Saravanan666/pypi-org huggingface dataset:
Package: matchup-ir
MatchUpPURE-Python IR library.Nowadays, through search engines, it is common to make queries that have, as a result, a high number of references that do not meet their contexts. In order to provide relevant results, upon consultation, some models from the Information Retrieval area, called classics, were proposed: the Boolean, the Vector and the Probabilistic. In turn, with a view to improving the quality of the results generated by the application of the classic models of Information Retrieval, extended models of Information Retrieval were defined from them; among them, we have the Extended Boolean, the Generalized Vector and the Belief Network.
 
In 2018/1, the first version of the MatchUp tool was developed: a web tool for calculating similarity between a query, which may be a specific document or set of terms of interest to the user, and create a collection of documents, enabling the generation of a list of documents from this created collection that are relevant to the desired query. To calculate similarity, this version included the classic IR models and the extended Extended Boolean model. Through the analysis of the results of the experiments carried out, it was possible to notice that the Vector model, in general, presented the best results when compared to the other models implemented. However, MatchUp did not include the Generalized Vector and Belief Network extended models, which may present better results than the Vector Model. Therefore, this scientific initiation project has, as main objective, the development of version 2.0 of the MatchUp tool, in order to also include the extended models Generalized Vector and Belief Network. To validate version 2.0 of the MatchUp tool, experiments will be carried out, involving different collections of documents.DocumentationRead the docsTechnologyPython 3.7
Package: match-video
Match VideoThis is a Python library that simplifies working with video from soccer matches. It allows match video to be selected intuitively by period number and clock, instead of absolute video time.To accomplish this the start of each period is set as a chapter in the match video's metadata. Clips from the video can then be selected by period number and clock. ffmpeg handles both reading and writing the video chapter metadata and clip selection.InstallationRequirementsPython 3.9 or newerffmpegpipinstallmatch-videoUsageBefore the video can be used, the start time of each half needs to be set.match-videoset-half-startspath/to/video.mp40:0463:20Then it is easy to select match video by period and clock!importmatch_videoasmv# get the third minute of the matchclip=mv.get_clip("path/to/video.mp4",period=1,start_clock=180,end_clock=240)# get the start of each half and concatenate themclip_clocks=[{"period":1,"start_clock":0,"end_clock":30},{"period":2,"start_clock":0,"end_clock":30},]clips=mv.get_clips("path/to/video.mp4",clip_clocks)See theexamplesto see how to save or display video clips.Supportgrantwenzinger@gmail.comLicenseMIT
Package: matchy
matchyA tool for matching devices in analog layout in a streamlined and automated way.InstallationRunpip install matchyfor the latest stable version.DevelopmentGit clonethis repository.cdto the root folder where this repo is cloned.poetry installto install it withPython Poetry.UsageWritematchyin a terminal.The CLI will ask you for the number of devices you want to match.The CLI will ask you for the multiplicity of each device.Sit back and wait for the optimization to occur.You will be prompted with the system's best guess for the optimal matrix, as well as some key metrics, such as the centroid of each device and total error.You may want to usematchyonly to calculate the centroid of your devices. In that case:Save your device matrix configuration in a CSV file.Runmatchy --initial <PATH> --method do_nothingMatchy will print the centroid for each device in a table.
Package: matchzoo-lite
MatchZoo-Lite基于MatchZoo 2.0.0开发，并做了简化主要修改增增加数据加载器 dataloader 可方便进行数据的加载，训练数据和测试数据的文件格式统一为 json 文件，格式为：{"text_left":"xxx xxx xx","text_right":"xxx xxx xxx","label":1}其中text_left和text_right为空格分割的分词文本删改去除 nltk 相关语料库的调用（如停用词）去除预提供的 datasets更换了测试 tests去除部分模型，只保留以下模型：arciarciidssmcdssmconv_highwayduetmatch_pyramidmvlstmInstallMatchZoo is dependent onKeras, please install one of its backend engines: TensorFlow, Theano, or CNTK. We recommend the TensorFlow backend. Two ways to install MatchZoo:Install matchzoo-lite from the Github sourcegit clone http://gitlab.alipay-inc.com/niming.lxm/matchzoo-lite.git
cd matchzoo-lite
python setup.py installDockerdocker pull seanlee97/matchzoo-lite:latestTrain your modeltrain_duet.pytrain_arcii.pytrain_dssm.pytrain_mvlstm.pyGet Started in 60 SecondsTo train aDeep Semantic Structured Model, import matchzoo and prepare input data.importmatchzooasmztrain_pack=mz.datasets.wiki_qa.load_data('train',task='ranking')valid_pack=mz.datasets.wiki_qa.load_data('dev',task='ranking')predict_pack=mz.datasets.wiki_qa.load_data('test',task='ranking')Preprocess your input data in three lines of code, keep track parameters to be passed into the model.preprocessor=mz.preprocessors.DSSMPreprocessor()train_pack_processed=preprocessor.fit_transform(train_pack)valid_pack_processed=preprocessor.transform(valid_pack)predict_pack_processed=preprocessor.transform(predict_pack)Make use of MatchZoo customized loss functions and evaluation metrics:ranking_task=mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=4))ranking_task.metrics=[mz.metrics.NormalizedDiscountedCumulativeGain(k=3),mz.metrics.NormalizedDiscountedCumulativeGain(k=5),mz.metrics.MeanAveragePrecision()]Initialize the model, fine-tune the hyper-parameters.model=mz.models.DSSM()model.params['input_shapes']=preprocessor.context['input_shapes']model.params['task']=ranking_taskmodel.params['mlp_num_layers']=3model.params['mlp_num_units']=300model.params['mlp_num_fan_out']=128model.params['mlp_activation_func']='relu'model.guess_and_fill_missing_params()model.build()model.compile()Generate pair-wise training data on-the-fly, evaluate model performance using customized callbacks on prediction data.train_generator=mz.PairDataGenerator(train_pack_processed,num_dup=1,num_neg=4,batch_size=64,shuffle=True)pred_x,pred_y=predict_pack_processed.unpack()evaluate=mz.callbacks.EvaluateAllMetrics(model,x=pred_x,y=pred_y,batch_size=len(pred_x))history=model.fit_generator(train_generator,epochs=20,callbacks=[evaluate],workers=5,use_multiprocessing=False)ReferencesMatchZooTutorialsEnglish Documentation中文文档If you're interested in the cutting-edge research progress, please take a look atawaresome neural models for semantic match.LicenseApache-2.0MatchZoo LicenseApache-2.0Copyright (c) 2015-present, Yixing Fan (faneshion)
Package: mat-data
MAT-data: Data Preprocessing for Multiple Aspect Trajectory Data Mining [MAT-Tools Framework][Publication] [citation.bib] [GitHub] [PyPi]The present package offers a tool, to support the user in the task of data preprocessing of multiple aspect trajectories, or to generating synthetic datasets. It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods.Created on Dec, 2023
Copyright (C) 2023, License GPL Version 3 or superior (see LICENSE file)Main Modulesproprocess: Methods for trajectory preprocessing;generator: Methods for trajectory datasets generation.InstallationInstall directly from PyPi repository, or, download from github. (python >= 3.7 required)pipinstallmat-dataGetting StartedOn how to use this package, seeMAT-data-Tutorial.ipynb(or the HTMLMAT-data-Tutorial.html)CitingIf you usemat-dataplease cite the following paper (this package is fragmented fromautomatizerealease):Portela, Tarlis Tortelli; Bogorny, Vania; Bernasconi, Anna; Renso, Chiara. AutoMATise: Multiple Aspect Trajectory Data Mining Tool Library. 2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022, pp. 282-285, doi: 10.1109/MDM55031.2022.00060.Bibtex:@inproceedings{Portela2022automatise,title={AutoMATise:MultipleAspectTrajectoryDataMiningToolLibrary},author={Portela,TarlisTortelliandBogorny,VaniaandBernasconi,AnnaandRenso,Chiara},booktitle={202223rdIEEEInternationalConferenceonMobileDataManagement(MDM)},volume={},number={},address={Online},year={2022},pages={282--285},doi={10.1109/MDM55031.2022.00060}}Collaborate with usAny contribution is welcome. This is an active project and if you would like to include your code, feel free to fork the project, open an issue and contact us.Feel free to contribute in any form, such as scientific publications referencing this package, teaching material and workshop videos.Related packagesThis package is part ofMAT-Tools Frameworkfor Multiple Aspect Trajectory Data Mining:automatize: automatize for experimental evaluation of MAT classificationmovelets: movelets for MAT classification methods (based on movelets)mat-data: mat-data is a preprocessing library for MAT datamat-analysis: mat-analysis for MAT classification methodsmat-view: mat-view for MAT and movelets visualization, and interpratation toolsChange LogThis is a package under construction, seeCHANGELOG.md
Package: matdata2py
MatData2PyThis library imports Matlab data saved in V7.3 format to pythonA few important features:1 - loading most type of Matlab variables such as: vectors, arrays (n dimension),string, char, structure,structure array, cell, cell array and all different type of numerical precisions as well as boolean etc.(tables, timeframe are not supported yet)NOTE: This lib can correctly importstringorstrcutre arraysvariable types2- The structure data type of matlab can be presented as either dictionary data type in python or dot presented version in python similar to matlab,3- the variables in matlab file can be all exported in single dictionary or they can be exported as individual variables in python environmentInstallationpip install matdata2pyGet startedHow to use this lib:importmatdata2pyasmtp# load Matlab data file# StructsExportLikeMatlab = True/False structures are exported in dictionary format or dot base reference format similar to matlab# ExportVar2PyEnv = True/False export all variables in single dictionary or as separate individual variables into python environmentVariables_output=mtp.loadmatfile(file_Name,StructsExportLikeMatlab=True,ExportVar2PyEnv=False)print(Variables_output.keys())
Package: mate
Mate is an implementation of matchers for Python’sunittestmodule, which it largely tears out fromtesttools.There are two purposes in doing so:Extract portions suitable for submission upstream for inclusion in the
unittest module and modify them so that they’re able to be integratedSeparate out details, matchers, and delayed assertions fromtesttools.TestCaseso that they can be used with otherTestCasesubclassesthe second of which supports the first, and an ultimate goal is to contribute a
shared implementation that can be used by both the standard library, testtools,
and any other consumers.LicensingThe fundamental API and large portions of the code are taken with
slight modifications from the aformentioned testtools package, which
is MIT-licensed. Its license and authors are included in the license
distributed with this package.
Package: mate-cli
mate-cliA CLI for the MATE REST API.AcknowledgementsThis material is based upon work supported by the United States Air Force and
Defense Advanced Research Project Agency (DARPA) under
Contract No. FA8750-19-C-0004. Any opinions, findings and conclusions or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the United States Air Force or DARPA.
Approved for Public Release, Distribution Unlimited.
Package: mateco
FeaturesSupport text to speech with many pretty voices optionsSupport download file mp3 from TTSSupport convert text to IPA (International Phonetic Alphabet) with English UK and English USInstallationWindowspython -m pip install matecoLinuxpip install matecomacOSsudo pip3 install matecoHow does it work?Working with module TTSSetup Language for ConvertingTo setup language and voice using the methodsetup_voice(language_code), wherelanguage_code:English US :amEnglish UK :brPortuguese (Brazil):pt-brPortuguese (Portugal):ptThe other languages :ISO LANGUAGE CODE 639-1frommatecoimportTTS# Initialization for Module TTSmod=TTS()# Choice the voice for America Englishmod.setup_voice('am')# Convert to audio datamod.convert('Welcome to Master Text Converter library. I hope it\'s useful for you.')mod.speak()mod.save_to_file('audio.mp3')# Change the languagemod.setup_voice('fr')mod.convert('Je parle un peu français')mod.speak()mod.save_to_file('audio_french.mp3')mod.close()Output>> All voices for your language:
>>    1. Joey - Male - SAPI5
>>    2. Justin - Male - SAPI5
>>    3. Matthew - Male - SAPI5
>>    4. Salli - Female - SAPI5
>>    5. Joanna - Female - SAPI5
>>    6. Ivy - Female - SAPI5
>> Make your choice: 3Work with module IPAInitialize module IPAfrommatecoimportIPA# Setup English UK IPAmod=IPA('am')en_uk=mod.get_ipa('potato, tomato')print('Bristh say:',en_uk)# Setup English US IPAmod=IPA('br')en_us=mod.get_ipa('potato, tomato')print('America say:',en_us)# Working with Bulk - List of the textsbulk=['potato','tomato','schedule']results=mod.get_ipas(bulk)print(results)Output>> Bristh say: pəˈteɪˌtoʊ, təˈmeɪˌtoʊ
>> America say: pəˈteɪtəʊ, təˈmɑːtəʊ
>> ['pəˈteɪtəʊ', 'təˈmɑːtəʊ', 'ˈʃɛdjuːl']
Package: mate-common
mate-commonThis library provides shared models and utility code for the various MATE
components.AcknowledgementsThis material is based upon work supported by the United States Air Force and
Defense Advanced Research Project Agency (DARPA) under
Contract No. FA8750-19-C-0004. Any opinions, findings and conclusions or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the United States Air Force or DARPA.
Approved for Public Release, Distribution Unlimited.
Package: matemagica
matemagicaThe matemagica package is used to perform some math operations more intuitively:Addiction (adicao)Subtraction (subtracao)Multiplication (multiplicacao)Division (divisao)Potentiation (potenciacao)Root (radiciacao)InstallationUse the package managerpipto install matemagicapipinstallmatemagicaUsagefrommatemagicaimportoperacoesoperacoes.nome_operacao()AuthorLeonardoLicenseMIT
Package: matematicassimple
Bienvenido a matematicassimpleQue es matematicassimple?matematicassimple es una libreria de python especializado para programadores principiantes en el cual tiene funciones utiles para tu programaPor que usar matematicassimple?matematicassimple ofrece la posibilidad de ayudarte con tu proyecto con sus funcionalidades, como por ejemplo: ordenar, raiz, elevado, factorial y muchas mas utilidadesComo se instala?Puedes ejecutarpipinstallmatematicassimpleopip install git+https://github.com/aldogamer01/matematicassimple.git#egg=matematicassimpleComo se usa?Puedes ver los comandos disponibles y sus ejemplos enla documentacionComo contribuir con el proyectoPuedes ver los pasos enCONTRIBUTING.mdLicenciaEste proyecto estÃ¡ bajo la licencia MIT. Consulta el archivoLICENSEpara mÃ¡s detalles.ColaboradoresAldogamer01
Package: matematik
matematik is a package for providing and solving basic math formulasDocumentation :https://github.com/demirantay/matematik/blob/master/docs/index.mdSourcecode :https://github.com/demirantay/matematikPyPi :https://pypi.org/project/matematik/Website : coming soon ...It provides:Arithmetic formulasAlgebra1 formulasAlgebra2 formulasGeometry formulasYou can find how to use the package through documentation.FAQQ: Why even code something like this ?A: The content of the package is not that important, I have coded this project to learn
more about python packaging. However, the content of the package is still
little bit useful for people who want to rapidly get the answer of a math formula.
Package: mate-query
mate-queryThis library provides MATE's core Code Property Graph (CPG) models
and domain-specific language, as well as all associated database
models and scaffolding.AcknowledgementsThis material is based upon work supported by the United States Air Force and
Defense Advanced Research Project Agency (DARPA) under
Contract No. FA8750-19-C-0004. Any opinions, findings and conclusions or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the United States Air Force or DARPA.
Approved for Public Release, Distribution Unlimited.
Package: mate-rest-client
mate-rest-clientThis library provides a client for MATE's REST API.AcknowledgementsThis material is based upon work supported by the United States Air Force and
Defense Advanced Research Project Agency (DARPA) under
Contract No. FA8750-19-C-0004. Any opinions, findings and conclusions or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the United States Air Force or DARPA.
Approved for Public Release, Distribution Unlimited.
Package: materialdashboard
materialdashboardmaterialdashboardis a Dash component library exposingMaterial-UIcomponents.Dash components are automatically generated using TypeScript definitions from the source React components.InstallationpipinstallmaterialdashboardAlthough it is not listed in the requirements for this package, you will obviously needDashto include the components in a web app.DocumentationAlthough the component properties should be documented from the extracted TypeScript comments, please refer to theMaterial-UI documentationfor more information about each component.ExamplesExamples can be found onGitHub:Layout(mostly theGridcomponent)Input components(buttons, selects, switches, text fields, etc.)LimitationsChild nodesChild nodes are only supported for thechildrenproperty of each component. If Material-UI components expect child components for properties other thanchildren, you will not be able to pass a Dash component to them. You can however pass other types. For example, a string is a valid child node.EventsNot all events are implemented. Currently, all components support clicks through then_clicksproperty. If you need an event that's currently missing, please open an issue or a pull request.
Package: materializationengine
Materialization EngineA product of the CAVE (Connectome Annotation Versioning Engine) infrastructureThis is a microservice for creating materialized versions of an analysis database,  merging together spatially bound annotation and a segmentation data stored in a chunkedgraph that is frozen in time. The data is stored in a PostgreSQL database where the spatial annotations are leveraging PostGIS point types. The materialization engine can create new time locked versions periodically on a defined schedule as well as one-off versions for specific use cases.Present functionality:A flask microservice as REST API endpoints for creating and querying the materialized databases.The backend is powered by workflows running asCeleryworkers, a task queue implementation used to asynchronously execute work.InstallationThis service is intended to be deployed to a Kubernetes cluster as a series of pods.
Local deployment is currently best done by using docker. A docker-compose file is included that will install all the required packages and create a local PostgreSQL database and redis broker that is leveraged by the Celery workers for running tasks.Docker compose example:$ docker-compose build
    $ docker-compose upAlternatively one can setup a docker container running PostgreSQL database and a separate Redis container then create a python virtual env and run the following commands:Setup a redis instance:$ docker run -p 6379:6379 redisSetup a Postgres database (with postgis):$ docker run --name db -v /my/own/datadir:/var/lib/postgresql/data -e POSTGRES_PASSWORD=materialize postgis/postgisSetup the flask microservice:$ cd materializationengine
    $ python3 -m venv mat_engine
    $ source mat_engine/bin/activate
    (mat_engine) $: python setup.py install
    (mat_engine) $: python run.pyStart a celery worker for processing tasks. Open another terminal:$ source mat_engine/bin/activate
    (mat_engine) $ celery worker --app=run.celery --pool=prefork --hostname=worker.process@%h --queues=processcelery --concurrency=4 --loglevel=INFO -OfairWorkflow OverviewThe materialization engine runs celery workflows that create snapshots of spatial annotation data where each spatial point is linked to a segmentation id that is valid at a specific time point.There are a few workflows that make up the materialization engine:Bulk Upload(Load large spatial and segmentation datasets into a PostgreSQL database)Ingest New Annotations(Query and insert underlying segmentation data on spatial points with missing segmentation data)Update Root Ids(Query and update expired root ids from the chunkedgraph between a time delta)Create Frozen Database(Creates a time locked database for all tables)Complete Workflow(Combines the Ingest New Annotations, Update Root Id and Create Frozen Workflows in one, run in series)MetaDistributed under the MIT license. SeeLICENSEfor more information.ContributingFork it (https://github.com/seung-lab/MaterializationEngine/fork)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull Request
Package: materialize
Materialize is a simple toolkit for replacing env files with etcd configurations.Step #1: Manually (or via a docker environment setting from the docker command line DO NOT BAKE IN!)export etchost={host of etcd server}
export etccluster{dev/prod/uat}Step #2: Edit the .env file so anything that has to go into shared config (between apps on a cluster) has #common after it. Then type the following;-./materialize dematerialize {cluster} {appname}With the .env file now scanned into etcd the app is ready to go.Two optionsReconstruct .env from the command line in the container. The cluster is gleaned from the environment variable../materialize magic {appname}Insert the following into manage.py in the django.import materializer
materializer.magic_rematerializer()And let the magic take hold.
Package: materialize-ui
MaterializeMaterialize web components for enaml web.InstallingInstall with pippip install materialize-uiUsageSimply have your page/templates extend the baseMaterializePageas follows.fromweb.components.apiimport*fromweb.core.apiimportBlockfrommaterialize.pageimportMaterializePageenamldefPage(MaterializePage):stylesheets=['/static/custom.css',]scripts=['/static/custom.js']Block:block=parent.headerH2:text="Page header"Block:block=parent.contentP:text="Content..."Block:block=parent.footerDiv:text="Page footer"Seeenaml-webfor more info.
Package: material-mechanics
Material MechanicsThe “Material Mechanics” package contains tools needed in the analysis of the mechanics of materials,
including fiber reinforced materials and laminates.Free software: MIT licenseDocumentation:https://material-mechanics.readthedocs.io.InstallationTo install simply use pip>>> pip install material_mechanicsFeaturesMaterials:Isotropic materialsTransverse isotropic materialsOrthotropic materialsfiber reinforced plastics (FRP)LaminatesAnalytics:Stiffness analysisfracture mechanics of FRPPuck 2D and 3DClassical Lamination Theory (CLT)RoadmapMaterialsNon linear material lawsAnalyticsFracture mechanics for isotropic materials (von Mises Stress)Addition of damage criteria for FRPstrain criteria for whole FRP laminatesTsai-Wu criterionintegration of fatigue damage analysisUsageCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-11-01)First release on PyPI.