Saravanan666/pypi-org huggingface dataset:
Package: neurolab
Neurolab is a simple and powerful Neural Network Library for Python.
Contains based neural networks, train algorithms and flexible framework
to create and explore other neural network types.Features:Pure python + numpyAPI like Neural Network Toolbox (NNT) from MATLABInterface to use train algorithms form scipy.optimizeFlexible network configurations and learning algorithms. You may change: train, error, initialization and activation functionsUnlimited number of neural layers and number of neurons in layersVariety of supported types of Artificial Neural Network and learning algorithmsExample:>>> import numpy as np
>>> import neurolab as nl
>>> # Create train samples
>>> input = np.random.uniform(-0.5, 0.5, (10, 2))
>>> target = (input[:, 0] + input[:, 1]).reshape(10, 1)
>>> # Create network with 2 inputs, 5 neurons in input layer and 1 in output layer
>>> net = nl.net.newff([[-0.5, 0.5], [-0.5, 0.5]], [5, 1])
>>> # Train process
>>> err = net.train(input, target, show=15)
Epoch: 15; Error: 0.150308402918;
Epoch: 30; Error: 0.072265865089;
Epoch: 45; Error: 0.016931355131;
The goal of learning is reached
>>> # Test
>>> net.sim([[0.2, 0.1]]) # 0.2 + 0.1
array([[ 0.28757596]])Links:Home PagePyPI PageDocumentationExamples
Package: neurolang
NeuroLangNeurolang enables the analysis of NeuroImaging data through probabilistic logic programming. It seamlessly allow to combine, images, databases, and ontologies within a single framework.Important linksOfficial source code repo:https://github.com/Neurolang/Neurolang/In-development interactive gallery of examples (best viewed with Chrome)http://neurolang-u18.saclay.inria.fr/hub/galleryHTML documentation (stable release):http://neurolang.github.io/HTML documentation (dev release):http://neurolang.github.io/dev
Package: neuro-logging
Neuro Platform LoggingUsage:importloggingfromneuro_loggingimportinit_logginginit_logging()logging.info("Some info")By defaultinit_logging()will forward allerrorsandcriticalmessages tostderr. All other type of messages will be forwarded tostdout.
You can pass own dict-based config with custom setting i.e. for disable warning in asyncio and concurrentfromneuro_loggingimportDEFAULT_CONFIG,init_loggingcustom_config=dict(DEFAULT_CONFIG)custom_config.update({"loggers":{"asyncio":{"level":"ERROR"},"concurrent":{"level":"ERROR"}}})init_logging(custom_config)
Package: neurolucidaxml2ex
Neurolucida XML 2 Ex====================This program converts Neurolucida XML to Ex format suitable for OpenCMISS-Zinc.Install-------::pip install neurolucidaxml2exUsage-----::neurolucidaxml2exconverter /path/to/input.xmlFor more information use the help::neurolucidaxml2exconverter --helpKeywords: Neurolucida OpenCMISS-ZincPlatform: UNKNOWNClassifier: Development Status :: 3 - AlphaClassifier: Programming Language :: PythonClassifier: Programming Language :: Python :: 2.7Classifier: Programming Language :: Python :: 3Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.Classifier: Topic :: Utilities
Package: neurom
NeuroMNeuroM is a Python toolkit for the analysis and processing of neuron morphologies.DocumentationNeuroM documentation is built and hosted onreadthedocs.latest snapshotlatest releaseMigration to v2 or v3 versionsRefer tothe doc pageon this topic.Reporting issuesIssues should be reported to theNeuroM github repository issue tracker.
The ability and speed with which issues can be resolved depends on how complete and
succinct the report is. For this reason, it is recommended that reports be accompanied
withA minimal but self-contained code sample that reproduces the issue. Minimal means no
code that is irrelevant to the issue should be included. Self-contained means it should
be possible to run the code without modifications and reproduce the problem.The observed and expected output and/or behaviour. If the issue is an error, the python
error stack trace is extremely useful.The commit ID of the version used. This is particularly important if reporting an error
from an older version of NeuroM.If reporting a regression, the commit ID of the change that introduced the problemIf the issue depends on data, a data sample which reproduces the problem should be
up-loaded. But check first whether the error can be reproduced with any of the data
samples available in thetests/datadirectory.CitationWhen you use the NeuroM software, we ask you to cite the following (this includes poster presentations):Funding & AcknowledgementsThis work has been partially funded by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270, 785907 (Human Brain Project SGA1/SGA2) and by the EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.For license and authors, seeLICENSE.txtandAUTHORS.mdrespectively.Copyright (c) 2015-2022 Blue Brain Project/EPFL
Package: neuromaps
Theneuromapstoolbox is designed to help researchers make easy,
statistically-rigorous comparisons between brain maps (or brain annotations).
Documentation can be foundhere.The accompanying paper is published inNature Methods(postprint).FeaturesA growing library of brain maps (“annotations”) in their original coordinate space, including microstructure, function, electrophysiology, receptors, and moreRobust transforms between MNI-152, fsaverage, fsLR, and CIVET spacesIntegrated spatial null models for statistically assessing correspondences between brain mapsInstallation requirementsCurrently,neuromapsworks with Python 3.8+.
You can install stable versions ofneuromapsfrom PyPI withpip install neuromaps.
However, we recommend installing from the source repository to get the latest features and bug fixes.You can installneuromapsfrom the source repository withpip installgit+https://github.com/netneurolab/neuromaps.gitor by cloning the repository and installing from the local directory:gitclonehttps://github.com/netneurolab/neuromapscdneuromapspipinstall.You will also need to haveConnectome Workbenchinstalled and available on your path in
order to use most of the transformation / resampling functionality ofneuromaps.CitationIf you use theneuromapstoolbox, please cite ourpaper.Importantly,neuromapsimplements and builds on tools that have been previously developed, and we redistribute data that was acquired elsewhere.
Please be sure to cite the appropriate literature when usingneuromaps, which we detail below.If you use volume-to-surface transformations (registration fusion), please citeBuckner et al 2011(original proposition) andWu et al 2018(first implementation of MNI152 to fsaverage transformation).If you use surface-to-surface transformations (multimodal surface matching), please citeRobinson et al 2014andRobinson et al 2018.If you use data included inneuromaps, please cite the the original papers that publish the data. A table with references for each brain annotation can be found in ourwiki, or more specifically, atthislink.If you use the spatial null models, there is an associated citation with each type of null model. They can be found in the docstring of the function, and also thefull listof functions.License informationThis work is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Licensecc-by-nc-sa.
The full license can be found in theLICENSEfile in theneuromapsdistribution.
Package: neuromation
PrefaceWelcome to Neuromation API Python client forhttps://neu.ro/.
Package ship command line tool calledneuro. With it you can:Execute and debug jobsManipulate DataMake some funApihttps://neuromation-sdk.readthedocs.io/en/latest/ContributingFor 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.envsource.env/bin/activateDevelopment flow:Install dependencies:make setupReformat code:make formatLint:make lintRun tests:make testRun end-to-end tests:make e2e
Package: neuromeka-clients
Neuromeka ClientsThis package provides client protocols for users to interact with Neuromeka's products, including Indy, Moby, Ecat, and Motor.InstallationYou can install the package from PyPI:pipinstallneuromeka-clientsUsageThe package contatins the following client classes:IndyClient in indy.pyIndyClient3 in indy_client3.pyMobyClient in moby.pyEcatClient in ecat.pyMotorClient in motor.pyTo use a client class, simply import it and create an instance:fromneuromekaimportEcatClient,MobyClient,IndyClient,IndyClient3,MotorClientecat=EcatClient("192.168.214.20")moby=MobyClient("192.168.214.20")indy=IndyClient("192.168.0.11")indyclient3=IndyClient3("192.168.0.11")motor=MotorClient("192.168.0.20")Replace EcatClient with the desired client class and the IP address with the appropriate address for your device.DependenciesThis package requires the following dependencies:grpciogrpcio-toolsprotobufThese dependencies will be automatically installed when you install the package using pip.ExamplesPlease refer to the 'example.py' file in the package for usage examples.SupportIf you encounter any issues or need help, please open an issue on the project's repository.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: neuromeka-clients3
Neuromeka ClientsThis package provides client protocols for users to interact with Neuromeka's products, including Indy, Moby, Ecat, and Motor.InstallationYou can install the package from PyPI:pipinstallneuromeka-clientsUsageThe package contatins the following client classes:IndyClient in indy.pyIndyClient3 in indy_client3.pyMobyClient in moby.pyEcatClient in ecat.pyMotorClient in motor.pyTo use a client class, simply import it and create an instance:fromneuromekaimportEcatClient,MobyClient,IndyClient,IndyClient3,MotorClientecat=EcatClient("192.168.214.20")moby=MobyClient("192.168.214.20")indy=IndyClient("192.168.0.11")indyclient3=IndyClient3("192.168.0.11")motor=MotorClient("192.168.0.20")Replace EcatClient with the desired client class and the IP address with the appropriate address for your device.DependenciesThis package requires the following dependencies:grpciogrpcio-toolsprotobufThese dependencies will be automatically installed when you install the package using pip.ExamplesPlease refer to the 'example.py' file in the package for usage examples.SupportIf you encounter any issues or need help, please open an issue on the project's repository.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: neurometa
NeurometaInstallationMost users don't need to installneurometaas they can just grab the output from the backups folder.pipinstallneurometaContentHuman brain structure treeMap-typed tree fromlist of regions in the human brain.Model organismMap fromlist of model organisms: Model organism to description. The user will most likely use parse and only use the keysNeurotransmitterMap from table in Wikipediaarticle: Full neurotransmitter name to anarraythat stores most commonly used aliases and abbreviations.
Package: neurommsig-epilepsy
Mechanistic pathway knowledge on Epilepsy encoded in Biological Expression
Language.InstallationTo install theneurommsig_epilepsypython package for programmatic access
to the BEL files in this repository, use the following code in your shell:$pipinstallneurommsig_epilepsyTo install theneurommsig_epilepsypython package for programmatic access
to the BEL files in this repository, use the following code in your shell:$gitclonehttps://github.com/neurommsig-epilepsy/neurommsig-epilepsy.git$cdneurommsig_epilepsy$pipinstall-e.To optionally re-compile all of the BEL files, use$neurommsig-epilepsycompileUsageTo get the knowledge and summarize it, use:importneurommsig_epilepsygraph=neurommsig_epilepsy.get_graph()graph.summarize()
Package: neurommsig-knowledge
NeuroMMSig KnowledgeThis repository contains knowledge curated in Biological Expression Language (BEL)
for NeuroMMSig during theAETIONOMYproject.InstallationThe latest version can be installed from GitHub with:$pipinstallgit+https://github.com/neurommsig/neurommsig-knowledge.gitUsageThe graph can be loaded with:fromneurommsig_knowledgeimportrepositoryfrompybelimportunion# Get all graphsgraphs=repository.get_graphs()# Combine them all using pybel.uniongraph=union(graphs.values())
Package: neuromorpho-api
neuromorpho-apiQuerying theneuromorpho.orgdatabase in Python
withrequests.Installationpipinstallneuromorpho-apiQuickstart>>>fromneuromorpho_apiimportrequestorasrequests>>>neuron_endpoint="https://neuromorpho.org/api/neuron/">>>resp=requests.get(neuron_endpoint+"fields")>>>resp.json(){'Neuron Fields':['neuron_id','neuron_name','archive','age_scale','gender',...What is this package?Theneuromorpho-apipackage provides arequests.Sessioninstance with a
customSSLContextneeded for interacting withneuromorpho.org.Why can't I just userequestsdirectly?You may see something like the following:>>>importrequests>>>requests.get("https://neuromorpho.org/api/neuron/id/1")Traceback(mostrecentcalllast)...SSLError:HTTPSConnectionPool(host='neuromorpho.org',port=443):Maxretriesexceededwithurl:/api/neuron/id/1(CausedbySSLError(SSLError(1,'[SSL: DH_KEY_TOO_SMALL] dh key too small (_ssl.c:1006)')))It appears the key provided byneuromorpho.orgis incompatible with the
default Python SSL cipher.
This package works around this issue usingRobin De Schepper's solution
posted on Stack Overflow.
At some point in the future, the neuromorpho certificate may (hopefully) be
updated, at which point this package will be archived.
Package: neuromorpholib
neuromorpholibInstallationpip3installneuromorpholibUsageDownloadingA simple download of known dataset and namefromneuromorpholibimportneuromorphonmo=neuromorpho.NeuroMorpho()acc1=nmo.download_swc("martone","ACC1")A query for all species=mouse neuronsmouse_neurons=nmo.search({"species":"mouse"})Download a SWC for a mouse neuronswc_demo=nmo.download_swc(mouse_neurons[0])If you know the archive name and neuron name, you can also download the swc directly by passingarchiveandneuron_namearguments.If you only want the SWC text and don't want it to be converted into aNeuronMorphologyobject, you can passtext_only=True.SWC ManagementRead a SWC file from diskfromneuromorpholib.swcimportload_swc,NeuronMorphologymy_morphology=load_swc("my_neuron.swc")# This is a NeuronMorphology object.Get a list of branch pointsbranch_points=my_morphology.get_branch_points()Get a simplified graph (only leaves and branch points) of a morphologymorphology_graph=my_morphology.smoothed()# nx.DiGraphSWC Geometry OperationsRotate, translate, or scale a geometry withNeuronMorphologyfunctions:n=NeuronMorphology()n.scale(3)n.translate([4,44,10])n.rotate([0,0,math.pi/2])Rendering SWCsAll of the following visualization techniques assume you have a single NeuronMorphology object, downloaded perhaps like this:fromneuromorpholibimportneuromorphonmo=neuromorpho.NeuroMorpho()acc1=nmo.download_swc("martone","ACC1")Using matplotlibimportnetworkxasnxg=acc1.get_graph()nx.draw(g,pos={n:a['xyz'][:2]forn,aing.nodes(data=True)},node_size=0)UsingpytriThis will return an interactive 3D turntable in a Jupyter Notebook.from pytri import Figure

fig = Figure()
fig.graph(acc1.get_graph(), pos_attribute="xyz")
fig.show()
Package: neuronbridge-python
NeuronBridge Python APIA Python API for theNeuronBridgeneuron similarity search service.Seethis notebookfor usage examples.Development NotesCreate a conda env with all the dependencies including Jupyter:conda env create -f environment.yml
conda activate neuronbridge-pythonThen install it as a Jupyter kernel:python -m ipykernel install --user --name=neuronbridge-pythonInstall for developmentYou can install the module for development like this:conda create --name neuronbridge-python python=3.8
conda activate neuronbridge-python
pip install -e .Useful shell commandsTo update conda_requirements.txt:conda env export --from-history --file conda_requirements.txtTo update requirements.txt:pipreqs --savepath=requirements.txt && pip-compileRegenerate the JSON schemas:python neuronbridge/generate_schemas.pyRun the unit tests:pytest testsPublishing a new releaseUpdate the version in setup.pyPush all changes and tag a release in GitHubBuild PyPI distribution:python setup.py sdist bdist_wheelUpload to PyPI:twine upload dist/*Running validation using RayYou can run validation multithreaded on a single machine like this:./neuronbridge/validate_ray.pyTo run the validation script in a distributed manner on the Janelia cluster, you must first installray-janeliain a sister directory to where this code base is cloned. Then run a script to bsub the Ray cluster:./scripts/launch_validation.sh
Package: neurondm
neurondmA data model for neuron types.Neuron TypesFor a an overview of how to use neuron-lang to describing neuron types please seethis introduction.
For a more in-depth look at the structures that neurons translate to in OWL
and some of the modelling decisions related to which reasoner you plan to use see anOverview of OWL modelling decisionsTo get started, follow the installation instructions below and
then includefrom neurondm.lang import *in your import statements.If you want to get started quickly, take a look at the notebook ofexamples of how to use neuron-lang to create new neuronsIf you want to use neurondm in a jupyter notebook, see the docs for how toset up neurondm for jupyter notebooks.InstallationYou can installneurondmusing the following commands.BasicspipinstallneurondmOnce installation is complete you should be able to run the following python code.fromneurondmimport*config=Config()n=Neuron(Phenotype('TEMP:myPhenotype'))config.write()config.write_python()SciGraph APISet the API key in a separate terminal to avoid losing
additional history after setting the api key.unsetHISTFILE
ontutilssetscigraph-api-key<key>Once that is done you should be able to run the following.python-mneurondm.models.huang2017NIF-OntologyTo work with the NIF-Ontology and build existing models
you need to clone the ontology repository and set your
SciGraph API key or set up a local SciGraph instance.gitclonehttps://github.com/SciCrunch/NIF-Ontology.git
ontutilssetontology-local-repo./NIF-Ontologypushd./NIF-Ontology
gitcheckoutneuronspopdFurther configurationIf you need more details on configuration see the pyontutilsconfigurationsection.Use outside the NIF ontologyIt is possible to use neurondm outside the NIF ontology and the pyontutils repository,
however it has not been fully abstracted to support that use case. If you want to do something
more complicated than the example shown in the installation section you will need to perform
some additional configuration. The way to do this is to set all the relevant values vianeurondm.Config. Seetest_neruons.test_roundtrip_pyfor an example.
Package: neurone-loader
NeuroneLoader is a python module for loading neurophysiological data recorded with Bittium NeurOne (formerly MegaEMG). It
therefore allows using the data in pure python processing workflows using the python scientifc software stack (e.g.numpy) without the need of prior conversion using other (proprietary) software (e.g. MATLAB).
It can also export it to container objects used by the popularpython-mneframework.Props to Andreas Henelius at Finnish Institute of Occupational Health for figuring out how
to read the NeurOne binary format in pure python as part of hisexport2hdfproject.InstallationQuick startDocumentationInstallationpipinstallneurone_loaderIf you want to export topython-mneyou must also install MNE and
all it’s dependencies.pipinstallmneQuick start>>>fromneurone_loaderimportRecording>>>rec=Recording(path_to_recording_folder)>>>rec.event_codesarray([0,1,12,13,99,128],dtype=int32)Please note that because raw EEG recordings can be quite large this package is very memory aware. Most data will be
loaded from disk lazily, i.e. the moment you’re actually accessing it, and redundant data will be removed from memory
as soon as it has been copied - unless you specify otherwise. Be advised that working with big recordings might still
require a lot of memory.I recommend looking at the docstrings before executing anything and maybe having a look at Concepts section in
theDocumentationbefore you start working with this package.ContributingIf you encounter any problem feel free to open aissueon GitHub. If you found a bug and want to
supply a fix or if you want to contribute a new feature open apull request. Just make sure that
your code is not breaking any tests and you also supply tests for your code.TestingTo run the tests you must first get the test data and then you can run the test with the following commands.
Please run them in the repository directory, not in the test subdirectory.To get the test data (~2.8GB) you need to installwget. Then you can
download the data by runningbashtest/get_test_data.shThen you can run the tests withpython-munittestdiscover-stest-t.
Package: neuroneq
neuroneqA GUI-based python tool to visualize equations used in simulated neurons.Code exampleEasily convert from alpha/beta to tau/infimport matplotlib.pyplot as plt
from neuroneq import ab2inf_tau, fit_inf
import numpy as np

alpha = "((v+45)/10)/(1-exp(-(v+45)/10))"
beta = "4*exp(-(v+70)/18)"

# convert to inf, tau
inf, tau = ab2inf_tau(alpha,beta)

# fit the equation to standard form
fit_equation = fit_inf(inf)

print(fit_equation)
# 1.0/(1.0+(exp((v+44.57)/(-9.55))))

# plot
min_v, max_v = -100, 50
v = np.linspace(float(min_v),float(max_v),2000)
fit_equation_np = fit_equation.replace("exp(","np.exp(")
y = eval(fit_equation_np)
plt.plot(v, y, 'r-',label="fit")
plt.show()