Saravanan666/pypi-org huggingface dataset:
Package: brainfucky
Brainfucky is a brainfuck interpreter written in python.About BrainfuckBrainfuck is an esoteric programming language. The language has only 8 symbols,
each performing certain functions, see below.+Increment the value of current cell by 1.-Decrement the value of current cell by 1.>Move the current cell pointer to right, increment the pointer by 1.<Move the current cell pointer to left, decrement the pointer by 1..Print the ascii character corresponding to the value of current cell.,Take an ascii input from user, and store its ascii value in current cell[if the byte at the data pointer is zero, then instead of moving the
instruction pointer forward to the next command, jump it forward to
the command after the matching]command.]if the byte at the data pointer is nonzero, then instead of moving the
instruction pointer forward to the next command, jump it back to the
command after the matching[command.About ImplementationThe memory element, the tape is emulated using the pythonlist.Value of ith element in the list corresponds to the value of celli.A cell pointer is used to point towards the current cell.+and-increments the value of cell pointed by the cell pointer.>and<increments and decrements the cell pointer effectively, moving
the tape right or left.Instructions in a block are executed and at the end of the execution of last
instruction, value of current cell is checked, if it is 0, move on, else
execute the block again.Install$pipinstallbrainfuckyRunTo run your brainfuck program do the following$brainfucky--fileexamples/hello_world.bfexecutingfileexamples/hello_world.bfHelloWorld!$brainfucky--fileexamples/triangle.bfexecutingfileexamples/triangle.bf***************************************************************************************************************************************************************************************************************************************************
Package: brainglobe
BrainGlobeThis package provides allBrainGlobetools in one place, installablein a single command.
Installing this package will provide you with the complete suite of BrainGlobe tools, without needing to worry about dependency resolution.
A complete list of the tools provided by this packageis provided on our website, and you can also read more about these tools there too.If you are interested in running some of our analysis workflows, you might want to head over tobrainglobe-workflowsand install the analysis pipelines offered by that package too.Installing and UpdatingSee theinstallation instructions over on the BrainGlobe websitefor full information.
But the TLDR is;pipinstallbrainglobeand to update,pipinstallbrainglobe--updateContributingContributors to BrainGlobe are absolutely encouraged, whether to fix a bug, develop a new feature, or add a new atlas.
If you would like to contribute, please take a look at ourdeveloper documentation.The Brainglobe InitiativeThe BrainGlobe Initiative exists to facilitate the development of interoperable Python-based tools for computational neuroanatomy.We have three aims:Develop specialist software for specific analysis and visualisation needs, such ascellfinderandbrainrender.Develop core tools to facilitate others to build interoperable tools in Python, e.g. theBrainGlobe Atlas API.Build a community of neuroscientists and developers to share knowledge, build software and engage with the scientific, and open-source community (e.g. by organising hackathons).FundingInformationThe BrainGlobe project is only possible due to grant funding and the generous support of host institutions, whose informationcan be found on this website.
Package: brainglobe-napari-io
napari-brainglobe-ioVisualise cellfinder and brainreg results with napariInstallationThis package is likely already installed
(e.g. with cellfinder, brainreg or another napari plugin), but if you want to
install it again, either use the napari plugin install GUI or you can
installbrainglobe-napari-ioviapip:pip install brainglobe-napari-ioUsageOpen napari (however you normally do it, but typically just typenapariinto your terminal, or click on your desktop icon)brainregSample spaceDrag yourbrainregoutput directory (the one with the log file) onto the napari window.Various images should then open, including:Registered image- the image used for registration, downsampled to atlas resolutionatlas_name- e.g.allen_mouse_25umthe atlas labels, warped to your sample brainBoundaries- the boundaries of the atlas regionsIf you downsampled additional channels, these will also be loaded.Most of these images will not be visible by default. Click the little eye icon to toggle visibility.N.B. If you use a high resolution atlas (such asallen_mouse_10um), then the files can take a little while to load.Atlas spacenapari-brainregalso comes with an additional plugin, for visualising your data
in atlas space.This is typically only used in other software, but you can enable it yourself:Open napariNavigate toPlugins->Plugin Call OrderIn thePlugin Sorterwindow, selectnapari_get_readerfrom theselect hook...dropdown boxDragbrainreg_read_dir_atlas_space(the atlas space viewer plugin) abovebrainreg_read_dir(the normal plugin) to ensure that the atlas space plugin is used preferentially.cellfinderLoad cellfinder XML fileLoad your raw data (drag and drop the data directories into napari, one at a time)Drag and drop your cellfinder XML file (e.g.cell_classification.xml) into napari.Load cellfinder directoryLoad your raw data (drag and drop the data directories into napari, one at a time)Drag and drop your cellfinder output directory into napari.The plugin will then load your detected cells (in yellow) and the rejected cell
candidates (in blue). If you carried out registration, then these results will be
overlaid (similarly to the loading brainreg data, but transformed to the
coordinate space of your raw data).Loading raw dataLoading cellfinder resultsContributingPlease see thedevelopers guide.LicenseDistributed under the terms of theMITlicense,
"brainglobe-napari-io" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: brainglobe-segmentation
brainglobe-segmentationSegmentation of anatomical structures in a common coordinate spaceInstallationPyPIpip install brainglobe-segmentationcondaconda install -c conda-forge brainglobe-segmentationN.B. Your data will need to be registered to an anatomical atlas first.UsageSeeuser guide.If you have any questions, head over to theimage.sc forum.ContributingContributions are very welcome. Please see thedevelopers guide.Citing brainglobe-segmentationIf you find brainglobe-segmentation 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: brainglobe-utils
brainglobe-utilsShared general purpose tools for the BrainGlobe project, includingcitation generation.Installationpipinstallbrainglobe-utilsTo also include the dependencies required fornapari, use:pipinstallbrainglobe-utils[napari]For development, clone this repository and install the dependencies with one of the following commands:pipinstall-e.[dev]pipinstall-e.[dev,napari]Citations for BrainGlobe toolsbrainglobe-utilscomes with thecite-brainglobecommand line tool, to write citations for BrainGlobe tools for you so you don't need to worry about fetching the data yourself.
You can read abouthow to use the toolon the documentation website.
Package: brainhance
Braingan3d Software ReleaseUsageThe usage of this software is relatively simple,brainsganinput_fileoutput_fileTheinput_fileandoutput_filefiles can be either path to NIFTI images
(with extension either.niior.nii.gz) or to a file (normally in the.txtformat) containing a list of images. For the upsampling for more
than on file, the second form is preferred, as it avoids loading the model onto
the GPU over and over again.It is possible to choose the network used with the-n/--networkflag. E.g.,brainsganLR_scan.nii.gzSR_scan.nii.gz--networkdHCPRecommended settingsThis code does not take advantage of more than one GPU. Thus, used in a
multi-GPU system, it is recommended to add theCUDA_VISIBLE_DEVICESbefore
running the codeCUDA_VISIBLE_DEVICES=<GPU_to_be_used>brainsganinput_fileoutput_fileIt is also possible to use only the CPU by leavingGPU_to_be_usedempty.RuntimeRunning on a single Nvidia P100, the upsampling takes roughly one second per
scan and roughtly 4s on the CPU.Developer InformationBuildThe following information is only useful for individuals who are actively
contributing to the program.We use setuptool and wheel to build the distribution code. The process is
described next. More information can be foundhere.Be sure that setuptools, twine, and wheel are up-to-datedpython3-mpipinstall--user--upgradesetuptoolswheeltwineRun the build commandpython3setup.pysdistbdist_wheelUpload the package to pippython3-mtwineuploaddist/*
Package: brainiak
Brain Imaging Analysis KitThe Brain Imaging Analysis Kit is a package of Python modules useful for
neuroscience, primarily focused on functional Magnetic Resonance Imaging (fMRI)
analysis.The package was originally created by a collaboration between Intel and the
Princeton Neuroscience Institute (PNI).To reduce verbosity, we may refer to the Brain Imaging Analysis Kit using theBrainIAKabbreviation. Whenever lowercase spelling is used (e.g., Python
package name), we usebrainiak.QuickstartIf you haveConda:conda install -c brainiak -c defaults -c conda-forge brainiakOtherwise, or if you want to compile from source, install the requirements (seedocs/installation) and then install from PyPI:python3 -m pip install brainiakNote that to use thebrainiak.matnormalpackage, you need to install
additional dependencies. As of October 2020, the required versions are not
available as Conda packages, so you should install from PyPI, even when using
Conda:python3 -m pip install -U tensorflow tensorflow-probabilityNote that we do not support Windows.DockerYou can also test BrainIAK without installing it using Docker:docker pull brainiak/brainiak
docker run -it -p 8888:8888 -v brainiak:/mnt --name demo brainiak/brainiakTo run Jupyter notebooks in the running container, try:python3 -m notebook --allow-root --no-browser --ip=0.0.0.0Then visithttp://localhost:8888in your browser and enter the token. Protip:
runscreenbefore running the notebook command.Note that we do not support MPI execution using Docker containers and that performance will not be optimal.SupportIf you have a question or feedback, chat with us onGitteror email our list atbrainiak@googlegroups.com. If you find a problem with BrainIAK, you can alsoopen an issue on GitHub.ExamplesWe include BrainIAK usage examples in the examples directory of the code
repository, e.g.,funcalign/srm_image_prediction_example.ipynb.To run the examples, download an archive of thelatest BrainIAK release from
GitHub. Note that we only
support the latest release at this moment, so make sure to upgrade your
BrainIAK installation.DocumentationThe documentation is available athttp://brainiak.org/docs.ContributingWe welcome contributions. Have a look at the issues labeled “easy” for
starting contribution ideas. Please read the guide inCONTRIBUTING.rstfirst.CitingPlease cite BrainIAK in your publications as: “Brain Imaging Analysis Kit,http://brainiak.org.” Additionally, if you useRRIDsto identify resources, please mention
BrainIAK as “Brain Imaging Analysis Kit, RRID:SCR_014824”. Finally, please cite
the publications referenced in the documentation of the BrainIAK modules you
use, e.g.,SRM.
Package: braininvaders2012
Brain Invaders 2012 DatasetRepository with basic scripts for using the Brain Invaders 2012a dataset developed at GIPSA-lab. The dataset files and their documentation are all available athttps://zenodo.org/record/2649069The code of this repository was developed inPython 3using MNE-Python [1, 2] as tool for the EEG processing.To make things work, you might need to install some packages. They are all listed in therequirements.txtfile and can be easily installed by doingpip install -r requirements.txtin your command line.Then, to ensure that your code finds the right scripts whenever you doimport braininvaders2012, you should also dopython setup.py develop # because no stable release yetNote that you might want to create avirtual environmentbefore doing all these installations.References[1] Gramfort et al. "MNE software for processing MEG and EEG data"DOI[2] Gramfort et al. "MEG and EEG data analysis with MNE-Python"DOI
Package: brain-isotopic-distribution
:mod:brainpyis a small Python library implementing theBafflinglyRecursiveAlgorithm forIsotopic PatterNgeneration [Dittwald2014]_. It includes three implementations,
a pure-Python object oriented implementation, a :title-reference:Cythonaccelerated
version of the object oriented implementation, and a pure :title-reference:Cimplementation,
listed in order of ascending speed. The C implementation is used by default when available.BRAIN, implemented in :func:brainpy.isotopic_variants, takes an elemental
composition represented by  any :class:~.collections.abc.Mapping-like Python object and uses
it to compute its aggregated isotopic distribution. All isotopic variants of the same number
of neutrons are collapsed into a single centroid peak, meaning it does not consider isotopic
fine structure.Installing:mod:brainpyhas three implementations, a pure Python implementation, a Cython translation
of that implementation, and a pure C implementation that releases the :title-reference:GIL.To install from a package index, you will need to have a C compiler appropriate to your Python
version to build these extension modules. Additionally, there are prebuilt wheels for Windows
available onPyPI <https://pypi.org/project/brain-isotopic-distribution/>_:.. code-block:: sh$ pip install brain-isotopic-distributionTo build from source, in addition to a C compiler you will also need to install a recent version
ofCython <https://pypi.org/project/Cython/>_ to transpile C code.ReferencesThis package is an implementation of the algorithm originally described in
P. Dittwald, J. Claesen, T. Burzykowski, D. Valkenborg, and A. Gambin,
"BRAIN: a universal tool for high-throughput calculations of the isotopic distribution for mass spectrometry.",
Anal. Chem., vol. 85, no. 4, pp. 1991–4, Feb. 2013.H. Hu, P. Dittwald, J. Zaia, and D. Valkenborg,
"Comment on 'Computation of isotopic peak center-mass distribution by fourier transform'.",
Anal. Chem., vol. 85, no. 24, pp. 12189–92, Dec. 2013.
Package: brainite
Module providing NeuroImaging & Genetic Data Integration Tools for PyTorch.Home-page:https://github.com/neurospin-deepinsight/brainiteAuthor:
brainite developersAuthor-email:antoine.grigis@cea.frLicense: CeCILL-B
Description: Module providing NeuroImaging & Genetic Data Integration Tools 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: brainles-aurora
AURORADeep learning models for brain cancer metastasis segmentation based on the manuscripts:Identifying core MRI sequences for reliable automatic brain metastasis segmentationDevelopment and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter studyInstallationWith a Python 3.10+ environment, you can install directly frompypi.org:pip install brainles-auroraRecommended EnvironmentCUDA 11.4+ (https://developer.nvidia.com/cuda-toolkit)Python 3.10+GPU with CUDA support and at least 8GB of VRAMUsageBrainLes features Jupyter Notebooktutorialswith usage instructions.CitationPlease support our development by citing the following manuscripts:Identifying core MRI sequences for reliable automatic brain metastasis segmentation@article{buchner2023identifying,
  title={Identifying core MRI sequences for reliable automatic brain metastasis segmentation},
  author={Buchner, Josef A and Peeken, Jan C and Etzel, Lucas and Ezhov, Ivan and Mayinger, Michael and Christ, Sebastian M and Brunner, Thomas B and Wittig, Andrea and Menze, Bjoern H and Zimmer, Claus and others},
  journal={Radiotherapy and Oncology},
  volume={188},
  pages={109901},
  year={2023},
  publisher={Elsevier}
}also consider citing the original AURORA manuscript, especially when using thevanillamodel:Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study@article{buchner2022development,
  title={Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study},
  author={Buchner, Josef A and Kofler, Florian and Etzel, Lucas and Mayinger, Michael and Christ, Sebastian M and Brunner, Thomas B and Wittig, Andrea and Menze, Bj{\"o}rn and Zimmer, Claus and Meyer, Bernhard and others},
  journal={Radiotherapy and Oncology},
  year={2022},
  publisher={Elsevier}
}LicensingThis project is licensed under the terms of theGNU Affero General Public License v3.0.Contact us regarding licensing.Contact / Feedback / QuestionsIf possible please open a GitHub issuehere.For inquiries not suitable for GitHub issues:Florian Kofler
florian.kofler [at] tum.deJosef Buchner
j.buchner [at] tum.de
Package: brainles-preprocessing
BrainLes-PreprocessingBrainLes preprocessingis a comprehensive tool for preprocessing tasks in biomedical imaging, with a focus on (but not limited to) multi-modal brain MRI. It can be used to build to build modular preprocessing pipelines:This includesnormalization,co-registration,atlas registrationandskulstripping / brain extraction.BrainLes is writtenbackend-agnosticmeaning it allows to swap the registration and brain extration tools.InstallationWith a Python 3.10+ environment you can install directly frompypi.org:pip install brainles-preprocessingUsagePlease have a look at ourJupyter Notebook tutorialsillustrating the usage of BrainLes preprocessing.FAQPlease credit the authors by citing their work.Atlas ReferenceWe provide the SRI-24 atlas from thispublication.
However, custom atlases can be supplied.Brain extractionWe currently provide support forHD-BET.RegistrationWe currently provide support forniftyreg.
Package: brainlit
BrainlitSummaryThis repository is used for various tasks in processing, and analyzing light microscopy images of brains for the purpose of studing neuron morphology. Documentation can be foundhere.Brainlit is a product of theneurodata laband theNeuro Data Designcourse. It is actively maintained by Thomas Athey (@tathey1). We strive to follow the samecode of conductthat applies to the Microsoft open source community.Contributing and QuestionsWe welcome all contributors, and encourage them to follow our contribution guidelines gound inCONTRIBUTING.md. Issues with the "good first issue" tag are meant for contributors that are either new to open source coding, or new to the package. Additionally, users are encouraged to use issues not only to discuss code-related problems, but for more general discussions or questions about the package.
Package: brain-loop-search
Brain Loop SearchTools for screening significant loop structures in a graph, typically a
brain structure graph with physiological or anatomical edge data.Installation$pipinstallbrain-loop-searchUsagePacking a bigger graph for regions of interestPacking vertices:importbrain_loop_searchasblsvertices=[322,329,981,337,453,8,1070]# ccf brain id# ccf ontologyontology=bls.brain_utils.CCFv3Ontology()vp=bls.packing.VertexPacker(vertices,ontology)# filtering by levelvp.filter_by_level(fro=1,to=2)Packing a graph:importbrain_loop_searchasblsimportpandasaspdimportnumpyasnpvertices=[322,329,981,337,453,1070,345,353,361]# ccf brain id# adjacent matrixadj_mat=pd.DataFrame(np.array([[0,1,1,0,0,0,1,1,0],[0,0,1,1,1,1,0,1,0],[0,0,0,0,1,1,1,1,1],[0,0,1,0,0,1,0,1,0],[1,0,1,0,0,1,0,1,0],[0,1,1,1,1,1,0,1,0],[0,0,0,0,0,1,0,0,1],[1,0,0,0,0,1,1,1,0],[1,1,1,0,0,1,0,1,1]]),index=vertices,columns=vertices)# ccf ontologyontology=bls.brain_utils.CCFv3Ontology()# packingnew_rows=[322]new_cols=[337,329,353]graph_packer=bls.packing.GraphPacker(adj_mat,ontology)new_mat=graph_packer.pack(new_rows,new_cols,def_val=0,superior_as_complement=True,aggr_func=np.sum)Shortest Path Loop SearchScreen simple loops from the graph.importbrain_loop_searchasblsimportpandasaspdedges=pd.DataFrame({"a":[1,2,3,4,5],"b":[4,-1,-1,1,-1],"c":[5,6,7,8,9],"d":[-1,2,3,1,2],"e":[-1,-1,-1,-1,-1]},index=["a","b","c","d","e"])g=bls.search.ShortestPathLoopSearch()g.add_subgraph(edges)# search by single shortest path with a reverse edgeloops=g.pair_complement(axis_pool=['a','b','c'])# search by chaining 3 of the shortest paths foundloops,sssp=g.chain_screen(n_axis=3)Max Flow Loop SearchGenerate a new graph of potentially integrated loops.importbrain_loop_searchasblsimportpandasaspdedges=pd.DataFrame({"a":[1,2,3,4,5],"b":[4,-1,-1,1,-1],"c":[5,6,7,8,9],"d":[-1,2,3,1,2],"e":[-1,-1,-1,-1,-1]},index=["a","b","c","d","e"])g=bls.search.ShortestPathLoopSearch()g.add_subgraph(edges)# find a single max flow with a reverse edge (like a magnet field)new_g=g.magnet_flow(s='b',t='a')# find cycled max flows and merge them into a new graphnew_g=g.merged_cycle_flow(axes=['b','c','a'])VisualizationVisualize a single loopimportbrain_loop_searchasbls# a loop is a list of list, with the head and tail of the sublist as axes# here are some random picked brain regionsloop=[[950,974,417],[417,993],[993,234,289,950]]bls.brain_utils.draw_single_loop(loop,'test.png')Figure:Visualize a graphimportnumpyasnpimportpandasaspdimportbrain_loop_searchasblsvertices=[322,329,981,337,453,1070,345,353,361]adj_mat=pd.DataFrame(np.array([[0,2,1,0,0,0,1,8,0],[0,0,3,1,5,1,0,5,0],[0,0,0,0,1,3,2,1,2],[0,0,6,0,0,1,0,4,0],[1,0,1,0,0,1,0,4,0],[0,1,7,1,4,1,0,1,0],[0,0,0,0,0,1,0,0,1],[1,0,0,0,0,2,2,1,0],[1,2,2,0,0,1,0,1,1]]),index=vertices,columns=vertices)g=bls.search.GraphMaintainer()g.add_subgraph(adj_mat)bls.brain_utils.draw_brain_graph(g.graph,'test2.png',thr=3)Figure:Useful LinksGithub project:https://github.com/SEU-ALLEN-codebase/brain-loop-searchDocumentation:https://SEU-ALLEN-codebase.github.io/brain-loop-search
Package: brainmaps
brainmaps-apiInstall instructionsInstallvirtualenvCreate a new Python virtual environment withvirtualenv envActivate the virtual environment withvirtualenv envInstallrequirements.txtwithpip install -r requirements.txtAlternative Docker install instructionsIf you're familiar with Docker you can also install the system from the base Docker image.docker build -t .BrainMaps is a Python library for use with theBrainMaps Platform
Package: brainmodels
BrainModelsBrainModelsprovides standard and canonical brain models (including various neurons, synapses, networks, and intuitive paper examples) which are implemented withBrainPysimulator. Moreover, we welcome your brain model implementations, and publish them through ourGitHubhomepage. In such a way, once your new model is implemented, it can be easily shared with other BrainPy users.Currently, we provide the following standard models:Neuron ModelsSynapse ModelsLearning RulesLeaky integrate-and-fire modelAlpha SynapseSTDPExponential integrate-and-fire modelAMPA / NMDABCM ruleQuadratic integrate-and-fireGABAA / GABABOja's ruleAdaptive Exponential IF modelExponential Decay SynapseAdaptive Quadratic IF modelDifference of Two ExponentialsGeneralized IF modelShort-term plasticityIzhikevich modelGap junctionHodgkin-Huxley modelVoltage jumpMorris-Lecar modelHindmarsh-Rose modelInstallationInstallBrainModelsusingpip:>pipinstallbrainmodelsInstall from source code:>pipinstallgit+https://github.com/PKU-NIP-Lab/BrainModelsBrainModelsis based on Python (>=3.6), and the following packages need to be installed to useBrainModels:brain-py >= 1.1.0