Saravanan666/pypi-org huggingface dataset:
Package: metabeyond
metabeyond - metadata utilities for modern Python codeA small set of utilities for writing self-documenting code, and for allowing introspection of metadata applied
using decorators at runtime in a similar fashion to Java 6 annotation interfaces. This entire module is heavily
influenced by Java 6 annotations such as the following nonsensical example:@Configuration@FunctionalInterface@SafeForSpeedsNotExceeding(value=1.15572735,units=ATTOPARSECS_PER_MICROFORTNIGHT)publicclassQuantumDecoherenceStabilizer{@HaikupublicvoidcheckNodes(){if(tree.hasAnyRealLeafNodes()){freshenLeavesOn(tree);}}}thus providing the following Python solution:@Configuration()@FunctionalInterface()@SafeForSpeedsNotExceeding(value=1.15572735,units=ATTOPARSECS_PER_MICROFORTNIGHT)classQuantumDecoherenceStabilizer:@haikudefcheck_nodes(self)->str:iftree.has_any_real_leaf_nodes():freshen_leaves_on(tree)(See theGoogle Annotation Galleryfor more esoteric annotation examples!)HintsHints are designed to add some description to a docstring for utilities such as Sphinx. The idea behind using
this as a decorator is that decorators are often much more visible to the reader than text in docstrings, so
it is a simple way to exploit readability. Apart from manipulating the docstring, no reference to this decorator
is ever actually kept. It is essentially transparent.Definition of a hint:frommetabeyondimporthintshaiku=hints.Hint('This is designed to be poetic to read.')Applying a hint.Hints never have parenthesis after their name in a decoration.@haikudefbasho():"""old pond"""frog.leap()inwatersound()This provides the following effect:>>>importinspect>>>get_docstring=lambdao:inspect.cleandoc(inspect.getdoc(o))>>>print(get_docstring(basho))oldpondThisisdesignedtobepoetictoread.RemarksRemarks are the next step up from hints and are designed to register themselves unto a class or function to be detected
later. This is akin to how Java 6 annotations work.Defining a remarkfrommetabeyondimportremarksclassBean(remarks.Remark):"""Marks the object as a bean."""Applying a remark.Remarks always have parenthesis. Failure to add parenthesis will result in the decorated item
being replaced by the decorator that would otherwise be called, which will cause any dependant code to break, so don't
try it.@Bean()defbar():return69# lolWe may provide more complex definitions with validation constraints on the decorated element, or with
attributes specific to each decoration. See the documentation for a full explanation, but the following gives you
the general idea. This example defines a route decorator similar to what is provided byflask. Applying it to a class will result in a failure.classRoute(remarks.Remark,constraint=inspect.isfunction):def__init__(self,route):self.route=route@Route('/about-me')defabout_me():...Inspecting any applied remarks is also pretty easy.>>>frommetabeyondimportremarks>>>remarks.get_remark(Route,about_me)Route(route='/about_me')Other methods for searching and querying in various ways can be found in the documentation.
Package: metablock
A Python Client for Metablock APIThis is an asynchronous python client formetablock API.InstallationThis is a simple python package you can install via pip:pip install metablockUsageCreate the clientfrommetablockimportMetablockcli=Metablock()# get the user associated with the API tokenuser=awaitcli.get_user()For the authentication token, you can use theMETABLOCK_API_TOKENenvironment variable,
alternatively, you can pass it to the client constructor:cli=Metablock(auth_key="your-token")
Package: metabltest
Testing utilities in Python, such as assert matchers and scratch
directories.Featuresattrspeedsupress_outputskip_if_ciskip_if_unavailableBackupEnvMixinSuppressLoggingMixinAndExtraAssertionsMixinwith methodsassertAlmostEqualassertDictOfArraysEqualassertDictOfArraysAlmostEqualassertTextEqualassertFilesEqualassertDerivativeEqualsassertSparseArrayEqual
Package: metabo-adni
Metabo_ADNIMetabolomics data processing for the ADNI data sets.
Currently, only supports the Biocrates p180 and Nightingale NMR platforms.InstallationClone the repogitclonehttps://github.com/tomszar/adni_metabolomics.gitInstall metabo_adnicdadni_metabolomics
pipinstall.UsageIn the folder with the required datasets, simply run:clean_filesAnd metabo_adni will run with the default parameters.Note:do not change the original name of the files.Options-D: define the directory were the files are located. Default, current working directory-P: define the platform, either p180 or nmr. Default, p180-F: define the fasting file. Default, BIOMARK.csv-L: define the directory were the LOD p180 files are located. Default, current working directory--mmc: remove metabolites with missing proportions greater than cutoff. Default, 0.2--mpc: remove participants with missing proportions greater than cutoff. Default, 0.2--cv: remove metabolites with CV values greater than cutoff. Default, 0.2--icc: remove metabolites with ICC values lower than cutoff. Default, 0.65--log2: apply log2 transformation to metabolite concentration values--merge: merge data frames across cohorts--zscore: apply zscore transformation to metabolite concentration values--winsorize: winsorize extreme values (more than 3 std of mean)--remove-moutliers: remove multivariate outliers using the Mahalanobis distance--residualize-meds: replace metabolite values with residuals from a regression with medication intake. Note that residuals are scaled to unit variance
Package: metaboatrace.scrapers
概要ボートレース関連の WEB サイトのスクレイピングライブラリ以下の機能を備えるボートレース公式サイトに対応する下記URL の生成スクレイピングパッケージの構成名前空間パッケージになっており、共通の名前空間 (metaboatrace) を保持する同種のパッケージがある。metaboatrace.modelsボートレース公式サイトに対応する規約名前空間は以下のように切られている。metaboatrace.scrapers.official.website.v1707このv1707の部分はボートレース公式サイトのバージョンに対応している。バージョニングは、Ubuntu でのそれに近い。Ubuntu は 22.04 のように年と月という形でバージョニングされている（22.04.1 のようセキュリティパッチのリビジョンも付くことがある）。ボートレースの公式サイトが現行のものになったのは 2017 年の 7 月なので、それに合わせてここではv1707としている。機能metaboatrace/scrapers/official/website/v1707/pages直下に、公式サイトのページに対応した名前空間がある。例えば、公式サイトの月間スケジュールに対応するものはmonthly_schedule_pageである。これらの配下にlocationとscrapingというモジュールがある。前者は引数（日付など）をもとに公式サイトの URL を生成するような責務を負った関数が包含されている。例えば、年と月を与えたら "https://boatrace.jp/owpc/pc/race/monthlyschedule?ym=202209" といったそのデータに対応する公式サイトの月間スケジュールの URL を返すような関数が入っている。後者は、公式サイトの HTML ファイルをスクレイピングのモジュールである。例えば、ここに入ってる関数は "https://boatrace.jp/owpc/pc/race/monthlyschedule?ym=202209" のファイルをスクレイピングしてエンティティを返すような処理を行う。※ ここでいうエンティティはクリーンアーキテクチャの定義上のエンティティのことであり、metaboatrace.modelsはそういったものを提供しているパッケージ
Package: metabodirect
MetaboDirectA comprehensive command-line based pipeline for the analysis of direct injection FT-ICR mass spectrometry data.InstallationMetaboDirect can be installed directly fromPyPiusing:pip install metabodirectAdditionally it can be installed from source by cloning itsGitHub repositorygit clone https://github.com/Coayala/MetaboDirect.git
cd MetaboDirect
python setup.py installMetaboDirect requires Python (3.5 and above), R (4 and above) and Cytoscape (3.8 and above) with the following libraries/modules:Pythonargparsenumpypandasseabornmore-itertoolspy4cytoscapestatsmodelsRtidyverseRColorBrewerveganggnewscaleggpubrKEGGRESTfactoextraUpSetRpmartR (for normalization tests)SYNCSAggvennggrepelCytoscapeFileTransferUsageInformation about the arguments can be obtaining using the option -h/--helpmetabodirect -hFor more information please check theUser Manual.
Package: metaboharrison
harrisonTime a block of code.Use as the context expression of awithstatement:>>> from harrison import Timer
>>> with Timer() as t:
>>>     ...
>>> print t.elapsed_time_ms
12345When a description string is passed on initialization, the elapsed time will
be printed on completion, keyed by this description.>>> with Timer('My expensive block of code'):
>>>     ...
My expensive block of code: 12345 msYou can also start and stop a Timer explicitly:timer=Timer()timer.start()some_expensive_function(...)printtimer.elapsed_time_sanother_expensive_function(...)timer.stop()printtimer.elapsed_time_sYou can also time each execution of a function using a decorator:fromharrisonimportprofile@profile('Describes the function')defsome_function():pass# Without args, the function name (e.g. 'some_function') will be used# as the description.@profile()defanother_function():passYou can also useRegisteredTimer, which groups together a bunch of named
timers, provides utilities for serializing their times, and an optional global
timer registry.Named after John Harrison, the English carpenter and clockmaker who
invented themarine chronometer.Similar librariesThis is similar to the librarycontexttimer, but that library is licensed
under the GPLv3 which is more restrictive than two-clause BSD license used
here.Developmentpipinstall-rrequirements_dev.txt
rakelintContributeIssue Tracker:https://github.com/bodylabs/harrison/issuesSource Code:https://github.com/bodylabs/harrisonPull requests welcome!SupportIf you are having issues, please let us know.LicenseThe project is licensed under the two-clause BSD license.
Package: metabolabpy
Python package to process 1D and 2D NMR spectroscopic data for metabolomics and tracer based metabolism analysisDocumentation:https://ludwigc.github.io/metabolabpySource:https://github.com/ludwigc/MetaboLabPyBug reports:https://github.com/ludwigc/MetaboLabPy/issuesInstallationSee theInstallation pageof
theonline documentation.Command line$ metabolabpy --helpBugsPlease report any bugs that you findhere.
Or fork the repository onGitHuband create a pull request (PR). We welcome all contributions, and we
will help you to make the PR if you are new togit.Developers & ContributorsChristian Ludwig (C.Ludwig@bham.ac.uk) -University of Birmingham (UK)LicenseReleased under the GNU General Public License v3.0 (seeLICENSE file)
Package: metabolabpytools
Python package to process 1D and 2D NMR spectroscopic data for metabolomics and tracer based metabolism analysisDocumentation:https://ludwigc.github.io/metabolabpySource:https://github.com/ludwigc/MetaboLabPyBug reports:https://github.com/ludwigc/MetaboLabPy/issuesInstallationSee theInstallation pageof
theonline documentation.Command line$ metabolabpy --helpBugsPlease report any bugs that you findhere.
Or fork the repository onGitHuband create a pull request (PR). We welcome all contributions, and we
will help you to make the PR if you are new togit.Developers & ContributorsChristian Ludwig (C.Ludwig@bham.ac.uk) -University of Birmingham (UK)LicenseReleased under the GNU General Public License v3.0 (seeLICENSE file)
Package: metabolace
This is an active fork oflace,
the Body Labs-developed polygonal mesh library.The fork’s goals are ambitious:Keep the library working in current versions of Python and other
tools.Make bug fixes.Provide API stability and backward compatibility with the upstream
version.Expand functionality to fully support quad meshes, and additional
analysis and manipulation functionality where appropriate.Respond to community contributions.InstallationInstall dependenciesMac OS:brewupdate&&brewinstallboostpipinstallnumpy==1.13.1pipinstalllaceLinux:apt-getinstall-y--no-install-recommendslibsuitesparse-devlibboost-devpipinstallnumpy==1.13.1pipinstalllaceDocker:docker build .Install the librarypipinstallmetabolaceAnd import it just like the upstream library:fromlace.meshimportMeshDevelopmentpipinstall-rrequirements_dev.txtpipinstall-e.raketestrakelintContributeIssue Tracker:https://github.com/metabolize/lace/issuesSource Code:https://github.com/metabolize/lacePull requests welcome!SupportIf you are having issues, please let us know.AcknowledgementsThis library was refactored from legacy code at Body Labs byAlex
Weiss, with portions byEric
Rachlin,Paul
Melnikow,Victor
Alvarez, and others. It was
extracted from the Body Labs codebase and open-sourced byGuillaume
Marceau.LicenseThe project is licensed under the two-clause BSD license.This project uses the RPly library to read and write PLY files, by Diego
Nehab, IMPA, distributed under the MIT License. *http://www.impa.br/~diego/software/rply
Package: metabolenv-flag
Get boolean values from environment variables in Python.fromenv_flagimportenv_flag# When unset, default to `False`.debug=notenv_flag('PRODUCTION')# When unset, use explicit default.is_local=get_bool('IS_LOCAL',default=True)Values are coerced as follows:When the variable is unset, or set to the empty string, returndefault.When the variable is set to a truthy value, returnTrue. These
are the truthy values:1true, yes, onWhen the variable is set to the anything else, returnFalse.
Example falsy values:0noIgnore case and leading/trailing whitespace.Developmentpipinstall-rrequirements_dev.txtraketestrakelintContributeIssue Tracker: github.com/bodylabs/env-flag/issuesSource Code: github.com/bodylabs/env-flagPull requests welcome!SupportIf you are having issues, please let us know.LicenseThe project is licensed under the two-clause BSD license.
Package: metabolike
A Python package that aggregates the data fromBRENDAandBioCycinto one unifiedgraph database.
The MetaCyc (BioCyc) database is imported to a graph database using the provided SBML file and various.datfiles.
AStreamlitapp isincluded in the codefor easy route search with both example TCGA datasets and custom CSV files.To-DoAdd tutorial for the packageAdd Dockerfile and docker-compose file
Package: metabolinks
Metabolinksis a Python package that provides a set of tools for high-resolution
MS metabolomics data analysis.Metabolinks aims at providing several tools that streamline most of
the metabolomics workflow. These tools were written having ultra-high
resolution MS based metabolomics in mind.Features are a bit scarce right now:peak list alignmentcommon metabolomics data-matrix preprocessing, based onpandasandscikit-learncompound taxonomy retrievalBut our road map is clear and we expect to stabilize in a beta version pretty soon.Stay tuned, and check out the examples folder (examples are provided as
jupyter notebooks).InstallingMetabolinksis distributed onPyPIand can be installed with pip on
a Python 3.6+ installation:pip install metabolinksHowever, it is recommended to install the the scientific Python packages that are
required byMetabolinksbefore usingpip. These are listed below, but they
can be easily obtained by installing one of the “Scientific/Data Science Python” distributions.
One of these two products is highly recommended:Anaconda Individual Edition(orMinicondafollowed by the necessaryconda install’s)Enthought Deployment Manager(followed by the creation of suitable Python environments)The formal requirements are:Python 3.6 and abovesetuptools,pip,requests,six,pandas-flavorandpytestand, from the Python scientific ecossystem:numpy,scipy,matplotlib,pandasandscikit-learnThe installation of theJupyterplatform is also recommended since
the examples are provided asJupyter notebooks.
Package: metabooby
Booby is a standalone datamodelingandvalidationlibrary written in Python. Booby is under active development (visitthis blog postfor more info and the roadmap) and licensed under theApache2 license, so feel free tocontributeandreport errors and suggestions.This is an active fork.UsageSee the sample code below to get an idea of the main features.fromboobyimportModel,fieldsclassToken(Model):key=fields.String()secret=fields.String()classAddress(Model):line_1=fields.String()line_2=fields.String()classUser(Model):login=fields.String(required=True)name=fields.String()email=fields.Email()token=fields.Embedded(Token,required=True)addresses=fields.Collection(Address)jack=User(login='jack',name='Jack',email='jack@example.com',token={'key':'vs7dfxxx','secret':'ds5ds4xxx'},addresses=[{'line_1':'Main Street'},{'line_1':'Main St'}])ifjack.is_valid:printjack.to_json(indent=2)else:printjson.dumps(dict(jack.validation_errors)){"email":"jack@example.com","login":"jack","token":{"secret":"ds5ds4xxx","key":"vs7dfxxx"},"name":"Jack","addresses":[{"line_1":"Main St","line_2":null},{"line_1":"Main Street","line_2":null}]}InstallationYou can install the last stable release of Booby from PyPI using pip or easy_install.$pipinstallboobyAlso you can install the latest sources from Github.$pipinstall-egit+git://github.com/jaimegildesagredo/booby.git#egg=boobyTestsTo run the Booby test suite you should install the development requirements and then run nosetests.$pipinstall-rtest-requirements.txt$noseteststests/unit$noseteststests/integrationChangesSeeChanges.
Package: metabook
metabookUpdates pdf book filenames and tags with book metadata for easier indexing.Metabook is a simple to use CLI utility that searches for pdf files inside a directory then searches for ISBN numbers
inside the files, gets the book metadata then updates the filename and meta tags inside the file.InstallationMetabook is best installed with pipxpipxinstallmetabookUsage exampleA few motivating and useful examples of how your product can be used. Spice this up with code blocks and potentially more screenshots.For more examples and usage, please refer to theWiki.Development setupDescribe how to install all development dependencies and how to run an automated test-suite of some kind. Potentially do this for multiple platforms.pipinstall--editablemetabookDocumentationRead the DocsExample UsageCreditsChangelogAPI ReferenceWikiMetaAuthor: Stephen R A King (sking.github@gmail.com)Distributed under the MIT license. Seefor more information.Created with Cookiecutter template:version 1.3.3Digital object identifier:
Package: metabox
metabox: A High-Level Python API for Diffractive Optical System Designmetabox is a Python package built on TensorFlow, enabling the design, evaluation and optimization of complex diffractive optical systems with ease, flexibility, and high performance.metaboxis a high-level Python package specifically designed for the creation, evaluation, and inverse optimization of diffractive optical systems. Leaning on the robust capabilities of TensorFlow,metaboxoffers a comprehensive and user-friendly API for optical system design.The package is built with flexibility at its core, making it easy to add new components, define custom merit functions, and employ various optimization algorithms. It’s designed to be highly performant and scalable, capable of managing systems with millions of degrees of freedom. With its intuitive structure,metaboxfacilitates the design of intricate diffractive optical systems with minimal lines of code.Key features ofmetaboxinclude:Arcwasolver, derived fromrcwa_tf, for direct computation of meta-atoms’ diffraction efficiency.A built-inrastermodule for parameterizing meta-atoms’ features.An easy-to-use sampling system for features, which can train a metamodel to replace thercwasolver, thus significantly speeding up simulations and optimization processes.A module for sequential optics to model light propagation through the optical system.Anassemblymodule offering a suite of tools for building the optical system from meta-atoms, apertures, and other optical components.Ameritmodule for evaluating and inverse-designing the performance of the optical system.Anrcwa.Materialclass for accessing pre-defined materials and their optical properties.Anexportmodule that allows for the export of the diffractive optical design to a.gdsfile for fabrication.Overall,metaboxis a powerful tool for both beginners and experienced users in the field of optical system design. By simplifying and accelerating the design process, it paves the way for innovative developments in the optical industry.InstallInstallmetaboxviapip:pip install metaboxGetting StartedTry outmetaboxfor free on Google Colab. Here are some tutorials on Colab. You can find the local versionshere.Tutorial 1: MetamodelingTutorial 2: Lens Optimization and ExportingTutorial 3: Optimization SerializationTutorial 4: Zemax Binary2 ImportTutorial 5: Refractive Surfaces SimulationTutorial 6: Refractive Surfaces OptimizationTutorial 7: Hologram OptimizationDocumentationModule ReferenceHome PageContributorsLuocheng Huang:luocheng@uw.edu,https://github.com/LuochenghuangCitingmetaboxThe manuscript is in preparation.Making Changes & ContributingThis project usespre-commit, please make sure to install it before making any
changes:pip install pre-commit
cd metabox
pre-commit installIt is a good idea to update the hooks to the latest version:pre-commit autoupdateTo make an editable installation, run the following commands:git clone https://github.com/Luochenghuang/metabox.git
cd metabox
pip install -e .
Package: metacall
AbstractMETACALLPython Port is the port ofMETACALLto Python
Programming Language. WithMETACALLPython Port you can
transparently execute code from Python to any programming language, for
example, calling JavaScript, NodeJS, Ruby or C# code from Python.InstallInstall MetaCall binaries first:curl -sL https://raw.githubusercontent.com/metacall/install/master/install.sh | bashThen install MetaCall Python package through MetaCall:metacall pip3 install metacallExamplemultiply.rbdefmultiply(left,right)returnleft*rightendmain.pyfrommetacallimportmetacall_load_from_file,metacallmetacall_load_from_file('rb',['multiply.rb']);metacall('multiply',3,4);# 12Running the example:metacall main.py
Package: metacase
MetaCaseUniversal test case metadata exporter tool.This tool can be used to convert and export Test Cases defined
using anFMFtree.The test cases must be defined according to aninternal schemaand the MetaCase can parse them and invoke a selected adapter to convert / export the
select test cases into an external ALM related tool.Format for defining the test case is YAML.Example herePre-requisitesPython 3.9+Installationpip install metacaseorpip install -e git+https://github.com/enkeys/metacase.gitUsageFor basic usage information, use:metacase --helpAdaptersThis tool provides a genericmetacase.adapter.Adapterinterface that can be implemented
for new external ALM related tools.Polarion ALMAdapter (early stage) that can export a test case defined using FMF (compliant with internal FMF Test Case metadata
schema) into Polarion test case importer API.For help, use:metacase polarion --helpConnectorsConnector are helpers that can obtain information from external sources such as issue tracker, source repository, etc.Contributorshttps://github.com/enkeys/metacase/graphs/contributorsAcknowledgmentsfmf- Flexible Metadata Format - Makes it easier to document
and query for your metadata.
Package: metacatalog
metacatalogCIStatusUnit testsDocumentationDeploymentManagement tool for the V-FOR-WaTer metadata database application. Although developed in and for theV-FOR-WaTer project, metacatalog is a standalone database application, that can be used on its own.DocsThe full documentation is available at:https://vforwater.github.io/metacatalogInstallation description is given at:https://vforwater.github.io/metacatalog/home/install.htmlQuickstartInstall metacatalog:pipinstallmetacatalogWith a Postgis database calledmetacataloginstalled atlocalhost:5432you can store a default connection like:Be aware that any password saved along with the default connection is saved in clear-text!!metacatalogconnection--savepostgresql://postgres:<masterpassword>@localhost:5432/metacatalogRefer to theconnection documentationto learn about all possibilities to connect to a database.The database table can be installed and populated like:metacataloginit-Cpostgresql://postgres:<password>@localhost:5432/metacatalogAnd now you can use the database via:theCLIthePython APIor directly interface to thedatabase model classesUsing metacatalog on WindowsOn one of my Windows systems the setuptools scripts does not get recognized
properly and thus the CLI does not work properly if not called by full path.
Therefore with version 0.1.4 the CLI is implemented the module main entrypoint.Wherever the docs call the metacatalog script, you can use the module, like:Instead of:metacatalog[options]<commad>you can use:python-mmetacatalog[options]<command>This should work cross-platform. Tested on Ubuntu 18, debian 9, Windows 7 and
Windows 10.