Saravanan666/pypi-org huggingface dataset:
Package: bioio-lif
bioio-lifA BioIO reader plugin for reading LIF (Leica) images usingreadlifDocumentationSee the full documentation on our GitHub pages site- the generic use and installation instructions there will work for this package.Information about the base reader this package relies on can be found in thebioio-baserepositoryhereInstallationStable Release:pip install bioio-lifDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-lif.gitExample Usage (see full documentation for more examples)Install bioio-lif alongside bioio:pip install bioio bioio-lifThis example shows a simple use case for just accessing the pixel data of the image
by explicitly passing thisReaderinto theBioImage. Passing theReaderinto
theBioImageinstance is optional asbioiowill automatically detect installed
plug-ins and auto-select the most recently installed plug-in that supports the file
passed in.frombioioimportBioImageimportbioio_lifimg=BioImage("my_file.lif",reader=bioio_lif.Reader)img.dataIssuesClick here to view all open issues in bioio-devs organization at onceor check this repository's issue tab.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: bioio-nd2
bioio-nd2A BioIO reader plugin for reading nd2 (Nikon microscope) files usingnd2DocumentationSee the full documentation on our GitHub pages site- the generic use and installation instructions there will work for this package.Information about the base reader this package relies on can be found in thebioio-baserepositoryhereInstallationStable Release:pip install bioio-nd2Development Head:pip install git+https://github.com/bioio-devs/bioio-nd2.gitExample Usage (see full documentation for more examples)Install bioio-nd2 alongside bioio:pip install bioio bioio-nd2This example shows a simple use case for just accessing the pixel data of the image
by explicitly passing thisReaderinto theBioImage. Passing theReaderinto
theBioImageinstance is optional asbioiowill automatically detect installed
plug-ins and auto-select the most recently installed plug-in that supports the file
passed in.frombioioimportBioImageimportbioio_nd2img=BioImage("my_file.nd2",reader=bioio_nd2.Reader)img.dataIssuesClick here to view all open issues in bioio-devs organization at onceor check this repository's issue tab.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: bioio-ome-tiff
bioio-ome-tiffA BioIO reader plugin for reading OME TIFF files usingtifffileDocumentationSee the full documentation on our GitHub pages site- the generic use and installation instructions there will work for this package.Information about the base reader this package relies on can be found in thebioio-baserepositoryhereInstallationStable Release:pip install bioio-ome-tiffDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-ome-tiff.gitExample Usage (see full documentation for more examples)Install bioio-ome-tiff alongside bioio:pip install bioio bioio-ome-tiffThis example shows a simple use case for just accessing the pixel data of the image
by explicitly passing thisReaderinto theBioImage. Passing theReaderinto
theBioImageinstance is optional asbioiowill automatically detect installed
plug-ins and auto-select the most recently installed plug-in that supports the file
passed in.frombioioimportBioImageimportbioio_ome_tiffimg=BioImage("my_file.ome.tiff",reader=bioio_ome_tiff.Reader)img.dataIssuesClick here to view all open issues in bioio-devs organization at onceor check this repository's issue tab.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: bioio-ome-tiled-tiff
bioio-ome-tiled-tiffA BioIO reader plugin for reading tiled OME TIFF files usingbfioDocumentationSee the full documentation on our GitHub pages site- the generic use and installation instructions there will work for this package.Information about the base reader this package relies on can be found in thebioio-baserepositoryhereInstallationStable Release:pip install bioio-ome-tiled-tiffDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-ome-tiled-tiff.gitExample Usage (see full documentation for more examples)Install bioio-ome-tiled-tiff alongside bioio:pip install bioio bioio-ome-tiled-tiffThis example shows a simple use case for just accessing the pixel data of the image
by explicitly passing thisReaderinto theBioImage. Passing theReaderinto
theBioImageinstance is optional asbioiowill automatically detect installed
plug-ins and auto-select the most recently installed plug-in that supports the file
passed in.frombioioimportBioImageimportbioio_ome_tiled_tiffimg=BioImage("my_file.ome.tiff",reader=bioio_ome_tiff.Reader)img.dataIssuesClick here to view all open issues in bioio-devs organization at onceor check this repository's issue tab.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: bioio-ome-zarr
bioio-ome-zarrA BioIO reader plugin for reading OME ZARR images usingome-zarrDocumentationSee the full documentation on our GitHub pages site- the generic use and installation instructions there will work for this package.Information about the base reader this package relies on can be found in thebioio-baserepositoryhereInstallationStable Release:pip install bioio-ome-zarrDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-ome-zarr.gitExample Usage (see full documentation for more examples)Install bioio-ome-zarr alongside bioio:pip install bioio bioio-ome-zarrThis example shows a simple use case for just accessing the pixel data of the image
by explicitly passing thisReaderinto theBioImage. Passing theReaderinto
theBioImageinstance is optional asbioiowill automatically detect installed
plug-ins and auto-select the most recently installed plug-in that supports the file
passed in.frombioioimportBioImageimportbioio_ome_zarrimg=BioImage("my_file.zarr",reader=bioio_ome_zarr.Reader)img.dataIssuesClick here to view all open issues in bioio-devs organization at onceor check this repository's issue tab.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: bioio-sldy
bioio-sldyA BioIO reader plugin for reading 3i Slidebook filesDocumentationSee the full documentation on our GitHub pages site- the generic use and installation instructions there will work for this package.Information about the base reader this package relies on can be found in thebioio-baserepositoryhereInstallationStable Release:pip install bioio-sldyDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-sldy.gitExample Usage (see full documentation for more examples)Install bioio-sldy alongside bioio:pip install bioio bioio-sldyThis example shows a simple use case for just accessing the pixel data of the image
by explicitly passing thisReaderinto theBioImage. Passing theReaderinto
theBioImageinstance is optional asbioiowill automatically detect installed
plug-ins and auto-select the most recently installed plug-in that supports the file
passed in.frombioioimportBioImageimportbioio_sldyimg=BioImage("my_file.sld",reader=bioio_ome_tiff.Reader)img.dataIssuesClick here to view all open issues in bioio-devs organization at onceor check this repository's issue tab.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: bioio-tifffile
bioio-tifffileA BioIO reader plugin for reading TIFFs usingtifffileDocumentationSee the full documentation on our GitHub pages site- the generic use and installation instructions there will work for this package.Information about the base reader this package relies on can be found in thebioio-baserepositoryhereInstallationStable Release:pip install bioio-tifffileDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-tifffile.gitExample Usage (see full documentation for more examples)Install bioio-tifffile alongside bioio:pip install bioio bioio-tifffileThis example shows a simple use case for just accessing the pixel data of the image
by explicitly passing thisReaderinto theBioImage. Passing theReaderinto
theBioImageinstance is optional asbioiowill automatically detect installed
plug-ins and auto-select the most recently installed plug-in that supports the file
passed in.frombioioimportBioImageimportbioio_tifffileimg=BioImage("my_file.tiff",reader=bioio_tifffile.Reader)img.dataIssuesClick here to view all open issues in bioio-devs organization at onceor check this repository's issue tab.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: bioio-tiff-glob
bioio-tiff-globA BioIO reader plugin for reading globbed TIFF files usingtifffileDocumentationSee the full documentation on our GitHub pages site- the generic use and installation instructions there will work for this package.Information about the base reader this package relies on can be found in thebioio-baserepositoryhereInstallationStable Release:pip install bioio-tiff-globDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-tiff-glob.gitExample Usage (see full documentation for more examples)Install bioio-tiff-glob alongside bioio:pip install bioio bioio-tiff-globThis example shows a simple use case for just accessing the pixel data of the image
by explicitly passing thisReaderinto theBioImage. Passing theReaderinto
theBioImageinstance is optional asbioiowill automatically detect installed
plug-ins and auto-select the most recently installed plug-in that supports the file
passed in.frombioioimportBioImageimportbioio_tiff_globimg=BioImage("my_file.tiff",reader=bioio_ome_tiff.Reader)img.dataIssuesClick here to view all open issues in bioio-devs organization at onceor check this repository's issue tab.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: bioio-types
bioio-typesTyping, base classes, and more for BioIO projects.InstallationStable Release:pip install bioio-typesDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-types.gitQuickstartfrombioio_types.readerimportReaderclassCustomTiffReader(Reader):# Your code herefromtypingimportListfrombioio_types.reader_metadataimportReaderMetadataasBaseReaderMetadataclassReaderMetadata(BaseReaderMetadata):@staticmethoddefget_supported_extensions()->List[str]:return["tif","tiff"]@staticmethoddefget_reader()->base_image_reader.reader.Reader:from.custom_tiff_readerimportCustomTiffReaderreturnCustomTiffReaderDocumentationFor full package documentation please visitbioio-devs.github.io/bioio-types.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.BSD License
Package: bio-jade
========Bio-Jade========.. image:: https://img.shields.io/pypi/v/jade.svg:target: https://pypi.python.org/pypi/bio-jade.. image:: https://img.shields.io/travis/jadolfbr/jade.svg:target: https://travis-ci.org/SchiefLab/Jade.. image:: https://readthedocs.org/projects/jade/badge/?version=latest:target: https://bio-jade.readthedocs.io/en/latest/?badge=latest:alt: Documentation StatusA repository for modules and applications to aid in the design and analysis of Biological molecules, especially when working with Rosetta or PyRosetta.* Free software: BSD license* Documentation: https://bio-jade.readthedocs.io.Features--------* A suite of modules for working with biological modules in python.* A suite of public and pilot applications to make day-to-day tasks easier.* Commonly used ones include score_analysis.py, get_seq.py, and RunRosettaMPI.pyCredits-------This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template... _Cookiecutter: https://github.com/audreyr/cookiecutter.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
Package: biokevlar
kevlarDaniel Standage, 2016-2019https://kevlar.readthedocs.ioWelcome tokevlar, software for predictingde novogenetic variants without mapping reads to a reference genome!
kevlar'sk-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model.
This software is free for use under the MIT license.Where can I find kevlar online?Source repository: https://github.com/dib-lab/kevlarDocumentation: https://kevlar.readthedocs.ioStable releases: https://github.com/dib-lab/kevlar/releasesIssue tracker: https://github.com/dib-lab/kevlar/issuesIf you have questions or need help with kevlar, theGitHub issue trackershould be your first point of contact.How do I install kevlar?Seethe kevlar documentationfor complete instructions, but the impatient can try the following.pip3 install git+https://github.com/dib-lab/khmer.git
pip3 install biokevlarHow do I use kevlar?Installation instructions: http://kevlar.readthedocs.io/en/latest/install.htmlQuick start guide: http://kevlar.readthedocs.io/en/latest/quick-start.htmlTutorial: http://kevlar.readthedocs.io/en/latest/tutorial.htmlHow can I contribute?We welcome contributions to the kevlar project from the community!
If you're interested in modifying kevlar or contributing to its ongoing development, feel free to send us a message or submit a pull request!The kevlar software is a project of theLab for Data Intensive Biologyand theComputational Genomics Labat UC Davis.
Package: biokinepy
IntroductionThebiokinepylibrary provides utilities for processing, computing, and analyzing joint kinematics - although at the moment it is not comprehensive. It aims to provide a starting point for creating a comprehensive Python library for analyzing motion capture datasets.Installationpip install biokinepyVisionbiokinepyprocessing starts with either skin marker trajectories or segment kinematics. It does NOT derive skin marker trajectories from videos, nor does it derive segment kinematics from biplane recordings. These tasks have already been implemented by other utilities, or are fulfilled by software supplied by the manufacturer (e.g.Vicon). What is missing in the Python ecosystem is a comprehensive library for analyzing motion capture datasets, which is wherebiokinepycomes in. Tools such asVisual3Dcurrently fulfill this function but 1) are not free, 2) are not open-source, 3) are inflexible, 4) are now outdated.Current research efforts both within biomechanics and computer science community focus on new methods for:Filling occluded marker trajectoriesAttenuating the effect of soft tissue artefactComputing the center of rotation of joints from noisy dataFiltering/smoothing marker trajectories and segment kinematics (not a trivial task since rotations do not form a vector space)Analyzing joint kinematic trajectories in terms of Euler angles, helical angles, etc.The vision forbiokinepyis to provide a central library where all the aforementioned algorithms are implemented. This gives researchers the ability to:Implement custom motion capture processing pipelines by picking the algorithms they deem appropriateCompare and contrast the effects of various algorithms on their datasetEasily share new algorithmsThe current version ofbiokinepyis nowhere close to fulfilling the goals outlined above, but hopefully one day it (or another package) will.Summary of current implementationabsor- methods for computing the kinematics of a segment from the trajectories of the markers that were attached to it.cs-  methods for manipulating homogeneous transformation matrices.euler_angles- methods for computing Euler angle trajectories from rotation matrices, currently only shoulder relevant Euler angle sequences have been implemented.filling- methods for filling occluded skin marker trajectories, currently only rigid body filling is implemented.joint_center- methods for computing the joint center of rotation from skin marker trajectories, currently only one method is implemented.segment_cs- methods for establishing the anatomical coordinate of segments, currently only the International Society of Biomechanics scapula definition is implemented.mean_smoother- mean smoother for position and orientation (using quaternions for averaging).np_utils- set of utilities fornumpy.trajectory- a class that represents a rigid body trajectory and provides easy access to methods for analyzing the trajectory.vec_ops- common vector operations.vel_acc- code for computing (angular) velocity and acceleration from a rotation matrix trajectory.
Package: biokit
BioKitGeneral Bioinformatics tools in Pythonnote:BioKit is tested with Travis for the following Python version: 3.7, 3.8, 3.9contributions:Please joinhttps://github.com/biokit/biokitissues:Please usehttps://github.com/biokit/biokit/issuesContentsBioKit is a set of tools gathered from several other Python packages. The goal
is to gather tools that should be useful to develop computational biology
software. Biokit contains a few plotting tools (viz module), some statistical
analysis (mixture model), some tools to access to Taxon and GO identifier, some basic tools to manipulate sequences and so on. It is linked to BioServices package to provide access to biological resources. Lots of biological software are developed in R. We have also added a module to ease the installation and usage of R tools within BioKit.WARNING: This package is maintained and used in production but its
contents are moving slowly to another project called sequana
(sequana.readthedocs.io) (.e.g mixture model from biokit.stats and biokit.viz
will most probably be moved in the current of 2021-2022).Installationpip install biokitor from bioconda:conda install bioconda
Package: biolearn
BiolearnBiolearn enables easy and versatile analyses of biomarkers of aging data. It provides tools to easily load data from publicly available sources like theGene Expression Omnibus,National Health and Nutrition Examimation Survey,
and theFramingham Heart Study. Biolearn also contains reference implemenations for common aging clock such at the
Horvath clock, DunedinPACE and many others that can easily be run in only a few lines of code. You can read more about it in ourpaper.WarningThis is a prerelease version of the biolearn library. There may be bugs and interfaces are subject to change.Important linksSource code:https://github.com/bio-learn/biolearn/Documentation Homepage:https://bio-learn.github.io/RequirementsPython 3.10+InstallInstall biolearn using pip.pipinstallbiolearnTo verify the library was installed correctly open python or a jupyter notebook and run:frombiolearn.data_libraryimportDataLibraryIf it executes with no errors then the library is installed. To get started check outsome code examplesDiscord serverThe biolearn team has adiscord serverto answer questions,
discuss feature requests, or have any biolearn related discussions.IssuesIf you find any bugs with biolearn please create a Github issue including how we can replicate the issue and the expected vs actual behavior.ContributingDetailed instructions on developer setup and how to contribute are availablein the repo
Package: biolib
biolib[This library is in active development and not currently recommended for public use.]Biolib is a python package for common tasks in bioinformatics.AnnouncementsPorted to Python 3 starting with version 0.1.0InstallThe simplest way to install this package is through pip:python-mpipinstallbiolibCiteIf you find this package useful, please cite this git repositoryhttps://github.com/dparks1134/biolib.CopyrightCopyright © 2015 Donovan Parks. See LICENSE for further details.
Package: biolink-model
Biolink Model:https://w3id.org/biolink/biolink-model.yamlQuickstart docs:For a good overview of the biolink-model,watch Chris Mungall's talkat ICBO 2020.Browse the model:https://biolink.github.io/biolink-modelnamed thingassociationpredicateRefer to the following resources for a quick introduction to the Biolink Model:Introduction to the Biolink DatamodelBiolink Model - A community driven data model for life sciences(Biocuration 2020)Slides:https://bit.ly/biolink-model-workshop-biocuration-2020Video:https://www.youtube.com/watch?v=RE1hFm8lvJASee alsoBiolink Model Guidelinesfor help understanding, curating, and working with the model.IntroductionThe purpose of the Biolink Model is to provide a high-level datamodel of
biological entities (genes, diseases, phenotypes, pathways, individuals, substances, etc),
their properties, relationships, and enumerate ways in which they can be associated.The representation is independent of storage technology or metamodel (Solr documents, neo4j/property graphs,
RDF/OWL, JSON, CSVs, etc). Different mappings to each of these are provided.The specification of the Biolink Model is asingle YAML filebuilt usinglinkml.
The basic elements of the YAML are:Class Definitions: definitions of upper levelclassesrepresenting both 'named thing' and 'association'Slot Definitions: definitions ofslots(aka properties) that can be used to relate members of these classes to other classes or data types. Slots collectively refer to predicates, node properties, and edge propertiesThe model itself is being used in the following projects:NCATS Biomedical Data TranslatorMonarch InitiativeKG-COVID-19KG MicrobeIlluminating the Druggable GenomeOrganizationThe main source of truth isbiolink-model.yaml. This is a YAML file that is intended to
be relatively simple to view and edit in its native form.The yaml definition is currently used to derive:JSON SchemaPython dataclassesJava code genProtoBuf definitionsGraphQLRDFOWLRDF Shape ExpressionsJSON-LD contextGraphvizGOlr YAML schemasthese can be compiled down to Solr XML schemasthese are also intermediate targets used within the BBOP/AmiGO frameworkMarkdown documentationCiting Biolink ModelUnni DR, Moxon SAT, Bada M, Brush M, Bruskiewich R, Caufield JH, Clemons PA, Dancik V, Dumontier M, Fecho K, Glusman G, Hadlock JJ, Harris NL, Joshi A, Putman T, Qin G, Ramsey SA, Shefchek KA, Solbrig H, Soman K, Thessen AE, Haendel MA, Bizon C, Mungall CJ, The Biomedical Data Translator Consortium (2022).
Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science. Clin Transl Sci. Wiley; 2022 Jun 6;https://onlinelibrary.wiley.com/doi/10.1111/cts.13302
Package: biolmai
BioLM AIPython client and SDK forhttps://biolm.aiFree software: Apache Software License 2.0Documentation:https://biolm-ai.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-09-04)First release on PyPI.