Saravanan666/pypi-org huggingface dataset:
Package: marvel
# Marvel APIInstallationUsingpip:pip install marvelThis is an API wrapper for [Marvel](https://developer.marvel.com/docs).UsageUsage is as simple as it gets:from marvel import Marvel
m = Marvel(PUBLIC_KEY, PRIVATE_KEY)Now there are six objects presented namely,characters,comics,creators,events,seriesandstoriesas listed at [Developers](https://developers.zomato.com/documentation)characters = m.characters
comics = m.comics
creators = m.creators
events = m.events
series = m.series
stories = m.storiesEach of the above object returns back the appropriate response (json) as mentioned in official [docs](https://developer.marvel.com/docs)Module ExamplesModule Examples:m = Marvel(PUBLIC_KEY, PRIVATE_KEY)
characters = m.charactersGet All Characters:all_characters = characters.all()Get Single Character:character = characters.get(1011334)Get Some Character’s Comics:comics = characters.comics(1011334)Similarly, you could apply the same logic to different objects, an example:m = Marvel(PUBLIC_KEY, PRIVATE_KEY)
stories = m.stories
all_stores = stories.all()
story = stories.get(id)
events = stories.events(id)Finally, the sub resources that each object has are as follows:CharactersallgetcomicseventsseriesstoriesComicsallgetcharacterscreatorseventsstoriesCreatorsallgetcomicseventsseriesstoriesEventsallgetcharacterscomicscreatorsseriesstoriesSeriesallgetcharacterscomicscreatorseventsstoriesStoriesallgetcharacterscomicscreatorseventsseries# Exception HandlingYou can catch any exception caused by Marvel API such as authentication error, bad input error, server down error, etc by handling theMarvelExceptionatmarvel/exceptions.py.# Running Testspython -m pytest tests/
Package: marvelapp-cli
marvelcliA CLI wrapper for Marvelapp (https://marvelapp.com)The wrapper will enable you to interact with your Marvel
account from the comfort of your command line.Getting started:pip install marvelapp-clihttps://pypi.org/project/marvelapp-cli/Authing requests:You will need an access token to use the CLI, generate a new one or find the one you already have set up here:https://marvelapp.com/oauth/devtokenYou can export your token by running this command from the same dir you installed the CLI:export MARVEL_CLI_TOKEN='your access token'The CLI will then automatically use this token for each command you run (so you don't have to auth each one individually).To unauth:unset MARVEL_CLI_TOKENExample:To invite users to your workspace:marvelcli add-collabs-to-project --project 12345 --email "janis@bigbrotherandtheholdingcompany.com" --email "stevie@fleetwoodmac.com" --email "grace@jeffersonairplane.com"Commands available:You can find out how to use each command with:marvelcli [command name] --help

Command Name:                Command Use:

about-user                   User statistics
add-collabs-to-project       Add collaborators to a project
add-groups-to-project        Add groups to a project
add-members-to-group         Add members to a group
create-folder                Create a folder
create-group                 Create a group
create-project               Create a new project
delete-groups                Delete groups
delete-project               Delete a project
get-billing-info             Get billing information
get-personal-projects        List all projects owned by you
remove-collabs-from-project  Remove collaborators from a project
remove-groups-from-project   Remove groups from a project
remove-members-from-group    Remove members from a group
remove-users-from-workspace  Remove users from your workspace
update-group-name            Update a group nameFeature requestsAnything you'd like to see that isn't here?
Emailsophierfitzpatrick@gmail.comwith your request!
Package: marvelator
Python abstraction for developer.marvel.com requestsFree software: BSD licenseInstallationpip install marvelatorDocumentationhttps://marvelator.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2017-09-03)First release.
Package: marvelous
Code on GithubPublished on PyPiRead the project documentationMarvel API documentationTo install:pipinstallmarvelousExample Usage:importmarvelous# Your own config file to keep your private key local and secretfromconfigimportpublic_key,private_key# Authenticate with Marvel, with keys I got from http://developer.marvel.com/m=marvelous.api(public_key,private_key)# Get all comics from this week, sorted alphabetically by titlepulls=sorted(m.comics({'format':"comic",'formatType':"comic",'noVariants':True,'dateDescriptor':"thisWeek",'limit':100}),key=lambdacomic:comic.title)forcomicinpulls:# Write a line to the file with the name of the issue, and the# id of the seriesprint('{}(series #{})'.format(comic.title,comic.series.id))Output available in full documentationContributingTo run the test suite, runpython -m nosein this folderWhen running a new test for the first time, set the environment variablesPUBLIC_KEYandPRIVATE_KEYto any Marel API keys. The result will be
stored in thetests/testing_mock.sqlitedatabase without your keys.To release:Update version numberCreate tag on GithubWait for Travis to publish
Package: marvin-brain
This repository is mainly a dependency forhttps://github.com/sdss/marvin, containing some core
logic and functions pertaining to Flask API infrastructure for the Marvin API.  It is not useable as a product or python package by itself.  The original ideas behindmarvin_brainhas largely been expanded intosdss_brainandvalis.Useful linksGitHub:https://github.com/sdss/marvin_brainIssues:https://github.com/sdss/marvin_brain/issues
Package: marvin-sqlalchemy-boolean-search
ChangesThis repo has diverged from the original fork.  It includes expanded parsing rules; see 0.1.1 changes in the CHANGELOG.  It has also been restructured as a standalone pypi package calledmarvin-sqlalchemy-boolean-search, as its
primary use is forhttps://github.com/sdss/marvin.  For a more general standalone package, seehttps://github.com/havok2063/boolean_parser, which rebuilds this repo functionality into a fully fledged python package.This repo is maintained athttps://github.com/havok2063/SQLAlchemy-boolean-search, with changes
pushed occassionally.SQLAlchemy-boolean-searchSQLAlchemy-boolean-search translates a boolean search string such as:"field1=*something* and not (field2==1 or parent.field3<=10.0)"into its corresponding SQLAlchemy query filter:and_(DataModel.field1.ilike('%something%'),
     not_(or_(DataModel.field2.__eq__(2),
              DataModel.parent.field3.__le__(10.0))))Relationship field names such as 'parent.grandparent.name' are accepted.The code is stable, is used in production, and enjoys a test coverage of 100%.DocumentationSQLAlchemy-boolean-search documentationAuthorsLing Thio - ling.thio [at] gmail.comAcknowledgementsThis project would not be possible without the use of the following amazing offerings:FlaskSQLAlchemypyparsingAlternative modulesSQLAlchemy-Searchableadds full text searching and relies on PostgreSQL vectors and triggers.sqlalchemy-elasticquery
Package: marv-ludwig
This package is part of the MARV Robotics Community Edition.MARV Robotics is a powerful and extensible data management platform,
featuring a rich dynamic web interface, driven by your algorithms,
configurable to the core, and integrating well with your tools to
supercharge your workflows.For more information please see:MARV RoboticswebsiteMARV RoboticsdocumentationMARV RoboticsGitLab project
Package: marv-robotics
This package is part of the MARV Robotics Community Edition.MARV Robotics is a powerful and extensible data management platform,
featuring a rich dynamic web interface, driven by your algorithms,
configurable to the core, and integrating well with your tools to
supercharge your workflows.For more information please see:MARV RoboticswebsiteMARV RoboticsdocumentationMARV RoboticsGitLab project
Package: marwan
DescriptionThis tool allows me to conveniently access some of my frequently used webpages from the command line.InstallationThemarwanpackage can be installed directly usingpip.pip install marwanUsagemarwancan be run from the command line and will automatically open your web browser.Open one of my GitHub repositories:$ marwan -g pyinterview
Opening https://www.github.com/marwanhawari/pyinterviewOpen my PyPI page:$ marwan -p
Opening https://pypi.org/user/marwanhawari/Optionsusage: marwan [-h] [-g [REPO]] [-l] [-p] [-w]

optional arguments:
  -h, --help            show this help message and exit
  -g [REPO], --github [REPO]
                        Open my GitHub page. You can specify the repository or leave empty for my homepage.
  -l, --linkedin        Open my LinkedIn page.
  -p, --pypi            Open my PyPI page.
  -w, --website         Open my personal website.
Package: maryam
OWASP MaryamOWASP Maryam is a modular open-source framework based on OSINT and data gathering. It is designed to provide a robust environment to harvest data from open sources and search engines quickly and thoroughly.InstallationSupported OSLinuxFreeBSDDarwinOSX$pipinstallmaryamAlternatively, you can install the latest version with the following command (Recommended):pipinstallgit+https://github.com/saeeddhqan/maryam.gitUsage# Using dns_search. --max means all of resources. --api shows the results as json.# .. -t means use multi-threading.maryam-edns_search-dibm.com-t5--max--api--form# Using youtube. -q means querymaryam-eyoutube-q"<QUERY>"maryam-egoogle-q"<QUERY>"maryam-ednsbrute-ddomain.tld# Show framework modulesmaryam-eshowmodules# Set framework options.maryam-esetproxy..
maryam-esetagent..
maryam-esettimeout..# Run web APImaryam-ewebapi127.0.0.11313Latest UpdatesAdded image_search moduleClustering, Meta Search Engine, Dark-Web SearchIris: the first beta versionAdded famous_personCore speedup optimizationsAdded setup.pyContributionContributes are welcome! Here is a start guide:Development GuideYou can add a new search engine to the util classes or use the current search engines to write a new module.
The best help to write a new module is checking the current modules.RoadmapImproving Iris page rankingWrite a complete metacrawler engine based on OSINT by using the current search enginesAdd clustering algorithms: DoneWeb user interfaceLinksOWASPWikiInstallModules GuideDevelopment GuideTo report bugs, requests, or any other issues pleasecreate an issue.
Package: marytts-cli
marytts-cliA command-line client for the HTTP server of the MaryTTS Text-to-Speech System
implemented in Python 3.Table of ContentsAboutInstallationUsageAuthorLicenseAboutThemarytts-clicommand-line client for the MaryTTS Text-to-Speech System is
a handy tool for experimenting with the TTS server itself and for simplifying
the automation process of regular TTS tasks.It performs a query to the HTTP server of MaryTTS with a range of configurable
parameters and returns the server's response. Input data for the query is read
fromstdinwhile parameters are set by invoking their corresponding
command-line arguments.If the query was successful, received output data is written tostdoutand
the program terminates with exit code0. Otherwise, an error message is
written tostderrand the program terminates with exit code1.InstallationTo install the requiredrequestslibrary for Python 3, run:pip install -r requirements.txtUsageusage: marytts-cli [-h] [--audio AUDIO] [--input INPUT] [--locale LOCALE]
                   [--output OUTPUT] [--url URL] [--voice VOICE]

A command-line client for the HTTP server of the MaryTTS Text-To-Speech System.

optional arguments:
  -h, --help       show this help message and exit
  --audio AUDIO    set format of MaryTTS audio output
  --input INPUT    set type of MaryTTS input
  --locale LOCALE  set identifier of Locale
  --output OUTPUT  set type of MaryTTS output
  --url URL        set request URL
  --voice VOICE    set name of MaryTTS voiceExampleecho "Hello World!" | ./marytts-cli > output.wavContributionPull requests, patches and feedback are always welcome. Feel free to use the
issue tracker or to contact the author.Authortrevor <trevor@destroyed.today>LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: marzano
============================Marzano Power Law Calculator============================.. image:: https://img.shields.io/pypi/v/marzano.svg:target: https://pypi.python.org/pypi/marzano.. image:: https://img.shields.io/travis/sreyemnayr/marzano.svg:target: https://travis-ci.org/sreyemnayr/marzano.. image:: https://readthedocs.org/projects/marzano/badge/?version=latest:target: https://marzano.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/sreyemnayr/marzano/shield.svg:target: https://pyup.io/repos/github/sreyemnayr/marzano/:alt: UpdatesRequires modern Python* Free software: MIT license* Documentation: https://marzano.readthedocs.io.Features--------* Simple CLI interface for inputting scores* marzano.powerlaw() takes a list and returns the power scoreCredits-------This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template... _Cookiecutter: https://github.com/audreyr/cookiecutter.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage=======History=======2019.02.14 (2019-02-14)------------------* First release on PyPI.
Package: masai
Masaï, a framework for processing, analysing and modelling
of solid state NMR spectraVersion:0.1WarningMasaïis still experimental and under active development.
It is not mature nor stable.
Its current design is subject to major changes, reorganizations, bugs and crashes!!!.Should I say? be patient as I expect a fully working version with the upcoming 0.2 release.What isMasaï?Masaïis a software written in python and partially in fortran.It (will) provides a general framework for the processing, analysis and modelling
of Solid State NMR spectra.AGUIapplication allows users to make basic processing and fitting of NMR spectra
(for now limited to the Bruker format).TheAPIshould also be accessible to be included in other programs.DocumentationFor more information, have a look to the (under work!) web pages athttp://fernandezc.github.com/masaiCitingMasaïWhen using Masaï for your own work, and before a paper is published
on this software, you are kindly requested to cite the program this way:Christian Fernandez,Masaï: a framework for processing, analysing and modelling
of solid state NMR spectra,http://fernandezc.github.com/masai,
version 0.1, Laboratoire Catalyse et Spectrochimie - ENSICAEN/Université de Caen/CNRS, 2012Release historyv0.1, 2012-11-24 – initial experimental release
Package: masakari
Virtual Machine High Availability (VMHA) service for OpenStackMasakari provides Virtual Machine High Availability (VMHA) service
for OpenStack clouds by automatically recovering the KVM-based Virtual
Machine(VM)s from failure events such as VM process down,
provisioning process down, and nova-compute host failure.
It also provides API service for manage and control the automated
rescue mechanism.NOTE:
Use masakari only if instance path is configured on shared storage system
i.e, ‘instances_path’ config option of nova has a path of shared directory
otherwise instance data will be lost after the evacuation of instance from
failed host if,
* instance is booted from image
* flavor using ephemeral disks is usedOriginal version of Masakari:https://github.com/ntt-sic/masakariTokyo Summit Session:https://www.youtube.com/watch?v=BmjNKceW_9AMasakari is distributed under the terms of the Apache License,
Version 2.0. The full terms and conditions of this license are
detailed in the LICENSE file.Free software: Apache license 2.0Documentation:https://docs.openstack.org/masakari/latestRelease notes:https://docs.openstack.org/releasenotes/masakari/Source:https://opendev.org/openstack/masakariBugs:https://bugs.launchpad.net/masakariConfigure masakari-apiCreate masakari user:openstack user create --password-prompt masakari(give password as masakari)Add admin role to masakari user:openstack role add --project service --user masakari adminCreate new service:openstack service create --name masakari --description "masakari high availability" instance-haCreate endpoint for masakari service:openstack endpoint create --region RegionOne masakari --publicurl http://<ip-address>:<port>/v1/%\(tenant_id\)sClone masakari usinggit clone https://github.com/openstack/masakari.gitRun setup.py from masakarisudo python setup.py installCreate directory/etc/masakariCopymasakari.conf,api-paste.iniandpolicy.yamlfile
frommasakari/etc/to/etc/masakarifolderTo run masakari-api simply use following binary:masakari-apiConfigure masakari databaseCreate ‘masakari’ databaseAfter running setup.py for masakari (sudo python setup.py install),
runmasakari-managecommand to sync the databasemasakari-manage db syncFeaturesTODO
Package: masakari-dashboard
Horizon plugin for masakariFree software: Apache licenseSource:https://opendev.org/openstack/masakari-dashboardBugs:https://bugs.launchpad.net/masakari-dashboardFeaturesTODOEnabling in DevStackAdd this repo as an external repository into yourlocal.conffile:[[local|localrc]]
enable_plugin masakaridashboard https://github.com/openstack/masakari-dashboardManual InstallationBegin by cloning the Horizon and Masakari dashboard repositories:git clone https://github.com/openstack/horizon
git clone https://github.com/openstack/masakari-dashboardCreate a virtual environment and install Horizon dependencies:cd horizon
python tools/install_venv.pySet up yourlocal_settings.pyfile:cp openstack_dashboard/local/local_settings.py.example openstack_dashboard/local/local_settings.pyOpen up the copiedlocal_settings.pyfile in your preferred text
editor. You will want to customize several settings:OPENSTACK_HOSTshould be configured with the hostname of your
OpenStack server. Verify that theOPENSTACK_KEYSTONE_URLandOPENSTACK_KEYSTONE_DEFAULT_ROLEsettings are correct for your
environment. (They should be correct unless you modified your
OpenStack server to change them.)Install Masakari dashboard with all dependencies in your virtual environment:tools/with_venv.sh pip install -e ../masakari-dashboard/And enable it in Horizon:ln -s ../masakari-dashboard/masakaridashboard/local/enabled/_50_masakaridashboard.py openstack_dashboard/local/enabled
ln -s ../masakari-dashboard/masakaridashboard/local/local_settings.d/_50_masakari.py openstack_dashboard/local/local_settings.d
ln -s ../masakari-dashboard/masakaridashboard/conf/masakari_policy.yaml openstack_dashboard/confTo run horizon with the newly enabled Masakari dashboard plugin run:./run_tests.sh --runserver 0.0.0.0:8080to have the application start on port 8080 and the horizon dashboard will be
available in your browser athttp://localhost:8080/
Package: masakari-monitors
Monitors for Masakari provides Virtual Machine High Availability (VMHA) service
for OpenStack clouds by automatically detecting the failure events
such as VM process down, provisioning process down, and nova-compute host failure.
If it detect the events, it sends notifications to the masakari-api.Original version of Masakari:https://github.com/ntt-sic/masakariTokyo Summit Session:https://www.youtube.com/watch?v=BmjNKceW_9AMonitors for Masakari is distributed under the terms of the Apache License,
Version 2.0. The full terms and conditions of this license are
detailed in the LICENSE file.Free software: Apache licenseDocumentation:https://docs.openstack.org/masakari-monitorsSource:https://git.openstack.org/cgit/openstack/masakari-monitorsBugs:https://bugs.launchpad.net/masakari-monitorsConfigure masakari-monitorsClone masakari using:$ git clone https://github.com/openstack/masakari-monitors.gitCreate masakarimonitors directory in /etc/.Run setup.py from masakari-monitors:$ sudo python setup.py installCopy masakarimonitors.conf and process_list.yaml files from
masakari-monitors/etc/ to /etc/masakarimonitors folder and make necessary
changes to the masakarimonitors.conf and process_list.yaml files.
To generate the sample masakarimonitors.conf file, run the following
command from the top level of the masakari-monitors directory:$ tox -egenconfigTo run masakari-processmonitor, masakari-hostmonitor and
masakari-instancemonitor simply use following binary:$ masakari-processmonitor
$ masakari-hostmonitor
$ masakari-instancemonitorFeaturesTODO
Package: masakhanePreprocessor
masakhanePreprocessorAn effective language-first preprocessing tool for African languages (🔧 Beta version).We build on theclean-textpreprocessor.How to UseInstall:git clone https://github.com/masakhane-io/masakhanePreprocessor.git
cd masakhanePreprocessor
pip install .PreprocessorYou only need to specify your language and it loads the important preprocessing style for You!You initialize thePreprocessorin Python as follows:frommasakhanePreprocessorimportPreprocessormy_prep=Preprocessor(lang='ig')You can also directly include some additional parameters you want:my_prep=Preprocessor(lang='ig',lower=True,strip_punctuation=True,strip_symbols=True)preproces_strTo preprocess a string use thepreproces_strfunction:clean_text=my_prep.preprocess_str('''Dịka● ndọrọndọrọọchịchị maka ntuliaka ọkwa GọvanọAnambra steeti si na-aga nke afọ 2021, ndị nọ.''')You get the following as output:Dịka ndọrọndọrọọchịchị maka ntuliaka ọkwa Gọvanọ Anambra steeti si na-aga nke afọ 2021 ndị nọNotice how the●character has been removed, but the-, which is an important part of Igbo, remains untouched.preprocess_fileTo preprocess a file use thepreprocess_filefunction:my_prep.preprocess_file('ig.txt',output_path=None#Specify the output path. If unspecified, uses the parent directory of input file)On successful completion you get this message:Clean file(s) saved successfully to xxxxxxx/ig_CLEAN.txtProperties of the preprocessing toolLanguage-first
It can:map any African language name provided to its language code. You can writePreprocessor(lang='yoruba')using just the name.map any language code to its BCP47 variant. So even if you useyooryorit does not matter.Simple to useContributionWe are open to and grateful for ideas to make this better. You can propose ideas as issues or pull requests.With 💙 FromThe Contributors
Package: masav
Python API for Israeli Masav paymentsInstallpip install masavUsagefrom JSON# TODOlow level APIfrommasavimportMasavPayingInstitute,MasavPaymentDetailsinstitute=MasavPayingInstitute(institute_code=12345678,institute_name="Company ISRAEL LTD.",sending_institute_code=12345)payee_details_1=MasavPaymentDetails(amount=85,bank_number="11",branch_number="303",account_number="007008629",payee_id="123123127",payee_name="Leto II Atreides",payee_number="00000000000001313131")payee_details_2=MasavPaymentDetails(amount=1346.37,bank_number="31",branch_number="051",account_number="000283487",payee_id="123456782",payee_name="Thorin Oakenshield",payee_number="00000000000001122233")payments_list=[payee_details_1,payee_details_2]file="tests/payment_file_generated_by_test.bin"institute.create_payment_file(file=file,payments_list=payments_list,payment_date="200507",serial_number=404,creation_date="200507")Developmentgit clone https://github.com/omrirz/masav.gitcd masavpip install -r requirements.txtTesttox
Package: masced-bandits
MASCed_banditsThis is a library of multi-armed bandit policies.
As of the most recent version the following policies are included:
UCB, UCB-Improved, EXP3, EXP3S, EXP4, EwS, ETC, Discounted UCB, Sliding Window UCB, e-greedy.Examplefrom masced_bandits.bandit_options import initialize_arguments
from masced_bandits.bandits import init_bandit
import numpy as np

initialize_arguments(["Arm1","Arm2"], 0)

ucb_instance = init_bandit(name='UCB')
for i in range(100):
    arms_chosen = []
    reward = np.random.random()
    arms_chosen.append(ucb_instance.get_next_arm(reward))