Saravanan666/pypi-org huggingface dataset:
Package: autopress
Auto documentationGenerate the markdown documentation (like Sphinx) to be used for vitepress, using GPT4.
It uses the OpenAI API to generate the documentation based on the docstrings of the code.UsageExampleDistribution on pypiUsageInstall the package using pypipipinstallautopressConfigure the package. The OpenAI API key will be asked if not already set.autodocconfigureThe configure command will ask for the OpenAI API key and let you change the default prompt for the OpenAI API.The commands can be listed usingautopress--helpCommands:
  Autopress.from_class      Use this method to generate a docstring from...
  Autopress.from_docstring  Use this method to generate a docstring from...
  Autopress.from_file       Open a markdown file and replace the tags...
  Autopress.from_method     Use this method to generate a docstring from...
  configure                 Set your configuration settingsThe main command isAutopress.from_filewhich will replace the tags in the markdown file with the actual documentation. Go in the root of the package and run the following command:cddemo
autopressAutopress.from_file--fileREADME.md--outputREADME_out.mdThe above command will generate the documentation for the README.md file and save it in the README_out.md file.ExampleThe below tag will be replaced with the actual documentation of the methodconvert_tagfrom the moduleautopress.utils.Before:<MethodAutopress module="autopress.utils" method="convert_tag" />After:<!--<MethodAutopress module="autopress.utils" method="convert_tag"  id="9d66f8"/>-->
<!--<AutoDoc.Content id="9d66f8">-->### `autopress.utils.convert_tag`Converts a tag to a docstring and replaces it in the content. This operation is useful when modifying file contents dynamically by substituting placeholders or tags with actual docstrings.### Parameters| Name | Type | Default | Description |
|------|------|---------|-------------|
|`content`|`str`| | The content of the file. |
|`tag`|`str`| | The tag to convert. |
|`docstring`|`str`| | The docstring to replace the tag with. |
|`existing_id`|`re.Match`| | The existing ID of the tag. |### Returns| Type | Description |
|------|-------------|
|`str`| The content with the tag replaced by the docstring. |

<!--</AutoDoc.Content id="9d66f8">-->You can excute again the command and the autpress will update the content.Distribution on pypiMake sure the version number is updated in theinit.pyfile and in thesetup.pyfile.The distribution on done with continuous integration using github actions. The secretPYPI_API_TOKENis set in the repository settings.Then, to trigger the release, we need to create a tag with the version number. The release will be automatically created and the package will be uploaded to pypi.For example, to release version 1.0.0, we need to do the following:gittagv1.0.0
gitpushoriginv1.0.0You can also create a release with a new tag in the github interface.
Package: autoprof
AutoProf is a pipeline for basic and advanced non-parametric galaxy image analysis.
Its design allows for fast startup and provides flexibility to explore new ideas and support advanced users.
It was written byConnor Stonewith contributions fromNikhil Arora,Stephane Courteau,
andJean-Charles Cuillandre.Installpip install autoprofDocumentationSee ourdocumentationfor a full description of AutoProf's capabilitiesCitationPlease see theADS Bibliographic Recordof the AutoProf paper for proper citation.NoticeThis is the AutoProf isophotal code, it works great in its domain which is wherever one would use isophotal fitting. Thus it is suitable for mostly isolated, mostly resolved, objects. If you are limited by the PSF, crowding, or want to model multi-band/epoch data you may want to consider "AstroPhot" a full forward modelling code. Justfollow this linkto check it out!
Package: autoprotocol
Autoprotocolis the standard way to express experiments in life science. This repository contains a python library for generating Autoprotocol.InstallationTo work from the latest stable release:pipinstallautoprotocolcheck the thereleasesfor more information about the changes that will be downloaded.Alternatively to get more up-to-date features:gitclonehttps://github.com/autoprotocol/autoprotocol-pythoncdautoprotocol-pythonpythonsetup.pyinstallcheck thechangelogfor information about features included onmasterbut not yet released.Building a ProtocolA basic protocol is written by declaringProtocol.refobjects and acting on them withProtocol.instructionmethods.importjsonfromautoprotocol.protocolimportProtocol# instantiate a protocol objectp=Protocol()# generate a ref# specify where it comes from and how it should be handled when the Protocol is doneplate=p.ref("test pcr plate",id=None,cont_type="96-pcr",discard=True)# generate seal and spin instructions that act on the ref# some parameters are explicitly specified and others are left to vendor defaultsp.seal(ref=plate,type="foil",mode="thermal",temperature="165:celsius",duration="1.5:seconds")p.spin(ref=plate,acceleration="1000:g",duration="1:minute")# serialize the protocol as Autoprotocol JSONprint(json.dumps(p.as_dict(),indent=2))which prints{"instructions":[{"op":"seal","object":"test pcr plate","type":"foil","mode":"thermal","mode_params":{"temperature":"165:celsius","duration":"1.5:second"}},{"op":"spin","object":"test pcr plate","acceleration":"1000:g","duration":"1:minute"}],"refs":{"test pcr plate":{"new":"96-pcr","discard":true}}}ExtrasSelect SublimeText snippets are included with this repository.
To use them copy theautoprotocol-python SublimeText Snippetfolder to your local Sublime/Packages/Userdirectory.DocumentationFor more information, see thedocumentation.ContributingFor more information, seeCONTRIBUTING.
Package: autoptim
# autoptim: automatic differentiation + optimizationDo you have a new machine learning model that you want to optimize, and do not want to bother computing the gradients? Autoptim is for you.## Short presentationAutoptim is a small Python package that blends `autograd` automatic differentiation in `scipy.optimize.minimize`.The gradients are computed under the hood using automatic differentiation; the user only provides the objective function:```pythonimport numpy as npfrom autoptim import minimizedef rosenbrock(x):return (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2x0 = np.zeros(2)x_min, _ = minimize(rosenbrock, x0)print(x_min)>>> [0.99999913 0.99999825]```It comes with the following features:- **Natural interfacing with Numpy**: The objective function is written in standard Numpy. The input/ output of `autoptim.minimize` are Numpy arrays.- **Smart input processing**: `scipy.optimize.minimize` is only meant to deal with one-dimensional arrays as input. In `autoptim`, variables can be multi-dimensional arrays or lists of arrays.- **Preconditioning**: Preconditioning is a simple way to accelerate minimization, by doing a change of variables. `autoptim` makes preconditioning straightforward.### DisclaimerThis package is meant to be as easy to use as possible. As so, some compromises on the speed of minimization are made.## InstallationTo install, use `pip`:```pip install autoptim```## Dependencies- numpy>=1.12- scipy>=0.18.0- autograd >= 1.2## ExamplesSeveral examples can be found in `autoptim/tutorials`
Package: autopub
AutoPubAutoPub enables project maintainers to release new package versions to PyPI by merging pull requests.EnvironmentAutoPub is intended for use with continuous integration (CI) systems such asGitHub Actions,CircleCI, orTravis CI. Projects used with AutoPub are built viabuildand published viaTwine. Contributions that add support for other CI and build systems are welcome.ConfigurationAutoPub settings can be configured via the[tool.autopub]table in the target project’spyproject.tomlfile. Required settings include Git username and email address:[tool.autopub]git-username="Your Name"git-email="your_email@example.com"Release FilesContributors should include aRELEASE.mdfile in their pull requests with two bits of information:Release type: major, minor, or patchDescription of the changes, to be used as the changelog entryExample:Release type: patch

Add function to update version strings in multiple files.UsageThe followingautopubsub-commands can be used as steps in your CI flows:autopub check: Check whether release file exists.autopub prepare: Update version strings and add entry to changelog.autopub build: Build the project.autopub commit: Add, commit, and push incremented version and changelog changes.autopub githubrelease: Create a new release on GitHub.autopub publish: Publish a new release.For systems such as Travis CI in which only one deployment step is permitted, there is a single command that runs the above steps in sequence:autopub deploy: Runprepare,build,commit,githubrelease, andpublishin one invocation.
Package: autoPVM
autoPVM v0.3Automatically conduct Price-Volume-Mix analysis on datasets.Explore the docs »Report Bug·Request FeatureAbout The ProjectThis project aims at conducting the Price Variance Mix analysis automatically. The main purpose of PVM analysis is to provide a high-level overview view into the past, and to break down the change in revenue or margins into some key components or categories. The categories are used to highlight and help explain how much of the overall change in revenue or margins was caused by, e.g. the implemented Price changes, versus changes in total costs, versus the impact from change in Volumes, versus changes other effects, comparing two different time periods.(back to top)InstallationThe autoPVM package can be installed using pip.autoPVM uses Numpy, Pandas & Plotly as dependencies.Install packagepip install autoPVM(back to top)UsageImport the PVM class usingfrom autoPVM import PVMRead a Pandas dataframedata = pd.read_csv('Sample Dataset/Supermarket Sales.csv')Create an analysis object and pass the dataframepvm = PVM.PVMAnalysis(data=data)Set column name markers of required quantities and marginsPVM.setMarkers(\
                 quantity_pr='QTY_PM'
               , quantity_ac='QTY_AM'
               , margin_pr='MARGIN_PM'
               , margin_ac='MARGIN_AM'
               , hierarchy=['Customer type', 'Gender', 'Branch', 'Product line'])quantity_prmarks previous time period quantity.quantity_acmarks current/next time period quantity.margin_prmarks previous time period margin.margin_acmarks current/next time period margin.hierarchymarks dimensional heirarchy:[Highest Level, .. , Lowest Level].Calculate the margin bridge usingpvm.calculateMarginBridge()Plot the bridge usingpvm.plotPVMBridge()Final dimension aggregate can be exported usingpvm.exportMarginBridgeFile()(back to top)ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate 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(back to top)LicenseDistributed under the Apache-2.0 License. SeeLICENSE.txtfor more information.(back to top)ContactAkash Sonthalia -@LinkedIn-axsonthalia@gmail.comProject Link:https://github.com/asonthalia/autoPVM(back to top)AcknowledgmentsSupermarket Sales DatasetPVM Analysis Explanation(back to top)
Package: autopvs1-batch
autoPVS1 batch该包集成autoPVS1，实现以VCF/BED文件为输入对多个变异批量VEP注释及autoPVS1注释。
拟解决的问题：autoPVS1集成VEP注释命令行，一次只能注释一个变异，若需要对大量变异进行分析，则需要多次运行vep注释命令行。本工具首先使用vep对保存大量变异的VCF/BED文件进行注释，并重写了AutoPVS1.vep_run逻辑（由运行vep注释单个变异改为根据vep结果过滤出指定变异的注释信息）VCF：对应SNV变异BED：对应CNV变异参数说明-i/--input: 输入文件, VCF for SNV, BED for CNV-o/--outfile: 输出autoPVS1结果-r/--hg19_fa: hg19 参考序列fasta文件路径-R/--hg38_fa: hg38 参考序列fasta文件路径-d/--vep_cahce: vep cache目录，即vep数据库目录路径-g/--genome_version: 当前变异对应的基因组版本，可选项：hg19, hg38安装运行zhuy@ubuntu:/projects/example$pipinstallautopvs1-batch
zhuy@ubuntu:/projects/example$autopvs1-batch.py-itest.vcf-otest.autopvs1_anno.txt-r/path/to/hg19.fa-R/path/to/hg38.fa-d~/.vep-ghg19结果说明变异PVS1 criterionPVS1 strength_rawPVS1 strength1:5935162:5935162:A:T           SS9    Strength.Moderate      Strength.Moderate
3:142281601:142281604:GAGT:-    NF1    Strength.VeryStrong    Strength.Unset限制本工具仅在python>=3.8环境下进行测试过。本工具引入autoPVS1项目代码并本着不对该项目代码进行任何改动的原则，由于autoPVS1源码内部机制读取config.ini相对路径写死了，因此本工具运行时需要修改site-packages/autopvs1/config.ini文件内容，要求执行用户对该文件局具有写权限。root用户环境下运行此工具或在用户所属pyenv，conda等对config.ini有写权限的环境下运行此工具
Package: autopxd2
python-autopxd2A friendly fork of autopxdhttps://github.com/tarruda/python-autopxdIt generates.pxdfiles automatically from.hfiles.Tested against:Python 3.7Python 3.8Python 3.9Python 3.10installation:pipinstallautopxd2Usage:Usage:autopxd[OPTIONS][INFILE][OUTFILE]GenerateaCythonpxdfilefromaCheaderfile.

Options:-v,--versionPrintprogramversionandexit.-I,--include-dir<dir>AllowtheCpreprocessortosearchforfilesin<dir>.-D,--compiler-directive<directive>AdditionaldirectivesfortheCcompiler.--debug/--no-debugDumppreprocessoroutputtostderr.-h,--helpShowthismessageandexit.Release History:v2.3.0 - 2023-01-08Add: Support for const & volatile qualifiers from Emmanuel Leblond#42v2.2.3 - 2022-10-04Move tohttps://github.com/elijahr/python-autopxd2v2.2.0 - 2022-08-03Add: Microsoft Visual C++ support from Steve Dower#40v2.1.1 - 2022-05-24Add:--regexfor arbitrary conversionsFix: Various other fixes and improvements from Mads Ynddal in#38v2.0.4 - 2021-11-23Fix: Windows CRLF issue (#24)v2.0.3 - 2021-10-08Fix: remove unnecessaryimportlib_resourcesfrominstall_requiresv2.0.2 - 2021-10-07Migrate tosetup.cfgv2.0.1 - 2021-10-06Add:--compiler-directiveoption to pass along to the compilerAdd: some type annotations (nodes.py)Deprecation: Drop support for Python 2Add: linting, format with blackAdd: Migrate from Travis CI to Github Actionsv1.1.0 - 2020-01-03Add: Support for macOSRoadmap:Refactoring of the codeDONEAdding tests for PEP8DONEUploading to PyPiDONECheck that the generated code is correct by comparing it to the libc in CythonMore testsMerge it into Cython so that the.pxdfiles aren't necessary anymore? Maybe.Please raise an issue if the generated code isn't correct.It's difficult to catch all the corner cases.
Package: auto-py-notion
NotionPyfor creating,updating and querying databases and pages ,
NotionPy is a python module to help you integrete notion with your software/workflowinstallationin your terminal :pip install auto-py-notionRequirmentsbefore we get started there are a couple of things we need to do :create an integration and grab a token fromhereshare the integration with you database or pageget the id of the database or page :https://www.notion.so/myworkspace/a8aec43384f447ed84390e8e42c2e089?v=...the database id is before the question mark and after the backslashmore details fromofficial notion docsGetting Startedlet's create a sample page inside a database :fromNotionPy.notionpyimportNotionClientinst=NotionClient("Your integration token")inst.create.page(database_id="the id of database of choice",data=[# List of tuples # provided properties must be created first in the db("Name","title","kareem"),("price","number",254),("to-do","checkbox",False),],#Optionalicon="ðŸ”¥",#Optionalcover="https://images.unsplash.com/photo-1523867574998-1a336b6ded04?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Mnx8Y292ZXJ8ZW58MHx8MHx8&w=1000&q=80",)-> Note that the data inserted must be [ ( prop_name ,type ,value ) ]currently there is no support for creating empty pages or empty values but look forward for feature updatesretreiving data from a database :inst.query.db("id of the database",in_json=True,#Optionaljson_indent=2,#Optionalprint_data=True#Optional)Supported properties type-title          -rich_text
    -select         -multi_select
    -relation       -people
    -checkbox       -url
    -files          -email
    -phone          -numberNotionPy 0.1.2 , What's New !sorting in queryring databasesfiltering in queyring databasesBug fxNotesthere are other modules that interact with notion's API so what makes this module different ? it is the fact that , as far as my research went , it is the most userfriendly and practical oneThis module is still has a lot to offer in the future , so all of your suggestions , issues , contributions are very welcomed
Package: auto-pypi
Why Should I Use This?This is a Python command line tool to automatically setup your (updated version) python package onto PyPi.As you may know, PyPi indexes (both real and test index) do not allow you to reuse package name (considering version numner), which means you can not upload your package with the same package name together with an identical version numner.In another word, you have to change the version number in yoursetup.pyfile before each time you want to upload your modified package. What’s more, you also need to remove the old build and egg folder before you run the setup tools.You’ll find it not convenient at all if you are uploading and testing your package frequently. Even if you are not going to upload and test frequently, each time you remove the previous setup related folders comes with some risks and is still time-consuming.By using this command line tool, you will be all set after a single commandautopypi. The only thing you need to care about now is the package version number.You could chage the version number in thesetup.pyfile as usual, but I highly recommend you to change a little bit in yoursetup.pyfile making the version number as an input value from the terminal:version_number=input("Input the new version number you are going to use: ")setuptools.setup(name="auto_pypi",version=version_number,author="Sen LEI",...)By doing this, you just need to run the commandautopypi, and specify a version number later when it pops up.UsageUse As A Command Line ToolJust runautopypiin terminal, providingyour package’s locationandnew version numberlater:Usage:autopypi[OPTIONS]PKG_DIRPythoncommandlinetooltosetupPythonpackageautomatically.Example:$autopypiyour-package-root-directory-rExample:$cdyour-package-root-directory$autopypi.-rOptions:-r,--realUsetherealPyPiindex(insteadoftestPyPi).--helpShowthismessageandexit.Then you’ll be asked to input the username and passcode of PyPi / Test-PyPi as usual.DocumentationCheck out the latestauto_pypidocumentation atRead the Docs
Package: auto-pypi-setup
Why Should I Use This?This is a Python command line tool to automatically setup your (updated version) python package onto PyPi.As you may know, PyPi indexes (both real and test index) do not allow you to reuse package name (considering version numner), which means you can not upload your package with the same package name together with an identical version numner.In another word, you have to change the version number in yoursetup.pyfile before each time you want to upload your modified package. What’s more, you also need to remove the old build and egg folder before you run the setup tools.You’ll find it not convenient at all if you are uploading and testing your package frequently. Even if you are not going to upload and test frequently, each time you remove the previous setup related folders comes with some risks and is still time-consuming.By using this command line tool, you will be all set after a single commandautopypi. The only thing you need to care about now is the package version number.You could chage the version number in thesetup.pyfile as usual, but I highly recommend you to change a little bit in yoursetup.pyfile making the version number as an input value from the terminal:version_number=input("Input the new version number you are going to use: ")setuptools.setup(name="auto_pypi",version=version_number,author="Sen LEI",...)By doing this, you just need to run the commandautopypi, and specify a version number later when it pops up.UsageUse As A Command Line ToolJust runautopypiin terminal, providingyour package’s locationandnew version numberlater:Usage:autopypi[OPTIONS]PKG_DIRPythoncommandlinetooltosetupPythonpackageautomatically.Example:$autopypiyour-package-root-directory-rExample:$cdyour-package-root-directory$autopypi.-rOptions:-r,--realUsetherealPyPiindex(insteadoftestPyPi).--helpShowthismessageandexit.Then you’ll be asked to input the username and passcode of PyPi / Test-PyPi as usual.DocumentationCheck out the latestauto_pypidocumentation atRead the Docs
Package: autopyreadme
AutoPyReadme:This is a Readme Generator for Python Projects. This can be used to generate Readme(s) faster, without putting a lot of efforts to assist the community developers in a much better and easier way!How to Use?from autopyreaadme import readme

readme.create() # call to create a readme
readme.update() # call to update the readme after changing data in readme.jsonAuthors:Nishant SharmaLicense:mitScripts:create readme:readme.create() // callable functionupdate readme:readme.update() // callable functionReader's Notes:Main File to Use:readme.pyGuess What?:This Readme is made using this library only!Feel free to Reach me out for collabs!:I am a curious developer, who wants to build 2 Impact, Eager to Connect to other Curious Developers!Check These Out:My Instagram|My LinkedInProject Dependencies:os
json
colorama==0.4.4
Package: autopysta
AutopystaAutopysta is a Python library for modeling 2D traffic on highways. It is written natively in C++ for better perfomance and wrapped by SWIG for use on Python.ImportantIf the installation is on Windows you have to keep in mind that the Python version has to be 3.8. Otherwise it will install but not work properly due to probelms with the dll.Linux doesn't have this problem and works for 3.8 onwards.DetailsThe package comes with a .so (for Linux systems) and a .pyd (for Windows) and a .py that use those files depending on the platform.Currently there is no support for Mac OS for it is planned for the future.How to use itTo use autopysta you just have to import it in Python:import autopystaThen it can be used to make simulations with vehicles that move and change tracks according to the models chosen in a highway defined by the user. We are working on a documentation to explain how to use all of it.
Package: autopytest
autopytestAn implementation ofautotestfor Python inspired byautotestandguard.autopytestobserves file change events and whenever you save a file it runs the appropriate tests withpytest.Featuresautopytestobserves filemodifiedevents and will perform the following:source fileswill find and run the associated individual test fileupon success, will run the entire suiteif we can't find a matching test, run the entire suitetest fileswill run that test fileupon success, will run the entire suiteInstall# pippipinstallautopytest# poetrypoetryaddautopytestConfigurationIn yourpyproject.tomladd the following.[tool.autopytest]source_directories=["app"]test_directory="tests"Usagecd{project}autopytest

autopytest{path}Project StructureTest naming iscurrentlyimportant.Multiple nested directory structures are supported as long as the convention is followed.Applicationspyproject.tomlfor applications[tool.autopytest]source_directories=["app","lib"]test_directory="tests"Given the above configuration. You should use a directory structure like the following. e.g. Ifapp/package/module.pyis edited we will attempt to locate and runtests/app/package/test_module.py📁 app
    📄__init__.py
    📁 package
        📄__init__.py
        📄 module.py
📁 lib
📁 tests
    📄__init__.py
    📁 app
        📁 package
            📄 test_module.py
    📁 libLibrariespyproject.tomlfor libraries[tool.autopytest]include_source_dir_in_test_path=falsesource_directories=["src"]test_directory="tests"If you are developing library and want your folder structure like the following. e.g. Ifsrc/package/module.pyis edited we will attempt to locate and runtests/package/test_module.py📁 src
    📁 package
        📄__init__.py
        📄 module.py
📁 tests
    📁 package
        📄 test_module.py