Saravanan666/pypi-org huggingface dataset:
Package: omneer
Omneer SDKOmneer SDK is a state-of-the-art toolkit designed for the development and deployment of AI and machine learning powered personalized medicine applications in the neuroscience field. The SDK, with its Python-centric approach, provides an easy-to-use platform for developers to create and interact with their applications.Fueled by Airflow, a dynamic and highly scalable workflow orchestration engine known for its efficiency and flexibility, Omneer SDK ensures seamless processing and management of complex data workflows. Airflow's Directed Acyclic Graph (DAG) approach ensures atomicity at a task level, allowing for a comprehensive and orderly execution of tasks.This SDK further enhances workflow execution with containerization options, offering independence in task scheduling and a vast array of scalable computing resources. As such, the Omneer SDK represents a perfect convergence of neuroscience, personalized medicine, and cutting-edge technology, promising a new era of advanced and personalized healthcare solutions.Discord Community Community•Docs•Installation•Quickstart•OmneerUtilizing the Omneer SDK, developers can deliver:Personalized AI and machine learning toolsAdvanced disease progression diagnosis and trackingCreation of digital twins for individualized healthcareInstant no-code interfaces for rapid deploymentHigh-performing, reliable cloud infrastructureFlexibility to define resources (CPU, GPU, etc.) for serverless executionOmneer SDK continues to be a ground-breaking platform for personalized healthcare solutions. Browse our collection of existing and actively maintained solutions atOmneer Community.Getting StartedSee the SDK in action by following the steps below to register your first workflow with Omneer.First, install omneer throughpip.$ pip install omneerThen, create some boilerplate code for your new workflow.$ omneer init diagnosisThe registration process, which could take a few minutes depending on your network connection, involves building a Docker image with your workflow code, serializing the code, registering it with your Omneer account, and pushing the Docker image to a managed container registry.Upon successful registration, your new workflow should be visible in your Omneer Console.For issues with registration or other queries, please raise an issue on GitHub.InstallationOmneer SDK is distributed via pip. We recommend installing it in a clean virtual environment for the best user experience.Virtualenv is our recommended tool for creating isolated Python environments.Virtualenvwrapperis recommended.pip install omneerExamplesOmneer Examplesfeatures list of well-curated workflows developed by the Omneer team.Parkinson's DiagnosisParkinson's ProgressionParkinson's PersonalizedWe'll maintain a growing list of well documented examples developed by our community members here. Please open a pull request to feature your own:Parkinson's DiagnosisEarly Detection
Package: omni
Omniscient Multiple Networked IdentificationInstallationThe stable releases are uploaded toPyPI, so
you can install and upgrade usingpip:pip install omniAlternatively, you can install development versions —at your own risk—
directly from the Git repository:pip install -e git://github.com/aperezdc/omniDocumentationThe documetation for OMNI is available at:http://omni.readthedocs.org/en/latest/ImportantNote that the documentation is work-in-progress and
for the moment it is lacking a lot of content.DevelopmentIf you want to contribute to OMNI, please use the usual GitHub workflow:Clone the repository.Hack on your clone of OMNI.Send a pull request for review.If you do not have programming skills, you can still contribute byreporting issuesthat you may
encounter.
Package: omni-api
Omni APISimple REST API wrapper for the Omni infra-structure.ConfigurationNameTypeDescriptionOMNI_BASE_URLstrThe base URL that is going to be used for API connections (defaults tohttp://localhost:8080/mvc/).OMNI_OPEN_URLstrThe open URL for the Omni connection (defaults toOMNI_BASE_URL).OMNI_PREFIXstrThe default prefix to be used for API requests (defaults toadm/).OMNI_IDstrThe client id to be used for API connections (defaults toNone).OMNI_SECRETstrThe secret key to be used for API connections (defaults toNone).OMNI_REDIRECT_URLstrThe URL to be used for redirection OAuth based workflow (defaults tobase,base.user,base.admin,foundation.store.list).OMNI_USERNAMEstrThe username to be used on direct workflow (defaults toNone).OMNI_PASSWORDstrThe password to be used on direct workflow (defaults toNone).UsageTypical usage of the Omni client implies the providing of the username and the
password inside the target omni instance (Direct Mode).api=omni.Api(base_url="http://frontdoorhq.com",username="YOUR_USERNAME",password="YOUR_PASSWORD")Alternatively it's possible to use the api throught an OAuth base approach using
the provided client id and secret values.api=omni.Api(base_url="http://frontdoorhq.com",client_id="YOUR_OAUTH_ID",client_secret="YOUR_OAUTH_SECRET")For these type of handling theOAuthAccessErrorexception must be handled and then
the user must be redirect to the url provided byapi.oauth_autorizemethod call.Running then theapi.oauth_accesscall with the returnedcodefrom the server side
that should be used to redeem theaccess_tokenrequired for session authentication.ExamplesExamples are locatedhere.LicenseOmni API is currently licensed under theApache License, Version 2.0.Build Automation
Package: omniart-eye-classifier
OmniArt Eye ClassifierThis package is an (painted) eye colour classifier. It is trained on a dataset of 2600 different painted eyes that were extracted from theOmniArtdataset.
The model achieved an accuracy of 83% for following ten classes:amber, blue, brown, gray, grayscale, green, hazel, irisless, negative, red.Thenegativeis used to classify images that are not eyes, or where, for example, the eye lid is closed.Project originThis package is part of a Master's thesis at the University of Amsterdam.
Package: omniart-eye-dataset
OmniArt Eye DatasetThis package provides a dataset of 118,576 painted eyes. These eyes are extracted from about 245,000 paintings from theOmniArtdataset.
The dataset provides the eyes as images, the colour of the iris, and metadata from the OmniArt dataset.UsageThe dataset can be used like any other PyTorch dataset. It extends theImageFolderclass to provide the images and labels/colour and in addition attaches the OmniArt metadata as a dictionary.
The following classes are used, and how many of that class exist in the dataset:ColourCountAmber3,114Blue18,926Brown42,094Gray7,637Grayscale16,234Green5,220Hazel1,578Irisless18,282Negative96,042Red5,491Thenegativeclass exists to be able to classify non-eye images. It contains samples of primarily noise and facial areas, such as closed eyelids.ExampleThe dataset can be used in the following wayimporttorchimportmatplotlib.pyplotaspltimportnumpyasnpimporttorchvision.utilsasvutilsfromtorchvision.transformsimporttransformsfromomniart_eye_datasetimportOmniArtEyeDatasetdataset=OmniArtEyeDataset(transform=transforms.Compose([transforms.Resize(50),transforms.CenterCrop(50),transforms.ToTensor(),]))dataloader=torch.utils.data.DataLoader(dataset,batch_size=64,shuffle=True,num_workers=4)# Take 64 random entriesimages,color,metadata=next(iter(dataloader))# Plot the entriesplt.figure(figsize=(10,10))plt.axis("off")plt.title("OmniArt eyes")plt.imshow(np.transpose(vutils.make_grid(images,padding=5,normalize=True),(1,2,0)))plt.show()RelatedThis dataset has already been used to train aclassifierand painted eye generator.Project originThis package is part of a Master's thesis at the University of Amsterdam.
Package: omniart-eye-generator
OmniArt Eye GeneratorThis package is an eye generator. It is a cDCGAN trained on theOmniArt Eye DatasetThe generator can generate eyes with the following eight colours:ColourAmberBlueBrownGrayGrayscaleGreenHazelRedUsageThe generator can be used in the following wayfromomniart_eye_generatorimportgenerate_eye# Eye will be a PIL Image if eye_count is 1, otherwise a list of Image is returnedeye=generate_eye('hazel',eye_count=1)eye.show()Project originThis package is part of a Master's thesis at the University of Amsterdam.
Package: omnia-timeseries-sdk
OMNIA Timeseries SDK for PythonPython software developer kit for the Omnia Timeseries API.GeneralAboutThis is the Python SDK for developers and data scientists working with Omnia Timeseries. The package integrates with
pandas to help you work easily and efficiently with the data.Getting startedRun the below command in a Python environment to install the latest releasepip install omnia_timeseries_sdk.. or upgrade existing installationpip install --upgrade omnia_timeseries_sdkImport the Omnia client in your own scripts...fromomnia_timeseries_sdkimportOmniaClient... and get to work.Hereis an introduction.AuthenticationThe SDK supportsauthentication by user impersonation and using a shared secretWhen accessing Omnia from a service with a shared client secret you must set the following environmental variables. The
values are provided by the Omnia administrators.set omniaResourceId="omnia-resource-id"set omniaClientId="your-client-id"set omniaClientSecret="very-very-secret-shared-key"Take a look at the resources listed below to learn more.ResourcesSourceIssuesChangelogDocumentationDownloadExamplesContributeThese instructions will get you a copy of the project up and running on your local machine for development and testing
purposes. See deployment for notes on how to deploy the project on a live system.PrerequisitesInstall Python version 3.7 or later from eitherhttps://www.python.orgorhttps://www.anaconda.com.Clone the source code repositoryAt the desired location, run:git clone https://github.com/equinor/omnia-timeseries-sdk-python.gitInstallingTo get the development environment running:... create an isolated Python environment and activate it,python -m venv /path/to/new/virtual/environment/path/to/new/virtual/environment/Scripts/activate... install the dev dependencies inrequirements.txt,pip install -r requirements.txt.. and install the package in development mode.python setup.py developYou should now be able to import the package in the Python console,importomnia_timeseries_sdkhelp(omnia_timeseries_sdk)Running the testsWe apply theunittestandpytesetframeworks.Run the tests and check test coverage bypytest --cov=omnia_timeseries_sdk --cov-report term-missing tests/Building the packageBuild tarball and wheel distributions by:python setup.py sdist bdist_wheelThe distribution file names adhere to thePEP 0427convention{distribution}-{version}(-{build tag})?-{python tag}-{abi tag}-{platform tag}.whl.DeploymentThe packagereleasesare deployed
toPyPi.We use GitHub Actions to automate the build and deployment (CI-CD).VersioningWe apply the "major.minor.micro" versioning scheme defined inPEP 440.We cut a new version by applying a Git tag like0.1.0at the desired commit and thensetuptools_scmtakes care of the rest at build time.
See thetags on this repository.AuthorsPer Voie-tovopLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: omnibelt
omni-beltThis is a simple tool belt containing pure python utilities for downstream projects.Note: This package is still very much under development, so keep a close eye on the version number,
as I cannot make any promises about forward or backward compatibility (at least until version 1.0). Additionally,
by my estimates about a fourth of the code in this repo is either partially or completely obsolete,
and will be removed in the near future.While there are some unit tests (see badge above for status), they are far from comprehensive,
and documentation is spotty at best.Related Projectsomni-fig: A versatile configuration management (fully documented and maintained)omni-crypt: A handful of simple cryptographic utilities (under development)
Package: omnibench-cli
omnibench-cliA command line interface to query and access datasets and workflows project-wide across omnibenchmark.InstallInstall via pip:pip install omnibench-cliWhile developing, you can use poetry:pip install poetry
poetry shellUse❯omni_cli
Usage:omni_cli[OPTIONS]COMMAND[ARGS]...Interactwithomnibenchmarkdatasetsandworkflows.

Options:--helpShowthismessageandexit.

Commands:benchInspectbenchmarksdatasetInspectdatasetsdockerManipulatelocaldockerimagesusedforworkflowrunsepochQueryandmanipulateOrchestratorEpochsintheKnowledge...graphLocalknowledgegraphoperationsinitInitializeomnibenchmarkcachequeryQuerythelocalgraphupdateUpdateomnibenchmarkcacheworkflowInteractswithworkflows

❯omni_clibenchls
omni_clustering
spatial-clustering

❯omni_clibenchstagesomni_clustering
omni_clustering:build
omni_clustering:data_run
omni_clustering:process_run
omni_clustering:param_run
omni_clustering:method_run
omni_clustering:metric_run
omni_clustering:summary_runLocal Graph ServerThe local graph needsoxigraph_serverto be installed.You can install it withcargo:❯cargoinstalloxigraph_serverUpdatingcrates.ioindexDownloadedoxigraph_serverv0.3.18Downloaded1crate(34.4KB)in1.93sInstallingoxigraph_serverv0.3.18Updatingcrates.ioindex[...longbuildfollows...]Now you can start the graph server (which supports Federated SPARQL. You can
configure a different graph for annotations:❯omni_cligraphenableEnablinglocalSPARQLendpoint
❯omni_cligraphrun
RunninglocalSPARQLendpoint
Runningoxigraph_serverwith--location/home/user/OmniBenchmark/graph
Listeningforrequestsathttp://localhost:7878
❯omni_cligraphrun--annotations
RunninglocalSPARQLendpoint
Runningoxigraph_serverwith--location/home/user/OmniBenchmark/annotations
Listeningforrequestsathttp://localhost:8080Do note, however, that no authentication or TLS endpoint is configured by
default. If you plan to deploy the graph in production, is up to you to make
sure you deploy an authentication frontend (support in the client is still
needed, see #2).Useful pointersInitial Functionality Requestsomnibenchmark-py, apythonlibrary to interact with omnibenchmark.omnibus, abash/Rtool to interact withomnibenchmarkfrom the terminal.omni_site, the static site generator that powershttps://omnibenchmark.org/.
Package: omniblack.caddy
Omniblack CaddyOmniblack Caddy offer a pythonic interface
to control aCaddyserver
using caddy'sJSON API.Basic Examplefromomniblack.caddyimportCaddy# omniblack.caddy will use the caddy file to find the# Admin api's addresswithCaddy(caddy_file='./caddyfile.json')ascaddy:caddy.get('/reverse_proxy/upstreams')
Package: omniblack.logging
Omniblack LoggingConfigures logging to use a standard format and enables color output.Color OutputOmniblack Logging supports color logging usingRich.
Color output will be disabled when eitherNO_COLORor theOMNIBLACK_NO_COLORenviroment variables.OMNIBLACK_NO_COLORis custom to Omniblack Logging to disable our color output without
disable color output for other programs.
Package: omniblack.setup
Omniblack SetupOmniblack Setup is a library that gives setuptoolsPEP 621compatibility.At the moment omniblack.setup does not support all ofPEP 621in particular when there is a string or table option this library only
supports the table option right now.UsageTo use this library callomniblack.setup:setupinsetup.py.fromomniblack.setupimportsetupsetup()
Package: omniblack.work
Omniblack work offers an development enviroment manager.
The primary entry point of this package is the shell functionwrk.This function can be accesed adding one of these lines to your shell’s
configuration file.# In ~/.bashrc for bashsource"$(wrk_fileenv_files/wrk.bash)"# In ~/.zshrc for zshsource"$(wrk_fileenv_files/wrk.zsh)"
Package: omnibus-driver
GUI Component for Omnibus-Filetransfer--------------------------------------With Omnibus you can send files to a friend or get files a friend has preparedfor you to download. It's designed to "just work" in a variety of networkingcontexts like: over the Internet, in a LAN (with or without Internet connection)or just over a bluetooth link. Transfers can comfortably be paused and resumed.And there is also a chat functionality.This package contains the GUI component which acts as a frontend for theomnibus-backend.Features implemented in the GUI:- File Transfer- User Management- Chat
Package: omni-camera
OmniCameraA library for querying and capturing from cameras, based onnokhwacrate.ExamplesQuery available cameras:print(*omni_camera.query(),sep='\n')Example output:CameraInfo(index=2, name='UVC Camera (046d:0809)', description='Video4Linux Device @ /dev/video2', misc='')
CameraInfo(index=0, name='USB2.0 VGA UVC WebCam: USB2.0 V', description='Video4Linux Device @ /dev/video0', misc='')Save an image (note: requires pillow to be installed):importomni_cameraimporttimecam=omni_camera.Camera(camerata.query()[0])# Open a camerawhilecam.poll_frame_pil()isNone:# Note that .poll_frame_* functions never blockstime.sleep(0.1)# Wait until we get at least one frame from the camera#time.sleep(1) # You might want to wait a bit longer while camera is calibratingimg=cam.poll_frame_pil()img.save("img.png")See examples/ for more
Package: omniclip
omniCLIPomniCLIP is a Bayesian peak caller that can be applied to data from CLIP-Seq experiments to detect regulatory elements in RNAs.This is a fork version by Chang Y.Please cite the original paper: Drewe-Boss, P., Wessels, HH. & Ohler, U. omniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq data. Genome Biol 19, 183 (2018).https://doi.org/10.1186/s13059-018-1521-2OverviewIntroductionInstallationUsageContributorsLicenseIntroductionomniCLIP can call peaks for CLIP-Seq data data while accounting for confounding factors such as the gene expression and it automatically learns relevant diagnostic events from the data. Furtermore, it can leverage replicate information and model technical and biological variance.InstallationThis package is not release on pypi, you can install it by:pipinstallomniclipUsageAn omniCLIP analysis is run into four different steps :Generating the annotation databaseParsing the background RNA-seq filesParsing the CLIP filesRunning the omniCLIP algorithmThe following is an example of the commands. The commands in this example only show therequiredarguments for the analysis. The following files are necessary to run an analysis.File nameDescription$GFF_fileGenome annotation file$GENOME_dirDirectory containing FASTA files with each of the chromosomes sequence$BG_file[1,2,...]BAM files for the background library. The alignments need to have the MD and NM tags.$CLIP_file[1,2,...]BAM files for the CLIP library. The alignments need to have the MD and NM tags.The following files will be created.File nameDescription$DB_fileSQL database of the genome annotation.$BG_datH5PY file of the parsed background library.$CLIP_datH5PY file of the parsed CLIP library.$OUT_dirDirectory containing the final results1. Generating the annotation databaseomniCLIPgenerateDB\--gff-file$GFF_file--db-file$DB_file2. Parsing the background RNA-seq filesomniCLIPparsingBG\--db-file$DB_file--genome-dir$GENOME_dir--bg-files$BG_file1--bg-files$BG_file2--out-file$BG_dat3. Parsing the CLIP filesomniCLIPparsingCLIP\--db-file$DB_file--genome-dir$GENOME_dir--clip-files$CLIP_file1--clip-files$CLIP_file2--out-file$CLIP_dat4. Running the omniCLIP algorithmomniCLIPrun_omniCLIP\--db-file$DB_file--bg-dat$BG_dat--clip-dat$CLIP_dat--out-dir$OUT_dirOptional argumentsFor the full list of optional arguments of the different step, consult the help of the commands using :omniCLIPparsingBG--helpContributorsLicenseGNU GPL license (v3)
Package: omniCLIP-test
omniCLIPomniCLIP is a Bayesian peak caller that can be applied to data from CLIP-Seq experiments to detect regulatory elements in RNAs.OverviewIntroductionDependenciesInstallationUsageContributorsLicenseIntroductionomniCLIP can call peaks for CLIP-Seq data data while accounting for confounding factors such as the gene expression and it automatically learns relevant diagnostic events from the data. Furtermore, it can leverage replicate information and model technical and biological variance.Dependencies and RequirementsomniCLIP requires Python (v.3.7) and the libraries described in theenvironment.ymlfile. All required dependencies can be installed usingcondaby executing the following in the main project directory :$ conda env create -f environment.ymlThe environment then needs to be activated in order to run omniCLIP :$ conda activate omniEnvInstallationManual installationThe latest stable release in themasterbranch can be downloaded by executing:$ git clone -b master https://github.com/simojoe/omniCLIP.gitAfter this the following command has to be executed:$ python3 setup.pyThis compiles the Cython code for the viterbi algorithm.UsageAn omniCLIP analysis is run into four different steps :Generating the annotation databaseParsing the background RNA-seq filesParsing the CLIP filesRunning the omniCLIP algorithmThe following is an example of the commands. The commands in this example only show therequiredarguments for the analysis. The following files are necessary to run an analysis.File nameDescription$GFF_fileGenome annotation file$GENOME_dirDirectory containing FASTA files with each of the chromosomes sequence$BG_file[1,2,...]BAM files for the background library. The alignments need to have the MD and NM tags.$CLIP_file[1,2,...]BAM files for the CLIP library. The alignments need to have the MD and NM tags.The following files will be created.File nameDescription$DB_fileSQL database of the genome annotation.$BG_datH5PY file of the parsed background library.$CLIP_datH5PY file of the parsed CLIP library.$OUT_dirDirectory containing the final results1. Generating the annotation database$ python3 omniCLIP.py generateDB \
--gff-file $GFF_file --db-file $DB_file2. Parsing the background RNA-seq files$ python3 omniCLIP.py parsingBG \
--db-file $DB_file --genome-dir $GENOME_dir \
--bg-files $BG_file1 --bg-files $BG_file2 \
--out-file $BG_dat3. Parsing the CLIP files$ python3 omniCLIP.py parsingCLIP \
--db-file $DB_file --genome-dir $GENOME_dir \
--clip-files $CLIP_file1 --clip-files $CLIP_file2 \
--out-file $CLIP_dat4. Running the omniCLIP algorithm$ python3 omniCLIP.py run_omniCLIP \
--db-file $DB_file --bg-dat $BG_dat --clip-dat $CLIP_dat \
--out-dir $OUT_dirOptional argumentsFor the full list of optional arguments of the different step, consult the help of the commands using :$ python3 omniCLIP.py parsingBG --helpContributorsLicenseGNU GPL license (v3)