessingfromAutoforecast.preprocessingimportpreprocessingX_train,y_train,X_test,y_test=preprocessing(df=df,target_name=['sales'],categoricals=['store_id','dpt_id','holiday','zipcode',...],numericals=['employee_num','store_surface',...],date_col=['date'],train_size=0.8,engineering=True,selection=True)Fitting and predictingfromautoforecast.automlimportAutoForecastmodel=AutoForecast()print('Autoforecast() model fitting...')model.fit(X_train=X_train,y_train=y_train)print('Autoforecast() model predicting...')y_pred=model.predict(X_test=X_test)print(f'y_pred={y_pred})Run the example functionfromautoforecast.examplesimportautoforecast_bitcoinautoforecast_bitcoin.run()Fetch historical cryptocurrency dataThis function is a wrapper ofhttps://developers.coinbase.com/api/v2#pricesn: integer, number of days we want since todaytype: str, ['buy', 'sell', 'spot']currency_pair: str, crypto & currencyfromautoforecast.datasets.import_bitcoin_priceimportget_price_for_last_n_dayscrypto_df=get_price_for_last_n_days(n=1,type='spot',currency_pair='BTC-USD'))
Package: autoformat
autoformatAutomatic, range-aware formatting of time-axis labels for timeseries data using matplotlib.This package is supported for Python 3 only. This project works for time ranges on the order of milliseconds or millenia, and produces a decent-looking result with a few lines:fromautoformatimportautoformatautoformat.scale(start_date,end_date)# your matplotlib plotting code hereInstallationpip install autoformatProjectgithub link
Package: autofunc
autofuncEasily define Python functions for structured output from GPT-4.:warning: This was primarily written for personal use, and is neither well-tested nor documented. There are similar and much better projects out there, such asmagentic.Installpip install autofuncUsageExpectsOPENAI_API_KEYto be defined in your environment.Define your functionfromautofuncimportAutoFuncswap_animals=AutoFunc("Swap the animals in the sentence. Eg. The dog barked at the cat -> the cat meowed at the dog")Call itswap_animals("The tigers growled at each other until they saw the snakes slithering near the trees.")'The snakes hissed at each other until they saw the tigers prowling near the trees.'Usepydanticfor structured outputfrompydanticimportBaseModel,FieldclassTravelDescription(BaseModel):from_city:str=Field(description="City name including the country")to_city:str=Field(description="City name including the country")mode_of_travel:str=Field(description="eg. train or flight")description:str=Field(description="A one-line description of a fictional journey, in the style of an adventure game. Simple present tense.")duration:strtravel_fn=AutoFunc("Describe travelling between the two provided cities.",TravelDescription)travel_fn("londres to paris"){'from_city':'London, United Kingdom','to_city':'Paris, France','mode_of_travel':'train','description':'You embark on a journey from the bustling city of London, crossing the English Channel through the Eurotunnel, to the romantic city of Paris.','duration':'2 hours 20 minutes'}CHANGES2023.10.05: Initial release
Package: autofxn
AutoFunctionCreate AI prediction functions by describing what they do. This project explores using an AI agent to write AI prediction functions, which can then be used to make predictions from anywhere usingFunction.This project is what powers creating predictors from prompts onfxn.ai/create.Installing AutoFunctionAutoFunction is distributed on PyPi. This distribution contains both the Python library and the command line interface (CLI):# Open a terminal and run the following command:pipinstallautofxnWriting Predictor Code with AIFirst, you will need to generate anOpenAI API keywith GPT-4 access:# Specify your OpenAI API keyexportOPENAI_API_KEY="<Your OpenAI API key>"✅ If you don't have an OpenAI account, you can generate predictors from prompts at zero cost atfxn.ai/create.Next, run the following command to generate a predictor notebook:# Create a predictor notebook that returns a greeting# This will generate a `predictor.ipynb` notebook in the current directoryautofxncreate--output-dir.\"Create a predictor that accepts a user's 'name' and returns a greeting"Usage from CodeThis library exports a single function,create_predictor_notebookwhich generates a predictor notebook from a given prompt:fromautofxnimportcreate_predictor_notebookfromnbformatimportNotebookNode,write# Generate the notebookprompt="Create a predictor that accepts a user's 'name' and returns a greeting"notebook:NotebookNode=create_predictor_notebook(prompt)# Write to filewithopen("predictor.ipynb","w")asf:write(notebook,f)Making Predictions from Anywhere with FunctionWithFunction, you can use the predictor notebook to make predictions from JavaScript, Python, Unity, Zapier, Slack, and more.First, register or login to Function andgenerate an access key:Then login to the Function CLI:# Open a terminal and run the following command:fxnauthlogin<ACCESSKEY>Now you can create and provision an AI-generated predictor notebook on Function by simply specifying a predictor tag:# Create a predictor notebook that returns a greetingautofxncreate--tag@username/greeting\"Create a predictor that accepts a user's 'name' and returns a greeting"Replaceusernamewith your Function username.The predictor tag uniquely identifies every predictor on Function.Once the predictor is active, you can use it to make predictions!# Make a prediction with our AI-generated predictorfxnpredict@username/greeting--name"Carti"RoadmapGenerate simple predictors that can be provisioned and used for predictions onFunction.Support other LLM providers (Anthropic, on-device LLM usingedgefxnetc).Plug into organization data sources (Snowflake, Databricks) to leverage highly-relevant information.Fault tolerance with OpenAI API requests.External program correctness and validation testing.Discover and composeFunction predictors.Discover and compose GitHub repositories.Useful LinksDiscover predictors to use in your apps.Join our Discord community.Check out our docs.Learn more about uson our blog.Reach out to us athi@fxn.ai.Thank you very much!
Package: autogame
Auto GameA python framework for automatic game playing, which can be used to develop automatic game scripts.Explore the docs »View Demo·Report Bug·Request FeatureAbout The ProjectNow many games in the market need to log in every day and do similar operations repeatedly, which is very time-consuming and boring.
Therefore, I want to create a program that can automatically play games instead of us.
I need a general auto game library to achieve my goal.
The library should have these features:Firstly, I need an API of games simulators, which can capture the game outputs through images (sounds may be captured in the future), and enter inputs into the game similar to mouse and keyboard, etc.;Secondly, it should integrate most image algorithms to identify key information quickly and accurately in the game scene;Moreover, a GUI interface is also necessary for users, and so on.
There are many automatic game playing frameworks available on GitHub, however, I didn't find one that really suit my needs, so I created this.
Maybe this library can also meet your needs.Getting StartedTo use this library just running follow these simple example steps.PrerequisitesThis is a library written in Python, so you need to ensure thatPythonexists.Installationpipinstallautogame-pythonUsageimportautogameUsageUse this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.For more examples, please refer to theDocumentationRoadmapSee theopen issuesfor a list of proposed features (and known issues).ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork this Project.Create your Feature Branch (git checkout -b feature/AmazingFeature).Commit your Changes (git commit -m 'Add some AmazingFeature').Push to the Branch (git push origin feature/AmazingFeature).Open a Pull Request.LicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactInvoker -invoker-bot@outlook.comProject Link:https://github.com/invokerrrr/autogame-pythonAcknowledgementsSimple Logo GeneratorBest README TemplateImg ShieldsChoose an Open Source LicenseGitHub PagesAnimate.cssLoaders.cssSlick CarouselSmooth ScrollSticky KitJVectorMapFont Awesome
Package: autoGDC
# autoGDC![logo](https://raw.githubusercontent.com/chasealanbrown/autoGDC/master/autoGDC_logo.png)This package was created for automatic downloading and processing of Genomic Data Commons data.## Why?
The GDC is a great resource; however the process of downloading the data and combining it into a simple format can be time consuming. This package makes this more streamlined.Examples of a few questions that may be explored are provided:DNA Methylation Clock: What sites are chosen when training a DNA methylation clock model?  How do they differ when different techniques are applied to retreive these sites?</li>RNA vs. DNA methylation: What is the correlation between transcription and DNAm for paired data?</li>DNA methylation sequence modeling: How much information is contained within sequence information of DNA methylation with respect to transcription?</li>Glioblastoma Differential Gene Expression: What is the DEG signature for glioblastoma patients?</li>These examples can help provide as a basis for how to use this package effectively.## Installation
The package autoGDC is available on pypi:`sh pip install autoGDC `## Installation from source`sh git clonehttps://github.com/chasealanbrown/autoGDCcd autoGDC `followed by either:`sh python setup.py install `or for development mode installation`sh python-mpip install-e. `
Package: autogenes
AutoGeneSAutoGeneSautomaticallyextracts informative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes bysimultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. It can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations.For a multi-objective optimization problem, there usually exists no single solution that simultaneously optimizes all objectives. In this case, the objective functions are said to be conflicting, and there exists a (possibly infinite) number ofPareto-optimal solutions. Pareto-(semi)optimal solutions are a set of all solutions that are not dominated by any other explored solution. Pareto-optimal solutions offer a set of equally good solutions from which to select, depending on the datasetInstallationPyPI onlypip install autogenesDevelopment Version (latest version on github)git clone https://github.com/theislab/AutoGeneSpip install dist/autogenes-1.0.3-py3-none-any.whlExampleExample on pseudo bulksDocumentationDocumentationGetting StartedDependenciespython>=3.6pandas>=0.25.1anndata>=0.6.22.post1numpy>=1.17.2dill>=0.3.1.1deap>=1.3.0scipy>=1.3cachetools>=3.1.1scikit-learn>=0.21.3matplotlib>=3.0CitationAliee, Hananeh and Theis, Fabian, AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution
Package: autogenfiles
# AutoGenFilesBased on a predefined templating structure create new files## ExamplesCheck [AutoGenFiles Playground](https://github.com/jtemporal/autogenfiles_playground).## TODOThere are a few TODO marked on the code but here’s a list of most general ones[x] guarantee file structure from templates folder[ ] test all[ ] make it into an installable cli[ ] document the variables.yaml structure[ ] document how to contribute[x] document example with the project in case[x] move all logic into an module structure
Package: autogenui
AutoGen UIExperimental UI for working withAutoGenagents, based on theAutoGenlibrary. The UI is built using Next.js and web apis built using FastApi.Why AutoGen UI?AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve complex tasks. A UI can help in the development of such applications by enabling rapid prototypingand testing and debugging of agents/agent flows (defining, composing etc) inspecting agent behaviors, and agent outcomes.Note:This is early work in progress.Note that you will have to setup your OPENAI_API_KEY or general llm config using an environment variable.
Also See this article for how Autogen supports multiplellm providersexportOPENAI_API_KEY=<yourkey>Getting StartedInstall dependencies. Python 3.9+ is required. You can install from pypi using pip.pipinstallautogenui.or to install from sourcegitclonegit@github.com:victordibia/autogen-ui.gitcdautogenui
pipinstall-e.Run ui server.autogenui# or with --port 8081Openhttp://localhost:8081in your browser.To modify the source files, make changes in the frontend source files and runnpm run buildto rebuild the frontend.RoadmapFastApi end point for AutoGen.
This involves setting up a FastApi endpoint that can respond to end user prompt based requests using a basic two agent format.Basic Chat UIFront end UI with a chatbox to enable sending requests and showing responses from the end point for a basic 2 agent format.Debug Tools: enable support for useful debugging capabilities like viewing# of agent turns per requestdefine agent config (e.g. assistant agent + code agent)append conversation history per requestdisplay cost of interaction per request (# tokens and $ cost)Streaming UI
Enable streaming of agent responses to the UI. This will enable the UI to show agent responses as they are generated, instead of waiting for the entire response to be generated.Flow based Playground UIExplore the use of a tool like React Flow to add agent nodes and compose agent flows. For example, setup an assistant agent + a code agent, click run and view output in a chat window.Create agent nodesCompose agent nodes into flowsRun agent flowsExplore external integrations e.g. withFlowiseReferencesAutoGen.@inproceedings{wu2023autogen,
      title={AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework},
      author={Qingyun Wu and Gagan Bansal and Jieyu Zhang and Yiran Wu and Shaokun Zhang and Erkang Zhu and Beibin Li and Li Jiang and Xiaoyun Zhang and Chi Wang},
      year={2023},
      eprint={2308.08155},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
Package: auto-git-flow
Auto git flowFeaturesAuto complete the versions when typinggit flow release startorgit flow hotfix startUsageInstallpipinstallauto-git-flowStart a new minor releaseX.X+1.Xgfrs# instance for "git flow release start X.X+1.X"Start a new major releaseX+1.X.Xgfrsm# instance for "git flow release start X+1.X.X"gfrs-m
gfrs--majorStart a new hotfixX.X.X+1gfhs# instance for "git flow hotfix start X.X.X+1"NoteExpected (and supported) tag format is "X.X.X"Checkoutchangelogfromtagsto auto generate a changelog from tags
Package: auto-gitignore
auto-gitignoreCreategitignorefiles right from your command line. Choose your templates faster than ever withautocompletion.Installationauto-gitignore can be installed viapipx.pipxinstallauto-gitignoreYou don't know pipx, yet? pipx is a tool to help you install and run end-user applications written in Python. In a way, it turns Python Package Index (PyPI) into a big app store for Python applications. You should definitelycheck it out.Usage$gitignoreIf you want to test the app before installation, the following command will run the app in a temporary environment:$pipxrunauto-gitignoreIf a.gitignorefile already exists in your current directory you can choose to append or overwrite.CreditsThis app uses thegitignore.ioAPI so credits go out to them.Big thanks to creators ofpipxfor turning PyPI in a big app store.
Package: autogluon.cloud
AutoGluon-CloudAutoGluon-Cloud aims to provide user tools to train, fine-tune and deployAutoGluonbacked models on the cloud. With just a few lines of codes, users could train a model and perform inference on the cloud without worrying about MLOps details such as resource management.Currently, AutoGluon-Cloud supportsAWS SageMakeras the cloud backend.Installationpipinstall-Upip
pipinstall-Usetuptoolswheel
pipinstallautogluon.cloudExamplefromautogluon.cloudimportTabularCloudPredictorimportpandasaspdtrain_data=pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv")test_data=pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv")test_data.drop(columns=['class'],inplace=True)predictor_init_args={"label":"class"}# init args you would pass to AG TabularPredictorpredictor_fit_args={"train_data":train_data,"time_limit":120}# fit args you would pass to AG TabularPredictorcloud_predictor=TabularCloudPredictor(cloud_output_path='YOUR_S3_BUCKET_PATH')cloud_predictor.fit(predictor_init_args=predictor_init_args,predictor_fit_args=predictor_fit_args)cloud_predictor.deploy()result=cloud_predictor.predict_real_time(test_data)cloud_predictor.cleanup_deployment()# Batch inferenceresult=cloud_predictor.predict(test_data)
Package: auto-gpg
auto-gpgA tool that switches gpg home on different working directoryInstallationpipinstallauto-gpgUsageConfigurationWrite configuration file~/.auto-gpg~/Developer/src/github.com:~/.gnupgs/hi@guoyk.xyz
~/Developer/src/gitlab.mycompany.com:~/.gnupgs/guoyk@mycompany.comExecuteauto-gpgauto-gpginvokesgpgcommand withGNUPGHOMEenvironment variable, and the value ofGNUPGHOMEis determined by the configuration file above.auto-gpg-k
auto-gpg-abs# ...Configuregitgitconfig--globalgpg.programauto-gpg
gitconfig--globalcommit.gpgsigntruegitconfig--globaltag.gpgsigntrueCreditsGUO YANKE, MIT License
Package: autogpy
autogpy - AutoGnuplot.pyAutomatic generation of gnuplot figures/scripts/data from python.Author:Alessandro Corbetta, 2019-2020Documentation:https://acorbe.github.io/autogpy/ExamplesLinkGithubLinkWhich problem does it solve?autogpyeliminates annoying duplications of code and data when employing python for data analysis andgnuplotfor figures. Providing a gnuplot-like or a matplotlib-like syntax,autogpyautomatically generates gnuplot scripts and dumps suitably the data.Links: [Source] [Output]Main featuresanything that be obtained by the gnuplot commandplotcan be producedoutput figures are shipped in a folder which includes scripts, data and makefileany gnuplot state modification can be achievedterminals epslatex, tikz/pgfplot and jpgmultiplotsplt.hist-like gnuplot histogram figures generatorjupyter notebook figure previewjupyter notebook gnuplot script inspectioneasy scp-based synchronization between a machine in which the figures are generated (e.g. from even larger datasets) and the "paper writing" machine.RequirementsLinux/MacOs (could work on Windows, yet it has not been adjusted for)Python 3Working latex distributionGnuplot, optionally with tikz terminalpdftoppm(viapoppler-utils) orimagemagickto convert the output pdf figures in png format for jupyter notebook previewGetting autogpyViapippipinstallautogpyOn Google Colab!aptinstallgnuplottexlivepoppler-utilsghostscripttexlive-latex-extra
!pipinstallautogpyFrom sourcegitclonegit@github.com:acorbe/autogpy.git
pipinstallautogpy/In a nutshellPlease see also theexamplesand thedocumentation.importautogpyimportnumpyasnpxx=np.linspace(0,6,100)yy=np.sin(xx)zz=np.cos(xx)withautogpy.AutogpyFigure("test_figure")asfigure:# gnuplot-like syntaxfigure.plot(r'with lines t "sin"',xx,yy)# matplotlib-like syntaxfigure.plot(xx,zz,u='1:2',w='lines',label='cos')generates the following figure (also appearing in jupyter)Most importantly, the following source and data are created in the foldertest_figure$lstest_figure

.gitignore
Makefile
sync_me.sh
fig__0__.dat
fig__1__.dat
fig__.core.gnu
fig__.jpg.gnu
fig__.pdflatex_compile.sh
fig__.pdflatex.gnu
fig__.tikz_compile.sh
fig__.tikz.gnuWithmakeone can obtain jpg, epslatex, and tikz/pgfplot versions of the figure. Notice that the input data has been formatted automatically and dumped in the.datfiles.Inspecting thefig__.pdflatex.gnu, responsible of the epslatex version of the figure, one gets:setterminalepslatexsize9.9cm,8.cmcolorcolortextstandalone'phv,12 'linewidth2setoutput'fig.latex.nice/plot_out.tex'p"fig__0__.dat"withlinest"sin",\"fig__1__.dat"u1:2withlinest"cos"KWONW ISSUESCertain features require imagemagick and a workinggnuplot-tikz.lua. Some versions of these might have bugs. Callfigure.display_fixes()to show known fixes.