Saravanan666/pypi-org huggingface dataset:
Package: biofrills
Utilities for biomolecular sequence analysis, underpinned by Biopython.Requires Python version 2.7 or a recent PyPy. Optionally uses Cython to speed up
some CPU-intensive operations.License: BSDInstallationThis is an ordinary Python package. You can install it from source with the
setup.py script:python setup.py build
python setup.py installStable releases are uploaded to PyPI as well, so you can install BioFrills with
Python package managers:pip install biofrillsOr:easy_install biofrillsSome scripts that I find useful are in the scripts/ directory. By default these
are not installed, but you can include them by uncommenting the line in setup.py
that starts withscripts=glob…Alternatively, you can just copy those scripts into another directory in your$PATH.
Package: bioFuzz
# bioFuzz## Overview
Fuzz testing for bioinformatics development.An idea I’ve been trying to motivate myself to do for some time now. more of a musing at this stage than an actual project.## InstallationShould be as simple aspip install bioFuzz## Example usageNone yet…## HelpIf you experience any problems using bioFuzz, open an [issue](https://github.com/minillinim/bioFuzz/issues) on GitHub and tell us about it.## Licence and referencingProject home page, info on the source tree, documentation, issues and how to contribute, seehttp://github.com/minillinim/bioFuzzThis software is currently unpublished## CopyrightCopyright (c) 2013 Michael Imelfort. See LICENSE.txt for further details.
Package: biogeme
BiogemeBiogeme is an open source freeware designed for the maximum likelihood estimation of parametric models in general, with a special emphasis on discrete choice models.InstallBiogeme requires Python >= 3.8 and can be installedfrom PyPI.pip install biogemeInstalling Biogeme from source can be done after cloning the repository with the following command executed in the directory where the filesetup.pyis located:pip install -ve .DocumentationFurther documentation is available onhttps://biogeme.epfl.ch/
Package: biogeme-optimization
biogeme_optimizationVarious optimization algorithms used for teaching and researchThe package contains the following modules:algebraIt contains functions dealing with linear algebra:A modified Cholesky factorization introduced by Schnabel and Eskow (1999)The calculation of a descent direction based on this factorization.bfgsThe functions in this module calculatethe BFGS update of the hessian approximation (see Eq. (13.12) in Bierlaire (2015)),the inverse BFGS update of the hessian approximation (see Eq. (13.13) in Bierlaire (2015)).boundsThis module mainly defines the classBoundsthat manages the bound constraints.diagnosticsThis module defines the diagnostic of some optimization subproblems
(dogleg, and comjugate gradient).exceptionsIt defines theOptimizationErrorexception.formatIt defines the classFormattedColumnsthat formats the information
reported at each iteration of an algorithm.functionIt defines the abstract classFunctionToMinimizethat encapsulate
the calculation of the objective function and its derivatives.hybrid_functionIt defines the classHybridFunctionthat calculates the objective
function and its derivatives, where the second derivative can be
either the analytical hessian, or a BFGS approximation.linesearchThis module implements the line search algorithms (see Chapter 11 in Bierlaire, 2015).simple_boundsThis module implements the minimization algorithm under bound
constraints proposed by Conn et al. (1988).trust_regionThis module implements the trust region algorithms (see Chapter 12 in
Bierlaire, 2015).ReferencesBierlaire, M. (2015).Optimization: Principles and
Algortihms. EPFL
Press.Conn, A. R., Gould, N. I. M and Toint, Ph. L. (1988)Testing a
Class of Methods for Solving Minimization Problems with Simple
Bounds on the
Variables. Mathematics
of Computation, 50(182), 399-430.Schnabel, R. B. and Eskow, E. (1999)A Revised Modified Cholesky Factorization Algorithm. SIAM Journal on Optimization, 9(4), 1135-1148.
Package: biogemeutilities
ThebiogemeUtilitiesis intended to add some quality-of-life features to the biogeme package.
It inherit the features from biogeme, while adding some new functionality.
The simplest way to use it, is to create a biogemeObject using the biogemeUtilities package,
but it can also be used in existing script using biogemeObjects created directly in the biogeme package.
Examples of how to use this package are included below the relevant functions.The full credit for the awesome biogeme package goes to Prof. Michel Bierlaire.
Please visit the biogeme website for at full documentation of biogeme:https://biogeme.epfl.ch/Version 1.0.5
2023-10-29
- First (beta) release on PyPI
- Compatible with biogeme 3.2.12
- Package renamed from “biogemeUtility” to “biogemeutility” to be aligned with the all lower case naming scheme in pip-install.Version 1.0.1
2023-10-28
- First (beta) release on PyPI
- Compatible with biogeme 3.2.12
- v1.0.0 had an error in the package structure, which is corrected in v1.0.1Version 1.0.0b1
2023-10-28
- First (beta) release on PyPI
- Compatible with biogeme 3.2.12
Package: biogl
bioglA collection of (semi) useful small bioinformatics functionsUsage withpippython3 -m pip install bioglThereafter, within Python, you should be able to simply doimport bioglto use.Using withoutpip:There are a variety of ways to use this module within other Python code, some of which are platform-dependent.On Linux, my preferred method is to simply add the location of thebiogldirectory to thePYTHONPATHenvironmental variable in your preferred shell. For example, if you are using Bash, simply add the following to your~/.bashrcfile:export PYTHONPATH=$PYTHONPATH:{biogl_directory_path}
Package: bio-gopher
GOPHER:GenOmicProfile-model compreHensiveEvaluatoRInstallation$ pip install bio-gopherNote that for proper installation, numpy needs to be installed before pyBigWig.This repository contains scripts for data preprocessing, training deep learning models for DNA sequence to epigenetic function prediction and evaluation of models.The repo contains a set of tutorial jupyter notebooks that illustrate these steps on a toy dataset. The two notebooks below are required prerequisites for the rest of tutorials:preprocessing/preprocessing/quant_dataset_tutorial.ipynbtutorials/train_model.ipynbTo replicate the results of the manuscript run the scripts in the analyzis directory. As a prerequisite download and unzip dataset.zip, trained_models.zip from zenodohttps://doi.org/10.5281/zenodo.6464031within the git repo. These contain test sets and pre-trained models. The analysis scripts can be ran in any order as long as paper_run_evaluate.py is ran first, in order to produce model evaluations which is required for further steps.
Package: biograder
Automatic Homework Grading and HintsThebiograderpackage is a Python package that automatically grades bioinformatics homework assignments.
The package additionally provides hints to students as they work through the assignments in Google Colab.
These homework assignments allow students to have hands-on coding experiences with real data. The data used
in the homework problems comes from thecptacpackage. (For more information about thecptacpackage,
visithttps://paynelab.github.io/cptac/).How to use the biograder packageThebiograderpackage is intended to be accessed and used through the
Google Colab interface. Homework assignments have been designed in Google
Colab for the following classes:BIO 165: Introduction to BioinformaticsBIO 462: Computational Cancer BiologyDeveloper documentationDocumentation for anyone wanting to understand the internal workings of
the package is available on the GitHub repository in thedevdocs folder.LicenseSee theLICENSE.mddocument on the GitHub repository.ContactThis package is maintained bythe Payne labat Brigham Young University.
Package: bioib
# bioIB
bioIB package for scRNA-seq analysisThis repository provides the code for bioIB, described in our article -
“bioIB: Identifying informative metagenes in single-cell RNA-seq with the Information Bottleneck algorithm”![image](https://github.com/SimaDubnov/bioIB/assets/76960862/daf3b06c-263f-4792-9de2-3d72832b6c50)bioIB is a novel method for scRNA-seq analysis, based on the Information Bottleneck algorithm.
Given the cellular labels representing the signal of interest, such as cell type, bioIB generates probabilistic gene clusters, or metagenes, that capture the major biological processes differentially activated between the defined cellular populations.# This repositoryThis repository contains:The bioIB package code.Jupyter notebook tutorial exemplifying the use of the bioIB package functions.
Package: bioimage
bioimageLibrary for convenient handling of bioimagesContains the following modules:ioConvenient file management, uses pims and pycomfort under the hood.
For example:load_bio_image(from_file: Path) that allows loading microscopy imagesload_frame(from_file: Path): that loads first frame of the microscopy image from the system pathfiltersUseful filters to process images.
For example:rolling ball filter to fix some issues on the backgroundlocal_contrast filter that can be used together with thresholding to find boundaries of the objectsgray2color that deals with fluorescent imagesglowing - for dealing with comparisons between normal and fluorescent imagescomparisonsWhen you need to compare multiple conditions
For example:show_images that displays images from the whole folder as a grid plotfolder_to_conditions that allows splitting images in the folder to multiple conditions based on their namingsget_glowing_overlays that allows combining fluorescent and phase-contrast images
Package: bioimageio.core
core-bioimage-io-pythonPython specific core utilities for running models in theBioImage Model Zoo.InstallationVia CondaThebioimageio.corepackage can be installed from conda-forge viaconda install -c conda-forge bioimageio.coreif you don't install any additional deep learning libraries, you will only be able to use general convenience functionality, but not any functionality for model prediction.
To install additional deep learning libraries use:Pytorch/Torchscript:# cpu installation (if you don't have an nvidia graphics card)condainstall-cpytorch-cconda-forgebioimageio.corepytorchtorchvisioncpuonly# gpu installation (for cuda 11.6, please choose the appropriate cuda version for your system)condainstall-cpytorch-cnvidia-cconda-forgebioimageio.corepytorchtorchvisionpytorch-cuda=11.6Note that the pytorch installation instructions may change in the future. For the latest instructions please refer topytorch.org.Tensorflow# currently only cpu version supportedcondainstall-cconda-forgebioimageio.coretensorflowONNXRuntime# currently only cpu version supportedcondainstall-cconda-forgebioimageio.coreonnxruntimeVia pipThe package is also available via pip:pip install bioimageio.coreSet up Development EnvironmentTo set up a development conda environment run the following commands:conda env create -f dev/environment-base.yaml
conda activate bio-core-dev
pip install -e . --no-depsThere are different environment files that only install tensorflow or pytorch as dependencies available.Command Linebioimageio.coreinstalls a command line interface for testing models and other functionality. You can list all the available commands via:bioimageioCheck that a model adheres to the model spec:bioimageio validate <MODEL>Test a model (including prediction for the test input):bioimageio test-model <MODEL>Run prediction for an image stored on disc:bioimageio predict-image <MODEL> --inputs <INPUT> --outputs <OUTPUT>Run prediction for multiple images stored on disc:bioimagei predict-images -m <MODEL> -i <INPUT_PATTERN> - o <OUTPUT_FOLDER><INPUT_PATTERN>is aglobpattern to select the desired images, e.g./path/to/my/images/*.tif.From pythonbioimageio.coreis a python library that implements loading models, running prediction with them and more.
To get an overview of this functionality, check out the example notebooks:example/model_usagefor how to load models and run prediction with themexample/model_creationfor how to create bioimage.io compatible model packagesexample/dataset_statistics_demofor how to use the dataset statistics for advanced pre-and-postprocessingModel SpecificationThe model specification and its validation tools can be found athttps://github.com/bioimage-io/spec-bioimage-io.Changelog0.5.10Fix critical bug in predict with tiling
Package: bioimageit-formats
BioImageIT formatsManage data formats for the BioImageIT projectDocumentationThe documentation is availablehere.DevelopmentRun testsTest are written with unittest python package. All tests are located in the subpackage bioimagepy/tests.
Run tests with the command:cdbioimageit_core
pipenvrunpython-munittestdiscover-vBuild the documentationThe documentation is written with Sphinx. To build is run the commands:cddocs
pipenvrunsphinx-build-bhtml./source./buildGenerate the requirements.txtTherequirements.txtfile is generated from Pipenv with:pipenvlock--requirements>requirements.txt
Package: bio-image-unet
Bio Image U-NetImplementations of U-Net and Siam U-Net for biological image segmentationAuthorsDaniel Härtter(Duke University, University of Göttingen)Yuxi Long(Duke University)Andreas PrimeßnigInstallationInstall withpip install git+https://github.com/danihae/bio-image-unetUsage exampleiPython Notebook for getting started with U-NetiPython Notebook for getting started with Siam U-NetDocumentationTBD
Package: bioinformagic-messages
Bioinformagic MessagesGenerate completely useless bioinformatics-related loading messages.Insert into any project to show users just howadvancedandcleveryour application is!Useimportbioinformagic_messagesasbmmessage=bm.Message()foriinrange(0,10):print(message.random_message())Output"Inspecting algorithms""Shaking domains""Hand-crafting the cloud""Splurging 00010111001""Generating nucleotides""Shaking URLs""Synthesizing AI""Parsing URLs""Reticulating extensions""Downloading sequences"
Package: bioinfo-toolset
BioInformatics ToolsInstallationpython3-mpipinstallbioinfo-toolsetUsagebit--helpExamplebitvep-lENST00000257430.9:c.3927_3931delResultforENST00000257430.9:c.3927_3931del
GRCh38:ENST00000257430.9:c.3927_3931del->5:112839521-112839525(AAAGA>-)GRCh37:ENST00000257430.9:c.3927_3931del->5:112175218-112175222(AAAGA>-)assembly_name:GRCh38id:ENST00000257430.9:c.3927_3931delseq_region_name:5start:112839521end:112839525allele_string:AAAGA/-strand:1most_severe_consequence:frameshift_variant
Colocatedvariants(1):id:COSV57321812seq_region_name:5start:112839521end:112839525allele_string:COSMIC_MUTATIONstrand:1somatic:1---
Transcriptconsequences(8):transcript_id:ENST00000257430gene_symbol:APCgene_id:ENSG00000134982hgnc_id:HGNC:583amino_acids:EKI/DXconsequence_terms:['frameshift_variant']protein_position:1309-1311transcript_name:EKI1309-1311DX---transcript_id:ENST00000502371gene_symbol:APCgene_id:ENSG00000134982hgnc_id:HGNC:583consequence_terms:['3_prime_UTR_variant','NMD_transcript_variant']protein_position:N/Atranscript_name:N/A---transcript_id:ENST00000504915gene_symbol:APCgene_id:ENSG00000134982hgnc_id:HGNC:583consequence_terms:['downstream_gene_variant']protein_position:N/Atranscript_name:N/A---transcript_id:ENST00000507379gene_symbol:APCgene_id:ENSG00000134982hgnc_id:HGNC:583consequence_terms:['downstream_gene_variant']protein_position:N/Atranscript_name:N/A---transcript_id:ENST00000508376gene_symbol:APCgene_id:ENSG00000134982hgnc_id:HGNC:583amino_acids:EKI/DXconsequence_terms:['frameshift_variant']protein_position:1309-1311transcript_name:EKI1309-1311DX---transcript_id:ENST00000508624gene_symbol:APCgene_id:ENSG00000134982hgnc_id:HGNC:583consequence_terms:['3_prime_UTR_variant','NMD_transcript_variant']protein_position:N/Atranscript_name:N/A---transcript_id:ENST00000512211gene_symbol:APCgene_id:ENSG00000134982hgnc_id:HGNC:583consequence_terms:['downstream_gene_variant']protein_position:N/Atranscript_name:N/A---transcript_id:ENST00000520401gene_id:ENSG00000258864consequence_terms:['intron_variant','NMD_transcript_variant']protein_position:N/Atranscript_name:N/A---
Package: bioio
BioIOImage Reading, Metadata Conversion, and Image Writing for Microscopy Images in Pure PythonDocumentationSee the full documentation on our GitHub pages siteExample Usage (see full documentation for more examples)Install bioio alongside OME TIFF and OME ZARR plug-ins with pip (this example won't use the OME ZARR plug-in):pip install bioio bioio-ome-tiff bioio-ome-zarrfrombioioimportBioImage# Get a BioImage objectimg=BioImage("my_file.tiff")# selects the first scene foundimg.data# returns 5D TCZYX numpy arrayimg.xarray_data# returns 5D TCZYX xarray data array backed by numpyimg.dims# returns a Dimensions objectimg.dims.order# returns string "TCZYX"img.dims.X# returns size of X dimensionimg.shape# returns tuple of dimension sizes in TCZYX orderimg.get_image_data("CZYX",T=0)# returns 4D CZYX numpy array# Get the id of the current operating sceneimg.current_scene# Get a list valid scene idsimg.scenes# Change scene using nameimg.set_scene("Image:1")# Or by scene indeximg.set_scene(1)# Use the same operations on a different scene# ...IssuesClick here to view all open issues in bioio-devs organization at once
Package: bioio-base
bioio-baseTyping, base classes, and more for BioIO projects.InstallationStable Release:pip install bioio-baseDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-base.gitQuickstartfrombioio_base.readerimportReaderclassCustomTiffReader(Reader):# Your code herefromtypingimportListfrombioio_base.reader_metadataimportReaderMetadataasBaseReaderMetadataclassReaderMetadata(BaseReaderMetadata):@staticmethoddefget_supported_extensions()->List[str]:return["tif","tiff"]@staticmethoddefget_reader()->bioio_base.reader.Reader:from.custom_tiff_readerimportCustomTiffReaderreturnCustomTiffReaderDocumentationFor full package documentation please visitbioio-devs.github.io/bioio-base.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.BSD License
Package: bioio-bioformats
bioio-bioformatsA BioIO reader plugin for readingmany file formats like as seen hereusingbioformatsDocumentationSee 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-bioformatsDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-bioformats.gitSpecial Installation InstructionsThis package utilizes bioformats which depends on javaTo install java and maven with conda, run:conda install -c conda-forge scyjavaYou may need to deactivate/reactivate your environment after installing. If you arestillgetting aJVMNotFoundException, try settingJAVA_HOMEas follows:Mac / Linuxexport JAVA_HOME=$CONDA_PREFIXWindowsset JAVA_HOME=%CONDA_PREFIX%\\LibraryExample Usage (see full documentation for more examples)Install bioio-bioformats alongside bioio:pip install bioio bioio-bioformatsThis 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_bioformatsimg=BioImage("my_file.tiff",reader=bioio_bioformats.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-czi
bioio-cziA BioIO reader plugin for reading CZIs usingaicspylibcziDocumentationSee 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-cziDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-czi.gitExample Usage (see full documentation for more examples)Install bioio-czi alongside bioio:pip install bioio bioio-cziThis 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_cziimg=BioImage("my_file.czi",reader=bioio_czi.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-dv
bioio-dvA BioIO reader plugin for reading dv (deltavision) files usingmrcDocumentationSee 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-dvDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-dv.gitExample Usage (see full documentation for more examples)Install bioio-dv alongside bioio:pip install bioio bioio-dvThis 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_dvimg=BioImage("my_file.dv",reader=bioio_dv.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-imageio
bioio-imageioA BioIO reader plugin for readingall these formatsusingimageioDocumentationSee 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-imageioDevelopment Head:pip install git+https://github.com/bioio-devs/bioio-imageio.gitExample Usage (see full documentation for more examples)Install bioio-imageio alongside bioio:pip install bioio bioio-imageioThis 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_imageioimg=BioImage("my_file.mp4",reader=bioio_imageio.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.