Saravanan666/pypi-org huggingface dataset:
Package: neuropredict
Documentation:https://raamana.github.io/neuropredict/NewsAs ofv0.6,neuropredict now supports regression applicationsi.e. predicting continuous targets (in addition to categorical
classes), as well as allow you toregress out covariates /
confoundswithin the nested-CV (following all the best practices).
Utilizing this feature requires the input datasets be specified in
thepyradigmdata structures: code @https://github.com/raamana/pyradigm,
docs @https://raamana.github.io/pyradigm/. Check the changelog below for more details.Older newsneuropredictcan handle missing data now (that are encoded withnumpy.NaN). This is done respecting the cross-validation splits
without any data leakage.OverviewOn a high level,On a more detailed level,Docs:https://raamana.github.io/neuropredict/Contributors most welcome:check ideasand the followingguidelines.
Thanks.Long term goalsneuropredict, the tool, is part of a broader initiative described below
to develop easy, comprehensive and standardized predictive analysis:CitationIfneuropredicthelped you in your research in one way or another,
please consider citing one or more of the following, which were
essential building blocks of neuropredict:Pradeep Reddy Raamana. (2017). neuropredict: easy machine learning and standardized predictive analysis of biomarkers (Version 0.4.5). Zenodo.http://doi.org/10.5281/zenodo.1058993Raamana et al, (2017), Python class defining a machine learning dataset ensuring key-based correspondence and maintaining integrity, Journal of Open Source Software, 2(17), 382, doi:10.21105/joss.00382Change Log - version 0.6Major feature: Ability to predict continuous variables (regression)Major feature: Ability to handle confounds (regress them out, augmenting etc)Redesigned the internal structure for easier extensibilityNewCVResultsclass for easier management of a wealth of outputs generated in the Classification and Regression workflowsAPI access is refreshed and easierChange Log - version 0.5.2Imputation of missing valuesAdditional classifiers such asXGBoost, Decision TreesBetter internal code structureLot more testsMore precise tests, as we vary number of classes wildly in test
suitesseveral bug fixes and enhancementsMore cmd line options such as--print_optionsfrom a previous run
Package: neuroptica
neuropticaneuropticais a flexible chip-level simulation platform for nanophotonic neural networks written in Python/NumPy. It provides a wide range of abstracton levels for simulating optical NN's: the lowest-level functionality allows you to manipulate the arrangement and properties of individual phase shifters on a simulated chip, and the highest-level features provide a Keras-like API for designing optical NN by stacking network layers.InstallationThe easiest way to get started withneuropticais to install directly from the Python package manager:pip install neuropticaAlternately, you can clone the repository source code and edit it as needed withgit clone https://github.com/fancompute/neuroptica.gitand in your program or notebook, addimportsyssys.path.append('path/to/neuroptica')Getting startedFor an overview ofneuroptica, read thedocumentation. Example notebooks are included in theneuroptica-notebooksrepository:Planar data classification using electro-optic activation functionsAuthorsneuropticawas written byBen Bartlett,Momchil Minkov,Tyler Hughes, andIan Williamson.
Package: neuropull
neuropullA lightweight tool for pulling connectome networks and metadataDocumentation:https://neurodata.github.io/neuropullGitHub:https://github.com/neurodata/neuropullPyPI:https://pypi.org/project/neuropull/Free software: MITFeaturesTODOCreditsThis package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.
Package: neuropy3
DescriptionPython3 library to read data from Neurosky Mindwave Mobile 2 on linux using
pybluez2.Contents:RequirementsPre-executionExecutionCommand line interfaceGraphical user interfaceImporting libraryAcknowledgementsLicenseRequirementslibbluetooth-devPre-executionYou can install dependencies via setup.py$python-mpipinstall.
$python-mpipinstall.[gui]# to install gui dependenciesvia python package index (PyPI)$python-mpipinstallneuropy3
$python-mpipinstallneuropy3[gui]# to install gui dependenciesor manually$python-mpipinstall-rrequirements.txt
$python-mpipinstall-rgui_requirements.txt# to install gui dependenciesExecutionCommand line interface$python-mneuropy3
$python-mneuropy3--addressXX:YY:ZZ:AA:BB:CC
$python-mneuropy3--helpAddress argument helps speed up connection process, otherwise a neuropy3
will run a scan of nearby bluetooth devices (which takes a lot).Graphical user interface$python-mneuropy3--gui
$python-mneuropy3--addressXX:YY:ZZ:AA:BB:CC--guiNote: requires neuropy3[gui] extra dependenciesScreenshotsImporting library>>>fromneuropy3.neuropy3importMindWave>>>mw=MindWave(address='XX:YY:ZZ:AA:BB:CC',autostart=False,verbose=3)>>># mw = MindWave(autostart=False, verbose=3)  # Autoscan for MindWave Mobile>>>mw.set_callback('eeg',lambdax:print(x))>>>mw.set_callback('meditation',lambdax:print(x))>>>mw.set_callback('attention',lambdax:print(x))>>>mw.start()>>>mw.unset_callback('eeg')>>>mw.stop()AcknowledgementsThis work has been supported by National R&D Project TEC2017-84197-C4-1-R and by the
Comunidad de Madrid project CYNAMON P2018/TCS-4566 and co-financed by European Structural
Funds (ESF and FEDER)Based onlihas/NeuroPylibrary.Communication protocol fromneurosky.Licenseneuropy3  Copyright (C) 2022-2023 Sergio Chica Manjarrez @ pervasive.it.uc3m.es.
Universidad Carlos III de Madrid.
This program comes with ABSOLUTELY NO WARRANTY; for details check below.
This is free software, and you are welcome to redistribute it
under certain conditions; check below for details.LICENSE
Package: neuroqc
neuroqcquality control for neuroscience data, including imagingFree software: Apache Software License 2.0Documentation:https://neuroqc.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-05-13)First release on PyPI.
Package: neuroquery
NeuroQueryNeuroQuery is a tool and a statistical model for meta-analysis of the functional
neuroimaging literature.Given a text query, it can produce a brain map of the most relevant anatomical
structures according to the current scientific literature.It can be used through a web interface:https://neuroquery.orgTechnical details and extensive validation are provided inthis paper.This Python package permits using NeuroQuery offline or integrating it in other
applications.Getting startedQuick demoDependenciesNeuroQuery requires Python 3, numpy, scipy, scikit-learn, nilearn, pandas,
regex, lxml, and requests.nltk is an optional dependency needed only if you use stemming or lemmatization
for tokenization of input text.python-Levenshtein is an optional dependency used only in some parts of
tokenization. If you use the vocabulary lists provided withneuroqueryor inneuroquery_datait is not needed.Installationneuroquerycan be installed withpip install neuroqueryUsageIn theexamplesfolder,minimal_example.ipynbshows basic usage ofneuroquery.neuroqueryhas a function to download a trained model so that users can get
started right away:fromneuroqueryimportfetch_neuroquery_model,NeuroQueryModelfromnilearn.plottingimportview_imgencoder=NeuroQueryModel.from_data_dir(fetch_neuroquery_model())# encoder returns a dictionary containing a brain map and more,# see examples or documentation for detailsview_img(encoder("Parkinson's disease")["brain_map"],threshold=3.).open_in_browser()neuroqueryalso provides classes to train new models from scientific
publications' text and stereotactic peak activation coordinates (seetraining_neuroquery.ipynbin the examples).BSD 3-Clause LicenseCopyright (c) 2023, Jérôme Dockès
All rights reserved.Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: neurorosettes
neurorosettesAn agent-based framework to model the formation of rosette patterns in tissues of the nervous systemneurorosettesis a Python package that implements agent-based modelling to study the formation of rosette patterns in tissues
during neuronal development. It is built on top of thevedopackage (version 2022.1.0), which provides an interface
to plot 3D objects using VTK, to prioritize the visualization of the simulations in real time, without requiring additional processing.InstallationThe package can be downloaded through pip usingpip install neurorosettes.Usageneurorosettesoffers multiple modules to simulate and test new biological hypothesis, such as:Cell cycle and death;Creation and extension of neuronal processes;Physical interactions between cell bodies and neuronal processes;
Package: neuro-sdk
PrefaceWelcome to Python Neuro-SDK forhttps://neu.ro/.Documentationhttps://neuro-sdk.readthedocs.io/en/latest/InstallationInstall from PyPI:$pipinstallneuro-sdkContributingFor OSX users install coreutils to properly interpret shell commands:brew install coreutilsBefore you begin, it is recommended to have clean virtual environment installed:$python-mvenv.env
$source.env/bin/activateDevelopment flow:Install dependencies:make setupReformat code:make formatLint:make lintRun tests:make test
Package: neuroseries
### NeuroseriesThis package includes tools to process neural data series,
providing function for handling of timestamped data, and for irregular
intervals. It is based on Pandas for most functionality, enhanced in the
areas where Pandas does not satisfy the needs of the neural data analyst.This package requires Python 3. The current version, 0.3.1 is compatible with pandas 0.23 and
has been tested with Python 3.7.
Look at the test suite for use examples.
Package: neuroshape
Theneuroshapetoolbox is designed to run both functional gradients and geometric eigenmodes using the Connectopic Laplacian approach for resting-state functional gradientsHaak et al. (2018), our lab’s approach (Borne et al. (2023)) to producing and analyzing task-driven gradients using psychophysiological interactions, and the Laplace-Beltrami Operator on a finite vertex mesh as built inShapeDNA(see alsoReuter et al. (2006)andWachinger et al. (2015)).Installation requirementsneuroshapeworks with Python 3.8+ and utilizes the following dependencies:nibabel (>=3.0)nilearn (>=0.7)numpy (>=1.14)scikit-learn (>=0.17)scipylapy (>=0.7)scikit-sparse (>=0.4.8)neuromapsVERY IMPORTANT:In order to use much of the functionality of this code, you must:InstallFreeSurferand source it on your OS path.InstallConnectome Workbenchand source it on your OS path.InstallGmshand source it on your OS path.InstallMRtrix3and source the<MRtrix3 installationdirectory>/binfolder on your OS path.RunexportMRTRIX=<MRtrix3installation directory>.Source MATLAB on your OS path.Installscikit-sparse’s libraries. Follow the installation process over there first.See instructions here on how to source binaries to path.Thepythonscriptvolume_eigenmodes.pywas sourced from theBrainEigenmodesrepository. Please cite theirNature paper (Pang et al. 2023)if you use that.The MATLAB scripts inneuroshape/functions/wishartwere sourced from theHCPpipelines repositoryand relatedNeuroimage paper (Glasser et al. 2013). Please be sure to cite them if you use the--filterfunctionality inconnectopic_laplacian.py.InstallationAfter installing the above dependencies, install the neuroshape toolbox with:$pipinstallneuroshapeYou can also compile from source (and install the dependencies automatically):$gitclonehttps://github.com/breakspear/neuroshape$cdneuroshape$condaenvcreate-fenvironment.yml$pythonsetup.pybuild$pythonsetup.pyinstallNOTE: The above must be performed in that order, otherwise the setup won’t run properly. If you don’t wish to initialize a whole new environment (or you don’t useconda), install the dependencies separately and forgo theconda env createstep. Either way, the above will install the module in your environment’s (or/usr/local/python/) site-packages directory under the packageneuroshape. You can then import the extensions into your own code, e.g.:fromneuroshape.etaimporteta_squaredsimilarity=eta_squared(matrix_2d)We are working on implementing full documentation for all extensions and tools in this package. As the project is in a rapid development stage, we appreciate your patience.CitationIf you use theneuroshapetoolbox, please cite our paper …..
If you use the subroutines involved, such aslapyorvolume_eigenmodes.py, please be sure to cite the original authors.LicenseThis work is licensed under a BSD 3-Clause “New” or “Revised” License.Copyright (C) 2023 Systems Neuroscience Group Newcastle. Please read the full licenseherebefore use.
Package: neuroshare
The Neuroshare API is a standardized interface to accesselectrophysiology data stored in various different fileformats. To do so, it uses format- specific shared libraries.Refer to the official websitehttp://neuroshare.orgfor more information.The aim of this library is to provide a high level interfaceto the Neuroshare API, i.e. it focuses on API usability morethen being a mere python version of the C API. Thus none ofthe original Neuroshare API calls are directly exposed butthe interface is through python objects that resemble (moreor less) the Neuroshare Entities.
Package: neurosift
Neurosift Python utilityThis is a simple command-line utility used to view local NWB files using Neurosift.InstallationpipinstallneurosiftUsageneurosiftview-nwb/path/to/file.nwbThis will open a web browser window with the Neurosift web app pointing to a symlinked copy of your file. You can then browse the file and visualize its contents.When finished, you can stop the server by pressing Ctrl-C in the terminal window.
Package: neurospeed
public API for working withNeuroSpeed.IOcloud-based biosignal processing engine.
NeuroSpeed.IO API main features:user-level access:operate raw sensor dataoperate processed dataoperate cognitive insightsattach callbacks to streaming data packets for easy operationhandle sensor connect/disconnect eventsoperate data recorder - export multi-sensor recordings to filecustomer-level access:handle user connectivityworking with the API requiresneurospeed.iouser account. Get one for free athttp://neurobrave.com
Package: neurosum
# Statistical Summary For Neuroscience
A statistical summary commandline tool for Axios raw data files.
Right now it create several plots:
* low passed PSD
* spike raster
* spike traces
* population spiking vector
* PSD of the population spiking vector
* spike traces of the population spiking vector## Installation
1. [Follow these instructions to install the Python engine for Matlab](https://www.mathworks.com/help/matlab/matlab_external/install-the-matlab-engine-for-python.html)
2. Runpython setup.py install## How To Use` neurosum (Raw file or folder name containing .raws) `It will output its plots into a folder titled(Name of raw file)_processed
Package: neurosynchro
Neurosynchrois a small Python package for creating and using neural
networks to quickly approximate the coefficients needed for fully-polarized
synchrotron radiative transfer. It builds on theKerasdeep learning library. Documentation may be foundon ReadTheDocs.Say that you have a code — such asRimphonyorSymphony— that calculates synchrotron
radiative transfer coefficients as a function of some input model parameters
(electron temperature, particle energy index, etc.). These calculations are
often accurate but slow. Withneurosynchro, you can train a neural network
that will quickly approximate these calculations with good accuracy. The
achievable level of accuracy will depend on the particulars of your target
distribution function, range of input parameters, and so on.This code is specific to synchrotron radiation because it makes certain
assumptions about how the coefficients scale with input parameters such as the
observing frequency.
Package: neurotechdevkit
Welcome to Neurotech Development KitTheNeurotech Development Kit(NDK)is an open-source, community-driven software library designed to lower the barrier of entry to the next generation of neurotechnology for current researchers and companies. It also enables software developers without access to hardware and human subjects to solve open problems in the field. The initial release of NDK provides support for transcranial focused ultrasound stimulation, along with comprehensive documentation, API flexibility, and 2D/3D visualizations. Future areas of interest may include photoacoustic and optical whole-brain imaging.As a community-driven project, we encourage you to contribute code, feedback, and features to help accelerate the development of transformative neurotechnology.Check out theNDK documentation page.RunningDockerYou can runneurotechdevkitinside a docker container with just a couple of steps:InstalldockerRun the following command:docker run -p 8888:8888 -it ghcr.io/agencyenterprise/neurotechdevkit:latestThe command above will start aJupyter notebookserver with example notebooks you can use to exploreneurotechdevkit. Use the printed URL to open it in your browser or connect to it using your IDE.All changes you make to these files will be lost once you stop the docker container.Note:You can have persistingJupyter notebooksby runningdocker run -p 8888:8888  -v $(pwd)/notebooks:/ndk/notebooks -it ghcr.io/agencyenterprise/neurotechdevkit:latestThe command above will create a foldernotebooksin your current directory where you can put your jupyter notebooks.We recommend downloading the.zipfile with example notebooks from thislink, and extracting it into your localnotebooksfolder so you can access them from the docker.Local installationTo install and runneurotechdevkitlocally check theinstallationpage.Usageimportneurotechdevkitasndkscenario=ndk.scenarios.built_in.Scenario0()scenario.make_grid()scenario.compile_problem()result=scenario.simulate_steady_state()result.render_steady_state_amplitudes(show_material_outlines=False)AcknowledgementsThanks to Fred Ehrsam for supporting this project, Quintin Frerichs and Milan Cvitkovic for providing direction, and to Sumner Norman for his ultrasound and neuroscience expertise. Thanks toStridefor facilitating ultrasound simulations and providing an MIT license for usage within NDK,Devitofor providing the backend solver,Naparifor great 3D visualization, and toJean-Francois Aubry, et al.for the basis of the simulation scenarios.
Package: neurotpr
NeuroTPROverall descriptionNeuroTPR is a toponym recognition model designed for extracting locations from social media messages. It is based on a general Bidirectional Long Short-Term Memory network (BiLSTM) with a number of additional features, such as double layers of character embeddings, GloVe word embeddings, and contextualized word embeddings ELMo.The goal of this model is to improve the accuracy of toponym recognition from social media messages that have various language irregularities, such as informal sentence structures, inconsistent upper and lower cases (e.g., “there is a HUGE fire near camino and springbrook rd”), name abbreviations (e.g., “bsu” for “Boise State University”), and misspellings. We tested NeuroTPR in the application context of disaster response based on a dataset of tweets from Hurricane Harvey in 2017.More details can be found in our paper:Wang, J., Hu, Y., & Joseph, K. (2020): NeuroTPR: A Neuro-net ToPonym Recognition model for extracting locations from social media messages. Transactions in GIS, 24(3), 719-735.Figure 1. The overall architecture of NeuroTPRUse the pretrained NeuroTPR modelUsing the pretrained NeuroTPR model for toponym recognition will need the following steps:Setup the virtual environment: Please create a new virtual environment using Anaconda and install the dependent packages using the following commands (please run them in the same order below):condacreate-nNeuroTPRpython=3.6condaactivateNeuroTPRcondainstallkeras-cconda-forgepipinstallgit+https://www.github.com/keras-team/keras-contrib.gitpipinstallneurotprDownload thepretrained model, and unzip it to a folder that you would prefer.Use NeuroTPR to recognize toponyms from text. A snippet of example code is below:fromneurotprimportgeoparse

geoparse.load_model("the folder path of the pretrained model; note that the path should end with /")result=geoparse.topo_recog("Buffalo is a city in New York State.")print(result)The input of the "topo_recog" function is a string, and the output is a list of JSON objects containing the recognized toponyms and their start and end indexes in the input string.Combine NeuroTPR with a geolocation serviceNeuroTPR is a toponym recognition model, which means that it will not assign geographic coordinates to the recognized toponyms. If you would like to add coordinates to the recognized toponyms, you could use thegeocoding function from GeoPandas,Google Place API, or other services. Note that these services are not doing place name disambiguation for you, since they don't know the contexts under which these toponyms are mentioned. However, it would be fine to use one of these services if the toponyms in your text are not highly ambiguous.Project dependencies:Python 3.6Keras 2.3.1Tensorflow 1.14.0Keras-contrib (https://github.com/keras-team/keras-contrib)Tensorflow Hub (https://www.tensorflow.org/hub)NLTK 3.5emoji 0.6.0
Package: neurotune
NeurotuneThis package providesNeurotune, a package for optimizing electical models of excitable cells.This package was originally developed byMike Vella. This has been updated byPadraig Gleesonand others (and moved toNeuralEnsemble) to continue development ofpyelectroandNeurotunefor use inOpenWorm,Open Source Brainand other projectsFull documentation can be foundhere.InstallationYou can install the dependencies usingpip:pip install -r ./requirements-dev.txtInstall Neurotune with the following command:sudo python setup.py installTheCI scriptshows the full set of commands for installation & execution of a number of examples.
Package: neurovista
neurovistaPython package for plotting neural morpholgies with PyVistaDocumentation:https://CAVEconnectome.github.io/neurovistaGitHub:https://github.com/CAVEconnectome/neurovistaPyPI:https://pypi.org/project/neurovista/Free software: MITFeaturesTODOCreditsThis package was created withCookiecutterand thebdpedigo/cookiecutter-pypackageproject template (which builds on several previous versions).
Package: neurowave-logger
coeval_logger{
"meta": {
"app_key": "xxx",
"session_id": "xxx",
"model_name": "GPT-4",
"version_tag": [
"v1",
"eval"
],
"user": {
"name": "xxxx",
"gender": "male"
},
"total_token": 3422,
"total_cost": 78,
"total_duration": 3452
},
"conversation": [
{
"question": "q1",
"prompt": "chat history  + system prompt",
"answer": "a1",
"token": 342,
"duration": 32, latency !!!
},
]
}