Saravanan666/pypi-org huggingface dataset:
Package: madarrays
MadArrays is a Python package that regroups various data structures adapted to
handle audio signals with missing data.InstallInstall the current release withpip:pip install madarraysFor additional details, see doc/install.rst.UsageSee thedocumentation.BugsPlease report any bugs that you find through theMadArrays GitLab project.You can also fork the repository and create a merge request.Source codeThe source code of MadArrays is available via itsGitLab project.You can clone the git repository of the project using the command:git clone git@gitlab.lis-lab.fr:skmad-suite/madarrays.gitCopyright © 2017-2018Laboratoire d’Informatique et SystèmesUniversité d’Aix-MarseilleCentre National de la Recherche ScientifiqueUniversité de ToulonContributorsRonan HamonValentin EmiyaFlorent JailletLicenseReleased under the GNU General Public License version 3 or later
(seeLICENSE.txt).
Package: madata
madatamadatasyncs the metadata of datasets betweenMADATA(Mannheim Data Repository) andWikidata. It provides access to the MADATA metadata records directly in Python.Table of contentsInstallationInitializationSyncingSPARQL queriesInstallationpip install madataorgit clone https://github.com/UB-Mannheim/madata
cd madata/
pip install .InitializationBy initializationmadataharvests the MADATA OAI-PMH interface, stores the Dublin Core metadata records inrecords.OAI_DCand queries the Wikidata SPARQL endpoint for the list of metadata records published at MADATA.
Example:frommadataimportMetadatarecords=Metadata()print(records)[('OAI','https://madata.bib.uni-mannheim.de/cgi/oai2'),('MADATA records from OAI-PMH',163),('MADATA records at Wikidata',1),('In sync?',False)]Every recordrecin the the listrecords.OAI_DChas the following attributes:rec.metadata(structured metadata record),rec.header(structured header for a metadata record) andrec.raw(raw DC metadata record). The raw header is available viarec.header.raw. Additionally, a pandas-dataframe with metadata records is stored inrecords.OAI_DC_df.SyncingIn order to upload the MADATA metadata records to Wikidata, you need an account at Wikidata. If you have an account, usefrommadataimportMetadatarecords=Metadata()records._sync()>>>Wikidatausername:>>>Wikidatapassword:Type your username and password, thenmadatastarts to sync the metadata records at MADATA and Wikidata.SPARQL queriesThe MADATA-subset at Wikidata:https://w.wiki/6s7R.
Package: madbg
madbgA fully-featured remote debugger for python.Provides a full remote tty, allowing sending keyboard signals to the debugger,
tab completion, command history, line editing and moreRuns the IPython debugger with all its capabilitiesAllows attaching to running programs preemptively (does not require gdb, unlike similar tools)Affects the debugged programminimally, although not yet recommended for use in production environmentsProvides TTY features even when debugged program is a deamon, or run outside a terminalInstallationpip install madbgUsageMadbg provide both a python API and a CLI.Attaching to a running processmadbg attach <pid>Orimportmadbgmadbg.attach_to_process(pid)Starting a debuggerUsing the CLIRun a python file with automatic post-mortem:madbg run path_to_your_script.py <args_for_script ...>Run a python module similarly topython -m:madbg run -m module.name <args_for_script ...>Start a script, starting the debugger from the first line:madbg run --use-set-trace script.py <args_for_script ...>Using the APIStart a debugger in the next line:madbg.set_trace()Continue running the program until a client connects, then stop it and start a debugger:madbg.set_trace_on_connect()After an exception has occurred, or in an exception context, start a debugger in the frame the exception was raised from:madbg.post_mortem()Connecting to a debuggerUsing the CLImadbg connectUsing the APImadbg.connect_to_debugger()ConnectionAll madbg API functions and CLI entry points allow using a custom IP and port (the default is127.0.0.1:3513), for example:madbg.set_trace(ip='0.0.0.0',port=1337)ormadbg connect 8.8.8.8 1337PlatformsMadbg supports linux with python>=3.7.Possible effectsWhat madbg does that might affect a debugged program:Changes the pgid and sid of the debugged processChanges the CTTY of the debugged processAffects child processes in unknown ways (Not tested yet)What madbg doesn't do:Writes or reads from stdioFeeds your cat
Package: madcc
A collection of useful cryptocurrency utils and functions. Makes use of:kraken API <https://www.kraken.com/help/api>krakenex <https://github.com/veox/python3-krakenex>.Included functions:None (yet)Scripts:kraken_limits - Used to show the current withdraw and deposit limits on Kraken.crypto_assers - Look up value of a list of crypto assets defined in a txt file.
Package: mad-datasets
mad-datasetsHelper to access to open-source gait datasets of the MaD-Lab (and maybe externals in the future).The aim of this package is to ensure that all datasets can be loaded in a similar fashion and all data (and annotations)
are in the same format (i.e. the same sensor orientations, units, etc.).
This should allow to easily run the same algorithm across multiple datasets.:warning: While this makes it easier to work with the datasets, the coordinate system and other data information
provided with the dataset might not match the format you get when using this library!All datasets APIs are built using thetpcp.Datasetinterface.
For available datasets see the table below.UsageInstall the package from Pippip install mad-datasetsThen download/obtain the dataset that you are planning to use (see below).
The best way to get started is to then check the example for the respective dataset on thedocumentation page.DatasetsDatasetInfo LinkDownloadEgaitSegmentationValidation2014https://www.mad.tf.fau.de/research/activitynet/digital-biobank/Email to data owner (see info link)EgaitParameterValidation2013https://www.mad.tf.fau.de/research/activitynet/digital-biobank/Email to data owner (see info link)StairAmbulationHealthy2021https://osf.io/sgbw7/https://osf.io/download/5ueq6/SensorPositionDataset2019https://zenodo.org/record/5747173https://zenodo.org/record/5747173TestingThe/testsdirectory contains a set of tests to check the functionality of the library.
However, most tests rely on the existence of the respective datasets in certain folders outside the library.
Therefore, the tests can only be run locally and not on the CI server.To run them locally, make sure datasets are downloaded into the correct folders and then runpoe test.Documentation (build instructions)Like the tests, the documentation requires the datasets to be downloaded into the correct folders to execute the
examples.
Therefore, we can not build the docs automatically on RTD.
Instead we host the docs via github pages.
The HTML source can be found in thegh-pagesbranch of this repo.To make the deplowment as easy as possible, we "mounted" thegh-pagesbranch as a submodule in thedocs/_build/htmlfolder.
Hence, before you attempt to build the docs, you need to initialize the submodule.git submodule update --init --recursiveAfter that you can runpoe docsto build the docs and thenpoe upload_docsto push the changes to the gh-pages
branch.
We will always just update a single commit on the gh-pages branch to keep the effective file size small.**WARNING: ** Don't delete thedocs/_buildfolder manually or by running the sphinx make file!
This will delete the submodule and might cause issues.
Thepoetask is configured to clean all relevant files in thedocs/_buildfolder before each run.After an update of the documentation, you will see that you also need to make a commit in the main repo, as the commit
hash of the docs submodule has changed.To make sure you don't forget to update the docs, thepoe prepare_releasetask will also build and upload the docs
automatically.
Package: maddlib
maddlibThis repository contains the source code of the Python package related to theMADD metric.InstallationThis package is available onpypiandGitHub.It can be installed usingpip(with command linepip install maddliborpython3 -m pip install maddlib).If an error occurs, please check first that your Python version is greater than 3.10.4.Getting startedSome example notebooks are intestfolder. You can use the MADD to evaluate algorithmic fairness or to mitigate algorithmic unfairness.ReferenceIf you are using this package, please cite:M. Verger, S. Lallé, F. Bouchet, and V. Luengo. Is Your Model ”MADD”? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models. In M. Feng, T. Käser, and P. Talukdar, editors,Proceedings of the 16th International Conference on Educational Data Mining, pages 91–102, Bengaluru, India, July 2023. International Educational Data Mining Society.LicenseThis work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.
Package: maddpkg
MADD packageThis repository contains the source code of the Python package related to theMADD metric.InstallationThis package is available onpypiand can be installed usingpip:pip install maddpkgorpython3 -m pip install maddpkgIf an error occurs, please check first that your Python version is greater than 3.10.4.ReferenceIf you are using this package, please cite:M. Verger, S. Lallé, F. Bouchet, and V. Luengo. Is Your Model ”MADD”? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models. In M. Feng, T. Käser, and P. Talukdar, editors,Proceedings of the 16th International Conference on Educational Data Mining, pages 91–102, Bengaluru, India, July 2023. International Educational Data Mining Society.LicenseThis work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.
Package: madeira
MadeiraThis is a python package which provides wrapper classes and convenience methods for the
Amazon Web Servces (AWS) python deployment SDK (boto3).In several deployment automation projects I've built over the years, I've found that
"bare metal control" over interaction with AWS services allows me to leverage features
released by AWS intoboto3the moment they're available, rather than waiting on 3rd
party CM tool authors/vendors to wrapallrequired functionality. Concurrently, this
reduces the footprint of project external dependencies.Given that most projects I work on are "pure python", this approach requires less mental
context switching compared to using template-driven CM tools.InstallationThis package is hosted onPyPI, so you may simply:pip install --user madeiraSupportIntended for use with Python 3.7 or later.LimitationsNot all AWS services are represented; similarly, not all features of AWS services
that are represented here are "wrapped".Package name originsSince the AWS python SDK is calledboto, which refers to Amazon River dolphins,
I figured I'd attempt to follow the pattern and name this after a tributary of the
Amazon River, given that the point of these wrappers is to deploy objects into AWS,
The analogy seemed to solve the problem of developer naming creativity...
:stuck_out_tongue_winking_eye:
Package: madeira-tools
Madeira ToolsThis is a collection of tools that wrapsmadeiraaiming to provide a standard set of functionality
for deployment of test and production apps to AWS.Command line toolsmadeira-deployDeploys the application to thetestenvironmentmadeira-deploy --debugDeploys the application to thetestenvironment with debuggingmadeira-deploy --mode productionDeploys the application to theproductionenvironmentmadeira-clean-layersDeletes any orphaned Lambda Layer versionsmadeira-package-layer <layer name>Packages dependencies for use as AWS lambda function layersMust be run from application project rootReferences thelayer_requirements.txtfile in said project rootmadeira-removeRemoves the application from thetestenvironmentmadeira-remove --mode productionRemoves the application from theproductionenvironmentmadeira-run-apiRuns the API container, if any, and attaches to its shell or log output in the foregroundmadeira-run-devOpens XFCE4 terminal with 2 tabs - each for API and UI container runtime + loggingMust be run from application project rootmadeira-run-uiRuns the API container, if any, and attaches to its shell or log output in the foreground
Package: madflow
MadflowReferences:https://arxiv.org/abs/2105.10529InstallmadflowFrom PyPITo be doneFrom the repositorygitclonehttps://github.com/N3PDF/madflow.gitcdmadflowpipinstall.External toolsmadflowrelies in a number of external tools.
Some of them are just used for convenience and are optional, some are necessary for the proper functioning of the program.MG5_aMCA valid installation of MG5_aMC (2.8+) is necessary in order to generate matrix elements.
If you already have a valid installation, please add the following environment variable pointing to the right directory:MADGRAPH_PATH.
Below are the instructions for MG5_aMC 3.1.0, for a more recent release please visit the MG5_aMC@NLOsite.wgethttps://launchpad.net/mg5amcnlo/3.0/3.1.x/+download/MG5_aMC_v3.1.0.tar.gz
tarxfzMG5_aMC_v3.1.0.tar.gzexportMADGRAPH_PATH=${PWD}/MG5_aMC_v3_1_0LHAPDFWhileLHAPDFis not strictly necessary to use themadflowlibrary or run any of the scripts,
having access to thelhapdfpython wrapper can be convenient in order to manage the different PDFsets.
Please install the latest version from the LHAPDFsite.Otherwise, if your installed version ofpdfflowis equal or greater than1.2.1,
you can manually install thePDF setsin a suitable directory
and ensure that either thePDFFLOW_DATA_PATHorLHAPDF_DATA_PATHenvironment variables are pointing to it.You can check your installed version ofpdfflowwith:python -c 'import pdfflow ; print(pdfflow.__version__);'Install plugin in MG5_aMCIn order to install themadflowplugin in MG5_aMC@NLO, it is necessary to link themadgraph_pluginfolder inside thePLUGINdirectory of MG5_aMC@NLO.
For instance, if the environment variable$MADGRAPH_PATHis pointing to the MG5_aMC root and you are currently in the repository root.ln-s${PWD}/madgraph_plugin${MADGRAPH_PATH}/PLUGIN/pyoutThe link can be performed automagically with themadflow --autolinkoption.UsemadflowFor a more precise description of whatmadflowcan do please visit the online documentation.For convenience a script is provided which should have been installed alongside the library.
Using this script is possible to run any process at Leading Order, integrated with aRAMBO-like phasespace.madflow--help[-h][-v][-pPDF][--no_pdf][-c][--madgraph_processMADGRAPH_PROCESS][-mMASSIVE_PARTICLES][-g][--pt_cutPT_CUT][--histograms]optionalarguments:-h,--helpshowthishelpmessageandexit-v,--verbosePrintextrainfo-pPDF,--pdfPDFPDFset--no_pdfDon'tuseaPDFfortheinitialstate-c,--enable_cutsEnablethecuts--madgraph_processMADGRAPH_PROCESSSetthemadgraphprocesstoberun-mMASSIVE_PARTICLES,--massive_particlesMASSIVE_PARTICLESNumberofmassiveparticles-g,--variable_gUsevariableg_s--pt_cutPT_CUTMinimumptfortheoutgointparticles--histogramsGenerateLHEfiles/histograms
Package: madforhooks
MadForHooksRandom assortedpre-commithooks.UsagePut this in therepossection of your.pre-commit-config.yamlfile:-repo:https://github.com/MarcoGorelli/madforhooksrev:0.4.1hooks:-id:conda-env-sorter-id:no-print-statements-id:check-execution-order-id:check-test-namingOr, from the commandline:pip install madforhookspython -m madforhooks.check_execution_order file1.ipynb file2.ipynb file3.ipynbExample of how to runcheck-test-namingon your test directory:git ls-files | xargs python -m madforhooks.check_test_namingHooks availableconda-env-sorterSort the dependencies in your conda environment file(s).no-print-statementsRaise if print statements are found (unless they havefile=).check-execution-orderRaise if notebook cells were executed out-of-order. Use--strictto ensure
strict monotonicity.check-test-namingCheck that test names start withtest. This is useful for finding
tests which are meant to be running in CI, but which don't because they
are misnamed.If there are any false positives, you prevent this tool from flagging them
by adding a# not a testcomment one the line where the function/class is
defined.
Package: madframe
madframe- Functional State-Machine Framework for Python💢 TypeCheck at runtime(coming soon !)☮️ Non intruisive🧰 Functionnal🕐 Event-BasedIncludes🤖 AutofillAutofill is the process of automaticaly composing a function usage deduced from the prototype naming and a given context.frommadframe.autofillimportautofillcontext={"value":"foo"}defsome_function(value):print(value)awaitautofill(some_function,args=[],context=context)# notice that we don't pass any argument>"foo"# autofill undertand it has to retrieve the value from context if you don't specify itawaitautofill(some_function,args["bar"],context=context)# we pass an argument>"bar"💁every madframe decorator usesautofill🕙 Routinefrommadframe.bindingsimportsetup@setupdefinitialize():return{"some_key":"foo"}@routine(1)defprint_foo(some_key):print(some_key)# -> prints "foo"🏗️ SetupSetup is used to define function to be ran at start-up. Those executions block the start-up and will abort the lauch if they fail.📝 Perpetuateperpetuateisautofillbut the context is also updated with the result of the function (given it's adict)🚩 Wirefrommadframe.bindingsimportwiresomething_is_done_when,do_something_when=wire()fetch=do_something_when(fetch_data)something_is_done_when(analyze_data)fetch# is equivalent of `analyze_data(fetch_data)`Installationpip3installmadframe
Package: mad-fw
# MAD: Multi-task Aggression Detection Framework### A Multi-Task Learning Framework for Aggression Detection using TransformersCreate a Pyhton 3.7 virtual environment` virtualenv--python=python3.7. source bin/activate pip install--upgradepip pip install-rrequirements.txt `## Language Modeler`cdlanguage-modeler`Change the language modeling parameters in`example_config.yaml`file.Run the language modeler`python language_modeler.py--config_yamlexample_config.yaml`To be updated!## Model Training`cdmodel-training`Change the transformer modeling parameters in`example_config.yaml`file.Training and test transformer modeles`python model_training.py--config_yamlexample_config.yaml`### Requirement Notes: TBU
Package: madgrad
MADGRAD Optimization MethodA Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic OptimizationDocumentation availiable athttps://madgrad.readthedocs.io/en/latest/.pip install madgradTry it out! A best-of-both-worlds optimizer with the generalization performance of SGD and at least as fast convergence as that of Adam, often faster. A drop-in torch.optim implementationmadgrad.MADGRADis provided, as well as a FairSeq wrapped instance. For FairSeq, just import madgrad anywhere in your project files and use the--optimizer madgradcommand line option, together with--weight-decay,--momentum, and optionally--madgrad_eps.The madgrad.py file containing the optimizer can be directly dropped into any PyTorch project if you don't want to install via pip. If you are using fairseq, you need the acompanying fairseq_madgrad.py file as well.Things to note:You may need to use a lower weight decay than you are accustomed to. Often 0.You should do a full learning rate sweep as the optimal learning rate will be different from SGD or Adam. Best LR values we found were 2.5e-4 for 152 layer PreActResNet on CIFAR10, 0.001 for ResNet-50 on ImageNet, 0.025 for IWSLT14 usingtransformer_iwslt_de_enand 0.005 for RoBERTa training on BookWiki usingBERT_BASE. On NLP models gradient clipping also helped.Mirror MADGRADThe mirror descent version of MADGRAD is also included asmadgrad.MirrorMADGRAD. This version works extremely well, even better than MADGRAD, on large-scale transformer training. This version is recommended for any problem where the datasets are big enough that generalization gap is not an issue.As the mirror descent version does not implicitly regularize, you can usually use weight
decay values that work well with other optimizers.Tech ReportAdaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic OptimizationWe introduce MADGRAD, a novel optimization method in the family of AdaGrad adaptive gradient methods. MADGRAD shows excellent performance on deep learning optimization problems from multiple fields, including classification and image-to-image tasks in vision, and recurrent and bidirectionally-masked models in natural language processing. For each of these tasks, MADGRAD matches or outperforms both SGD and ADAM in test set performance, even on problems for which adaptive methods normally perform poorly.@misc{defazio2021adaptivity,title={Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization},author={Aaron Defazio and Samy Jelassi},year={2021},eprint={2101.11075},archivePrefix={arXiv},primaryClass={cs.LG}}ResultsLicenseMADGRAD is licensed under theMIT License.
Package: mad-gui
MaD GUIMachine LearningandData AnalyticsGraphicalUserInterfaceWhat is it?The MaD GUI is a framework for processing time series data. Its use-cases include visualization, annotation (manual or automated), and algorithmic processing of visualized data and annotations. More information:DocumentationGithub RepositoryYouTube PlaylistUsing our exampleIn a python 3.8 environment, execute the following commands or use the sectionDevelopment installation:pip install mad_gui
mad-guiThis is just to get a first feeling of how the GUI looks like, you can test with our example data:
You candownload our example datato
test our built-in exemplary importer, exemplary algorithms and exemplary label.
To see how to open our example data within the GUI, please refer to our section about theUser Interface.Using your data / algorithmsVery short, you will create and inject plugins, similar to this:from mad_gui import start_gui
from my_plugin_package imoprt MyAlgorithm

start_gui(plugins=MyAlgorithm)For more information on how to create your plugins, refer toour readmeor ourmore extensive documentation.
Package: madic
Overviewmadic is a Python package for detecting and correcting interference in targeted
mass spectrometry data using high-performance Pandas data structures.PurposeMultiple reaction monitoring (MRM, also known as selected reaction monitoring /
SRM) data acquired during targeted mass spectrometry experiments can suffer
from complex interference patterns that hinder accurate quantification. This
software identifies and corrects interference, as well increases the confidence
of peptide calls according to three qualities of an ideal chromatogram:
relative transition ratios similar to synthetic standards, co-elution of all
individual target transitions with respective stable isotope labeled (SIL)
transitions, and a distinguished peak relative to the background. Additionally,
these criteria should be satisfied for all replicate injections of a given
sample.More informationFollow the github URL below for more information.
Package: madlab
MADLAB python libraryDescriptionMADLAB is a SaaS (Software as a Service) platform provided by LHS.Installpip install madlabSetuppython setup.py bdist
   python3 setup.py sdist
   twine upload dist/*Dev environmenmtMADLAB_HOST="http://localhost:5000"Rungunicorn app:app --bind 0.0.0.0:5001 --worker-class aiohttp.GunicornWebWorker  --workers 8On docker node :Add in file/etc/systemd/system/docker.service.d/override.conf[Service]
ExecStart=
ExecStart=/usr/bin/docker daemon -H fd://  -H tcp://0.0.0.0:2376 -s overlay --insecure-registry madlab:5000Then flush changes by executing :systemctl daemon-reloadverify that the configuration has been loaded:systemctl show --property=ExecStart dockerrestart docker:systemctl restart docker