Saravanan666/pypi-org huggingface dataset:
Package: melina
Melina can convert meta.xml to meta language (similar to protobuf) and back.melina --meta-out DIR1 meta.xml  # to meta
melina --xml-out DIR2 meta.meta  # back to xml

melina --meta-stdout meta.xml > x.meta  # or to stdoutThis is how melina language looks like:/// pdmeta: "1.1", domain: "f", product: "b", release: "AX", version: "1.5", revision: "198"// line above is a header with meta module versions.
// It needs to be the first symbol in file and start with '///'moSCALAR{// you can define managed objects like structs in C++boolx;inty;stringz;};moCOMPLEX{// enum/struct fields contain definition of enum/struct// enum/struct name should begin with small letter - after all it's a name of variableenumx{A=1,B,C=2};structy{intz;structv{intw;};};};moCARDINALITY{// you can define cardinality of each managed object or struct fieldintx;// required by defaultoptionalinty;// optional integer, may be nullrepeatedintz;// list of integersrepeated(15)intz;// at most 15 'z' integers};moDEFAULTS{// bool, int, string, enum can have default valuesboolx[default=true];inty[default=12];stringz[default="foo bar"];enumv[default=12]{A=1,B=12};};moRANGES{// int/string fields can specify allowed ranges// if int has a 3-param specifier, it's considered a "float"int(0..12)x;// allowed integers from 0 to 12 inclusiveint(-0.5,0.5,0.001)y;// allowed floats from -0.5 to 0.5 inclusive with step 0.001string(0..10)z;// allowed string length from 0 to 10 chars inclusive};moUNITS{// integers can have units (for documentational purposes)inty[units="kbps"];};/**
 * Example managed object description.
 */moCOMMENTS{// you can use C++-style '//foo' or '/*foo*/' comments in meta files// doxygen-style comments '///foo' and '/**foo*/' are treated as mo/field description,// if present immediately above or rightwards of mo/fieldintx;/// Example scalar field descriptioninty;/** Example scalar field description *//**
     * Example complex type description.
     */structz{};/// Example complex type description.structv{};};moCHILDREN->FIRST,SECOND,THIRD(10){// managed object can have children defined// children can have max count specified in parens (at most 10 THIRD children)};mo(hc)FLAGS{// each managed object has: hidden('h'), create('c'), update('u'), delete('d') flags.// If not specified, hidden is assumed false, rest is assumed true.// Specification may be provided as 'hcud' letters in parens.};
Package: meliora
About the packagemeliorais a Python package that provides a set of statistical tests and tools to assess the performance of the credit risk models. All tests are covered with unit tests and algorithms have been replicated in other tools like R, MATLAB and SAS to avoid errors. Whenever possible, the definition of the test was retrieved from the authoritive source like the EBA, the ECB or the Basel Committee.The main contributors started building their first statistical credit models back in 2003. Over the years, we have impemented similar set of tests in several different financial institutions.This package is standing on the shoulders of giants as it makes heavy use of the Python
ecosystem and especially Scikit-learn, Scipy and Statsmodels. Several functions are straightforward
wrappers using these resources and are provided to the user for convenience purposes. The authors
have taken great care to ensure that no part of this package contains proprietary code.Main aimThe aim of the package is to provide all common tests used by today's modellers when developing, maintaining and validating their PD, LGD, EAD and prepayment models. The aim of this package is to provide credit risk practioners with the tools to develop their credit risk models without reinventing the wheel.Main Featurestests cover both IFRS 9 and IRB models as well as non-regulatory modelsthe tool contains more than 30 testsall test have been covered with unit teststhe tests have been documented in detailcommonly accepted tresholds have been provided for convenience purposesFor the list of all tests, see Overview > List of testsTests that are currently included in the package#NameAreaEstimate1Binomial testCalibrationPD2Chi-Square test (Hoshmer-Lemeshow test)CalibrationPD3Normal testCalibrationPD4Spiegehalter testCalibrationPD5Redelmeier testCalibrationPD6Herfhindahl index / Concentration of rating gradesConcentrationPD7Brier scoreDiscriminationPD8Receiver Operating CharacteristicDiscriminationPD9Accuracy RatioDiscriminationPD10Kendall’s τDiscriminationPD11Somers’ DDiscriminationPD12Conditional Information Entropy RatioDiscriminationPD13Kullback-Leibler distanceDiscriminationPD14Information valueDiscriminationPD15Bayesian error rateDiscriminationPD16Cumulative LGD accuracy ratioDiscriminationLGD17Loss Capture RatioDiscriminationLGD18Kolmogorov-Smirnov testDiscriminationPD19Spearman’s rank correlationDiscriminationLGD20Jeffrey's testDiscriminationPD21ELBE back-test using a t-testDiscriminationLGD22Migration matrices testDiscriminationPD23Loss ShortfallPredictive powerLGD24Mean Absolute DeviationPredictive powerLGD25Population Stability IndexStabilityPD26Stability of transition matricesStabilityPDFull list of dependenciesNumPy (https://www.numpy.org)Pandas (https://pandas.pydata.org/)Statsmodels (https://www.statsmodels.org/)Scikit-learn (https://scikit-learn.org/)Scipy (https://scipy.org/)Getting HelpFor usage questions, send an email toanton.treialt@aistat.comLicenseMIT License
Package: melisa
The easiest way to create your ownoptimizedDiscord Bot.THIS LIBRARY IS CURRENTLY UNDER DEVELOPMENT!Every provided example or a feature is not ready or may be changed in the futureAboutMelisaPyis a Discord microframework for Python 3
for theDiscord API.It supports Discord V10 REST API and GatewayWe are trying to make our library optimized.
We are going to create really cool cache configuration, so don't worry about the RAM :)Install MelisaPyNOTE: Currently, installation is only possible using GitHubpip install git+https://github.com/MelisaDev/melisaEvents Listeningimportmelisaclient=melisa.Client("your cool token...")@client.listenasyncdefon_message_create(message):ifmessage.content.startswith('$greet'):awaitmessage.channel.send(f'Hello man!')client.run_autosharded()Also, we should tell you, that logging is enabled automatically (evil laugh).
But do not worry, only some important things will be logged, but it can be disabled or changed.If you wish to specify some intents, you should do it something like that:importmelisaclient=melisa.Client("your cool token...",intents=[melisa.Intents.GUILD_MEMBERS,melisa.Intents.GUILD_BANS])Or you can do something like this:importmelisaintents=melisa.Intents.all()-melisa.Intents.GUILD_PRESENCESclient=melisa.Client("your cool token...",intents=intents)Also sharding is fully supported in Melisa, but it is too long to write about it here,
so feel free to read our docs!Making your bot more efficientPython optimization flagsYou can specify some optimization flags in CPython interpreterpython bot.py- no optimizationpython -O bot.py- features such as internal assertions will be disabled.python -OO bot.py- more features (including all docstrings) will be removed from the loaded code at runtime.melisa[speed]Also you can speed up some features in Melisa, like json parsing.
It will installorjson.Want to help us?Contributing manual is not ready yet, but will be done in some days.
Feel free to us inour Discord Serverabout contributing to the Melisa.
Package: melissa
Melissa-------| |Build Status| |Codacy Badge| |codecov.io| |Join the chat at https://gitter.im/Melissa-AI/Melissa-Core||Melissa is a virtual assistant for OS X, Windows and Linux systems. Shecurrently uses Google Chrome's speech-to-text engine, OS X's ``say``command, Linux's ``espeak`` command or Ivona TTS along with some magicalscripting which makes her alive, developed by `TanayPant <http://tanaypant.com>`__ and a group of sorcerers. The Web UI forMelissa has been designed by `Nakul Saxena <http://nakulsaxena.in>`__... raw:: html<p align="center"><a href="https://www.youtube.com/watch?v=r1yoaq3BcUA"><img src="http://i.imgur.com/OmsI07v.gif"></a></p>Check out our`wiki <https://github.com/Melissa-AI/Melissa-Core/wiki>`__, where wehave installation and configuration instructions, usage guide as well asrelevant documentation.Discussion and Support~~~~~~~~~~~~~~~~~~~~~~If you face an issue or require support, please take a look through theGitHub Issues, as you may find some useful advice there. If you arestill facing issues, feel free to create a post at our `Google GroupForum <https://groups.google.com/forum/#!forum/melissa-support--discussion-forum/>`__describing the issue and the steps you have taken to debug it.Licence~~~~~~~`The MIT License(MIT) <https://github.com/Melissa-AI/Melissa-Core/blob/master/LICENSE.md>`__.. |Build Status| image:: https://api.travis-ci.org/Melissa-AI/Melissa-Core.svg?branch=master:target: https://travis-ci.org/Melissa-AI/Melissa-Core/.. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/b1394316e9eb40bbbf51a12530c4f86d:target: https://www.codacy.com/app/tanay1337/Melissa-Core?utm_source=github.com&utm_medium=referral&utm_content=Melissa-AI/Melissa-Core&utm_campaign=Badge_Grade.. |codecov.io| image:: http://codecov.io/github/Melissa-AI/Melissa-Core/coverage.svg?branch=master:target: http://codecov.io/github/Melissa-AI/Melissa-Core?branch=master.. |Join the chat at https://gitter.im/Melissa-AI/Melissa-Core| image:: https://badges.gitter.im/Melissa-AI/Melissa-Core.svg:target: https://gitter.im/Melissa-AI/Melissa-Core?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
Package: melizalab-tools
A collection of python code and scripts used by the Meliza Lab.To install:pip installmelizalab-toolsmodulesdlab.pprox: functions for working withpproxobjects, a data format
for storing multi-trial point process data (e.g. spike times evoked
by stimulus presentation).console scriptsgroup-kilo-spikes: sort spike times output fromkilosortandphy2into pprox files.other stuffscripts/extract_waveforms.py: extracts spike waveforms from a raw
recording (in ARF format) using spike times stored in a file (pprox
format). This script is mostly only used to verify that spike sorting
is working properly, because thegroup-kilo-spikesscript has an
option to store average waveforms.
Package: mellon
MellonMellon is a non-parametric cell-state density estimator based on a
nearest-neighbors-distance distribution. It uses a sparse gaussian process
to produce a differntiable density function that can be evaluated out of sample.InstallationTo install Mellon usingpipyou can run:pipinstallmellonor to install usingcondayou can run:condainstall-cconda-forgemellonor to install usingmambayou can run:mambainstall-cconda-forgemellonAny of these calls should install Mellon and its dependencies within less than 1 minute.
If the dependency jax is not autimatically installed, please refer tohttps://github.com/google/jax.DocumentationPlease read thedocumentationor use thisbasic tutorial notebook.Basic UsageimportmellonimportnumpyasnpX=np.random.rand(100,10)# 10-dimensional state representation for 100 cellsY=np.random.rand(100,10)# arbitrary test datamodel=mellon.DensityEstimator()log_density_x=model.fit_predict(X)log_density_y=model.predict(Y)CitationsThe Mellon manuscript is available onbioRxivIf you use Mellon for your work, please cite our paper.@article{Otto2023.07.09.548272,author={Dominik Jenz Otto and Cailin Jordan and Brennan Dury and Christine Dien and Manu Setty},title={Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon},elocation-id={2023.07.09.548272},year={2023},doi={10.1101/2023.07.09.548272},publisher={Cold Spring Harbor Laboratory},URL={https://www.biorxiv.org/content/early/2023/07/10/2023.07.09.548272},eprint={https://www.biorxiv.org/content/early/2023/07/10/2023.07.09.548272.full.pdf},journal={bioRxiv}}
Package: mellow
Machine Learning - just do it !mellow is a python package that contains some well known machine learning algorithms.Exclusive to first time learner.It provides:most popular machine learning algorithmssophisticated utility functions useful in these algorithmsand much moreOften Developers/Students feel hesitation to implement the machine learning algorithms because either they find it hefty or time consuming, what I believe is understanding the machine learning algorithms and the mathematics behind these algorithms is much more important than converting it into code. So I came up with this beautiful library mellow to help you (and myself tbh) out.Installation using pippip install mellow
Package: mellow-strategy-sdk
SDK for creating new strategies on Uniswap V3gitclonehttps://github.com/mellow-finance/mellow-strategy-sdk.gitcdmellow-strategy-sdkpython3-mvenv.venvsource.venv/bin/activatepipinstallpoetrypoetryinstallorpython3-mvenv.venvsource.venv/bin/activatepipinstallmellow_strategy_sdkGetting StartedImportfrommellow_sdk.primitivesimportPool,Token,Feefrommellow_sdk.dataimportRawDataUniV3frommellow_sdk.strategiesimportUniV3Passivefrommellow_sdk.backtestimportBacktestfrommellow_sdk.viewersimportRebalanceViewer,UniswapViewer,PortfolioViewerfrommellow_sdk.positionsimportBiCurrencyPosition,UniV3PositionChoose a poolA typical notebook would start with downloading and preparing data for a specific pool.
Let’s choose WBTC/WETH, fee 0.3% pool.pool=Pool(Token.WBTC,Token.WETH,Fee.MIDDLE)Get dataАt the first run you need to download the datadata=RawDataUniV3(pool,'data',reload_data=False).load_from_folder()Use implemented strategyuniv3_passive=UniV3Passive(lower_price=data.swaps['price'].min()-1,upper_price=data.swaps['price'].max()+1,pool=pool,gas_cost=0.,name='passive')BacktestNext step is to run backtest using your strategy and databt=Backtest(univ3_passive)portfolio_history,rebalance_history,uni_history=bt.backtest(data.swaps)VisualizeNext visualize resultsrv=RebalanceViewer(rebalance_history)uv=UniswapViewer(uni_history)pv=PortfolioViewer(portfolio_history,pool)# Draw portfolio stats, like value, fees earned, apyfig1,fig2,fig3,fig4,fig5,fig6=pv.draw_portfolio()# Draw Uniswap intervalsintervals_plot=uv.draw_intervals(data.swaps)# Draw rebalancesrebalances_plot=rv.draw_rebalances(data.swaps)# Calculate df with portfolio statsstats=portfolio_history.calculate_stats()intervals_plot.show()rebalances_plot.show()fig2.show()fig4.show()fig6.show()Congratulations! Now you have the results of your strategy backtest on the real UniV3 data!
Package: melmel
Hello Peoples!Thanks for downloading my Module, and if you haven't downloaded it yet please do so I worked hard on it and I am planning to adding lots of cool stuff to it.Hold up I just remembered, I am a YouTuber! here is my chanel:https://www.youtube.com/channel/UCIRBNR4hPcjGh4KSFrXKa_wAlso if you want some Cottage Cheese here is 568 cheeses of the cottage variety.
Package: melnor-bluetooth
Melnor BluetoothMelnor Bluetooth is a reverse engineered implementation of the Bluetooth protocol for all "smart" bluetooth-enabled watering valves under the Melnor, EcoAquastar, Eden, and other brands.The libraryshouldrun on MacOS, Linux, or Windows. It's primarily developed on MacOS and other platforms likely have bugs. PRs and tests are welcome to improve quality across all platforms.Getting StartedCLIA simple CLI has been provided for basic debugging purposes. It's not intended for any real use and isn't suitable for running a valve in the real world.This project uses poetry for dependency management and building. Running this project locally is as simple as the following steps:Clone the repositorypoetry installpoetry run cli.pyThe python API has been designed to be as easy to use as possible. A few examples are provided below:Read battery stateimportasynciofrombleakimportBleakScanner# type: ignore - bleak has bad export typesfrommelnor_bluetooth.deviceimportDeviceADDRESS="00:00:00:00:00"# fill with your device mac addressasyncdefmain():ble_device=awaitBleakScanner.find_device_by_address(ADDRESS)ifble_deviceisnotNone:device=Device(ble_device)awaitdevice.connect()awaitdevice.fetch_state()print(device.battery_level)awaitdevice.disconnect()asyncio.run(main())Turn on a zoneimportasynciofrombleakimportBleakScanner# type: ignore - bleak has bad export typesfrommelnor_bluetooth.deviceimportDeviceaddress="00:00:00:00:00"# fill with your device mac addressasyncdefmain():ble_device=awaitBleakScanner.find_device_by_address(ADDRESS)ifble_deviceisnotNone:device=Device(ble_device)awaitdevice.connect()awaitdevice.fetch_state()device.zone1.is_watering=Trueawaitdevice.push_state()awaitdevice.disconnect()asyncio.run(main())
Package: melnykmath
my_python_libraryщоб створити власну бібліотекувстановити :
% pip install wheel
% pip install setuptools
% pip install twineстворити папку, яку буде імпортуватисьобовʼязковий файл setup.py
'''
from setuptools import find_packages, setupsetup(
name='mypythonlib',
packages=find_packages(include=['mypythonlib']),
version='0.1.0',
description='My first Python library',
author='Me',
install_requires=[],
setup_requires=['pytest-runner'],
tests_require=['pytest==4.4.1'],
test_suite='tests',
)
'''
4. python setup.py bdist_wheelInstall library - pip install /path/to/wheelfile.whl
Package: melo
Margin-dependent Elo ratings and predictions modelDocumentationmoreland.dev/projects/meloQuick startRequirements: Python 2.7 or 3.3+ withnumpyandscipy.Install the latest release withpip:pip install meloExample usage:import pkgutil
import numpy as np
from melo import Melo

# the package comes pre-bundled with an example dataset
pkgdata = pkgutil.get_data('melo', 'nfl.dat').splitlines()
dates, teams_home, scores_home, teams_away, scores_away = zip(
    *[l.split() for l in pkgdata[1:]])

# define a binary comparison statistic
spreads = [int(h) - int(a) for h, a
    in zip(scores_home, scores_away)]

# hyperparameters and options
k = 0.245
lines = np.arange(-50.5, 51.5)
regress = lambda months: .413 if months > 3 else 0
regress_unit = 'month'
commutes = False

# initialize the estimator
nfl_spreads = Melo(k, lines=lines, commutes=commutes,
                   regress=regress, regress_unit=regress_unit)

# fit the estimator to the training data
nfl_spreads.fit(dates, teams_home, teams_away, spreads)

# specify a comparison time
time = nfl_spreads.last_update

# predict the mean outcome at that time
mean = nfl_spreads.mean(time, 'CLE', 'KC')
print('CLE VS KC: {}'.format(mean))

# rank nfl teams at end of 2018 regular season
rankings = nfl_spreads.rank(time, statistic='mean')
for team, rank in rankings:
    print('{}: {}'.format(team, rank))
Package: melobot
MeloBot插件化管理、基于异步会话机制的 python 的 qbot 开发框架郑重承诺和提示：本项目一切开发旨在学习，请勿用于商业用途和非法用途。任何使用本项目的用户群体，有义务遵守其所在国家或地区的相关法律规定。🎉 关于 MeloBotMeloBot 是一个基于 Python 的 qbot 开发框架。其以实现了 Onebot 标准的适配器作为前端，通过对来自适配器的各种“事件”的处理，再产生“行为”，最后提交给适配器与 qq 服务器进行通信，实现各类复杂的功能。v1 版本（main分支）已经完成。它本质上不是一个开发框架，只是一个可用的 qq 机器人项目。只支持 windows 平台，且需要 go-cq 作为适配器，现已经停止更新和维护。目前正开发 v2 版本（v2-dev分支）。v2 版本已经实现完毕，目前正在进行验证工作，验证完成后会发布 release。你可以参考用于本项目验证的具体机器人项目（MeloInf），这里有详细的使用示例。此外，你可以在 pypi.org 预先获得 MeloBot 的 pre-release 版本：pipinstall--premelobot🚧 MeloBot v2 的开发工作✅ 封装建立内部数据结构，与底层数据解耦✅ 取消线程池设计，全部使用协程设计✅ 事件接口、事件分发与事件回送✅ 权限检查、规则校验、解析组件等中间件设计✅ 完整的会话控制机制✅ 插件化管理✅ 插件交互✅ 生命周期 hook 设计⬜ 后期验证📦️ 版本支持python >= 3.8platform == All（mac 平台未测试）OneBot 标准 >= 11💬 更多请参阅文档（v1 版本）：MeloBot 文档文档开源（v1 版本）：MeloBot-docs
Package: melodia
Melodia is a Python library for MIDI music creation.It provides four core concepts – tone, time signature, note and track and various
abstractions built on them, such as chords and chord progressions. Library contains
MIDI writer which allows to output tracks to MIDI files in order to export them to
any DAW.InstallationMelodia is fully typed and requires Python 3.7 or greater.pipinstallmelodiaDocumentationDocumentation is available atmelodia.readthedocs.io.Examplefrommelodia.core.trackimportTrackfrommelodia.musicimportchordfrommelodia.ioimportmiditrack=Track(signature=(4,4))track.add(chord.maj('C3',(1,1)))track.add(chord.maj('D3',(1,1)))track.add(chord.min('A3',(1,1)))track.add(chord.maj7('G3',(1,1)))withopen('chords.mid','wb')asf:midi.dump(track,f)
Package: melody
# MelodyMelody is a lightweight parameter search and optimisation tool
originally designed to support searching and optimising over the
performance landscape of software. In particular, it is being designed
to support code composition options (loop ordering, loop fusion etc),
code compilation and link options (compilers and compiler flags) and
code configuration options (tunable parameters such as a cut off
radius, or preconditioner). However Melody has been written in a
relatively generic way so can be used to search over, or optimise any
search space.Melody takes its name from the fact that optimisation is often called
tuning and most good tunes rely on a great melody!# Badges[![Build Status](https://travis-ci.org/rupertford/melody.svg?branch=master)](https://travis-ci.org/rupertford/melody)
[![Coverage Status](https://coveralls.io/repos/github/rupertford/melody/badge.svg?branch=master)](https://coveralls.io/github/rupertford/melody?branch=master)
[![Code Health](https://landscape.io/github/rupertford/melody/master/landscape.svg?style=flat)](https://landscape.io/github/rupertford/melody/master)
[![Documentation Status](https://readthedocs.org/projects/melody/badge/?version=latest)](http://melody.readthedocs.io/en/latest/?badge=documentation)# LicensingMelody is available under a BSD 3-clause license. A copy of this
license is provided when Melody is downloaded and installed.# InstallationMelody can be installed and uninstalled using pip.` > sudo pip install melody > sudo pip uninstall melody `# DocumentationDocumentation is available here:http://melody.readthedocs.io/en/documentation
Package: meloetta
Meloetta is a Pokémon Battle Client for interacting with Pokémon Showdown written in Python. This project was born out of frustration for currently existing tools and their lack of dependency on Zarel's (PS Creator) existing code for handling client server interation.The client works by reading messages from an asyncio stream and forwarding these to the javascript client battle object with PyMiniRacer. This concept was taken frommetagrok.As is, the necessary javascript source files come with the pip install. Whenever Pokemon Showdown Client releases an update, the client code can also be automatically updated from the source.
Package: melomaniac
Listen to your Google Play Music and Soundcloud library from your terminal.RequirementsPython 3.5+mpvInstallationpip install melomaniacYou will also need to installmpvto actually play the songs.UsageTo launch the player, just do:melomaniac listen gmusicormelomaniac listen soundcloudIf this is the first time using a specific backend, Melomaniac will ask you
a few pieces of information:Gmusic:usernameandpassword. There is no way around it. If you have 2FA activated for you account you must create an application specific password.Soundcloud:client_id,client_secret,usernameandpassword. You need toacquire a client credential for your application, in order for Melomaniac to work. Bothusernameandpasswordwill only be used once to retrieve API tokens and will never be stored.The configuration is stored in~/.melomaniac/config.yml.### CommandsAccess an element (rightorenter)Navigate a list (up/down)Play a song (enter)Pause/Resume a song (space)Forward/Rewind (f/r)Quit (q)