Saravanan666/pypi-org huggingface dataset:
Package: matalg27
IntroductionThis module, written in Python2.7, is the backport of a similar module written in Python 3.2. There are many
Python users who have not yet moved to Python 3.x and because generally Python 3.x is not fully backwards
compatible with the earlier Python versions, it has been necessary to provide backports of Python 3.x modules to Python 2.x.This module, written in pure Python 2.7, does not require other modules, only
Python 2.x. Consequently the module is as portable as Python itself. The well documented module is easy to use and allows use of the usual
scalar variable syntax for the more complex matrix operations. Thus, for matrix multiplication of matricesamatandbmatone can simply write:resultmat = amat * bmatFor the inversion ofamat, one only needs to write:resultmat = ~ amatMatrix naming convention is freely chosen by the end user.  The module is well documented with pdf filesquickStart,userManualandreferenceMatalg. Reference can also be made to the documentation
of the Python 3.2 module “Matalg”. In the backport attention was given to user use compatibility between the
modules in Python 3.2 and Python 2.7.Documentation and ServicingQuick Startwill get an experienced user up to speed very quickly.User Manualhas an explanation and examples of most capabilities of the package, whilst theReference of Matalglists all the methods and functions of the module with brief explanation of each. In addition to the supplied pdf files, the information for the Python 2.7 module is also accessible on the web at:http://akabaila.pcug.org.au/matalg27-doc/index.htmlThe author is prepared to consider all reasonable requests for extensions and improvements and/or bug fixes of the module. Your emails will be appreciated:algis.kabaila@gmail.comLicenceThe module is licensed under LGPL and is Open Source Freeware. You are welcome to copy it and to share it.Algis Kabaila, Canberra, 2011.
Package: matanui
Project HostingProject hosting, bug tracker, etc.:http://launchpad.net/matanuiCode hosting (Bazaar repository):https://code.launchpad.net/matanuiDescription of Project/ConceptsMataNui is intending to provide a data serving platform for the Grid that
can replicate between various sites, store/query arbitrary amounts of
meta-data, and is interfaced with a RESTful web service. The concept also
allows for an alternative server front end using the Griffin GridFTP server.
The concept is (initially) based on storing all data/meta-data in the GridFS
mode of the MongoDB NoSQL database. This code consists of a WSGI based
RESTful web service that interfaces directly with the MongoDB, and can be
used together with Apache and mod_wsgi.The MataNui project is resulting from the ambitions of the New Zealand
BeSTGRID project (https://bestgrid.org/) to provide a reliable and easy to
use “Data Fabric” for the Globus Toolkit based Grid. The implementation is
also aligned with the ambitions of the Australian Research Collaboration
Service ARCS (http://www.arcs.org.au/).The project has been outlines at the New Zealand eResearch Symposium 2010 in
Auckland on this poster:http://www.slideshare.net/XEmacs/huge-matanui-building-a-grid-data-infrastructure-that-doesnt-suckSome external resources used together with this project.MongoDB data base:http://www.mongodb.org/Apache WSGI module:https://code.google.com/p/modwsgi/Griffin GridFTP server (alternative data access service front end):https://projects.arcs.org.au/trac/griffinUser end data management GUI application DataFinder:https://wiki.sistec.dlr.de/DataFinderOpenSourcehttps://launchpad.net/datafinder/
Package: matatika
MatatikaThematatikapackage allows a user to interact with the Matatika service. A command-line interface (CLI) and client library are included.Current FunctionalityLogin with Bearer token and endpoint URL (defaulthttps://catalog.matatika.com/api)Override Bearer token and endpoint URL for supported operationsUse a specific workspace by default for supported operationsList all available workspacesList all available datasets in a given workspacePublish a datasetFetch data from a datasetUpcoming FeaturesLogin with username and password though browser (CLI only)Create a datasetCreate a data pipeline to a workspaceList all data pipelinesRun a data pipeline
Package: matbench-discovery
Matbench DiscoveryTL;DR: We benchmark ML models on crystal stability prediction from unrelaxed structures finding universal interatomic potentials (UIP) likeCHGNet,MACEandM3GNetto be highly accurate, robust across chemistries and ready for production use in high-throughput materials discovery.Matbench Discovery is aninteractive leaderboardand associatedPyPI packagewhich together make it easy to rank ML energy models on a task designed to simulate a high-throughput discovery campaign for new stable inorganic crystals.We've tested models covering multiple methodologies ranging from random forests with structure fingerprints to graph neural networks, from one-shot predictors to iterative Bayesian optimizers and interatomic potential relaxers.Our results show that ML models have become robust enough to deploy them as triaging steps to more effectively allocate compute in high-throughput DFT relaxations. This work provides valuable insights for anyone looking to build large-scale materials databases.We welcome contributions that add new models to the leaderboard through GitHub PRs. See thecontributing guidefor details.If you're interested in joining this work, feel free toopen a GitHub discussionorsend an email.For detailed results and analysis, check out thepreprint.
Package: matcalc
MatCalcDocsmaterialsvirtuallab.github.io/matcalcIntroductionMatCalc is a Python library for calculating material properties from the potential energy surface (PES). The
PES can come from DFT or, more commonly, from machine learning interatomic potentials (MLIPs).Calculating material properties often requires involved setups of various simulation codes. The
goal of MatCalc is to provide a simplified, consistent interface to access these properties with any
parameterization of the PES.OutlineThe main base class in MatCalc isPropCalc(property calculator).AllPropCalcsubclassesshould implement acalc(pymatgen.Structure) -> dictmethod that returns a dictionary of properties.In general,PropCalcshould be initialized with an ML model or ASE calculator, which is then used by either ASE,
LAMMPS or some other simulation code to perform calculations of properties.CitematcalcIf you usematcalcin your research, seecitation.cffor the GitHub sidebar for a BibTeX and APA citation.
Package: match
The purpose of the moduleMatchis to get the offsets (as well as
the string between those offsets, for debugging) of a cleaned-up,
tokenized string from its original, untokenized source. “Big deal,” you
might say, but this is actually a pretty difficult task if the original
text is sufficiently messy, not to mention rife with Unicode characters.Consider some text, stored in a variableoriginal_text, like:I   am writing a letter !  Sometimes,I forget to put spaces (and do weird stuff with punctuation)  ?  J'aurai une pomme, s'il vous plâit !This will/should/might be properly tokenized as:[['I','am','writing','a','letter','!'],['Sometimes',',','I','forget','to','put','spaces','-LRB-','and','do','weird','stuff','with','punctuation','-RRB-','?'],["J'aurai",'une','pomme',',',"s'il",'vous','plâit','!']]Now:In[2]:importmatchIn[3]:match.match(original_text,['-LRB-','and','do','weird','stuff','with','punctuation','-RRB-'])Out[3]:[(60,97,'(and do weird stuff with punctuation)')]In[4]:match.match(original_text,['I','am','writing','a','letter','!'])Out[4]:[(0,25,'I   am writing a letter !')]In[5]:match.match(original_text,["s'il",'vous','plâit','!'])Out[5]:[(121,138,"s'il vous plâit !")]The return type frommatch()is alistbecause it will returnalloccurrences of the argument, be it alistof tokens or a
singlestring(word):In[6]:match.match(original_text,"I")Out[6]:[(0,1,'I'),(37,38,'I')]When passing in a singlestring,match()is expecting thatstringto be a single word or token. Thus:In[7]:match.match("****because,the****","because , the")Out[7]:[]Try passing in"because ,the".split('')instead, or better yet,
the output from a proper tokenizer.For convenience, a function calledmatch_lines()is provided:In[8]:match.match_lines(original_text,[...:['-LRB-','and','do','weird','stuff','with','punctuation','-RRB-'],...:['I','am','writing','a','letter','!'],...:"I"...:])Out[8]:[(0,1,'I'),(0,25,'I   am writing a letter !'),(37,38,'I'),(60,97,'(and do weird stuff with punctuation)')]The values returned will always be sorted by their offsets.Installationpip install matchorconda install-cets matchRequirementsPython >= 3.8nltkregexDocumentationHere!.
Package: matcha-johannes
maNUAL chaRMMPlease note that this an early incarnation, which may or may not be suitable for general use.It is recommended to set an alias for the script for convenient usage, e.g. in your .bashrc:# Make sure that the charmm binary is in the $PATH or you have set a location in the script.export PATH="/path/to/charmm/bin:$PATH"# Create an alias for the script e.g. in your .bashrc .alias macha="/path/to/manual_charmm_system_setup.sh"
Package: matcha-ml
Open source MLOps on Azure in one stepIf you train machine learning models, then you know the challenge of going fromexperimenttoproduction. There's a vast range of tools that promise to help, from experiment tracking through to model deployment, but setting these up requires a lot of time and cloud engineering knowledge.Matcha removes the complexity of provisioning your machine learning infrastructure. With one step, you'll have a completemachine learning operations (MLOps)stack up and running in your Microsoft® Azure cloud environment. This means you'll be able to track your experiments, train your models, as well as deploy and serve those models.🍵 Who is Matcha for?Matcha is for data scientists, machine learning engineers, and anybody who trains machine learning models. If you're using Azure, and want an intuitive way to deploy machine learning infrastructure, Matcha is for you.🏃 How do I get started?If you're new to Matcha, the best place to start isour guide to deploying your first model. If you're happy with the basics, then you might want to dive into ourMatcha exampleson Github.📖 DocumentationThe full Matcha documentation can be found atmymatcha.ai. This covers what Matcha is in more detail, how to use it, what Azure permissions are required, and how Matcha works internally.🗺️ RoadmapWe've put a lot of thought into what our users — data scientists, ML engineers, etc — need from their infrastructure, and we came up with 5 key pieces of functionality that are absolute musts:A place to track, version, and manage datasets.A place to track experiments and models assets.Scalable compute for running training workloads, with the option to use GPUs.Somewhere to deploy and serve models in a way that scales with your application needs.The ability to monitor models for things like drift and bias.Matcha is still in alpha release, and we don't support everything on that list yet. We support experiment tracking, training, and deployment, with plans to add data versioning and monitoring later. We very much welcome input on our roadmap from our early users.👏 ContributingMatcha at its very core is open source and we're eager for you to get involved whether through raising issues or by opening a PR.We have an in-depthcontributingguide which will describe how to do all of this.Note: Matcha is still in alpha release. While we've worked hard to ensure there are no defects, there's a small chance that you'll find a bug or something that hasn't been documented as well as it could be. If that happens, we'd really value your feedback, which you can send by submitting anissueto Matcha on Github.📜 LicenseThis library is released under the Apache License. SeeLICENSE.🤝 AcknowledgementsThank you toZenMLfor their contributions and inspiration through theirstack recipes.
Package: matchbook
# matchbook
Python wrapper for Matchbook API.[Matchbook Documentation](https://developers.matchbook.com/v1.0/reference)# Installation` $ python install matchbook `# Usage`python >>> from matchbook.apiclient import APIClient >>> api =APIClient('username','password') >>> sport_ids = api.reference_data.get_sports() >>> tennis_events =api.market_data.get_events(sport_ids=[9])`
Package: matchcaselib
Match-Case Librarya.k.a. matchcaselibWelcome to matchcaselib, an easy implementation ofPython 3.10's new featureof structural pattern matching.This project is currently in aBETA STAGE.InstallationYou can install this package by running the following command in your terminal:pip install matchcaseliborupdate itwith the commandpip install --upgrade matchcaselibUsagefrommatchcaselibimportmatcha=input("Enter in a number: ")a=int(a)b=input("Enter in another number: ")b=int(b)withmatch(a,b)asm:if(m.case(1,1)):print("All ones.")if(m.case(1,2)):print("One and two.")if(m.case(a,1)):print("The value of A can be anything!  A was{}and b was 1.".format(a))if(m.default):print("I don't know what you mean...")What you can do:Compare as many sets of variables as you would like (the amount you provide with the initialization of the class must be consistent with the amount you use for thecasemethod.)Make a "default" response - Run a block ifdefaultis true (no matches were found)Work in Progress features:Multi-pattern matching (test for multiple patterns)Break function - leave the current matchWhat not to do:Use different amounts of variables for the class initialization and the use of thecasemethodI don't know, there aren't many bugs right now LOL
Package: matchclot
# MatchCLOT[![Documentation Status](https://readthedocs.org/projects/matchclot/badge/?version=latest)](https://matchclot.readthedocs.io/en/latest/?badge=latest)Matching single cells across modalities with contrastive learning and
optimal transport.Preprint available on bioRxiv
([Gossi et al., 2022](https://www.biorxiv.org/content/10.1101/2022.11.16.516751v1))## Main functionalities
![overview](tutorials/figures/overview.png)1. MatchCLOT is a computational framework that is able to
match single-cells measured using different omic modalities.With paired multi-omic data, MatchCLOT uses contrastive learning to learn a common representation between two modalities after applying a preprocessing pipeline for normalization and dimensionality reduction.Based on the similarities between the cells in the learned representation, MatchCLOT finds a matching between the cell profiles in the two omic modalities using entropic optimal transport.Pretrained MatchCLOT models can be applied to new unpaired multiomic data to match two modalities at single-cell resolution.## Installation and Tutorials>_NOTE:_The package and the documentation are under development.MatchCLOT is available on PyPI and can be installed with pip:`pip install matchclot`The documentation is available at [https://matchclot.readthedocs.io/en/latest/](https://matchclot.readthedocs.io/en/latest/)
Package: matchco
Match organization names. See docs/ directory for more info.Basic usage:from matchco import subsequences, NoMatchError, bestmatchname = "mygreat korp"candidate1 = ("My Great Corp, Inc.", "My Great Corp", subsequences("My Great Corp".lower()))candidate2 = ("Other Corp Inc.", "Other Corp", subsequences("Other Corp Inc.".lower()))candidates = (candidate1, candidate2)try:match = bestmatch(name, candidates, multiple=True)except NoMatchError:match = Noneprint(match or "no match for '%s' found" % name)
Package: matchcode-toolkit
MatchCode toolkit is a Python library that provides the directory fingerprinting
functionality forScanCode toolkitandScanCode.ioby implementing the
HaloHash algorithm and using it in ScanCode toolkit and ScanCode.io plugins and
pipelines.InstallationMatchCode toolkit must be installed in the same environment as ScanCode toolkit
or ScanCode.io.From PyPI:pip install matchcode-toolkitA checkout of this repo can also be installed into an environment using pip’s--editableoption,# Activate the virtual environment you want to install MatchCode-toolkit into,
# change directories to the ``matchcode-toolkit`` directory
pip install --editable .or built into a wheel and then installed:pip install flot
flot --wheel --sdist # The built wheel will be in the dist/ directory
pip install matchcode_toolkit-*-py3-none-any.whlUsageMatchCode toolkit provides the--fingerprintoption for ScanCode toolkit.
This is a post-scan plugin that adds the fieldsdirectory_content_fingerprintanddirectory_structure_fingerprintto
Resources and computes those values for directories.scancode --info --fingerprint <scan target location> --json-pp <output location>MatchCode toolkit provides thescan_and_fingerprint_packagepipeline for
ScanCode.io. This is the same as thescan_single_packagepipeline, but has the
added step of computing fingerprints for directories.LicenseSPDX-License-Identifier: Apache-2.0The ScanCode.io software is licensed under the Apache License version 2.0.
Data generated with ScanCode.io is provided as-is without warranties.
ScanCode is a trademark of nexB Inc.You may not use this software except in compliance with the License.
You may obtain a copy of the License at:http://apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.Data Generated with ScanCode.io is provided on an “AS IS” BASIS, WITHOUT WARRANTIES
OR CONDITIONS OF ANY KIND, either express or implied. No content created from
ScanCode.io should be considered or used as legal advice. Consult an Attorney
for any legal advice.
Package: matchernet
What is MatcherNet?What is MatcherNet?MatcherNet is a probabilistic state-space model for dynamic system identification and control. With MatcherNet, you can easily design a dynamic world model of high-dimensional / multi-modal / multi-scale states and observations for robotics, image processing, sensor networks, and their hybrid cases. MatcherNet may provide you a solution better than a large state space model, a deep neural network of end-to-end structure, etc.AcknowledgmentMatcherNet was developped with support by the New Energy and Industrial Technology Development Organization (NEDO), Japan,
and by Post-K application development for exploratory challenges from the MEXT, Japan.DocumentSee anoverviewfor knowing what is MatcherNet in a little bit more detail.See thematchernet documentationfor important classes and functions.See adevelopers' info.for knowing some additional info. for installation.How to install?The beta version has been released below:https://pypi.org/project/matchernet/You can install the beta version with the following command:pipinstallmatchernetHow to try demos?See demo files under the/demosdirectory.Python3demos/demo_ekf.pySee also jupyter notebooks under the/examplesdirectory.How to set up in detail?For MacOSXInstall Python 3.7.*It is tested on 3.7.2 and 3.7.3.
3.8.* is not supported yet.Set up virtual environmentmkdir~/virt_envcd~/virt_env
virtualenv-pPython3mn
workonmnInstall librariespip3install-rrequirements.txtModify PYTHONPATHexportPYTHONPATH="/path/to/dir:$PYTHONPATH"You need this if you are to modify files in the matchernet original packages.Run a demoPython3demos/demo_ekf.py
Package: matcher-pcx-synomia
Matcher-pcx-synomiaExtract themes from verbatims.Version 1.1.4Release date: 2019-10-30Getting Startedpip install ...Prerequisitesflashtext:
$ pip install flashtextUsageYou would need a lexicon.txt with the following structure:manque de place=><manque de place;place;-1<
personnel désagréable=><personnel désagréable;personnel;-1<
train direct=><train direct;train;1<Init matcher:themes_matcher = matcher.ThemesMatcher('lexicon.txt')Extract themes from verbatims:verbatims = ["","Le confort et la propreté","Rapidité (Train Direct), mais personnel désagréable","c'est catastrophique… retards chroniques, personnel désagréable","Le manque de place ds le tgv"]
matches = themes_matcher.match(verbatims)
print(matches)
[[], [], [['train direct', 'train', '1'], ['personnel désagréable', 'personnel', '-1']], [['personnel désagréable', 'personnel', '-1']], [['manque de place', 'place', '-1']]]AuthorsSynomia
Package: matchfarm
Python match farmUse python to scrape matches from the Riot APIsSample usageSetup:from src.ingestor import Ingestor
from src.config import Config

API_KEY="<Riot API Key here>"

userConfig= Config(API_KEY)


test = Ingestor(userConfig)

print(test.REGION_NA1)Download fixed sizefrom src import ingestor
from src.config import Config
from src.download_Controller import Download_Controller
from src.local_download import Local_Download

configUser = Config("<Riot API Key here>")

downloadType = Local_Download()

ingestorEngine = ingestor.Ingestor(configUser)

downloader = Download_Controller(downloadType, ingestorEngine)

downloader.downloadFixed('Kascadian', 50)
Package: match-func
match_funcPattern matching as a function.frommatch_funcimportmatch# Use ranges!age=20match(age,{range(0,3):'Toddler',range(3,14):'Child',range(14,18):'Teen',range(18,65):'Adult'},default='Elder')>>'Adult'# Use tuples!name='Clara'match(name,{('Laura','John','Luke'):'First class',('Clara','Nathan'):'Business',('Jason','Mary'):'Economy'},default='Non customer')>>'Business'# Use conditions!value=5match(value,{value<10:'Small',10<=value<=20:'Medium',value>20:'Large'})>>'Small'# Use types!something='Hello'match(something,{int:'Its an integer',str:'Its a string'},default='Its something else')>>'Its a string'# Mix and match!earnings=10_000match(earnings,{str:'Invalid type: str',range(0,5_000):'Too low',5_000:'Exactly 5k',earnings>5_000:'Over the top!'},default=f'Invalid value:{earnings}')>>'Over the top!'
Package: matchgroup
杩欐槸鐢ㄤ簬鎷夐綈鍘熷垱銆佹椿璺冦佷骇鑳界殑python鍖咃紝閫氳繃杈撳叆涓や釜缇や綋鐨勫師鍒涚巼銆佹椿璺冨害銆佸彂鏂囬噺锛岄氳繃PSM鐨勬柟寮忓緱鍒板彲鍖归厤鐨勪袱缇や綋鏍锋湰锛屽苟杩涜杩斿洖銆
涓嬮潰鏄绋嬪簭鍖呯殑浣跨敤浠嬬粛锛
#鏁版嵁澶勭悊
data = pd.read_csv('D:\椤圭洰\X椤圭洰\鎷夐綈鍘熷垱銆佹椿璺冦佷骇鑳絓\X_鍒涗綔鍙锋媺榻愬琛.csv', encoding='utf-8')data.set_index('cpid', drop=1, inplace=True)data = data[['cp_type','active_index','pubnum','rate_org']].sample(20000)data['treatment'] = data['cp_type'].map({'X': 1, 'creator': 0})
#瀹炰緥鍖
mt = PsmData(data,treatment='treatment',exclude_vars=['cp_type'])
#鑾峰彇璇勫垎
print(mt.propensity_score(method=LogisticRegression()))
#寰楀埌鍖归厤鐨刬ndex
print(mt.matching(left_match=1, right_match=0, k=1, caliper=None, replace=True).head())
#寰楀埌鍖归厤鍚庣殑涓ょ兢浣撴牱鏈
print(mt.full_matched_data(left_match=1, right_match=0, k=1, caliper=None, replace=True).head())
#缁樺埗鍒嗗竷鍥
matchdata = mt.full_matched_data()
mt.plot_distribution_list(matchdata)
plt.show()