Saravanan666/pypi-org huggingface dataset:
Package: neuronetlearn
Multi-Experiment Network Estimation and Inference for High-Dimensional Point Process DataThis work is licensed under aCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.Python package:https://github.com/stevenwang/NeuroNetLearn/Modern high-dimensional point process data, especially those from neuroscience experiments, often involve observations from multiple conditions and/or experiments. Networks of interactions corresponding to these conditions are expected to share many edges, but also exhibit unique, condition-specific ones. However, the degree of similarity among the networks from different conditions is generally unknown. Existing approaches for multivariate point processes do not take these structures into account and do not provide inference for jointly estimated networks. To address these needs, we develop theneuronetlearnpackage that includes estimation and inference tools for networks of high-dimensional Hawkes processes over multiple experiments. Specifically,neuronetlearnincludes functions that implement a joint estimation procedure for networks of high-dimensional point processes that incorporates easy-to-compute weights in order to data-adaptively encourage similarity between the estimated networks. It also includes functions that implement a powerful hierarchical multiple testing procedure for edges of all estimated networks, which takes into account the data-driven similarity structure of the multi-experiment networks.For more details, please see the accompanying manuscripts:"Statistical Inference for Networks of High-Dimensional Point Processes."by Xu Wang, Mladen Kolar and Ali Shojaie, and"Joint Estimation and Inference for Multi-ExperimentNetworks of High-Dimensional Point Processes."by Xu Wang and Ali Shojaie.Installation:You can install a stable release ofneuronetlearnusingpip3by runningpython pip3 install neuronetlearnfrom a Terminal window.Dependencies includescipy,numpy,math,numba,igraph,sklearnCode was run using python 3.8.5Primary files:net_est_auto.py: joint estimation for multi-experiment networks where tuning parameters are automatically chosen based on eBIC (viagetBIC.py)net_inf.py: high-dimensional statistical inference for point process networknet_inf_threshold.py: fast version of 'net_inf' by first identifying sub-graphs and then applying 'net_inf' to each sub-graphspg_genlasso_solver_jit.py: generalized lasso solver using smoothing proximal gradient descent algorithmsimu_net.py: simulate multi-experiment point process data where the settings of networks are generated usinggenSetting.pyht.py: multi-experiment hierarchical testing controlling FWERExampleWe recommend starting with one of the following examples (hosted at the package githubpage) which demonstrate various features of the package.examples/demo_network_estimation.ipynb: examples of estimating multi/single-experiment point process network(s) usingnet_estfunction innet_est_auto.pyexamples/demo_network_inference.ipynb: examples of statistical inference over multi/single-experiment point process network(s) usingnet_inffunction innet_inf.py
Package: neuronetlib
Feedforward neural network library from scratch1. Introduction and motivationIn order to fully understand how artificial neural networks are trained, I wanted to write a constructor for feedforward neural networks, which creates a neural net only by having specified a list of the desired hidden layers and some custom parameters, like learning rate and batch size for gradient descent.I wrote the constructor from scratch, using only built-in python functions and NumPy. The training was optimized to make great use of vectorization and especially the highly optimized NumPy matrix multiplication. The constructor class stores a list of layers, which are their own class and store a weight matrix and various vectors for derivatives, intercepts and values each.For classification, the network uses the softmax function before the output layer and optimizes using the cross-entropy loss function.The constructor is demoed on the MNIST dataset, where it achieved 89.2% accuracy after a few minutes of training. The Jupyter Notebook file can be found in the main branch.2. How to access the constructor2.1 Create an instance of the class and pass the necessary argumentsTo access the constructor, one has to define a variable, which is then an instance of the class 'ffnn':model=ffnn([100,70])# call the constructor and set the parameters. In this case two hidden layers with 100 and 70 nodes each.The arguments in brackets are:List of the hidden layers (for example [20,20] for two hidden layers with 20 neurons each)Learning rate (step size for the adjustments in parameter learning. default is 0.04)Batch size (how much of the dataset is used for mini-batch gradient descent)Target Accuracy (constructor will stop training once this accuracy and sufficient convergence is achieved)
Below are all the possible arguments:model=ffnn(h_layers=[100,70],learning_rate=0.05,batch_size=0.1,targetAccuracy=0.92)2.2 Construct the networkThe model is now stored in a variable. In our case, the variable is called 'model'. We can now call the construct method on our model, which builds the specified layers and nodes. We need to specify both the x and y part of the dataset, so that the model knows how large the input and output layer need to be.model.construct(x,y) # construct the network with the dataset (needs it for shape of input and output layer)2.3 Train the modelNow we can call the train method on our model to learn the parameters. The necessary arguments for this method are the whole dataset (x and y). Make sure that the dimensions of the y vector is n * 1 (if the numpy array has shape (n,) instead of (n,1) you need to reshape it).model.train(x,y)# train the network with training data2.4 predict on the test datasetWe now possess a trained model and can use it to make predictions on the test set with the 'predict' method. This will return an array with the predictions as well as the accuracy of the model on the training data.model.predict(x,y)# predict on the test data with the trained model
Package: neuron-image-denoise
Neuron Image DenoisingThe 3D neuron image denoising filter cythonized.Install$pipinstallneuron-image-denoiseUser Guidefromneuron_image_denoise.filterimport*img:numpy.ndarray# 3D numpy array, 16bitparams={'ada_interval':(2,3,3),'flare_interval':(2,8,8),'ada_sampling':3,'flare_sampling':8,'flare_weight':.02,'atten_depth':4,'flare_x':True,'flare_y':True}out=adaptive_denoise(img,**params)out=adaptive_denoise_16to8(img,**params)# 16bit to 8bitout=adaptive_sectional_feedforward_filter(img,sigma=12.,truncate=2.,scaling=1,suppression=.8)Algorithm ExplanationNeurons are illuminated by the fluorescent proteins, which can be seen as dots in the space. Measuring the signal is to
locate where the dots are, but the problem is, these dots can diffuse to outside of neurons and their light can be
spreaded far away due to refraction and microcopy aberration.Adaptive thresholding can reduce this effect by assuming that true signals stand out from the background, as the
fluorescent given by the dots attenuates with distance.
Methods like difference of gaussian play similarly, but adaptive threshold has the best and most stable effect
together with way lower computation.Yet there's another problem caused by the microscopy, and varies depending on the instrument. The images can be polluted by
nearby slices, resulting in flares that look like a brush head. It is especially common with strong signals like somata,
making the nearby neurites discernible. There are two ways to solve this, either using more advanced microscopy to
reduce this effect to a small range, or measure this effect and cancel computationally.Useful LinksGithub project:https://github.com/SEU-ALLEN-codebase/neuron-image-denoise-pyDocumentation:https://SEU-ALLEN-codebase.github.io/neuron-image-denoise-py
Package: neuronjs
python-neuronjsPython utilities and middleware for neuron.jsneuronjs analyzes the dependencies from user defined facades according
to the dependency tree, and outputs<script>tags and configurations
forneuron.jsThe dependency tree can be generated byneuron-package-dependencyInstall$pipinstallneuronjsUsagefromneuronjsimportNeuronn=Neuron(dependency_tree=dependency_tree,# must definedresolve=resolve,version=version,cache=cache,debug=False)neuronjscould be used either in template file or your python
controller.Hereis a sample Jinja2 template file.dependency_treedictthejson.loads()ed dependency
treeresolvefunction(id)=(optional) implements your own custom
resolver.resolveaccepts one parameteridwhich can be
either astrof module id or alistof module ids. If astris passed in, the method should returns the resolved absolute
url of the module id. Ifidis alist, an url of comboed
script files should be returned.debugfunction|bool=Falsetells neuronjs whether should
switch on debug mode. When on debug mode, no javascript files of
dependencies will be preloaded, and the output will not be
compressed.ifdebugis callable, neuronjs will use the return value of
methoddebugifdebugis a boolean value, anddebugis true, the debug
mode will be on.cachedict=Noneifcacheis defined, it should contains 3
methods:cache.has(key)returnsboolcache.get(key)looks up and returns the cached value by keycache.set(key, value)sets the value by keyversionstronly works ifcacheis defined. neuronjs will
usesversionto generate the key to cache the output resultmodule id<name>@<version><path>#forexample:'jquery@2.0.0/jquery.js'n.facade(entry, data=None, defer=False)entrystrid of the entry moduledatadict=Nonethe data of the facade, which will be passed
to the entry module.deferbool=Falsewhether should defer the loading process of
the facade. IfTrue, the script of the facade and its
dependencies will not be loaded at the beginning of the page as much
as possible.Registers a facade entry withdata. neuronjs willReturns''(empty string), so you can use this method in python
templaten.combo(id…)Makes packages of ids to combine into a single file.idstrthe module id of commonjs module or css module.n.combo('jquery','underscore')n.combo('app-home/style.css','app-user/user.css')Returns''n.css(id)Registers an css module, and tell neuronjs to output it on the page.Returns''n.src(id)Returns the absolute path of the corresponding module.Methods below are used in base template.n.output_scripts()Outputs neuron.js, neuron initial configurations and the html of script
tags.For most cases, this method should be used in the base template file,
seeexample.Returnsstrn.output_facades()Outputs the initialization of facades.Returnsstrn.output_css()Outputs the html of link elements of csses.For most cases, this method should be used in the base template file,
seeexample.ReturnsstrLicenseMIT
Package: neuronlinear
Perceptron Package - neuronlinearWelcome to the neuronlinear ! This package is designed to provide a simple and efficient implementation of the Perceptron algorithm for binary classification tasks. Whether you are new to machine learning or an experienced practitioner, the Perceptron Package offers a versatile and easy-to-use tool for your classification needs.FeaturesLightweight and efficient implementation of the Perceptron algorithmSupports both online and batch learning modesFlexible customization options for data preprocessing and feature engineeringSimple and intuitive API for training and inference
Compatible with Python 3.xInstallationYou can easily install the Perceptron Package using pip:pip install neuronlinearQuick StartUsing the neuronlinear Package is straightforward. Here's a simple example to get you started:from neuronlinear.model import Perceptron, prepare_data


ETA = 0.1 #leaning rate
EPOCHS = 10 #epochs count

# Create a Perceptron object
perceptron = Perceptron(eta=ETA, epochs=EPOCHS)

data = {"x1":[0, 0, 1, 1], "x2" : [0, 1, 0, 1], "y" : [0, 1, 1, 1]}

X, y = prepare_data(data)

perceptron.fit(X, y)
print(f"Prediction for > 0, 1 is {perceptron.predict([0, 1])}")

print(f"Total loss is {model.total_loss}")ContributingWe welcome contributions from the community! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request on our GitHub repository.LicenseThe neuronlinear Package is licensed under the MIT License.AcknowledgmentsWe would like to express our gratitude to the contributors and supporters who have helped make this package possible.If you find the Perceptron Package useful, please consider giving it a star on GitHub. Your support is greatly appreciated!Note: The neuronlinear Package is provided as-is without any warranty. Use at your own risk.
Package: neuronmd
NeuronMdPackage for simulating different spiking neuron models.InstallationInstall usingpip:pipinstallneuronmdClone the repository:gitclonehttps://github.com/sowmyamanojna/neuronmdThen,cdinto the directory and run:pipinstall.Neuron ModelsHodgkin-Huxley Neuron ModelThe HH neuron model can be simulated by creating an instance of theHHNeuronclass.The code is as follows:importnumpyasnpfromneuronmdimportHHNeuron# Initialize a HH Neuron Classhhneuron=HHNeuron()# tmax - Max time (ms) to simulate the neuron# dt - time step of simulation# I - Current for simulating the neurontmax=100dt=0.01I=0.1t=np.arange(0,tmax,dt)# Simulate the neuron for a single current instancehhneuron.simulate(t,I)# Plot the resultshhneuron.plot()# View the variation in the state of the dynamical# system, as the input current is varied.current_list=np.arange(0.01,0.5,0.01)hhneuron.animate(t,ylim=[-85,40],current_list=current_list)hhneuron.animate(t,current_list=current_list,name="no_ylim")The results obtained from the above code:The default parameters used in the model are:v=-65# mVvna=50# mVvk=-77# mVvl=-54.387# mVgnamax=1.20# m.mho/cm**2gkmax=0.36# m.mho/cm**2gl=0.003# m.mho/cm**2cm=0.01# mF/cm**2m=0.0530h=0.5960n=0.3177The parameters can be changed using thechange_paramsfunction. A dict of params as keys and their corresponding values should be passed as the parameter.Izhikevich Neuron ModelThe Izhikevich neuron model can be simulated by creating an instance of theIzhNeuronclass.The code is as follows:izhneuron=IzhNeuron()tmax=1000dt=0.01t=np.arange(0,tmax,dt)I=5izhneuron.simulate(t,I)izhneuron.plot()current_list=np.arange(0.01,10,0.5)izhneuron.animate(t,ylim=[-80,35],current_list=current_list)izhneuron.animate(t,current_list=current_list,name="no_ylim")The results obtained from the above code:The default parameters used in the model are:a=0.02b=0.2c=-65d=8v=-65u=b*vThe above values correspond to Regular Spiking neuron.The parameters can be changed using thechange_paramsfunction. A dict of params as keys and their corresponding values should be passed as the parameter.Fitz-Hugh Nagumo Neuron ModelThe Fitz-Hugh Nagumo neuron model can be simulated by creating an instance of theFHNNeuronclass.The code is as follows:fhnneuron=FHNNeuron()tmax=100dt=0.01I=1.75t=np.arange(0,tmax,dt)fhnneuron.simulate(0.6,0,t,0.6)fhnneuron.plot(name="0.1")current_list=np.arange(0.01,I,0.01)fhnneuron.animate(t,current_list,ylim=[-0.45,1.5])fhnneuron.animate(t,current_list=current_list,name="no_ylim")The results obtained from the above code:The default parameters used in the model are:a=0.5b=0.1r=0.1The parameters can be changed using thechange_paramsfunction. A dict of params as keys and their corresponding values should be passed as the parameter.
Package: neuron-ml
NeuronA tiny and very high level transfer learning library for image classification 📚What is Neuron exactly?Neuron is a tiny library that aims to simplify image classification (If you don't know what Image classification is, it's the process to tell from an image what object / thing / feature is on it).Using Neuron, you'll be able to build production grade model under 5 lines of code. Yes, you read it correctly: 5 lines. Where as in common Machine Learning libraries like TensorFlow, PyTorch or Keras, you would do it in hundreds of lines.Of course, these libraries are much more complex and versatile than Neuron. Neuron isn't replacing these libraries if you need to build your own graph, but if you're doing so, you probably already know this.InstallCopy - paste that in a Terminalpip install neuron-mlDemoHere is an example of what Neuron can do:importneuron_mlasn# TensorFlowdata=n.load("./dataset/")# formats the datamodel=n.train(data)# train the datan.export(model,["./Model.pb","./Labels.txt"])# Exports everythingn.clean(model)# Clean temporary filesAnd it can also load files and classify them (before using it for production, make sure you have good hardware, as the model can take up to 5 seconds to load and run).importneuron_mlasnmodel=n.model(["./Model.pb","./Labels.txt"])# Load the modelgraph=n.graph(model)# Generate the graphlabels=n.labels(model)# Get the labelsn.classify(graph,labels,"./dataset/Celery/celery-1.jpg")# Classify. Will return a result objectSee the wiki for more informations.VersioningWe useSemVerfor versioning. For the versions available, see thetags on this repository.AuthorsArthur Guiot-Initial work-@arguiotAlso look at the list ofcontributorswho participated in this project. If you don't code but you have great ideas, don't hesitate to write your idea in the issue part. If your idea is accepted, I will add you to this list 😊.LicenseThis project is licensed under the MIT License - see the  file for detailsCopyright © 2018 Arthur Guiot All Rights Reserved.
Package: neuron-morphology
neuron_morphologyA package for working with single-neuron morphological reconstruction data, such as those in theAllen Cell Types Database. Provides tools for processing, visualizing, and analyzing such reconstructions.For usage and installation instructions, see thedocumentation.ContributingWe welcome contributions! Please see ourcontribution guidefor more information. Thank you!Level of SupportWe are planning on occasional updating this tool with no fixed schedule. Community involvement is encouraged through both issues and pull requests.
Package: neuronsphere
neuronspherePython bundle for all public NeuronSphere components.ComponentsThe following components are installed via dependencies:hmd-cli-app: core CLI applicationhmd-cli-configure: CLI tool for configuring local NeuronSphere environmenthmd-cli-repo: CLI tool for creating NeuronSphere compliant repositorieshmd-cli-neuronsphere: CLI tool for starting/stopping local NeuronSphere serviceshmd-cli-tools: helper library for HMD CLI toolsGetting startedAfter install, run the following commands:hmd configure
hmd neuronsphere upThen confirm the following URLs are accessiblehttp://localhost:8081/: Trino Query UIhttp://localhost:8888/: Jupyter Lab UIhttp://localhost:8088/: Explorer Dashboard Portalhttp://localhost:175/: Airflow UI
Package: neuron_ui
NEURON-UIExperimental prototype for a new user interface
forNEURONbased on web
technologies.This prototype is being developed in collaboration with theNeurosim
Laband theSense
Lab.The UI connects tonrnpythonthrough aGeppettoextension forJupyter
Notebook.See theRepoandWikifor more
info!Installationpipinstallneuron_uijupyternbextensionenable--pyjupyter_geppettoUsageNEURON-UIorjupyternotebook--NotebookApp.default_url=/geppetto--NotebookApp.token=''
Package: neuronvisio
What is itNeuronvisio is a Graphical User Interface forNEURON simulator environment.
Neuronvisio connect with NEURON using the new python NEURON interface.Features3D visualization of the model with the possibility to change it runtimeCreation of vectors to record any variables present in the sectionPylab integration to plot directly the result of the simulationExploration of the timecourse of any variable among time using a color coded scalethe GUI runs in its own thread so it’s possible to use the console (integrated with IPython)automatically download and load models fromModelDBMore info are available on the homepage:http://neuronvisio.orgOffline DocumentationTo create offline documentation similar to the one online you will need
sphinxhttp://sphinx.pocoo.org/installed.Move in the doc directory:cd docsand then launch:make htmlIn the_build/htmldirectory you will have the online doc.How To releaseUpdate ChangelogChange version atneuronvisio/__init__.pyChange to same version in conda.recipe/meta.yamlCommit everythingTag repopython setup.py tagUpload to pipy and anaconda.orgPiPy:python setup.py make_releaseAnaconda.org:Got to anaconda.org and click on the website so the last build will be uploaded
to the channel.
Package: neuropacs
NeuroPACS Python SDKConnect to NeuroPACS diagnostic capabilities with our Python SDK.Getting StartedInstallationpipinstallneuropacsUsageimportneuropacsapi_key="your_api_key"server_url="https://your_neuropacs_url"product_id="PD/MSA/PSP-v1.0"prediction_format="XML"# PRINT CURRENT VERSIONversion=neuropacs.PACKAGE_VERSION#INITIALIZE NEUROPACS SDKnpcs=neuropacs.init(server_url,api_key)#CONNECT TO NEUROPACSconnection=npcs.connect()#CREATE A NEW JOBorder_id=npcs.new_job()#UPLOAD AN IMAGE# --> data must be a valid path <String> or byte array <Bytes># --> order_id param is optionalupload_status=npcs.upload(data)#UPLOAD A DATASET# --> dataset_path must be a valid path to a dataset <String># --> order_id param is optionalupload_status=npcs.upload_dataset(dataset_path)#START A JOB# --> Valid product_id options: PD/MSA/PSP-v1.0# --> order_id param is optionaljob_start_status=npcs.run_job(product_id)#CHECK JOB STATUS# --> order_id param is optionaljob_status=npcs.check_status()#RETRIEVE JOB RESULTS# --> Valid prediction_format options: TXT, PDF, XML, JSON, DICOMSR# --> order_id param is optionaljob_results=npcs.get_results(prediction_format)AuthorsKerrick Cavanaugh -kerrick@neuropacs.comVersion History1.3.9Initial ReleaseSeerelease historyLicenseThis project is licensed under the MIT License - see the LICENSE.md file for details
Package: neuropercept-PratikBorkar04
neuropercept"NeuroPercept is a streamlined Python package designed for foundational neural network and perceptron models, offering intuitive functions for value prediction and basic neural computations. Ideal for both educational purposes and simple projects, it simplifies the process of training and predicting with perceptrons."How to use thisfromneuropercept.perceptronimportPerceptron## get X and y and then use below commandsmodel=Perceptron(eta=eta,epochs=epochs)model.fit(X,y)
Package: neuroplotlib
Neuroplotlibneuroplotlibis a Python package for plotting neuron models inLFPy.Installationneuroplotlibrequires LFPy to be installed, which in turn requires the NEURON simulator.You can download and install NEURON from thislink.Then you can install LFPy withpiporconda:pipinstallLFPyorcondainstallLFPyFinally,neuroplotlibcan be installed frompip:pipinstallneuroplotlibor from source:gitclonehttps://github.com/alejoe91/neuroplotlibcdneuroplotlib
pythonsetup.pyinstall(ordevelop)UsageCheck out thisnotebookfor a gallery ofneuroplotlibfeatures.