Saravanan666/pypi-org huggingface dataset:
Package: brainmri-ps
Brain MRI Pulse Sequences ClassificationAbout ProjectThis is official code of our packagebrainmri_ps. We provide a machine learning based tool to automatically classify Brain MRI series into different pulse sequence types:FLAIRT1CT2ADCDWITOFOTHERInstallationInstall via pip:pip install brainmri_psUsageLoad pretrained models:from brainmri_ps import PulseSequenceClassifier
classifier = PulseSequenceClassifier("mobilenet_v2").from_pretrained()NameInput Resolution#Params (M)MACs (G)Test AccuracyPretrainedMobileNet V22562.230.42100.0✓Example - predict from a study:In  : classifier.predict_study("*/1.2.840.113619.6.388.6361536015762131135133837693432843617")
Out :
{
    "1.2.840.113619.2.5.1821162425615901145251590114525252000":   "ADC", 
    "1.2.840.113619.2.388.57473.14165493.12954.1590103413.819":    "T2", 
    "1.2.840.113619.2.388.57473.14165493.12954.1590103413.822":   "DWI", 
    "1.2.840.113619.2.388.57473.14165493.12954.1590103413.823":   "T1C", 
    "1.2.840.113619.2.388.57473.14165493.12954.1590103413.821": "FLAIR"
}Functionpredict_studydoes the following steps:Read all dicom files in a study folder and group them into series by SeriesInstanceUID fieldDetermine the orientation plane (axial, sagittal, coronal) of the series by using the ImageOrientationPatient fieldPredict and return the pulse sequence types of axial series (ignore the non-axial ones)ContactIssues should be raised directly in the repository. For further support please email us at:lhkhiem28@gmail.comvantuan5644@gmail.com
Package: brain-observatory-utilities
brain_observatory_utilitiesTools for analysis, manipulation and visualization of  Brain Observatory data availible via the AllenSDK.Functions in this repository depend on the AllenSDKhttps://github.com/AllenInstitute/AllenSDKInstallationSet up a dedicated conda environment:conda create -n brain_observatory_utilities python=3.8Activate the new environment:conda activate brain_observatory_utilitiesMake the new environment visible in the Jupyterpip install ipykernel
python -m ipykernel install --user --name brain_observatory_utilitiesInstall brain_observatory_utilitiesif you intend to edit the source code, install in developer mode:git clone https://github.com/AllenInstitute/brain_observatory_utilities.git
cd brain_observatory_utilities
pip install -e .Level of SupportWe are planning on occasional updating these tools and repo structure with no fixed schedule. Community involvement is encouraged through both issues and pull requests.
Package: brainome
Brainome(tm)Project descriptionBrainome is adata compilerthat automatically solves supervised machine learning problems with repeatable and reproducible results and createsstandalone python predictors.Brainome’s philosophy is that data science should be accessible to all:Run on your machine or in your cloud.Keep your data local.Own your model python code - run it anywhere.Single “compiler like” command to convert you data in a model in a single step.Automatic data format conversion (text, numbers, etc..).No hyper-parameter tuning through measurements.Unlimited dimensionality (premium).Brainome offer unique data insight and helps answer:Do I have enough data and the right feature?What features are important (attribute ranking)?What model type will work best?Is my model overfitting?Brainome’s predictors:Run as executable or import as library.Are hardware independent.Are self contained in a single python file and integrate easily in standard CI/CD flow, Github, etc…Brainome is free for personal use or evaluation.
Package: brainome-linux-python3.7
Brainome(tm)Project descriptionBrainome is adata compilerthat automatically solves supervised machine learning problems with repeatable and reproducible results and createsstandalone python predictors.Brainome’s philosophy is that data science should be accessible to all:Run on your machine or in your cloud.Keep your data local.Own your model python code - run it anywhere.Single “compiler like” command to convert you data in a model in a single step.Automatic data format conversion (text, numbers, etc..).No hyper-parameter tuning through measurements.Unlimited dimensionality (premium).Brainome offer unique data insight and helps answer:Do I have enough data and the right feature?What features are important (attribute ranking)?What model type will work best?Is my model overfitting?Brainome’s predictors:Run as executable or import as library.Are hardware independent.Are self contained in a single python file and integrate easily in standard CI/CD flow, Github, etc…Brainome is free for personal use or evaluation.
Package: brainome-linux-python3.8
Brainome(tm)Project descriptionBrainome is adata compilerthat automatically solves supervised machine learning problems with repeatable and reproducible results and createsstandalone python predictors.Brainome’s philosophy is that data science should be accessible to all:Run on your machine or in your cloud.Keep your data local.Own your model python code - run it anywhere.Single “compiler like” command to convert you data in a model in a single step.Automatic data format conversion (text, numbers, etc..).No hyper-parameter tuning through measurements.Unlimited dimensionality (premium).Brainome offer unique data insight and helps answer:Do I have enough data and the right feature?What features are important (attribute ranking)?What model type will work best?Is my model overfitting?Brainome’s predictors:Run as executable or import as library.Are hardware independent.Are self contained in a single python file and integrate easily in standard CI/CD flow, Github, etc…Brainome is free for personal use or evaluation.
Package: brainome-linux-python3.9
Brainome(tm)Project descriptionBrainome is adata compilerthat automatically solves supervised machine learning problems with repeatable and reproducible results and createsstandalone python predictors.Brainome’s philosophy is that data science should be accessible to all:Run on your machine or in your cloud.Keep your data local.Own your model python code - run it anywhere.Single “compiler like” command to convert you data in a model in a single step.Automatic data format conversion (text, numbers, etc..).No hyper-parameter tuning through measurements.Unlimited dimensionality (premium).Brainome offer unique data insight and helps answer:Do I have enough data and the right feature?What features are important (attribute ranking)?What model type will work best?Is my model overfitting?Brainome’s predictors:Run as executable or import as library.Are hardware independent.Are self contained in a single python file and integrate easily in standard CI/CD flow, Github, etc…Brainome is free for personal use or evaluation.
Package: brainome-mac-python3.7
Brainome(tm)Project descriptionBrainome is adata compilerthat automatically solves supervised machine learning problems with repeatable and reproducible results and createsstandalone python predictors.Brainome’s philosophy is that data science should be accessible to all:Run on your machine or in your cloud.Keep your data local.Own your model python code - run it anywhere.Single “compiler like” command to convert you data in a model in a single step.Automatic data format conversion (text, numbers, etc..).No hyper-parameter tuning through measurements.Unlimited dimensionality (premium).Brainome offer unique data insight and helps answer:Do I have enough data and the right feature?What features are important (attribute ranking)?What model type will work best?Is my model overfitting?Brainome’s predictors:Run as executable or import as library.Are hardware independent.Are self contained in a single python file and integrate easily in standard CI/CD flow, Github, etc…Brainome is free for personal use or evaluation.
Package: brainome-mac-python3.8
Brainome(tm)Project descriptionBrainome is adata compilerthat automatically solves supervised machine learning problems with repeatable and reproducible results and createsstandalone python predictors.Brainome’s philosophy is that data science should be accessible to all:Run on your machine or in your cloud.Keep your data local.Own your model python code - run it anywhere.Single “compiler like” command to convert you data in a model in a single step.Automatic data format conversion (text, numbers, etc..).No hyper-parameter tuning through measurements.Unlimited dimensionality (premium).Brainome offer unique data insight and helps answer:Do I have enough data and the right feature?What features are important (attribute ranking)?What model type will work best?Is my model overfitting?Brainome’s predictors:Run as executable or import as library.Are hardware independent.Are self contained in a single python file and integrate easily in standard CI/CD flow, Github, etc…Brainome is free for personal use or evaluation.
Package: brainome-mac-python3.9
Brainome(tm)Project descriptionBrainome is adata compilerthat automatically solves supervised machine learning problems with repeatable and reproducible results and createsstandalone python predictors.Brainome’s philosophy is that data science should be accessible to all:Run on your machine or in your cloud.Keep your data local.Own your model python code - run it anywhere.Single “compiler like” command to convert you data in a model in a single step.Automatic data format conversion (text, numbers, etc..).No hyper-parameter tuning through measurements.Unlimited dimensionality (premium).Brainome offer unique data insight and helps answer:Do I have enough data and the right feature?What features are important (attribute ranking)?What model type will work best?Is my model overfitting?Brainome’s predictors:Run as executable or import as library.Are hardware independent.Are self contained in a single python file and integrate easily in standard CI/CD flow, Github, etc…Brainome is free for personal use or evaluation.
Package: brainowl
BrainOwlThis is a classifier tuned for neuroimaging. In particular for task-related
fMRI. It is meant to be used with the OWL norm (also called Ordered $l_1$ norm)
and uses a solver based on SpaRSA.The OWL norm should identify features relevant for the learning problem, even if
they are correlated. Weight maps based on OWL tend to be sparse, but not so
sparse like the solutions from LASSO, for example.InstallInstall usingpip(be sure you have python3>=3.5):pip install brainowlAnd done.If you want to have the source code, you can clone the repository usinggit:git clone https://github.com/jpvaldes/brainowl.gitand then install it:cd brainowl
pip install -e .Usage exampleThe included Jupyter notebook contains an example usage of the BrainOwl
classifier showing how to decode two categories of the classic neuroimaging
Haxby dataset.The dataset will be downloaded automatically if it is not found.AcknowledgmentsThescikit-learnlibrary for making it easier to
develop new ideas, thepyowlimplementation, and thenilearnproject (in
particular, the SpaceNet learners).This project contains code frompyowl.ReferencesX Zeng, M A T Figueiredo, The Ordered Weighted $l_1$ Norm:
Atomic Formulation, Projections, and Algorithms.

J. Bogdan, E. Berg, W. Su, and E. Candes, Statistical Estimation and
Testing via the Ordered $l_1$ Norm.

Stephen Wright, Robert Nowak, and Mario Figueiredo. Sparse Reconstruction
by Separable Approximation. IEEE Transactions on Signal Processing, 2009,
Vol. 52, No. 7, 2479-2493.

Marcos Raydan. The Barzilai and Borwein Gradient Method for the Large Scale
Unconstrained Minimization Problem. SIAM J. Optim., 1997, Vol. 7, No. 1,
26-33.
Package: brain-pipe
Brain PipePreprocess brain imaging datasets in a fast and re-usable way.MotivationThis repository contains code to efficiently preprocess brain imaging datasets in
python3, predominantly for machine learning downstream tasks.The initial goal of this code was to preprocess the public EEG dataset of theICASSP 2023 Auditory EEG challengecalledSparrKULee.Parts of this code were translated from our internal matlab codebase at ExpORL,
maintained primarily byJonas VanthornhoutandMarlies GillisInstallationYou can install this repository as apippackage.pipinstallbrain_pipeHow to useYou can write your own preprocessing script, seethe docs,
or you can use thecliwithconfiguration filesto preprocess your data.brain_pipeconfig_file.extension# e.g. brain_pipe sparrKULee.yamlRequirementsPython >= 3.8ContributingContributions are welcome. Please open an issue or a pull request.
For more information, seeCONTRIBUTING.md.
This package is created and maintained byExpORL, KU Leuven, Belgium.Example usage and starting guideRead thedocsfor a more detailed explanation of the pipeline.For a simple example, seethe docs.
For a more elaborate example, seeexamples/exporl/sparrKULee.py
Package: brainplotlib
brainplotlibis a Python package that plots data on cortical surface.
It's designed to have minimal requirements --- onlyNumPyandmatplotlib.InstallationThe package can be installed with pip:pipinstallbrainplotlibExample usageSee theexamples galleryfor all code examples with detailed explanations.importnumpyasnpfrombrainplotlibimportbrain_plot,example_data# The example_data is icoorder5 resolution (10242 vertices per hemisphere),# and the non-cortical vertices have been masked out (9372 and 9370 remaining# vertices for the left and right hemisphere, respectively).img,scale=brain_plot(example_data,vmax=10,vmin=-10,cmap='seismic',return_scale=True)The rendered image is a NumPy array.
It can be rendered usingmatplotlib:importmatplotlib.pyplotaspltfig=plt.figure(figsize=(img.shape[1]/200,img.shape[0]/200),dpi=200)plt.imshow(img)plt.axis('off')cbar=plt.colorbar(scale,shrink=0.8,aspect=30)plt.savefig('example_data_with_colorbar.png',bbox_inches='tight')plt.show()Alternatively, the high-resolution image can be saved directly usingOpenCV.importcv2cv2.imwrite('example_data.png',np.round(img[:,:,[2,1,0]]*255).astype(np.uint8))CitationIf you use this software in your publications, please cite it@software{brainplotlib,author={Ma Feilong and Guo Jiahui and M. Ida Gobbini and James V. Haxby},title={brainplotlib: plotting brain data on cortical surface},month=feb,year=2022,publisher={Zenodo},doi={10.5281/zenodo.5979819},url={https://doi.org/10.5281/zenodo.5979819}}
Package: brain-pred-toolbox
Brain Predictability Toolbox (BPt)The Brain Predictability toolbox (BPt) is a Python based Machine Learning library designed for working   with Neuroimaging data. This library is particularly suited towards working with large neuroimaging datasets, where a large number of subjects with potentially multi-modal data are available.Warning: As of 11/30/22, BPt is entering a sort of legacy mode, where it will be no longer actively
developed. The library should still continue to work, as before, but the required python packages
are now far more restrictive than before, and may require a dedicated conda or virtual environment.
Unfortunately this is the nature of open-source academic software when not maintained by a dedicated
community, but that said, if anyone is interested in taking over,
they can feel free to message me about it (though in all honesty it may be a better
use of time to contibute to other projects with a more stable developer community instead).Please check out the project documentation at:https://sahahn.github.io/BPt/This library is based on python and likely will require atleast some prior experience with python and machine learning.InstallNote:This library is only tested on python versions 3.7+ so while 3.6 might work, for the most reliable performance please use higher versions of python!The easiest way to install the latest stable release of BPt is via pip, just runpip install brain-pred-toolboxThe other method, to get the latest stable development version of the library is to clone this repository,
and then install it locally with once navigated into the main BPt folderpip install .Quick Start ExampleLoad a pre-set BPt dataset, then
run a default 5-fold cross validation.from BPt.datasets import load_cali
from BPt import evaluate

data = load_cali()
results = evaluate('elastic_pipe', data)The returned object, stored in variable results, is an instance of classEvalResults, which contains all types of information and metrics from the stored evaluation.Check out the documentation athttps://sahahn.github.io/BPt/for more examples on how to get started using BPt!
Package: brainprep
Package that provides tools for brain MRI Deep Learning pre-processing.Home-page:https://github.com/neurospin-deepinsight/brainprepAuthor:
brainprep developersAuthor-email:antoine.grigis@cea.frLicense: CeCILL-B
Description: Package that provides tools for brain MRI Deep Learning pre-processing.Platform: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: X11 Applications :: Qt
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Package: brainpy
BrainPy is a flexible, efficient, and extensible framework for computational neuroscience and brain-inspired computation based on the Just-In-Time (JIT) compilation (built on top ofJAX,Taichi,Numba, and others). It provides an integrative ecosystem for brain dynamics programming, including brain dynamicsbuilding,simulation,training,analysis, etc.Website (documentation and APIs):https://brainpy.readthedocs.io/en/latestSource:https://github.com/brainpy/BrainPyBug reports:https://github.com/brainpy/BrainPy/issuesSource on OpenI:https://git.openi.org.cn/OpenI/BrainPyInstallationBrainPy is based on Python (>=3.8) and can be installed on Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Install the latest version of BrainPy:$pipinstallbrainpy-UIn addition, many customized operators in BrainPy are implemented inbrainpylib.
Install the latest version ofbrainpylibby:# CPU installation for Linux, macOS and Windows$pipinstall--upgradebrainpylib# CUDA 12 installation for Linux only$pipinstall--upgradebrainpylib-cu12x# CUDA 11 installation for Linux only$pipinstall--upgradebrainpylib-cu11xFor detailed installation instructions, please refer to the documentation:Quickstart/InstallationUsing BrainPy with dockerWe provide a docker image for BrainPy. You can use the following command to pull the image:$dockerpullbrainpy/brainpy:latestThen, you can run the image with the following command:$dockerrun-it--platformlinux/amd64brainpy/brainpy:latestUsing BrainPy with BinderWe provide a Binder environment for BrainPy. You can use the following button to launch the environment:EcosystemBrainPy: The solution for the general-purpose brain dynamics programming.brainpy-examples: Comprehensive examples of BrainPy computation.brainpy-datasets: Neuromorphic and Cognitive Datasets for Brain Dynamics Modeling.《神经计算建模实战》 (Neural Modeling in Action)第一届神经计算建模与编程培训班 (First Training Course on Neural Modeling and Programming)第二届神经计算建模与编程培训班 (Second Training Course on Neural Modeling and Programming)CitingBrainPy is developed by a team in Neural Information Processing Lab at Peking University, China.
Our team is committed to the long-term maintenance and development of the project.If you are usingbrainpy, please consider citingthe corresponding papers.Ongoing development plansWe highlight the key features and functionalities that are currently under active development.We also welcome your contributions
(seeContributing to BrainPy).model and data parallelization on multiple devices for dense connection modelsmodel parallelization on multiple devices for sparse spiking network modelsdata parallelization on multiple devices for sparse spiking network modelspipeline parallelization on multiple devices for sparse spiking network modelsmulti-compartment modelingmeasurements, analysis, and visualization methods for large-scale spiking dataOnline learning methods for large-scale spiking network modelsClassical plasticity rules for large-scale spiking network models
Package: brain-py
BrainPy is a flexible, efficient, and extensible framework for computational neuroscience and brain-inspired computation based on the Just-In-Time (JIT) compilation (built on top ofJAX). It provides an integrative ecosystem for brain dynamics programming, including brain dynamicssimulation,training,analysis, etc.Website (documentation and APIs):https://brainpy.readthedocs.io/en/latestSource:https://github.com/PKU-NIP-Lab/BrainPyBug reports:https://github.com/PKU-NIP-Lab/BrainPy/issuesSource on OpenI:https://git.openi.org.cn/OpenI/BrainPyExamples from literature:https://brainpy-examples.readthedocs.io/InstallBrainPy is based on Python (>=3.7) and can be installed on  Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Install the latest version of BrainPy:$pipinstallbrainpy-UThe following packages are required forBrainPy:numpy >= 1.15andjax >= 0.3.0(how to install jax?)For detailed installation instructions, please refer to the documentation:Quickstart/InstallationLicenseGNU General Public License v3.0
Package: brainpy-datasets
Neuromorphic and Cognitive Datasets for Brain Dynamics Modelingbrainpy-datasetsaims to provide datasets for
brain dynamics modeling, including but not limited to
neuromorphic data, cognitive task or environment, traditional vision
or auditory datasets.Installbrainpy-datasetsis based on Python (>=3.7) and can be installed on  Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Install the latest version ofbrainpylib:$pipinstallbrainpy-datasets-UAcknowledgementsWe would like to thank following repos because we got so many inspirations from them:https://github.com/pytorch/pytorch/tree/master/torch/utils/datahttps://github.com/pytorch/visionhttps://github.com/keras-team/kerashttps://github.com/neurogym/neurogym
Package: brainpy-largescale
brainpy-largescaleRunBrainPyin multiple processes.brainpy-largescale depends onBrainPyandbrainpy-lib, use the following instructions toinstall brainpy package.InstallOnly supportLinuxpip install brainpy-largescaleImportimportbrainpyasbpimportbplSet platformbpl.set_platform('cpu')only support cpu.Create populationUse Leaky Integrate-and-Fire (LIF)a=bpl.neurons.LIF(300,V_rest=-60.,V_th=-50.,V_reset=-60.,tau=20.,tau_ref=5.)b=bpl.neurons.LIF(100,V_rest=-60.,V_th=-50.,V_reset=-60.,tau=20.,tau_ref=5.)Create synapsed=bpl.synapses.Exponential(a,b,bp.conn.FixedProb(0.4,seed=123),g_max=10,tau=5.,delay_step=1)Construct networknet=bpl.Network(a,b,d)net.build()Add inputadd current inputinputs=[bpl.device.Input(a,20),bpl.device.Input(b,10)]Add spike monitormonitor_spike=bpl.device.Monitor([a,b],bpl.device.MonitorKey.spike)Add volt monitormonitor_volt=bpl.device.Monitor([b],bpl.device.MonitorKey.volt)monitors=[monitor_spike,monitor_volt]Add spike and volt callbackdefspike(a:List[Tuple[int,float]]):ifa:print(a)defvolt(a:List[Tuple[int,float,float]]):# print(a)passRunrunner=bpl.runner.DSRunner(net,monitors=monitors,inputs=inputs,jit=False,spike_callback=spike,volt_callback=volt,)runner.run(10.)Visualizationimportmatplotlib.pyplotaspltif'spike'inrunner.mon:bp.visualize.raster_plot(runner.mon.ts,runner.mon['spike'],show=True)LicenseApache License, Version 2.0
Package: brainpylib
brainpylib: C++/CUDA Library forBrainPybrainpylibaims to provide operators for sparse and event-driven computations commonly used in brain dynamics programming.Installbrainpylibis based on Python (>=3.7) and can be installed on  Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Install the latest version ofbrainpylib:$pipinstallbrainpylib-UBuild from sourceOn CPU device:rm-rfbuild
pipuninstallbrainpylib-y
pythonsetup.pybdist_wheel
pipinstalldist/brainpylib*On GPU device:rm-rfbuild
pipuninstallbrainpylib-y
pythonsetup_cuda.pybdist_wheel
pipinstalldist/brainpylib*CitingIf you are usingbrainpylib, please consider citingthe corresponding papers.