Saravanan666/pypi-org huggingface dataset:
Package: brainpy-simulator
Brain modeling heavily relies on calculus. Focused on differential equations,
BrainPy provides an integrative simulation and analysis framework for
neurodynamics in computational neuroscience and brain-inspired computation.
It provides three core functions:General numerical solvers for ODEs and SDEs (future will support DDEs and FDEs).Neurodynamics simulation tools for various brain objects, such like neurons,
synapses and networks (future will support soma and dendrites).Neurodynamics analysis tools for differential equations, including phase plane
analysis and bifurcation analysis (future will support continuation analysis
and sensitive analysis).
Package: brain-radiodensity-feature-extractor
Brain Feature ExtractorA simple library that extracts brain features from a Brain Tomography. The constructed features refer to the regions:cerebrospinal fluid;clacification;gray matter;white matter;ischemic stroke;hemorrhagic stroke.NOTE:The input image must be a file in DICOM format.Installationpip install brain-radiodensity-feature-extractorGet startedHow to extract features from a brain scan:frombrain_feature_extractorimportBrainFeatureExtractorGMM# Instancing the MGABTD-percent extractorextractor=BrainFeatureExtractorGMM(percentage=0.3,pixel_level_feature=False)# Extracting features from the imageprint(extractor.extract_features(path='sample/image157_isquemico.dcm',verbose=True))
Package: brain-region-signal-statistics-calculalor
Welcome!This is a repository for computing region signal statistics for 3D brain volume.InstructionsTo use the brain-region-signal-statistics-calculalor library, either clone this repository and install the requirements listed in setup.py or install directly with pip.Install packagepip install brain-region-signal-statistics-calculalorThe script should take four inputs:an image volume to summarize (e.g. data/signal.npz)an image volume that maps voxels to brain region IDs (e.g. data/annotation.npz)a CSV describing brain regions (e.g. data/structures.csv)an output CSV file name (e.g. data/statistics.csv)TestingTo compute the statistics for the given signal image, run:./run.py --annotation_volume_path data/annotation.npz --signal_volume_path data/signal.npz --structures_df_path data/structures.csv --statistics_df_path data/statistics.csvContactDi Wang (di-wang@uiowa.edu)
Package: brainreg-segment
This package is deprecated!Please usebrainglobe-segmentationinstead.The old README can be found below.brainreg-segmentSegmentation of 1/2/3D brain structures in a common anatomical spacebrainreg-segmentis a companion tobrainregallowing manual segmentation of regions/objects within the brain (e.g. injection sites, probes etc.) allowing for automated analysis of brain region distribution, and visualisation (e.g. inbrainrender).brainreg-segmentandbrainregwere developed byAdam TysonandCharly Rousseauin theMargrie Lab, based onaMAPbyChristian Niedworok. The work was generously supported by theSainsbury Wellcome Centre.Installationbrainreg-segment comes bundled withbrainreg, so see thebrainreg installation instructions.brainreg-segment can be installed on it's own (pip install brainreg-segment), but you will need to register your data with brainreg first.UsageSeeuser guide.If you have any questions, head over to theimage.sc forum.ContributingContributions are very welcome. Please see thedevelopers guide.Citing brainreg-segmentIf you find brainreg-segment useful, and use it in your research, please let us know and also cite the paper:Tyson, A. L., Vélez-Fort, M.,  Rousseau, C. V., Cossell, L., Tsitoura, C., Lenzi, S. C., Obenhaus, H. A., Claudi, F., Branco, T.,  Margrie, T. W. (2022). Accurate determination of marker location within whole-brain microscopy images. Scientific Reports, 12, 867doi.org/10.1038/s41598-021-04676-9
Package: brainrender
Brainrenderbrainrenderis a python package for the visualization of three dimensional neuro-anatomical data. It can be used to render data from publicly available data set (e.g. Allen Brain atlas) as well as user generated experimental data. The goal of brainrender is to facilitate the exploration and dissemination of neuro-anatomical data by providing a user-friendly platform to create high-quality 3D renderings.Docs:books:brainrender docs📖brainrender paperInstallationYou caninstallbrainrenderwith:pip install brainrenderContributingContributions to brainrender are more than welcome. Please see thedevelopers guide. Note that some tests are only run locally, by specifying--runslow --runlocalinpytest.Citing brainrenderIf you usebrainrenderin your work, please cite:Claudi, F., Tyson, A. L., Petrucco, L., Margrie, T.W., Portugues, R.,  Branco, T. (2021) "Visualizing anatomically registered data with Brainrender&quot; <i>eLife</i> 2021;10:e65751 [doi.org/10.7554/eLife.65751](https://doi.org/10.7554/eLife.65751)ExamplesThe following images were created for the publication of thebrainrender paper. References to the original data being visualised can also be found in the paper.
Package: brainrender-napari
brainrender-napariVisualisation and management of BrainGlobe atlases in napari.A napari plugin to visualise and manage BrainGlobe atlases.brainrender-napariaims to port the functionality ofbrainrendertonapari.UsageCheck out the"Visualising an atlas in napari"tutorial in the BrainGlobe documentation.InstallationWe strongly recommend to use a virtual environment manager (likecondaorvenv). The installation instructions below will not specify the Qt backend for napari, and you will therefore need to install that separately. Please seethenapariinstallation instructionsfor further advice on this.You can installbrainrender-napariviapip:pip install brainrender-napariTo install latest development version :pip install git+https://github.com/brainglobe/brainrender-napari.gitContributingContributions are very welcome. Have a look at ourdeveloper conventionsfor more info. The core development team is happy to help and advise anyone wanting to contribute!LicenseDistributed under the terms of theBSD-3license,
"brainrender-napari" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.AcknowledgementsThis@napariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate and theNeuroinformatics Unit's template.
Package: brainrevenge
python-library-mockupTemplate repository to create a python library (like julearn)InstructionsCreate a repository on Github using this one as a template.Clone your repository.Change the name of the library (currentlymockup)Replace the following tokens with search and replace.TokenContentExamplebrainrevengePackage namejulearnhttps://github.com/brainrevenge/brainrevengeGithub URLhttps://github.com/juaml/julearn/https://brainrevenge.github.io/brainrevenge/DOCs URLhttps://juaml.github.io/julearnFede RaimondoAuthor's nameFede Raimondof.raimondo@fz-juelich.deAuthor's namef.raimondo@fz-juelich.deBrainRevenge Project LibraryShort descriptionFZJ AML LibraryGo tohttps://pypi.org/login (maybe create an account)Go to account settingsScroll down to API tokensCreate a token for all projects, take note.Repeat 4. but forhttps://test.pypi.orgOn your Github repository, go to "Settings" and the "Secrets". Add the respectivePYPI_TOKENandTESTPYPI_TOKENCommit the changes and push to your repository.Open the documentation and follow the instructions to release a version.
Package: brainrise
Module providing common Brain MRI Augmentation Methods for PyTorch.Home-page:https://github.com/neurospin-deepinsight/brainriseAuthor:
brainrise developersAuthor-email:antoine.grigis@cea.frLicense: CeCILL-B
Description: Module providing common Brain MRI Augmentation Methods for PyTorch.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: brains
brains-cli==========Command line interface to [brains](http://github.com/dev-coop/brains)installation============```pip install brains```usage=====##### `> brains init`asks for user information and how to run the program, saves to `brains.yaml` in current directory##### `> brains push [--dataset iris] [--description "My first try"] [--dont-wait-up]`requires `brains init` first. collects local source and pushes to server. `--dont-wait` will make the call return immediately and not wait for results##### `> brains results`TODO: Opens up results in browser`brains.yaml` examples==================Hello world```run: python hello_world.pycontent: hello_world.py```Hello world with glob file pattern```run: python hello_world.pycontent: *.py```Hello world multiple individual files AND glob patterns```run: python hello_world.pycontent:- hello_world.py- test_hello_world.py- yo_momma.dataset- src/*.py```# TODO- [ ] finish `get` command where to save it?- [ ] `brains run` should fetch dataset/cache it properly (use name not UUID)- [ ] TODO: Opens up results in browser/go to browser with current config already setup?- [x] change `init` to automatically put every file in contents except brains history- [x] support multiple datasets- [ ] add `brains datasets` to list datasets
Package: brainsatplay
brainsatplay.pyA Python library for interfacing with Brains@PlayInstallationSupportInstallationpipinstallbrainsatplayExample (with Brainflow)importbrainsatplayimporttimeimportmathfrombrainflow.board_shimimportBoardShim,BrainFlowInputParams,LogLevels,BoardIdsfrombrainflow.data_filterimportDataFilter,FilterTypesimportsys,signaldefmain():'''Connect to the Brainstorm'''# Authenticationusername='guest'password=''## Set Connection Detailsbrainstorm=brainsatplay.Brainstorm('http://localhost','80')# URL, Port## Connectres=brainstorm.connect(username,password)# All optional (defaults to guest)'''Subscribe to a Particular Game'''# # Connection Settingsappname='brainstorm'devices=[]props=['raw','times','sps','deviceType','format','eegChannelTags']sessionid=Nonespectating=False# Spectate to view data without sending itres=brainstorm.getSessions(appname)ifres['msg']!='appNotFound':sessionid=res['sessions'][0]['id']else:res=brainstorm.createSession(appname,devices,props)sessionid=res['sessionInfo']['id']# Handle Data from Subscribed GamesdefnewData(json):foruserinjson['userData']:name=user['username']print('Data for{}'.format(name))res=brainstorm.subscribeToSession(sessionid,spectating,newData)'''Stream your Data'''# Setup Brainflowparams=BrainFlowInputParams()board_id=BoardIds['SYNTHETIC_BOARD'].valueboard=BoardShim(board_id,params)board.rate=BoardShim.get_sampling_rate(board_id)board.channels=BoardShim.get_eeg_channels(board_id)board.time_channel=BoardShim.get_timestamp_channel(board_id)board.eeg_channels=BoardShim.get_eeg_channels(board_id)board.eeg_names=BoardShim.get_eeg_names(board_id)board.prepare_session()board.start_stream(num_samples=450000)# Handle CTRL-C ExitdefonStop():board.stop_stream()board.release_session()loopCount=0# Start Stream LoopdefstreamLoop():pass_data=[]rate=DataFilter.get_nearest_power_of_two(board.rate)data=board.get_board_data()t=data[board.time_channel]data=data[board.eeg_channels]forentryindata:pass_data.append((entry).tolist())data={}data['raw']=pass_datadata['times']=t.tolist()# Send Metadata on First LoopifloopCount==0:data['sps']=board.ratedata['deviceType']='eeg'data['format']='brainflow'tags=[]fori,channelinenumerate(board.eeg_channels):tags.append({'ch':channel-1,'tag':board.eeg_names[i],'analyze':True})data['eegChannelTags']=tagsreturndatares=brainstorm.startStream(streamLoop,onStop)if__name__=="__main__":main()SupportIf you are having issues, please email Garrett Flynn atgflynn@usc.edu
Package: brainseg
Brain SegA brain segmentation tool using machine learningUnder developmentThis software has NOT been approved for medical analysis/diagnosis. Please use other
approved software for medical use.ModelsSavedModels for use can be found atGoogle Drive.Models were trained using the Mindboggle-101 data:Klein, Arno, 2016, "Mindboggle-101 manually labeled individual brains",https://doi.org/10.7910/DVN/HMQKCK, Harvard Dataverse, V2A Klein, SS Ghosh, FS Bao, J Giard, Y Hame, E Stavsky, N Lee, B Rossa, M Reuter, EC Neto, A Keshavan. 2017.
Mindboggling morphometry of human brains.
PLoS Computational Biology 13(3): e1005350. doi:10.1371/journal.pcbi.1005350
(bioRxiv 2016 preprint)Optionspositional arguments:
  filepath              Path of file to segment

optional arguments:
  -h, --help            show this help message and exit
  -s SEGMENT, --segment Segments using provided SavedModel (Directory path required)
  --segment-precision   Float32 or float64 {32,64}
  --output OUTPUT, -o   Output filepath
  --output-channels     Number of channel in output image {1,3}Usage Examples# Segments a sample nifti scan$brainsegsample.nii.gz-ssampleSavedModel-ooutput.nii.gzModel Training GraphsSavedModel_210303
Package: brain-segmen
Brain SegmentationAuthorRakha AsyrofiVersion0.0.1Updated2021-09-12OverviewClick this link to seelive demoIf you want to seeCerebellum and Frontal Lobe Segmentation Based on K-Means Clustering and Morphological Transformation described inour paper at iSemantic2020. Please kindly cite the following paper when you use this tool. It would also be appreciated if you send me a courtesywebsiteandgoogle scholar, so I could survey what kind of tasks the tool is used for.@INPROCEEDINGS{9234262,  author={Asyrofi, Rakha and Winata, Yoni Azhar and Sarno, Riyanarto and Fajar, Aziz},  
booktitle={2020 International Seminar on Application for Technology of Information and Communication (iSemantic)},   
title={Cerebellum and Frontal Lobe Segmentation Based on K-Means Clustering and Morphological Transformation},   
year={2020},  
volume={},  
number={},  
pages={149-154},  
doi={10.1109/iSemantic50169.2020.9234262}}Developed by Asyrofi (c) 2021Cara menginstalinstalasi melalui pypi:pip install brain_segmenCara menggunakan programfrombrainsegmen.segmenimportbrainSegmentmySegmen=brainSegment()a=mySegmen.bukadata(data)Check out:https://youtu.be/ZzlAgxbpK4E
Package: brainSimulator
Functional brain image synthesis using the KDE or MVN distribution.
Currently in beta. Python code. Find the documentation athttp://brainsimulator.readthedocs.io/brainSimulatoris a brain image synthesis procedure intended to
generate a new image set that share characteristics with an original
one. The system focuses on nuclear imaging modalities such as PET or
SPECT brain images. It analyses the dataset by applying PCA to the
original dataset, and then model the distribution of samples in the
projected eigenbrain space using a Probability Density Function (PDF)
estimator. Once the model has been built, anyone can generate new
coordinates on the eigenbrain space belonging to the same class, which
can be then projected back to the image space.UseWith the new version, the whole interface has been switched to an
object. This allows to train the model once and then perform as many
sample drawings as required.#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA',method='mvnormal')simulator.fit(original_dataset,labels)images,classes=simulator.generateDataset(original_dataset,labels,N=200,classes=[0,1,2])CiteF.J. Martinez-Murcia et al (2017). “Functional Brain Imaging Synthesis
Based on Image Decomposition and Kernel Modelling: Application to
Neurodegenerative Diseases.” Frontiers in neuroinformatics (online).
DOI: 10.3389/fninf.2017.00065SafeguardsAs in the paper, it is best to use MVN modelling, but it is fundamental
to test the number of components (L) used in the modelling, otherwise it
will lead to overfitting. The KDE modelling works better `out of the
box’, but the results may be more disperse.LicenseThis code is released under the licenseGPL-3.0+.
Package: brainslug
WarningBrainSlug is a work in progress. Code may change rapidly in incompatible manners. Any question, please drop us an issueBrainSlug is a framework forparasitic computing. Allowing you to
write programs which code and logic live in a computer but actions orside-effectsare performed on another.Example:frombrainslugimportrun,slug,body@slug(remote=body.platform=='linux')defget_root_shell(remote):withremote.open('/etc/passwd','r')aspasswd:forlineinpasswd:ifline.startswith('root'):returnline.split(':')[-1]run(get_root_shell)This code should be run in a computer we callslug. Running this code
will open the port 8080 and stop execution until abodyconnects to
it.To connect abody, in a remote system run the following:$curlhttp://<slug-ip>:8080/boot/python|pythonThis will spawn thebodyto life and connect it with theslug.The program continues running in theslug, but the file reads are
performed in thebody.More examples on the example section.
Package: brainspace
BrainSpace is a lightweight cross-platform toolbox primarily intended
for macroscale gradient mapping and analysis of
neuroimaging and connectome level data. The current version
of BrainSpace is available in Python and MATLAB, programming
languages widely used by the neuroimaging and network neuroscience
communities. The toolbox also contains several maps that allow for
exploratory analysis of gradient correspondence with other
brain-derived features, together with tools to generate spatial null models.For installation instructions, examples and documentation of BrainSpace see
ourdocumentation.Happy gradient analysis!LicenseThe BrainSpace source code is available under the BSD (3-Clause) license.SupportIf you have problems installing the software or questions about usage
and documentation, or something else related to BrainSpace,
you can post to the Issues section of ourrepository.PaperIf you consider using BrainSpace, please cite our manuscript:
Vos de Wael R, Benkarim O, Paquola C, Lariviere S, Royer J, Tavakol S, Xu T, Hong S, Langs G, Valk S, Misic B, Milham M, Margulies D, Smallwood J, Bernhardt B (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 3, 103.Core development teamReinder Vos de Wael, MICA Lab - Montreal Neurological InstituteOualid Benkarim, MICA Lab - Montreal Neurological InstituteBoris Bernhardt, MICA Lab - Montreal Neurological Institute
Package: brainstat
Welcome to BrainStat’s documentation!BrainStat is a toolbox for the statistical analysis and context decoding of
neuroimaging data. It implements both univariate and multivariate linear models
and interfaces with the BigBrain Atlas, Allen Human Brain Atlas and Nimare
databases. BrainStat flexibly handles common surface, volume, and parcel level
data formats, and provides a series of interactive visualization functions. The
toolbox has been implemented in both Python and MATLAB, the two most widely
adopted programming languages in the neuroimaging and neuroinformatics
communities. It is openly available, and documentedhere.DevelopersSara Lariviere - MICA Lab, Montreal Neurological InstituteŞeyma Bayrak - Max Planck Institute for Human Cognitive and Brain SciencesReinder Vos de Wael - MICA Lab, Montreal Neurological InstituteOualid Benkarim - MICA Lab, Montreal Neurological InstituteRaul Cruces - MICA Lab, Montreal Neurological InstituteJessica Royer - MICA Lab, Montreal Neurological InstitutePeer Herholz - Montreal Neurological InstituteSeok-Jun Hong - Sungkyunkwan UniversitySofie Valk - Max Planck Institute for Human Cognitive and Brain SciencesBoris Bernhardt - Montreal Neurological InstituteLicenseThe BrainStat source code is available under theBSD (3-Clause) license.SupportIf you have problems installing the software or questions about usage and
documentation, or something else related to BrainStat, you can post to theIssuessection of our repository.
Package: brain-stitcher
Stitcher package is a 3D mesh builderIt was developed by metaBIO lab member Heitor Gessner as part of his master thesis.
The main usage is for recreating brain models from rare species, i.e., especies with low number of spiciements.It provides 3 classes:Points()Perimeters()Surface()Each Periemeter is a collection of Points. Each Surface is a collection of Perimeters.
For the points in points, it is expected to have a 3D coordinate reference for each point that delimits a plane contour.
The contours are made by manual or automatic segmentation, in the case of brains. This can be done in softwares such as ImageJ, Horos or Osirix - and than exported as a json file.Version 1.1:-New super resolution method using Fourier Transform
-Fixed fix_intersection() method from Perimeter() to properly handle rare parallel lines caseVersion 1.2:-FIX: Point division by number added as operation (division)
-NEW: Perimeter legth added
-NEW: estimation of volume and lateral area by numerical approximation prior to the reconstruction (doi: 10.3389/fnana.2013.00028)
-NEW: report of possible topological artifacts after build the 3D meshVersion 1.2.1:-CHANGE: fixing the intersection is optinal when making a new PerimeterVersion 1.2.2:-Fix: loading from cache now works as expected
-NEW: Perimeter.remove_by_angle(limit_in_radians) method to remove vertices that with angle<limitVersion 1.2.2.1:-Fix: Intersection check limit (reconstruction.Surface.intersection_limit) is now a property of Surface intead of being hard codedVersion 1.2.3:-NEW: Added Surface.self_intersection boolean property to report problematic surface buildVersion 1.3:-Fix: remove_overlap was improved and now can handle multiple overlaps on the same spot
Package: brainstorm
DocumentationThe full documentation is athttp://brainstorm.rtfd.org.History0.5 (2015-12-01)Changed Behaviourexamples now run on CPU by defaultaddedbrainstorm.tools.shuffle_dataandbrainstorm.tools.splitto help with data preparationSigmoidCEandSquaredDifferencelayers now outputs a loss for each dimension instead of summing over features.SquaredDifferencelayer does no longer scale by one half.Added aSquaredLosslayer that computes half the squared difference and
has an interface that is compatible with theSigmoidCEandSigmoidCElayers.Outputprobabilitiesrenamed topredictionsinSigmoidCEandSigmoidCElayers.New Featuresadded ause_convoption tobrainstorm.tools.create_net_from_specaddedcriterionoption tobrainstorm.hooks.EarlyStopperhookaddedbrainstorm.tools.get_network_infofunction that returns information
about the network as a stringaddedbrainstorm.tools.extractfunction that applies a network to some
data and saves a set of requested buffers.brainstorm.layers.masklayer now supports masking individual featuresaddedbrainstorm.hooks.StopAfterThresholdReachedhookImprovementsEarlyStopper now works for any timescale and intervalRecurrent, Lstm, Clockwork, and ClockworkLstm layers now accept inputs of
arbitrary shape by implicitly flattening them.several fixes to make building the docs easiersome performance improvements of NumpyHandler operationsbinarize_tandindex_m_by_vsped up testsseveral improvements to installation scriptsBugfixesfixedsqrtoperation forPyCudaHandler. This should fix problems with BatchNormalization on GPU.fixed a bug for task_type=’regression’ inbrainstorm.tools.get_in_out_layersandbrainstorm.tools.create_net_from_specremoved defunct name argument from input layerfixed a crash when applyingbrainstorm.hooks.SaveBestNetworktorolling_traininglossvarious minor fixes of thebrainstorm.hooks.BokehVisualizerfixed a problem withsum_toperation inbrainstorm.handlers.PyCudaHandlerfixed a blocksize problem in convolutional and pooling operations inbrainstorm.handlers.PyCudaHandler0.5b0 (2015-10-25)First release on PyPI.
Package: brainstorm-search
Brainstorm SearchPurposeTo summarize idea lists from Duckduckgo search to make brainstorming ideas and idea research more efficientInstallationpip install brainstorm-searchUsagebrainstorm <list related search term>Examplesbrainstorm "best flavors of icecream"
brainstorm "top 10 vacation destinations"
brainstorm "most popular american presidents"