Saravanan666/pypi-org huggingface dataset:
Package: mnawsprocesscreds
A collection of process-based credential providers to be used with the AWS CLI
and related tools.This is an experimental package, breaking changes may occur on any minor
version bump.This package supports multiple HTML Forms, so older ADFS setups are supported as well.InstallationThe easiest way to install is to use pip:pip install mnawsprocesscredsRequirementsThis package requires a version of python to be installed. Currently supported
python versions are:2.7.9+3.3.x3.4.x3.5.x3.6.xSAML Forms-Based AuthenticationIf you have a SAML identity provider, you can useawsprocesscreds-samlto
configure programmatic access to your AWS resources. It has four required
arguments:-e/--endpoint- Your SAML idp endpoint.-u/--username- Your SAML username.-p/--provider- The name of your SAML provider. Currently okta and
adfs are supported.-a/--role-arn- The role arn you wish to assume. Your SAML provider
must be configured to give you access to this arn.This will cache your credentials by default, which will allow you to run
multiple commands without having to enter your password each time. You can
disable the cache by specifying--no-cache.Additionally, you can show logs by specifying-vor--verbose.To configure this provider, you need create a profile using thecredential_processconfig variable. See theAWS CLI Config docsfor more details on this config option.Example okta configuration:[profile okta]
region = us-west-2
credential_process = awsprocesscreds-saml -e https://example.okta.com/home/amazon_aws/blob/123 -u 'monty@example.com' -p okta -a arn:aws:iam::123456789012:role/okta-devExample adfs configuration:[profile adfs]
region = us-west-2
credential_process = awsprocesscreds-saml -e 'https://corp.example.com/adfs/ls/IdpInitiatedSignOn.aspx?loginToRp=urn:amazon:webservices' -u Monty -p adfs -a arn:aws:iam::123456789012:role/ADFS-DevCustom ProvidersThe mechanism this package uses to provide credentials is generally available,
and not specific to this package. It can be used to implement any custom
credential provider that will work with the AWS CLI, boto3, and other SDKs as
they implement support.A detailed breakdown of this mechanism along with a live demo of implementing a
credential provider that hooks into the macOS keychain can be seen on this
recorded talk from re:Invent 2017:AWS CLI: 2107 and BeyondThe CLI will call the process provided as the value forcredential_process.
This process must return credentials on stdout in the following JSON form:{
   "Version": 1,
   "AccessKeyId": "string",
   "SecretAccessKey": "string",
   "SessionToken": "string",
   "Expiration": "2019-01-31T21:45:41+00:00"
}WhereExpirationis an RFC 3339 compatible timestamp. As the expiration
time nears, the process will be called again to get a new set of credentials.
TheVersiondenotes the version of this format, whose only current valid
value is1. The remaining keys are the AWS credentials you wish to use.
Package: mnd
Match 'n DispatchMatch python args and dispatch based on their contents.Getting startedCreate a dispatcherfrom mnd.dispatch import Dispatcher, handled = Dispatcher()Use the handy decorator>>> @handler(d, msg="hello")>>> def say(msg=None):...     print "got message: ", msgTry dispatching some events```python>>> d.dispatch(msg="gets filtered out..."))>>> d.dispatch(msg="hello"))got message: helloInstall$ pip install mnd
Package: mne-bids
MNE-BIDSMNE-BIDS is a Python package that allows you to read and writeBIDS-compatible datasets with the help ofMNE-Python.Why?MNE-BIDS links BIDS and MNE-Python with the goal to make your analyses faster to code, more robust, and facilitate data and code sharing with co-workers and collaborators.How?The documentation can be found under the following links:for thestable releasefor thelatest (development) versionGetting HelpFor any usage questions, please post to theMNE Forum. Be sure to add themne-bidstag to
your question.CitingIf you use MNE-BIDS in your work, please cite ourpublication in JOSS:Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C.,
Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C.,
Rockhill, A., Larson, E., Gramfort, A., & Jas, M. (2019):MNE-BIDS: Organizing
electrophysiological data into the BIDS format and facilitating their analysis.Journal of Open Source Software,4:1896. DOI:10.21105/joss.01896Please also cite one of the following papers to credit BIDS, depending on which data type you used:MEG-BIDSEEG-BIDSiEEG-BIDS
Package: mne-bids-pipeline
MNE-BIDS-PipelineMNE-BIDS-Pipeline is a full-flegded processing pipeline for your MEG and
EEG data.It operates on data stored according to theBrain Imaging Data
Structure (BIDS).Under the hood, it usesMNE-Python.💡 Basic concepts and features🏆 Automated processing of MEG and EEG data from raw data to inverse solutions.🛠️ Configuration via a simple text file.📘 Extensive processing and analysis summary reports.🧑‍🤝‍🧑 Process just a single participant, or as many as several hundreds of participants – in parallel.💻 Execution via an easy-to-use command-line utility.🆘 Helpful error messages in case something goes wrong.👣 Data processing as a sequence of standard processing steps.⏩ Steps are cached to avoid unnecessary recomputation.⏏️ Data can be "ejected" from the pipeline at any stage. No lock-in!☁️ Runs on your laptop, on a powerful server, or on a high-performance cluster via Dash.📘 Installation and usage instructionsPlease find the documentation atmne.tools/mne-bids-pipeline.❤ AcknowledgmentsThe original pipeline for MEG/EEG data processing with MNE-Python was built
jointly by theCognition and Brain Dynamics Teamand theMNE Python Team, based on scripts originally
developed for this publication:M. Jas, E. Larson, D. A. Engemann, J. Leppäkangas, S. Taulu, M. Hämäläinen,
A. Gramfort (2018). A reproducible MEG/EEG group study with the MNE software:
recommendations, quality assessments, and good practices. Frontiers in
neuroscience, 12.https://doi.org/10.3389/fnins.2018.00530The current iteration is based on BIDS and relies on the extensions to BIDS
for EEG and MEG. See the following two references:Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G.,
Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension
to the brain imaging data structure for electroencephalography. Scientific
Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A.,
Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J.,
Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data
structure extended to magnetoencephalography. Scientific Data, 5, 180110.https://doi.org/10.1038/sdata.2018.110
Package: mne-connectivity
MNE-ConnectivityMNE-Connectivityis an open-source Python package for connectivity and
related measures of MEG, EEG, or iEEG data built on top of theMNE-PythonAPI. It includes modules for data input/output, visualization,
common connectivity analysis, and post-hoc statistics and processing.This project was initially ported over from mne-python starting v0.23, by Adam
Li as part of Google Summer of Code 2021. Subsequently v0.1 and v0.2 releases
were done as part of GSoC period. Future development will occur in subsequent
versions.DocumentationStableMNE-Connectivity documentationis available online.Installing MNE-ConnectivityTo install the latest stable version of MNE-Connectivity, you can usepipin a terminal:pipinstall-Umne-connectivityFor more complete instructions and more advanced installation methods (e.g. for
the latest development version), see theinstallation guide.Get the latest codeTo install the latest version of the code usingpipopen a terminal and type:pipinstall-Uhttps://github.com/mne-tools/mne-connectivity/archive/main.zipTo get the latest code usinggit, open a terminal and type:gitclonehttps://github.com/mne-tools/mne-connectivity.gitAlternatively, you can also download azip file of the latest development version.Contributing to MNE-ConnectivityPlease see the documentation on the MNE-Connectivity homepage:https://github.com/mne-tools/mne-connectivity/blob/main/CONTRIBUTING.mdForumhttps://mne.discourse.groupA Note About ConnectivityIn the neuroscience community as of 2021, the term “functional connectivity” can
have many different meanings and comprises many different measures. Some of
these measures are directed (i.e. try to map a statistical causal relationship between
brain regions), others are non-directed. Please note that the interpretation of your
functional connectivity measure depends on the data and underlying
assumptions.
For a taxonomy of functional connectivity measures and information on the
interpretation of those measures, we refer toBastos and Schoffelen.In mne-connectivity, we do not claim that any of our measures imply causal
connectivity.
Package: mne-faster
This implements theFASTER pipelineusingMNE-Python.Nolan H, Whelan R, Reilly RB. FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection. J Neurosci Methods. 2010 Sep 30;192(1):152-62. doi:10.1016/j.jneumeth.2010.07.015. Epub 2010 Jul 21. PMID: 20654646.Installationpipinstallhttps://github.com/wmvanvliet/mne-faster/archive/refs/heads/main.zipUsageSee theexample
Package: mne-features
This repository provides code for feature extraction with M/EEG data.
The documentation of the MNE-Features module is available at:documentation.InstallationTo install the package, the simplest way is to usepipto get the latest release:$ pip install mne-featuresOr if you preferconda:$ conda install --channel=conda-forge mne-featuresOr to get the latest version of the code:$ pip install git+https://github.com/mne-tools/mne-features.git#egg=mne_featuresDependenciesThese are the dependencies to use MNE-Features:numpy (>=1.17)matplotlib (>=1.5)scipy (>=1.0)numba (>=0.46.0)llvmlite (>=0.30)scikit-learn (>=0.21)mne (>=0.18.2)PyWavelets (>=0.5.2)pandas (>=0.25)CiteIf you use this code in your project, please cite:Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT,
"An ensemble learning approach to detect epileptic seizures from long intracranial EEG recordings"
Proc. IEEE ICASSP Conf. 2018
Package: mne-icalabel
mne-icalabelThis repository is a conversion of the popular Matlab-basedICLabelclassifier for Python.
In addition,mne-icalabelprovides extensions and improvements in the form of other models.Why?EEG and MEG recordings include artifacts, such as heartbeat, eyeblink, muscle, and movement activity.
Independent component analysis (ICA) is a common method to remove artifacts, but typically relies on manual
annotations labelling which independent components (IC) reflect noise and which reflect brain activity.This package aims at automating this process, using the popular MNE-Python API for EEG, MEG and iEEG data.Basic UsageMNE-ICALabel estimates the labels of ICA components given
a MNE-PythonRaworEpochsobject and an ICA instance using theICA decompositionavailable in MNE-Python.from mne_icalabel import label_components

# assuming you have a Raw and ICA instance previously fitted
label_components(raw, ica, method='iclabel')The only current available method is'iclabel'.DocumentationStable versiondocumentation.Dev versiondocumentation.InstallationThe current stable release ofmne-icalabelcan be installed with pip, for example, by running:pip install mne-icalabelFor further details about installation, see theinstallpage.To get the latest (development) version, usinggit, open a terminal and type:git clone git://github.com/mne-tools/mne-icalabel.git
cd mne-icalabel
pip install -e .The development version can also be installed directly using pip:pip install https://api.github.com/repos/mne-tools/mne-icalabel/zipball/mainAlternatively, you can also download azip file of the latest development version.ContributingIf you are interested in contributing, please read thecontributing guidelines.Getting HelpFor any usage questions, please post to theMNE Forum. Be sure to add themne-icalabeltag to
your question.CitingIf you use theICLabelmodel, please consider citing our paper:Li et al., (2022). MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python. Journal of Open Source Software, 7(76), 4484, https://doi.org/10.21105/joss.04484with the corresponding BibTex:@article{Li2022,
    doi       = {10.21105/joss.04484},
    url       = {https://doi.org/10.21105/joss.04484},
    year      = {2022},
    publisher = {The Open Journal},
    volume    = {7},
    number    = {76},
    pages     = {4484},
    author    = {Adam Li and Jacob Feitelberg and Anand Prakash Saini and Richard Höchenberger and Mathieu Scheltienne},
    title     = {MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python},
    journal   = {Journal of Open Source Software}
}Future versions of the software are aimed at improved models and may have different papers associated with it.
Package: mnelab
MNELAB is a graphical user interface (GUI) forMNE-Python, a Python package for EEG/MEG analysis.DocumentationInstructions for installing and using MNELAB as well as step-by-step examples for different use cases are available in thedocumentation. Check out thechangelogto learn what we added, changed, or fixed.InstallationYou can install MNELAB withpip:pip install mnelabIf you want to use all MNELAB features, the full package including optional dependencies can be installed with:pip install "mnelab[full]"You can also usepipxto install MNELAB in a completely isolated environment:pipx install mnelabIf you want the latest development version, use the following command:pip install git+https://github.com/cbrnr/mnelabRunning MNELABMNELAB must be started from a terminal with the following command:mnelabIf you get an error, try the following alternative:python -m mnelabDependenciesMNELAB requires Python ≥ 3.9 and the following packages:mne≥ 1.6.0PySide6≥ 6.6.0numpy≥ 1.25.0scipy≥ 1.10.0matplotlib≥ 3.8.0pyxdf≥ 1.16.4pyobjc-framework-Cocoa≥ 10.0 (macOS only)Optional dependencies provide additional features:scikit-learn≥ 1.3.0 (ICA computation with FastICA)python-picard≥ 0.7.0 (ICA computation with PICARD)pyEDFlib≥ 0.1.35 (EDF/BDF export)pybv≥ 0.7.4 (BrainVision VHDR/VMRK/EEG export)ContributingThecontributing guidecontains detailed instructions on how to contribute to MNELAB.
Package: mne-lsl
MNE-LSL(Documentation website)provides a real-time brain signal streaming framework.MNE-LSLcontains an improved python-binding for the Lab Streaming Layer C++ library,mne_lsl.lsl, replacingpylsl. This low-level binding is used in high-level objects
to interact with LSL streams.Any signal acquisition system supported by native LSL or OpenVibe is also
supported by MNE-LSL. Since the data communication is based on TCP, signals can be
transmitted wirelessly. For more information about LSL, please visit theLSL github.InstallMNE-LSL supportspython ≥ 3.9and is available onPyPIand onconda-forge.
Install instruction can be found on thedocumentation website.AcknowledgmentMNE-LSLis based onBSLandNeuroDecode. The original version developed byKyuhwa Leewas recognised atMicrosoft Brain Signal Decoding competitionwith the First Prize Award (2016).MNE-LSLis based on the refactor version,BSLbyMathieu ScheltienneandArnaud Desvachezfor theFondation Campus Biotech Geneva (FCBG)and
development is still supported by theHuman Neuroscience Platform (FCBG).Copyright and licenseThe code is released under theBSD 3-Clause License.
Package: mne-microstates
Microstate analysis for use with MNE-PythonA small module that works with MNE-Python to perform microstate analysis in EEGand MEG data.To learn more about microstate analysis, read the paper:Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1995).  Segmentation ofbrain electrical activity into microstates: model estimation and validation.IEEE Transactions on Biomedical Engineering.https://ieeexplore.ieee.org/document/391164InstallationInstall this package using PIP:pip install mne-microstatesUsageimportmneimportmne_microstates# Load MNE sample datasetfrommne.datasetsimportsamplefname=sample.data_path()/'MEG/sample/sample_audvis_filt.fif'raw=mne.io.read_raw_fif(fname,preload=True)# Always use an average EEG reference when doing microstate analysisraw.set_eeg_reference('average')# Highpass filter the data a little bitraw.filter(0.2,None)# Selecting the sensor types to use in the analysis. In this example, we# use only EEG channelsraw.pick_types(meg=False,eeg=True)# Segment the data into 6 microstatesmaps,segmentation=mne_microstates.segment(raw.get_data(),n_states=6)# Plot the topographic maps of the found microstatesmne_microstates.plot_maps(maps,raw.info)# Plot the segmentation of the first 500 samplesmne_microstates.plot_segmentation(segmentation[:500],raw.get_data()[:,:500],raw.times[:500])AuthorMarijn van Vlietw.m.vanvliet@gmail.com
Package: mnemo
mnemo-assistantPyPi|docsA Python3.8+ web framework providing tools for journaling and note taking.Features (in short)Bottle-based web framework.Login system (currently supports one user at a time).Requirementsmnemo-assistanthas been tested with Python 3.8 and 3.9. We recommend installing it usingpipx(seebelow) to avoid dependency conflicts with other packages. The packages it depends on are listed in ourdependency graph. We usepoetryto handle our dependencies.InstallWe recommend installingmnemo-assistantusingpipx. Check how to installpipxhereif you don't have it yet! Once you havepipxready on your system, install the latest stable release ofmnemo-assistantby running:pipx install mnemo-assistant. If you see the stars (✨ 🌟 ✨), then the installation went well!UsageRunmnemoto access the barber's services. Add-hto see the full help page of a command! Also, runmnemo-autocomplete -s BASH_TYPEto activate autocompletion.BASH_TYPEcurrently supports:bash,fish, andzsh.ContributingWe welcome any contributions tomnemo-assistant. In short, we useblackto standardize code format. Any code change also needs to passmypychecks. For more details, please refer to ourcontribution guidelinesif this is your first time contributing! Also, check out ourcode of conduct.LicenseMIT License - Copyright (c) 2021 Gabriele Girelli
Package: mnemonic
python-mnemonicReference implementation of BIP-0039: Mnemonic code for generating
deterministic keysAbstractThis BIP describes the implementation of a mnemonic code or mnemonic sentence –
a group of easy to remember words – for the generation of deterministic wallets.It consists of two parts: generating the mnenomic, and converting it into a
binary seed. This seed can be later used to generate deterministic wallets using
BIP-0032 or similar methods.BIP PaperSeehttps://github.com/bitcoin/bips/blob/master/bip-0039.mediawikifor full specificationInstallationTo install this library and its dependencies use:pip install mnemonicUsage examplesImport library into python project via:frommnemonicimportMnemonicInitialize class instance, picking from available dictionaries:englishchinese_simplifiedchinese_traditionalfrenchitalianjapanesekoreanspanishturkishczechportuguesemnemo=Mnemonic(language)mnemo=Mnemonic("english")Generate word list given the strength (128 - 256):words=mnemo.generate(strength=256)Given the word list and custom passphrase (empty in example), generate seed:seed=mnemo.to_seed(words,passphrase="")Given the word list, calculate original entropy:entropy=mnemo.to_entropy(words)ChangelogAll notable changes to this project will be documented in this file.The format is based onKeep a Changelog, and this project adheres toSemantic Versioning.0.21- 2024-01-05AddedCzech and Portuguese wordlistsOption to provide custom list of words instead of loading from built-in fileChangedUsesecretsmodule for randomnessUse English as a default language if none is providedLanguage detection is unambiguous even if some words are ambiguousBuild system switched to PoetryRemovedSupport for Python below 3.8 was dropped0.20- 2021-07-27AddedType annotationsSupport for testnet private keysChangedProject directory structure was cleaned upLanguage on theMnemonicobject is remembered instead of repeatedly detectingRemovedSupport for Python 2.7 and 3.4 was dropped0.19 - 2019-10-01AddedStart of changelog
Package: mnemonic-major-encoder
Mnemonic Major EncoderEncode and Decode text by the Major SystemFree software: MIT licenseDocumentation:https://mnemonic-major-encoder.readthedocs.io.Featuresencode text by the mnemonic major systemdecode a number into a word according to the mnemonic major systemUsage>>> from mnemonic_major_encoder import encoder
>>> encoder.encode("dario")
'14'>>> from mnemonic_major_encoder import decoder
>>> decoder.decode("23095")
['inammissibile', 'inammissibili', 'inammissible']CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-10-29)First release on PyPI.
Package: mnemonicode
A python library for encoding binary data as a sequence of english words.Based on, and compatible withhttp://web.archive.org/web/20101031205747/http://www.tothink.com/mnemonic/InstallationRecommended method is to use the version frompypi$pipinstallmnemonicodePlease note that this library supports python 3 only.UsagePython mnemonicode exposes four functions:mnformatandmnparsefor handling conversions to and from formatted strings, andmnencodeandmndecodefor working with lower level lists of tuples of words.String encodingEncode a byte array as a sequence of grouped words, formatted as a single string:>>>mnformat(b"cucumber")'paris-pearl-ultra--gentle-press-total';Decode a mnemonicode string into a byte array:>>>mnparse('scoop-limit-recycle--ferrari-album')b'tomato'Both functions allow specifying the word and group separator.  It is safe for the word separator to match part of the group separator, but not the other way round.  Word and group separators that overlap with word in the dictionary should obviously be avoided.An example using custom separators:>>>mnemonicode.mnformat(...b'apricot',group_separator=', uhhh, ',word_separator=', um, '...)'arctic, um, dilemma, um, single, uhhh, presto, um, mask, um, jet'Tuple encodingEncode a bytes object as an iterator of tuples of words:>>>list(mnencode(b"avocado"))[('bicycle','visible','robert'),('cloud','unicorn','jet')]Decode an iterator of tuples of words to get a byte array:>>>mndecode([('turtle','special','recycle'),('ferrari','album')])b'potato'LinksSource code:https://github.com/bwhmather/python-mnemonicodeIssue tracker:https://github.com/bwhmather/python-mnemonicode/issuesContinuous integration:https://travis-ci.org/bwhmather/python-mnemonicodePyPI:https://pypi.python.org/pypi/mnemonicodeLicenseThe project is licensed under the BSD license.  SeeLICENSEfor details.