Saravanan666/pypi-org huggingface dataset:
Package: metalparser
metalparsermetalparseris a Python API for obtaining song lyrics from diverse lyrics websites.
At the moment there is only one supported website, which isDarkLyrics, an online database of lyrics for heavy metal music.DescriptionThis library scrapes the corresponding website for the lyrics and returns results according to the used API.Kindly read thedisclaimerto ensure that your use complies with it.Installationmetalparseris distributed as a Python package, freely available onPyPIand can easily be installed via pip.
Given that you are usingpython >= 3.5:pip install metalparserAlternatively, it can be manually installed by cloning this project on your local computer:git clone https://github.com/lucone83/metal-parser.git
cd metal-parser
pip install .DocumentationThe library comes (at the moment) with 6 APIs:get_artists_list()get_albums_info()get_songs_info()get_album_info_and_lyrics()get_albums_info_and_lyrics_by_artist()get_song_info_and_lyrics()More complete docs regarding this project can be found onreadthedocs.Some examplesI recommend not to change the default settings regarding requests rate per minute and the wait time (3 secs) after each request.
DarkLyrics does not have a robots.txt, so they don't really like scraping. Be gentle! :)from metalparser.darklyrics import DarkLyricsApi

api = DarkLyricsApi()Retrieve the lyrics given a song and the corresponding artist:song = 'under grey skies'
artist = 'kamelot'
lyrics = api.get_song_info_and_lyrics(song=song, artist=artist, lyrics_only=True)

print(lyrics)Get all the songs of a specific album:artist = 'pantera'
album = 'vulgar display of power'
songs_list = api.get_songs_info(artist, album=album, title_only=True)

print(songs_list)Get all the albums of a specific artist:artist = 'iron maiden'
albums_list = api.get_albums_info(artist=artist, title_only=True)

print(albums_list)SupportCurrently the following python versions are supported:3.4.*3.5.*3.6.*3.7.*3.8.*Thanks tores0nance and hisdarklyrics projectfor inspiration;
Package: metalpriceapi
metalpriceapimetalpriceapi is the official Python wrapper for MetalpriceAPI.com. This allows you to quickly integrate our metal price API and foreign exchange rate API into your application. Checkhttps://metalpriceapi.comdocumentation for more information.InstallationInstall the latest release with:pip install metalpriceapiUsagefrommetalpriceapi.clientimportClientapi_key='SET_YOUR_API_KEY_HERE'client=Client(api_key)DocumentationfetchSymbols()client.fetchSymbols()LinkfetchLive(base, currencies)base<string> Optional. Pass in a base currency, defaults to USD.currencies<List<string>> Optional. Pass in an list of currencies to return values for.client.fetchLive(base='USD',currencies=['XAU','XAG','XPD','XPT'])LinkfetchHistorical(date, base, currencies)date<string> Required. Pass in a string with formatYYYY-MM-DDbase<string> Optional. Pass in a base currency, defaults to USD.currencies<List<string>> Optional. Pass in an list of currencies to return values for.client.fetchHistorical(date='2024-02-05',base='USD',currencies=['XAU','XAG','XPD','XPT'])Linkconvert(from_currency, to_currency, amount, date)from_currency<string> Optional. Pass in a base currency, defaults to USD.to_currency<string> Required. Specify currency you would like to convert to.amount<number> Required. The amount to convert.date<string> Optional. Specify date to use historical midpoint value for conversion with formatYYYY-MM-DD. Otherwise, it will use live exchange rate date if value not passed in.client.convert(from_currency='USD',to_currency='EUR',amount=100,date='2024-02-05')Linktimeframe(start_date, end_date, base, currencies)start_date<string> Required. Specify the start date of your timeframe using the formatYYYY-MM-DD.end_date<string> Required. Specify the end date of your timeframe using the formatYYYY-MM-DD.base<string> Optional. Pass in a base currency, defaults to USD.currencies<List<string>> Optional. Pass in an list of currencies to return values for.client.timeframe(start_date='2024-02-05',end_date='2024-02-06',base='USD',currencies=['XAU','XAG','XPD','XPT'])Linkchange(start_date, end_date, base, currencies)start_date<string> Required. Specify the start date of your timeframe using the formatYYYY-MM-DD.end_date<string> Required. Specify the end date of your timeframe using the formatYYYY-MM-DD.base<string> Optional. Pass in a base currency, defaults to USD.currencies<List<string>> Optional. Pass in an list of currencies to return values for.client.change(start_date='2024-02-05',end_date='2024-02-06',base='USD',currencies=['XAU','XAG','XPD','XPT'])Linkcarat(base, date)base<string> Optional. Pass in a base currency, defaults to USD.date<string> Optional. Specify date to get Carat for specific date using formatYYYY-MM-DD. If not specified, uses live rates.client.carat(base='USD',date='2024-02-05')LinkOfficial documentationFAQHow do I get an API Key?Free API Keys are availablehere.I want more informationCheckout our FAQshere.SupportFor support, get in touch usingthis form.
Package: metalpy
简体中文|EnglishMETAL Essential Tools and Libraries (Python)(metalpy),
is a collection of common python tools and libraries for SimPEG and related workflows,
which currently includes:MEPA: a general-purposed parallelization framework.Mexin: a code injection framework for Python.SCAB: a collection of SimPEG related utilities and extensions.Carto: cartography related utilities, aiming to download tile maps and
save/load GeoTIFF images.InstallationMetalpy can be installed usingpip:pip install "metalpy[complete]"Notes on installing with pipmetalpymanages its dependencies separately, which means expected modules
should be specified inpipinstallation process.Supported commands are listed next:pip install "metalpy[complete]"    # Install all requirementspip install "metalpy[scab]"        # Install requirements for SCABpip install "metalpy[carto]"       # Install requirements for Cartopip install "metalpy[scab, carto]" # Install requirements for SCAB and Cartopip install "metalpy[mepa]"        # Install requirements for MEPApip install "metalpy[mexin]"       # Install requirements for Mexinpip install "metalpy[tests]"       # Install requirements for testspip install "metalpy[docs]"        # Install requirements for doc generationpip install "metalpy[dev]"         # Install requirements for development
Package: metal-sdk
🤘 Metal Python SDKDeveloper DocumentationPyPi PackageSetup$pip3installmetal-sdkUsagefrommetal_sdk.metalimportMetalmetal=Metal("api_key","client_id","index_id");metal.index({"text":"a"})metal.index({"text":"b"})metal.index({"text":"c"})Async exampleTesting$python3-munittesttests/test_metal.pyReference
Package: metalsmith
OverviewThis is a simple tool to provision bare metal machines usingOpenStack Bare
Metal Service (ironic)and,
optionally,OpenStack Image Service (glance)andOpenStack Networking
Service (neutron).Please note the Metalsmith project is in maintenance mode and it’s
functionality is slowly being introduced into Ironic’s API and usual clients.
No additional features are expected to be added to metalsmith directly.License: Apache License, Version 2.0Documentation:https://docs.openstack.org/metalsmith/Source:https://opendev.org/openstack/metalsmithBugs:https://bugs.launchpad.net/metalsmith/+bugsInstallationpip install --user metalsmithNoteThe current versions ofmetalsmithrequire Bare Metal API from the Stein
release or newer. Use the 0.11 release series for older versions.ContributingPull requests:Gerrit(seedeveloper’s guide)Bugs and RFEs:Launchpad(please do NOT report bugs to Github)
Package: metal-test
AboutThis framework provides facilities for automated execution of remote code. The core idea is, to utilize the debug symbols for automated testing, instead of developing custom tools.
It does however limit some things to the itanium abi, i.e. gcc and clang.At the current state it provides functionality for:Exit code propagationUnit testingSyscall stubsArgument assignmentAll tooling is written in python and easily extensible. Almost everything is written in C89, but we also have some modern C++ features.Tool Overviewmetal.gdbThe metal.gdb tool is the core. It uses thePython APIof gdb to run code and obtain detailed information.
This allows us to analyse and print values during unit tests without any allocations. Our toolset wraps around this and uses a single breakpoint to provide a variety of functionality, all written as plugins.Since it is gdb based you can use it with any of those targets:local executionremote server (gdbsever)openocdqemuSince everything is a gdb plugin, you can use any of the functionality during a debug session, not only standalone.metal.serialThe serial library provides a light-weight testing tool for environments that do not provide access for a debugger. It does utilize debug symbols, but to a minimal degree.
The debug symbols are used in conjunction with a preprocessor to conveniently generate you a binary protocol for uni- or bidirectional communication with the target device, e.g. through a serial port.
All you need is a function to write (and maybe receive) bytes you can enjoy the same features asmetal.gdb, but without the introspectio.I wrote two articles so far about how metal.serial works.Line Information Without StringsELFs & DWARFsGetting startedmetal.testis shipped as apippackage, since most is in python.pipinstallmetal_testThemetal-flagstool allows you get the compiler flags, which you could for example use in your CMake build throughexecute_process.metal-flags-I-SgdbDocumentationThe current master Documentation can be found in thewiki.
Package: metamaker
metamakerSimple command line tool to train and deploy your machine learning models with AWS SageMakerFeaturesmetamaker enables you to:Build a docker image for training and inference withpoetryandFastAPITrain your own machine learning model with SageMakerDeploy inference endpoint with SageMakerUsageCreate poetry project and install metamaker❯ poetry new your_module
❯ cd your_module
❯ poetry add git+https://github.com/altescy/metamaker#mainDefine scripts for traning and inference inmain.pyfrom pathlib import Path
from typing import Any, Dict

from metamaker import MetaMaker

from your_module import Model, Input, Output

app = MetaMaker[Model, Input, Output]()

@app.trainer
def train(
    dataset_path: Path,
    artifact_path: Path,
    hyperparameters: Dict[str, Any],
) -> None:
    model = Model(**hyperparameters)
    model.train(dataset_path / "train.csv")
    model.save(artifact_path / "model.tar.gz")

@app.loader
def load(artifact_path: Path) -> Model:
    return Model.load(artifact_path / "model.tar.gz")

@app.predictor
def predict(model: Model, data: Input) -> Output:
    return model.predict(data)Write metamaker configs inmetamaker.yamlhandler: main:app
dataset_path: s3://your-bucket/path/to/dataset/
artifact_path: s3://your-bucket/path/to/artifacts/
hyperparameter_path: ./hparams.yaml

image:
  name: metamaker
  includes:
    - your_module/
    - main.py
  excludes:
    - __pycache__/
    - '*.py[cod]'

training:
  execution_role: arn:aws:iam::xxxxxxxxxxxx:role/SageMakerExecutionRole
  instance:
    type: ml.m5.large
    count: 1

inference:
  endpoint_name: your_endpoint
  instance:
    type: ml.t2.meduim
    count: 1Build docker image and push to ECRmetamaker build --deploy .Train your model with SageMaker and deploy endpointmetamker sagemaker train --deploy
Package: metamaska
μετάμάσκα - malevolent payload classifiermeta.mask can detect different types of malicious payloads like SQL injection, XSS, path-traversal, and command injection payloads.Documentation:https://dogancanbakir.github.io/metamaskaGitHub:https://github.com/dogancanbakir/metamaskaPyPI:https://pypi.org/project/metamaska/Free software: MITTODOsupport more typessupport interoperable model formats, more athereCreditsCookiecutterwaynerv/cookiecutter-pypackageML-based-WAFWAF-A-MoLE
Package: metamaterialanalysiscode
Metamaterial Analysis Code1. IntroductionThis code is the core of the TFM (Final Master Project) "Simulación Mediante  Elementos Finitos Del Comportamiento No
Lineal De Metamateriales Auxéticos En Grandes Desplazamientos Para Uso Ingenieril." It is an OOP pacakage that
allows the user to generate .fem files with the mesh of components based on metamaterial structure.
This means that the material is not solid but consists of cells. Only works for Optistruct.2. Version updatev.1.0Bug fixingNew classes for nonlinear analysisExpansion of functionalities3. Property and LicenseThe code is property of Manuel Sanchez and the ETSIAE. It follows the MIT license.The Patent used in the code is exclusively property of their creators. Copy or modifying are not allowed
without permission of their owners.4. DocumentationThe package can be used by calling the functions that gets the different objects that are instances of
the different "MACClasses" defined in the code.Importing the package:importmetamaterialanalysiscodeasmacAn example of generating a model:material1=mac.set_material(id=1,type="MATS1",stressstrain=tabla_strainstress1,nonlinearity="PLASTIC",yieldstress=0.02)material2=mac.set_material(id=2,type="MAT1",e=70000,nu=0.3)beam2=mac.set_property(id=2,type="PBEAM",material=[material2],area=2000,i1=0.0001,i2=1,i12=1,j=1)beam1=mac.set_property(id=1,type="PBEAML",material=[material2],section="ROD",dim1=1.0)cellstructure1=mac.set_structure(type="Auxetic",djoint=5.3,dstar=-0.3,heightstar=0.3,hcapas=3,hprisma=15,stepx=10,stepy=10,nelem=4)modelo1=mac.set_model(modeldimensions=(150,300,70),cellstructure=cellstructure1,cellmaterial=[material2],cellproperty=[beam1])Printing the model into a .fem file:modelo1.write_fem(r"C:\Users\admin\Desktop\test1_model.fem")Generating and printing an analyisis:enforcedispl1=mac.set_load(id=1,type="SPC",nodes=nodesdisp,components=[3],displacement=-0.5,load=True)constraint1=mac.set_constraint(id=2,nodes=nodesdisp,components=[3],displacement=-0.5)constraint2=mac.set_constraint(id=2,nodes=nodespc,components=[1,2,3,4,5,6],displacement=0)subcase1=mac.set_subcase(id=1,label="linear",loads=[enforcedispl1],constraints=[constraint1,constraint2])analysis2=mac.set_analysis(model=modelo1,subcases=[subcase1])analysis2.write_fem(r"C:\Users\admin\Desktop\test1_analysis.fem")As all the nodes and elements are saved in two dictionaries, the user can modify coords, properties, materials,
etc.A basic knowledge of OOP is needed to understand and use the advance functionalities that the package bringsExample:elementtodel=set()nodetodel=set()forelementkeyinmodelo1.ElementDict.keys():fornodeinmodelo1.ElementDict[elementkey].Nodes:ifnode.Coords[2]<(minz+0.1)ornode.Coords[2]>(maxz-0.1):nodetodel.add(node.ID)elementtodel.add(elementkey)forelementkeyinelementtodel:delmodelo1.ElementDict[elementkey]fornodekeyinnodetodel:delmodelo1.NodeDict[nodekey]
Package: metametameta
metametametaGenerate dunder metadata file with__title__,__author__, etc. Also tools to discover these in other packages.InstallationpipxinstallmetametametaUsagemetametameta--sourcepyproject.toml--targetmypackage/__metadata__.pyMotivationThere are many modern ways to get metadata about packages, as of
2024,importlib.metadataand it's backports will get you
lots of metadata for yours and other packages.The newest way isPEP-621, see alsopackaging.python.orgThe oldest way to provide metadata was to use dunder variables in your package, e.g.__author__,__version__, etc.The method was never strongly standardized, neither officially nor informally.Here is one early proponent of this
sort of metadata.Metadata fields can appear in any or no python file in a project.Sometimes they are at the top of a single file python module, common locations for metadata:__init__.py__meta__.py__about__.pySome metadata elements could reasonably be in every single file.There are no particular standards for the type of__author__. It could be a string, space delimited string, list
or tuple. That is true for the other metadata elements as well.Sometimes the metadata values are code, e.g.__version__could be a string or some tuple or data class
representing a version.WorkflowOn each build, regenerate the__about__.py. Pick one source of your canonical metadata, e.g.pyproject.toml,setup.py,setup.cfg.Using metadataIf you have a lot of packages and you are doing analytics or something with them, you could compile all the metadata
as declared in the source code. It could be different from the metadata that shows on the PyPI page. If you are
searching for contact info for a package maintainer, this might be useful.Another marginal use case is in error logging. Error loggers gather up info from just about anywhere, anything can
be a clue including metadata of dependencies. So this is one more source of that. Seebug_trailfor a proof of
concept for this usage.Another marginal use case is that is a folksonomy, a taxonomy created by the people. The official metadata is
governed by the Python Packaging Authority and the Python Software Foundation. If, say, you wanted to add a metadata
item for__mailing_address__you could easily do it with source code metadata.
Package: metamist
MetamistIntroductionMetamistis a database designed for storingde-identified-omics metadata.PurposeThe project provides an interface to interact with the -omics database via the Python client as well as the GraphQL + HTTP APIs.FeaturesProject-Based Resource Organization: Every resource in Metamist is associated with a specific project.Access Control: Access to resources is controlled through membership in specific Google Groups:dataset-sample-metadata-main-read: For read-only access.dataset-sample-metadata-main-write: For write access.Efficiency Note: Members of Google Groups are cached in a blob to optimize performance, as group-membership identity lookups can be slow.High-Level ArchitectureIt comprises three key components:System-Versioned MariaDB Database: A robust database system for managing -omics metadata.Python Web API: This component is responsible for:Managing permissions.Storing frequently used queries.Providing a GraphQL/HTTP API for efficient querying of the database.Installable Python Library: Wraps the Python Web API using the OpenAPI generator, facilitating easier interaction with the system.SchemaAs of Jan 15, 2024 this schema should reflect the data structure on the tables:You can also find this atDbDiagram.The codebase contains the following modules worth noting:models-> General data models + enumsdb/python/tables-> Interaction with MariaDB / BigQuerydb/python/layers-> Logicapi/graphql: GraphQLapi/routes: HTTP + OpenAPIAnd metamist maintains two clients:web-> React app that consumes a generated Typescript API + GraphQLmetamist-> autogenerated Python APIInstallation and Running LocallyInstallation and developer setupLicenseThis project is licensed under the MIT License. You can see it in theLICENSEfile in the root directory of this source tree.
Package: metamock
metamockmetamock: because dev systems need 169.254.169.254 too.AWS instance metadata mockerForked from:https://github.com/dump247/aws-mock-metadataFree software: MIT licenseDocumentation:https://metamock.readthedocs.io.CreditsThis package was created with Cookiecutter andrstms/cookiecutter-python-cli, a fork of theaudreyr/cookiecutter-pypackageproject template.audreyr/cookiecutteraudreyr/cookiecutter-pypackagerstms/cookiecutter-python-cli
Package: metamorph
metamorphFirst line is the input followed by colorized suggestions.Doc:metamorph --helpStableUnstableDocumentationhttps://metamorph-apn.readthedocs.io/en/stable/https://apn-pucky.github.io/metamorph/index.htmlVersionsStablepipinstallmetamorph[--user][--upgrade]Devpipinstall--index-urlhttps://test.pypi.org/simple/metamorph[--user][--upgrade]ConfigurationFor a list of parameters runmetamorph -h.The root nodeflowcan have multiple different starting languages (givenstartis None).translator:"GoogleTranslator"start:"de"goal:"de"flow:de:fr:es:fr:de:es:fr:sv:fr:en:en:fi:de:fr:es:fr:de:es:fr:sv:sv:This exemplaryconfigs/config.yamlwill produce following results (note-sdfor diagrams and-cfor config, while most command line parameters take precedence over config (-gshere)).
A list of translators can be found herehttps://github.com/nidhaloff/deep-translator.metamorph-i-sd-gsen-cconfig.yaml(GoogleTranslategets abbreviated to only capital lettersGT)
Package: metaname
python-metanameA python JSON-RPC client for the Metaname APIDocumentation for the Metaname API is at:https://metaname.net/api/1.1/docUsage:>>> client = Client("account_reference", "api_key")
>>> client.domain_names()
>>> client.dns_zone("example.com")
>>> client.create_dns_record("example.com",
        {"name":"www",
            "type":"A",
            "aux":None,
            "ttl":86400,
            "data":"93.184.216.119"})License:See LICENSE.
Package: metanetwork
Meta-NetworkMeta-Network is an approach to ensemble learning where base learners are not constrained to the same task but rather take on distinct roles in the ensemble. Commonly, ensembles of classifiers do not have a distinction in each base learner's role. While each base learner may be diversified through bootstrapping or use of different parameters/models, their objectives are universally the same: classify its input to the best of its ability. Stacking and meta-classifiers introduce a sense of roles but each layer retains the same objective. Meta-Networks, however, remove this universal objective. By training the ensemble as a whole unit through backpropagation, each base learner may learn different objectives that orchestrate together to produce the ultimate objective, resulting in greater capacity for higher performance and solutions for complex problems.This package hosts the basic meta-network model as well as additional implementations of the principle.More details regarding Meta-Networks can be found in the documentationhere.InstallationOnce you have a suitable python environment setup,metanetworkcan be easily installed usingpip:pip install metanetworkmetanetworkis tested and supported on Python 3.4 up to Python 3.8. Usage on other versions of Python is not guaranteed to work as intended.UsageMeta-Networks are quite simple to use, although they do need some encouragement by creating the meta-network structure first. They generally followsklearnAPI style.frommetanetworkimportMetaNetwork,NeuralNetwork# Create ensemble with a first layer of 3 estimators and a final layer of 1 estimatorestimators=[NeuralNetwork(),NeuralNetwork(),NeuralNetwork(),NeuralNetwork()]foreinestimators[:-1]:e.n_classes_=2# Each estimator in the first layer produces 2 outputse[-1].n_classes_=4# The final estimator produces the 4 class outputsnetwork=[estimators[:-1],estimators[-1:]]# Create and train the metanetworkmn=MetaNetwork(network).fit(train_X,train_Y)# Predict some datap=mn.predict(test_X)For full details on usage, see thedocumentation.ChangelogSee thechangelogfor a history of notable changes tometanetwork.Developmentmetanetworkis in heavy development. Don't look, it's embarrassing!Help and SupportDocumentationDocumentation formetanetworkcan be foundhere.Issues and QuestionsIssues and Questions should be posed to the issue trackerhere.
Package: metanetx-sdk
Parse and process information fromMetaNetXforMIRIAMcompatibility using theIdentifiers.orgnamespaces.InstallIt’s as simple as:pip install metanetx-sdkUsageThe authoritative source on how to use the various commands is always accessible via
the commands’ help.mnx-sdk -hNormally you would start by loading the files from the MetaNetX FTP servermnx-sdk pull ./dataand then transforming each data table.mnx-sdk etl chem-prop ./data/chem_prop.tsv.gz ./data/transformed_chem_prop.tsv.gzYou can also directly use the functions from themetanetx_sdk.apimodule.CopyrightCopyright © 2019-2020, Moritz E. Beber.Free software distributed under theApache Software License 2.0.