Saravanan666/pypi-org huggingface dataset:
Package: material-plausible-plugin
Plausible Analytics for MaterialPlausible Analytics is a simple, open-source, lightweight and privacy-friendly
web analytics alternative to Google Analytics.This plugin implements Plausible Analytics support in Material for MkDocs.Live demoQuick startInstall the plugin:pipinstallmaterial-plausible-pluginAdd the following lines tomkdocs.yml:plugins:-material-plausibleextra:analytics:provider:plausibledomain:example.com#: If using custom domain proxy or self-hosting Plausible,#: uncomment and specify script path here:# src: "https://plausible.example.com/js/plausible.js"Feedback widgetTo enable the feedback widget, add the following lines inside theextra.analyticsblock:feedback:title:Was this page helpful?ratings:-icon:material/emoticon-happy-outlinename:This page was helpfuldata:goodnote:>-Thanks for your feedback!-icon:material/emoticon-sad-outlinename:This page could be improveddata:badnote:>-Thanks for your feedback! Help us improve this page byusing our <a href="..." target="_blank" rel="noopener">feedback form</a>.Then in your Plausible account, go to your website's settings and visit theGoalssection. For each rating defined, click on the+ Add goalbutton,
selectCustom eventas the goal trigger and enterFeedback: {rating data value}.For example, if you have two ratings –goodandbad, addFeedback: goodandFeedback: badgoals.Ratings will be shown in theGoal Conversionssection at the very bottom of the page, as soon as any are available:You can click on a specific “goal” to filter your dashboard by it. For example, if you filter by theFeedback: badgoal, you can see which pages need the most attention in theTop Pagessection.Site searchYou can track site search usage, too. Enable thesearchplugin, then in theGoalssection, set up a goal namedSearch. On your statistics page, you
can click it in the goals list to see what terms your users are searching for:Licensematerial-plausible-pluginis distributed under the terms of theISC license.
Package: materials
materials is a database of physical and chemical data, possibly with
temperature dependence, of various more or less common materials. All
data are given in SI units.For example, to plot the density of various materials between 274 and
370 K you can doimportmaterialsimportmatplotlib.pyplotaspltimportnumpyT=numpy.linspace(274.0,370.0,num=100)rho_air=materials.air.density(T)rho_argon=materials.argon.density(T)rho_copper=materials.copper.density(T)rho_water=materials.water.density(T)plt.semilogy(T,rho_copper,label='copper')plt.semilogy(T,rho_water,label='water')plt.semilogy(T,rho_argon,label='argon')plt.semilogy(T,rho_air,label='air')plt.title('densities')plt.xlabel('temperature (K)')plt.ylabel('density (kg/m^3)')plt.legend()plt.show()Installationmaterials isavailable from the Python Package
Index, so simply typepip install -U materialsto install or upgrade.TestingTo run the materials unit tests, check out this repository and typepytestDistributionTo create a new releasebump the__version__number,publish to PyPi and GitHub:make publishLicensematerials is published under theMIT
license.
Package: materials-commons
This package contains two modules:
(1) materials_commons.api, the Materials Commons Python API, an interface
to the Materials Commons servers; and (2) materials_commons.cli, the
Material Commons command line interface which is built on the API and provides
command line access to the Materials Commons servers.
We assume you have used (or are otherwise familiar with) the Materials
Commons web site,https://materialscommons.org/, or a similar site based on the
materials commons code (https://github.com/materials-commons/materialscommons.org),
and intend to use these tools in that context. There are additional examples and code at
the github site shown above; these include demo_project.py a script that builds
a demo project in the materials-commons web site.
Package: materials-compendium
Materials CompendiumTheMaterials Compendiumpackage facilitates the parsing of essential material composition data from the "Compendium of Material Composition Data for Radiation Transport Modeling," a comprehensive resource provided by the esteemed Pacific Northwest National Laboratory (PNNL). This package equips radiation transport modelers with the necessary tools to access material properties crucial for accurate simulation within various radiation transport codes.InstallationInstallation from PyPITo integrate theMaterials Compendiumpackage seamlessly into your workflow, you can use thePython Package Indexusingpipcommand:pipinstallmaterials-compendiumInstallation from RepositoryAlternatively, if you prefer to work directly from the repository, follow these steps:gitclonehttps://github.com/pyne/materials-compendium.gitcdmaterials-compendium
pipinstall.For running tests, use:pipinstall.[test]DocumentationFor comprehensive guidance on leveraging the capabilities of thematerials-compendiumpackage and an exhaustive API reference, kindly refer to ouronline documentation (working).DisclaimerThe material composition data enclosed within the JSON file are meticulously curated and aligned with Revision 2 of the Compendium. These data are thoughtfully annotated with references for user assurance. It's imperative to acknowledge the potential variances in composition or densities for certain materials, and we've diligently included ranges in references whenever feasible.For materials not cataloged in the provided references, users may find it necessary to supply application-specific impurity information.We stress the importance of meticulously aligning simulation parameters, such as reaction cross sections, within your selected radiation transport code to uphold the integrity of simulation outcomes.NoteworthyWhile this script is meticulously tailored to JSON file parsing for radiation transport modeling, its adaptability for other applications is an avenue worth exploring.ContributionsWe extend a warm invitation to contribute to theMaterials Compendium. We believe that fostering an environment of collaboration is paramount. Should you wish to contribute, the process is as straightforward as forking our repository on GitHub, implementing your modifications, and subsequently initiating a pull request. Should queries arise or assistance be required during this process, please don't hesitate to engage with us through the PyNE mailing list (https://groups.google.com/forum/#!forum/pyne-dev,pyne-dev@googlegroups.com). Your involvement will undoubtedly enrich the package and its utility.
Package: materials-eunomia
EunomiaAI Agent Chemist for Developing Materials DatasetsIterative-Chain-of-Verification (CoV)Eunomia employs a chain-of-verification process iteratively to minimize the likelihood of hallucination, as well as integrating chemistry-informed knowledge into decision-making and action-taking plannings of the LLM.Live Apphttps://eunomia.streamlit.app/Example 1: Molecular Targets and Peptide SequencesVEGFR exampleExample 2: Water Stable Metal-organic FrameworksWater stability exampleExample 3: Thermal Conductivity of Metal-organic FrameworksThermal conductivity exampleInstallationpipinstallmaterials-eunomiaor you can clone the source code and install in developer mode:gitclonehttps://github.com/AI4ChemS/Eunomia.git&&cdEunomia
pipinstall-e.Confirm correct installation by running the unittests:cdtests&&python-munittestdiscover
Package: materials-learning-algorithms
MALAMALA (Materials Learning Algorithms) is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional simulations.MALA is designed as a modular and open-source python package. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Sandia National Laboratories (SNL) and the Center for Advanced Systems Understanding (CASUS). SeeContributingfor contributing code to the repository.This repository is structured as follows:├── examples : contains useful examples to get you started with the package
├── install : contains scripts for setting up this package on your machine
├── mala : the source code itself
├── test : test scripts used during development, will hold tests for CI in the future
└── docs : Sphinx documentation folderInstallationWARNING: Even if you install MALA via PyPI, please consult the full installation instructions afterwards. External modules (like the QuantumESPRESSO bindings) are not distributed via PyPI!Please refer toInstallation of MALA.RunningYou can familiarize yourself with the usage of this package by running
the examples in theexample/folder.ContributorsMALA is jointly maintained bySandia National Laboratories(SNL), USA.Scientific supervisor: Sivasankaran Rajamanickam, code maintenance:
Jon VogelCenter for Advanced Systems Understanding(CASUS), Germany.Scientific supervisor: Attila Cangi, code maintenance: Lenz FiedlerA full list of contributors can be foundhere.Citing MALAIf you publish work which uses or mentions MALA, please cite the following paper:J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson,
A. Cangi, S. Rajamanickam (2021). Accelerating Finite-temperature
Kohn-Sham Density Functional Theory with Deep Neural Networks.Phys. Rev. B 104, 035120 (2021)alongside this repository.
Package: materials-lib
Materials_Lib - General Materials Python LibraryThis repo is intended to store the physical properties of materials used throughout coding projects. In case you want to define a new material, create a new toml file by duplicating thetemplate.tomlfile.Documentation(Under construction)Using UnitsIt is common to need to use physical units. It is recommended to use thepint. You can add apint.UnitRegistry()as an argument when instancing theMaterialclass. The class will then use the same units as the rest of the project.
Package: materialsml
materialsMLA Machine Learning package for materials informatics. A crafty data analysis complement to the materials database fromThe Materials ProjectGetting StartedLocal Installation and UsagepipinstallmaterialsmlIt is recommended to run this package using avirtualenvvirtual environment. To do so, follow the below simple protocol to create the virtual environment, run it, and install the package there:virtualenvvenvsourcevenv/bin/activatepipinstallmaterialsmlpython[your-script.py]To exit the virtual environment, simply typedeactivate. To access it at any other time, typesource venv/bin/activateon the directory containing thevenvfolder.viewer (in development)Disclaimer: Use At Your Own RiskThis program is free software. It comes without any warranty, to the extent permitted by applicable law. You can redistribute it and/or modify it under the terms of the MIT LICENSE, as published by Andrew Garcia. See LICENSE below for more details.MIT licenseCopyright 2023 ©Andrew Garcia.
Package: material-va-lighthouse
Material VA Lighthouse PluginThis plugin styles a Material for MKDocs site using CSS from theVA Design System.InstallationOpen a terminal and run the following command:pipinstallmaterial-va-lighthouseOr add the following to yourrequirements.txtfile:material-va-lighthouseUpdate yourmkdocs.ymlto include the plugin:plugins:-material-va-lighthouseUpdate thethemesection. Setmaterialas your theme (if you're not already)
and point the logo to the Lighthouse logo from this plugin:theme:name:materiallogo:'assets/images/va_lighthouse_logo.png'Then add anextra_csssection to yourmkdocs.ymlthat points to the CSS file from this plugin:theme:name:materiallogo:'assets/images/va_lighthouse_logo.png'extra_css:-'assets/stylesheets/va_lighthouse.css'Runmkdocs buildandmkdocs serveto see the changes.
Package: materialx
IntroductionMaterialX is an open standard for representing rich material and look-development content in computer graphics, enabling its platform-independent description and exchange across applications and renderers.  Launched atIndustrial Light & Magicin 2012, MaterialX has been a key technology in their feature films and real-time experiences sinceStar Wars: The Force AwakensandMillennium Falcon: Smugglers Run.  The project was released as open source in 2017, with companies including Sony Pictures Imageworks, Pixar, Autodesk, Adobe, and SideFX contributing to its ongoing development.  In 2021, MaterialX became the seventh hosted project of theAcademy Software Foundation.Quick Start for DevelopersDownload the latest version of theCMakebuild system.Point CMake to the root of the MaterialX library and generate C++ projects for your platform and compiler.Select theMATERIALX_BUILD_PYTHONoption to build Python bindings.Select theMATERIALX_BUILD_VIEWERoption to build the MaterialX viewer.Supported PlatformsThe MaterialX codebase requires a compiler with support for C++14, and can be built with any of the following:Microsoft Visual Studio 2017 or newerGCC 6 or newerClang 6 or newerThe Python bindings for MaterialX are based onPyBind11, and support Python versions 3.6 and greater.MaterialX ViewerTheMaterialX Viewerleverages shader generation to build GLSL shaders from MaterialX graphs, rendering the results using the NanoGUI framework.Figure 1:Procedural and uniform materials in the MaterialX viewerFigure 2:Textured, color-space-managed materials in the MaterialX viewerOpen Chess SetThe Open Chess Set is an open reference asset, consisting of aMaterialX filein the Standard Surface shading model and ageometry filein the glTF format.  It was authored by Moeen Sayed and Mujtaba Sayed, and was contributed to the MaterialX project by Side Effects.Figure 3:The Open Chess Set, rendered in Arnold for MayaFigure 4:The Open Chess Set, rendered in Karma XPU for HoudiniPre-Built BinariesThe following packages contain pre-built binaries for the latest release, including the MaterialX viewer, Python libraries, and example assets:Microsoft Windows (Visual Studio 2022, Python 3.11)MacOS (Xcode 14, Python 3.11)Linux (GCC 12, Python 3.11)Additional ResourcesTheDeveloper Guidecontains a developer-oriented overview of MaterialX with API documentation.ThePython Scriptsfolder contains standalone examples of MaterialX Python code.TheJavaScriptfolder contains details on building JavaScript bindings for MaterialX.Presentations atASWF Open Source Daysand theSIGGRAPH Physically Based Shading Courseprovide details on the roadmap for MaterialX development.
Package: materialxjson
MaterialX JSON I/OContentsIntroductionInstallationUsageDocumentationIntroductionThis is a Python package for supporting interoperability of  MaterialX documents by adding support conversion to / from aJSONrepresentation.TheJSONrepresentation is lossless and is targeted at integrations which prefer to useJSONas a data exchange format instead ofXML.DocumentationVisit thehome pagefor For further information about this package, including links to API documentation and a Jupyter notebook with sample code that expands on the basicUsageexample.InstallationThe Python package is losted onPyPiand can be installed
using `pip``:pipinstallmaterialxjsonor thesource repositorycan be cloned and the package built from the command line:python-mbuildThis will build a distribution folder calleddistwhich contains a zip file which can be installed using:pip--install<nameofzip>UsageThe following example shows bidirectional conversion:A MaterialX file inXMLformat is read in and written to a string inJSONformat.A MaterialX file inJSONformat is read in and written to a string inXMLformat.Note that anyJSONpackage can be used to performJSONI/O. Thejsonpackage is used by the utilities provided in this package.Package SetupThematerialjsonpackage can be loaded as follows, along with MaterialX and json packages:importjsonimportMaterialXasmxfrommaterialxjsonimportcoreXML to JSON Format ConversionA MaterialX document can be read in from an XML file as follows:importpkg_resources# Read in MaterialX filemtlxFileName=pkg_resources.resource_filename('materialxjson','data/standard_surface_default.mtlx')doc=mx.createDocument()mx.readFromXmlFile(doc,mtlxFileName)and then converted to JSON format as follows by creating aMaterialXJsonobject and calling thedocumentToJSONmethod:# Create I/O handlermtlxjson=core.MaterialXJson()# Write to JSON formatjsonObject=mtlxjson.documentToJSON(doc)The contents of the JSON object can be extracted out using thejsonpackage as follows. (An indentation level of2is
used to make the output more readable.)# Convert JSON object to JSON stringjsonString=core.Util.jsonToJSONString(jsonObject)JSON to XML Format ConversionA JSON file can be read in as follows. In this case we load the sample file included with the package:# Get file to loadjsonFileName=pkg_resources.resource_filename('materialxjson','data/standard_surface_default_mtlx.json')# Load JSON filejsonObject=core.Util.readJson(jsonFileName)The JSON object is then converted to a MaterialX document and written out to an XML string as follows:# Create I/O handlermtlxjson=core.MaterialXJson()# Read JSON object into documentdoc=mx.createDocument()mtlxjson.documentFromJSON(jsonObject,doc)# Write to XML Stringdocstring=core.Util.documentToXMLString(doc)CaveatsThe following are some caveats to be aware of when using this package:There is no equivalent support for "include files" for JSON conversion. The JSON representation is intended to be a single file representation.XML comments are not preserved in the JSON representation.AuthorLinkedIn:Bernard KwokGitHub:kwokcbEmail:kwokcb@gmail.com
Package: material-zui
InstallationPrerequisitesPython >=v3.10Install packagepip install poetrySetup and active virtual enviroment withpoetrypoetry add material-zuito install all dependenciesDone!! 😁Modulesbardbing_aicompresscrawlcrontabdate_timeenvfakefilegptimagegrayscalecolorizationsketchtransparent backgroundupscaleconvert to jpg/pnglistlognetworknumberosregexreplicate: AI Platform APIseleniumstringtelegram_botutilityvalidatePackagehttps://pypi.org/project/material-zui
Package: materia-spectra
Toolkit for managing spectral data.Toolkit for managing spectral data through Materia.Getting StartedInstallingFrompip:pip installmateria-spectraFromconda:conda install-cconda-forge-ckijanamateria-spectraDocumentationSee documentationhere.ExamplesSee example scripts inExamples.AuthorsKi-Jana CarterLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.ContributingSeeCONTRIBUTING.
Package: materia-structure
Streamlined interface for managing atomistic structure data.Streamlined interface for managing atomistic structure data through Materia. Defines unified and intuitive functionality for manipulating atomistic structure data leveraging the power of Openbabel.Getting StartedInstallingFrompip:pip installmateria-structureFromconda:conda install-cconda-forge-ckijanamateria-structureDocumentationSee documentationhere.ExamplesSee example scripts inExamples.AuthorsKi-Jana CarterLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.ContributingSeeCONTRIBUTING.
Package: materia-utils
Lightweight utilities for atomistic simulations of materials.Utilities for atomistic simulations of materials through Materia.Getting StartedInstallingFrompip:pip installmateria-utilsFromconda:conda install-cconda-forge-ckijanamateria-utilsDocumentationSee documentationhere.ExamplesSee example scripts inExamples.AuthorsKi-Jana CarterLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.ContributingSeeCONTRIBUTING.
Package: materya-mercury
A quantitative trading library.Mercuryis a major god in Roman mythology.He is, among other things, the god of financial gain and communication (including divination).Quick startInstallWith pip$pipinstallmaterya-mercuryFrom sourceClone this repo and run$makeExtras DependenciesThe library provides someextras modulesimplementing interfaces ofmercuryas examples and helpers to quickly setup your own strategies.You can install their eventual dependencies with$pipinstallmaterya-mercury[extra_<type>_<modulename>]e.g. to install extras dependencies for alphavantage datasource$pipinstallmaterya-mercury[extra_datasource_alphavantage]UsageComing soonSeeSamplesExtras ModulesBrokersInteractive BrokersOandaXAPIDatasourcesAlphavantageCSVQuandlStrategiesSMA CrossoverContributingDevelopmentA fully configuredVSCode Development Containeris available to quickly get into development without the hassle of setting up a local environment, you just have to jump in.Alternatively you can use a development container without VS Code, the setup is available withdocker-composeto inspect or run tests against the library in a compatible environment.$docker-composerun--rmdevbash
Creatingmercury_dev_run...donecloud@8e5fb622c5c5:/workspace$maketestIf you prefer to work locally (or don't use VSCode), you need first to install the library in a development mode:$makeinstall-devTestYou can simply run the test suite in the current environment with:$maketestLicenseGPL-3.0
Package: mate-shell
Mate: Extremely pluggable and modular shellFeaturesEasy plugin creation using setuptoolsManage group of plugins better bymate --shellsBuilt-in auto completeForced modularity in pluginsDope looking shellProvides command output redirect to embedded ipythonSupports batch executions bymate --execand JSON formatted outputInstallationUsing pypipip3installmate-shellUsing gitgitclonehttps://github.com/twisted-fun/mate.gitcdmate
pip3install-e.UsageHope thisasciinemawill help.PluginsCommands can be added into mate shell as plugins. And it's super easy!Writing a plugin# demo_plugin.pyfrommateimportadd_plugins,commanddefsxor(s1,s2):return"".join(chr(ord(a)^ord(b))fora,binzip(s1,s2))@command(option="xor")defbitwise_string_xor(self,str1,str2):"""A bitwise xor operation for two strings."""return{"result":sxor(str1,str2).__repr__()}add_plugins(modules=[bitwise_string_xor])# setup.pyfromsetuptoolsimportsetupsetup(name="mate-demo-plugin",install_requires="mate-shell",entry_points={"mate":["bitwise_str_xor = demo_plugin"]},py_modules=["demo_plugin"],)Installing the pluginpip3install-e.Accessing the pluginCheck out thisasciinema.More?Example Plugins
Package: mate-wrapper
ðŸ¦™ MATEThe Easy Telegram Application Makerpip install mate-wrapperWelcome to MATE, the open source wrapper for thePythonTelegramBot wrapperthat makes building telegram applications veryeasyandintuitiveby adopting
aminimalcoding style!DesignMATE is designed as a way to build telegram applications which involvesusing as little
custom code as possible, relying on tested and safe solutions to do~90%of the work
while injecting custom logic where necessary.It's components are also easily extensible and reusable to allow for a wide degree of
flexibility.There are 5 main types of components:Prompts:A Prompt is what is used to send messages to users, they support Markdown formatting,
web previews, keyboards and variable insertion without writing any additional
code other than the message itself!GettersA Getter is what is used to get any input from the User, it supports anything
from text to keyboard inputs and even photos!HandlersA convenient way of abstracting away some complexity tied to the PythonTelegramBot
wrapper and making the code more readable in the process!ChainsThese Chains aren't of the binding kind! Chains let you chain together multiple
functions (handlers, getters, custom functions or even other chains) in order to
build complex user interactions using the least possible amount of custom code!MenusConnect everything together in an easy way with Menus and the ready-made
Panels & Buttons that come with them!Simple exampleHere's how a simple echo bot looks like!fromMateWrapper.botimportTelegramBotfromMateWrapper.handlersimportTextHandlerfromMateWrapper.promptsimportPromptdefmain():bot=TelegramBot("[Your token]",name="test")bot.add_handler(TextHandler(Prompt("Hi{__name}, you said:{_text}")))bot.start_and_idle()if__name__=="__main__":main()Pretty easy right? Take a look at the bots found insamplesand see for yourself how easy it still is to make complex bots ;)DocsRead the documentationhere!ContributingFeel free to contribute with any fixes/suggestions, I'll get to them as soon as i have time!