Saravanan666/pypi-org huggingface dataset:
Package: pybioanalyzer
PyBioAnalyzerPython codes for analyzing bioanalyzer filesRequirementsChecked compatible with python3.7. Probably works in python >3.7.matplotlibscipy 1.2.1pandasInstallationpip install pybioanalyzerUsagepybioanalyzer --folder_name example/ --assay_type HS_DNA --min_lim 30 --max_lim 500 --disable_ladderresult files must be export as csv format.Results.csv has to contain ladder information.ContributionGeorg Urtel, Evgeniia Edeleva
Package: pyBiocFileCache
pyBiocFileCacheFile system based cache for resources & metadata. Compatible withBiocFileCache R packageNote: Package is in development. Use with caution!!InstallationPackage is published toPyPIpip install pybiocfilecacheInitialize a cache directoryfrom pybiocfilecache import BiocFileCache
import os

bfc = BiocFileCache(cache_dir = os.getcwd() + "/cache")Once the cache directory is created, the library provides methods toadd: Add a resource or artifact to cacheget: Get the resource from cacheremove: Remove a resource from cacheupdate: update the resource in cachepurge: purge the entire cache, removes all files in the cache directoryAdd a resource to cache(for testing use the temp files in thetests/datadirectory)rec = bfc.add("test1", os.getcwd() + "/test1.txt")
print(rec)Get resource from cacherec = bfc.get("test1")
print(rec)Remove resource from cacherec = bfc.remove("test1")
print(rec)Update resource in cacherec = bfc.get("test1"m os.getcwd() + "test2.txt")
print(rec)purge the cachebfc.purge()NoteThis project has been set up using PyScaffold 4.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: pyBiodatafuse
pyBioDataFuseA python package for integrating data from multiple resources💪 Getting StartedTODO show in a very small amount of space theMOSTuseful thing your package can do.
Make it as short as possible! You have an entire set of docs for later.Command Line InterfaceThe pyBiodatafuse command line tool is automatically installed. It can
be used from the shell with the--helpflag to show all subcommands:$pyBiodatafuse--helpTODO show the most useful thing the CLI does! The CLI will have documentation auto-generated
bysphinx.🚀 InstallationThe most recent release can be installed fromPyPIwith:$pipinstallpyBiodatafuseThe most recent code and data can be installed directly from GitHub with:$pipinstallgit+https://github.com/BioDataFuse/pyBiodatafuse.git👐 ContributingContributions, whether filing an issue, making a pull request, or forking, are appreciated. SeeCONTRIBUTING.mdfor more information on getting involved.👋 Attribution⚖️ LicenseThe code in this package is licensed under the MIT License.🍪 CookiecutterThis package was created with@audreyfeldroy'scookiecutterpackage using@cthoyt'scookiecutter-snekpacktemplate.🛠️ For DevelopersSee developer instructionsThe final section of the README is for if you want to get involved by making a code contribution.Development InstallationTo install in development mode, use the following:$gitclonegit+https://github.com/BioDataFuse/pyBiodatafuse.git
$cdpyBiodatafuse
$pipinstall-e.🥼 TestingAfter cloning the repository and installingtoxwithpip install tox, the unit tests in thetests/folder can be
run reproducibly with:$toxAdditionally, these tests are automatically re-run with each commit in aGitHub Action.📖 Building the DocumentationThe documentation can be built locally using the following:$gitclonegit+https://github.com/BioDataFuse/pyBiodatafuse.git
$cdpyBiodatafuse
$tox-edocs
$opendocs/build/html/index.htmlThe documentation automatically installs the package as well as thedocsextra specified in thesetup.cfg.sphinxplugins
liketexextcan be added there. Additionally, they need to be added to theextensionslist indocs/source/conf.py.📦 Making a ReleaseAfter installing the package in development mode and installingtoxwithpip install tox, the commands for making a new release are contained within thefinishenvironment
intox.ini. Run the following from the shell:$tox-efinishThis script does the following:UsesBump2Versionto switch the version number in thesetup.cfg,src/pyBiodatafuse/version.py, anddocs/source/conf.pyto not have the-devsuffixPackages the code in both a tar archive and a wheel usingbuildUploads to PyPI usingtwine. Be sure to have a.pypircfile configured to avoid the need for manual input at this
stepPush to GitHub. You'll need to make a release going with the commit where the version was bumped.Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can
usetox -e bumpversion -- minorafter.
Package: pybiolib
PyBioLibPyBioLib is a Python package for running BioLib applications from Python scripts and the command line.Python Example# pip3 install -U pybiolibimportbiolibsamtools=biolib.load('samtools/samtools')print(samtools.cli(args='--help'))Command Line Examplepip3install-Upybiolib
biolibrunsamtools/samtools--help
Package: pybiology
pybbiologyFreely available tools for Analsis of DNA
The Biopython Project is an international association of developers
of freely available Python tools for computational molecular biology.List of functions and usageseq_percent(dna_sequence,*char)has_stop_codon(dna,frame=0)1. compute DNA sequencedef seq_percent(dna_sequence,*char):
        sum=0
        listed=[]
        dna_length=len(dna_sequence)
        for arg in char:
            if listed.count(arg)==0:
                sum= sum + dna_sequence.count(arg)
                listed.append(arg)
        return (sum)*100.0/dna_length2. This function checks if given DNA has in frame stop codons.def has_stop_codon(dna,frame=0):
    stop_codon_found=False
    stop_codons=['tga','tag','taa']
    for i in range(frame,len(dna),3):
        codon=dna[i:i+3].lower()
        if codon in stop_codons:
            stop_codon_found=True
            break
    return stop_codon_found
Package: pybiomart
A simple and pythonic biomart interface for Python.The intent of pybiomart is to provide a simple interface to biomart, which can be used to easily query biomart databases from Python. In this sense, pybiomart aims to provide functionality similar to packages such as biomaRt (which provides access to biomart from R).DocumentationDocumentation is available at:https://jrderuiter.github.io/pybiomart.ExamplesRetrieving and querying a dataset using the server interface:frompybiomartimportServerserver=Server(host='http://www.ensembl.org')dataset=(server.marts['ENSEMBL_MART_ENSEMBL'].datasets['hsapiens_gene_ensembl'])dataset.query(attributes=['ensembl_gene_id','external_gene_name'],filters={'chromosome_name':['1','2']})Retrieving a dataset directly with known dataset name:frompybiomartimportDatasetdataset=Dataset(name='hsapiens_gene_ensembl',host='http://www.ensembl.org')dataset.query(attributes=['ensembl_gene_id','external_gene_name'],filters={'chromosome_name':['1','2']})LicenseReleased under the MIT license.
Package: pybiopax
PyBioPAX: A python implementation of the BioPAX object modelPyBioPAX implements the BioPAX level 3 object model (http://www.biopax.org/release/biopax-level3-documentation.pdf) as a set of
Python classes. It exposes API functions to read OWL files into this
object model, and to dump OWL files from this object model.
This allows for the processing and creation of BioPAX models natively in
Python.Gyori BM, Hoyt CT (2022). PyBioPAX: biological pathway exchange in Python. Journal of Open Source Software, 7(71), 4136,https://doi.org/10.21105/joss.04136InstallationPyBioPAX can be installed from PyPI as a package:$pipinstallpybiopaxUsageReading an OWL file into a BioPaxModel object:importpybiopaxmodel=pybiopax.model_from_owl_file('test.owl')Writing a BioPaxModel into an OWL file:importpybiopaxpybiopax.model_to_owl_file(model,'test.owl')Querying Pathway Commons to get a BioPaxModel object:importpybiopaxmodel=pybiopax.model_from_pc_query('pathsfromto',['MAP2K1'],['MAPK1'])Working with the elements of the Python object model:importpybiopaxmodel=pybiopax.model_from_pc_query('pathsfromto',['MAP2K1'],['MAPK1'])# Each BioPaxModel instance has an objects attribute which is a dict# whose keys are object URIs (strings) and values are BioPaxObject instances.assertisinstance(model.objects,dict)assertall(isinstance(obj,pybiopax.biopax.BioPaxObject)forobjinmodel.objects.values())# Let's look at a specific objectbcr=model.objects['BiochemicalReaction_4f689747397d98089c551022a3ae2d88']# This is a BiochemicalReaction which has a left and a right side. All list/set# types per the BioPAX specification are represented as lists in the Python# object model# Both left and right consist of a single proteinleft=bcr.left[0]assertisinstance(left,pybiopax.biopax.Protein)assertleft.display_name=='ERK1-2'right=bcr.right[0]assertisinstance(right,pybiopax.biopax.Protein)assertright.display_name=='ERK1-2-active'We can also use thepybiopax.pathsmodule to construct iterators over
objects based on a string specification from a given starting point.
Continuing from the block of code above, we take the BiochemicalReactionbcrand link to reactants on its left hand side, then linking to their
entity references, and finally linking back to all the physical entities
that those are references of.frompybiopax.pathsimportfind_objectserks=find_objects(bcr,'left/entity_reference/entity_reference_of')Contribution and supportTo contribute to the code, please submit a pull request after
reading thecontribution guidelines.
To report bugs and issues, or ask questions related to PyBioPAX, please
submit anissue.FundingDevelopment of this software was supported by the Defense Advanced Research
Projects Agency under award W911NF-15-1-0544 and the National Cancer Institute
under award U54-CA225088.
Package: pybioportal
pybioportal: a Python package to easily retrieve data from cBioPortalPackageMetaWhat is it?pybioportalis an open source, BSD-licensedPythonlibrary that provides a set of
functions to facilitate data acquisition from thecBioPortal Web Public APIand the transformation
of that data into useful data structures.It allows users to easily query the cBioPortal public API and retrieve the data directly in the form of apandasDataFrame, making the data accessible and ready for analysis.It is an essential tool for researchers and biological data analysts who wish to access and leverage
cBioPortal resources for their research and bioinformatic analysis projects.Where to get itThe source code is currently hosted on GitHub at:https://github.com/Matteo-Valerio/pyBioPortalBinary installers for the latest released version are available at thePython
Package Index (PyPI)and onAnaconda.# PyPIpipinstallpybioportal# Anacondacondainstall-cmatteo.valeriopybioportalLicenseBSD 3DocumentationThe official documentation is hosted onReadtheDocs.Contributing to pybioportalBug reports, bug fixes, documentation improvements, enhancements and ideas are welcome and can be made
onand/or by sending anormessage to the author.
Package: pybioprox
PyBioProx: 2 & 3d spatial distance analysis in PythonThis module,pybioprox, offers efficient 2d and 3d spatial analysis of colocalisation.
The analyses fall into the category of object-based colocalisation methods.InstallationTo install this Python module, you may use pip as follows:pip install pybioproxIf you wish to also install the optional Graphical User Interface script, this may be done
by specifying theguioptional extra:pip install pybioprox[gui]Getting startedOnce installed, the module can be invoked from the command-line using:python -m pybioprox <path_to_folder>to run the analysis in batch mode on all data files at the locationpath_to_folder.If you have installed the gui component or already have the required gui libraries,
present on your system, then you may also runpybioprox_guidirectly from the command line.
Package: pybiosci
PyBioSciPythonBioScience is a package containing different functionalities on different BioScience algorithms.
Initially the package will be focused on the Microbiome data analysis, later the functionalities will be expanded to other BioScience tasks.Diversity IndicesThis module contains differentalphaandbetadiversity indices to calculate the diversity of a sample and a group of samples. All the indices are discussed in thispaper.Below an example code for finding diversity indices has been shown:from pybiosci.diversity_indicies import alpha_index 
import pandas as pd

df = pd.read_csv('data/simCounts.csv')
print(alpha_index(df, index='Shannon'))The dataframe is expected to be in a definite format. Row represents the taxa and column represents each sample. Absolute read counts are taken as input and converted to relative abundance when required for an index. You can specifyindex=Allto get all indices as a feature table.
Package: pybiosig
PyBiosigA python framework to work with biomedical signalsVersion: 1.1.0InstallationSimply install with:pip install pybiosigFor a more recent version use:pip install git+https://github.com/aalcaineo/pybiosig.gitCredits:Alejandro Alcaine, Ph.DMESC Working Group on e-Cardiologylalcaine@usj.esFaculty of Health SciencesUniversity San JorgeVillanueva de GÃ¡llego (Zaragoza)Spain
Package: pybiosis
PybiosisThis project allows users to run python functions from existing devices and services like Stream Deck and Google Assistant. This is done through the use of python decorators that register user functions to those devices after a compiling stage. The compiling stage generates the data files used by Stream Deck while the program Push2Run is used to compile voice commands for Google Assistant. Python wrappers for useful Windows applications like ControlMyMonitor, VirtualDesktop are also included, in the applications folder.InstallationInstallPush2Run(for voice commands) (tested with dropbox method and the key phrase "on pc", since it is short).Install Pybiosis through pip withpip install pybiosis.Create a directory to hold your custom functions and set the Environment VariablePYBIOSIS_USER_PATHeither using the Windows gui or with the commandsetx PYBIOSIS_USER_PATH "your path here". You may need to restart your terminal.Add a file called driver.py in that directory that contains this code:frompybiosis.loaderimportload_user_modulesfrompybiosis.compilerimportDeckCompiler,GoogleCompilerdefmain():load_user_modules()DeckCompiler().compile()GoogleCompiler().compile()Create a new file in that directory (eg:my_first_commands.py) and consider the example below.Run the commandpython -m pybiosisto compile your functions in the command prompt with administrator privileges. Repeat this any time you add new functions.If you use multiple StreamDeck profiles, you can set the Environment VariablePYBIOSIS_PROFILE_IDto the desired identifier (without.sdProfile). The identifiers can be found inAppData\Roaming\Elgato\StreamDeck\ProfilesV2.ExampleA user wishes to launch a game with a google voice command and/or a StreamDeck button:frompybiosis.compilerimportgeneral,google,deck,multi_phrase@general(title='Spire',description="Launch Slay the Spire.")@google(voice=multi_phrase(['open','play','place'],['spire','fire','buyer']))@deck(location="Games/3,1",image='spire.jpg')defspire():path_to_game=R"C:\Users\...\Games\Slay the Spire.url"os.system(Rf'cmd.exe /C START "" "{path_to_game}"')Themulti_phrasefunction allows multiple words to trigger the function, since the voice recognition may not hear perfectly. Now the user can execture this function on their computer with a Google voice command and with a Stream Deck button. The user would say something like "Hey Google, on pc, play spire" and the game would be launched on their computer. Their Stream Deck would now have a button in the 4th (0 indexing) column and 2nd row in the Games folder that would also launch the game. Finally, an icon (likespire.jpg) can be specified by adding images to a folder calledIconslocated in thePYBIOSIS_USER_PATHdirectory.LimitationsCurrently only tested on Windows, at least some application functionality is windows only.Stream Deck folders must be manually created.If you get a password prompt from Push2Run, simply recompile.Questions?Email me atnawar@palfore.com.
Package: pybiotk
pybiotk: A python toolkit for bioinformatics analysisInstalluse PyPi(official)pip install pybiotkAn older version may be installedorgit clone https://gitee.com/liqiming_whu/pybiotk.git
cd pybiotk
pip install .Modulesconsole_scripts =
    gtf2bed = pybiotk.convert.gtf2bed:run
    bed2bedgraph = pybiotk.convert.bed2bedgraph:run
    fq2fasta = pybiotk.convert.fq2fasta:run
    bam2fastx = pybiotk.convert.bam2fastx:run
    bampe_order_by_name = pybiotk.convert.bampe_order_by_name:run
    bam_random = pybiotk.utils.bam_random:run
    gtf_filter = pybiotk.utils.gtf_filter:run
    fasta_filter = pybiotk.utils.fasta_filter:run
    fastq_uniq = pybiotk.utils.fastq_uniq:run
    fastq_join = pybiotk.utils.fastq_join:run
    fastx_rename = pybiotk.utils.fastx_rename:run
    genomefetcher = pybiotk.utils.genomefetcher:run
    reverse_fastx = pybiotk.utils.reverse_fastx:run
    seq_random = pybiotk.utils.seq_random:run
    merge_row = pybiotk.utils.merge_row:run
    read_tables = pybiotk.utils.read_tables:run
    rmats_filter = pybiotk.utils.rmats_filter:run
    count_normalize = pybiotk.utils.normalize:run
    reference_count = pybiotk.utils.reference_count:run
    pyanno = pybiotk.utils.pyanno:run
    rna_fragment_size = pybiotk.utils.fragment_size:run
    merge_subseq = pybiotk.utils.merge_subseq:run
    subseq_analysis = pybiotk.utils.subseq_analysis:runUsageusage: pyanno [-h] -i INPUT -o OUTPUT -g GTF [-l {transcript,gene}] [--tss_region TSS_REGION [TSS_REGION ...]] [--downstream DOWNSTREAM] [-s]
              [--rule {1+-,1-+,2++,2--,1++,1--,2+-,2-+,+-,-+,++,--}] [-p] [--ordered_by_name]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        input file, bam or bed. The file type will be inferred from the filename suffix ['*.bam', '*.bed']. (default: None)
  -o OUTPUT, --output OUTPUT
                        output file name. (default: None)
  -g GTF, --gtf GTF     gtf file download from Genecode, or a sorted gtf file. (default: None)
  -l {transcript,gene}, --level {transcript,gene}
                        annotation level, transcript or gene. (default: transcript)
  --tss_region TSS_REGION [TSS_REGION ...]
                        choose region from tss. (default: [-1000, 1000])
  --downstream DOWNSTREAM
                        downstream length from tes. (default: 3000)
  -s, --strand          require same strandedness. (default: False)
  --rule {1+-,1-+,2++,2--,1++,1--,2+-,2-+,+-,-+,++,--}
                        how read(s) were stranded during sequencing. only for bam. (default: 1+-,1-+,2++,2--)
  -p, --pair            annotate fragments instead of reads. (default: False)
  --ordered_by_name     if input bam is ordered by name, only for pair-end bam. (default: False)
Package: pyBioTools
pyBioTools v0.2.11pyBioTools is a collection of python tools to manipulate biological sequencesFull documentation is available athttps://adrienleger.com/pyBioTools/DisclaimerPlease be aware that pyBioTools is a research package that is still under development.It was tested under Linux Ubuntu 16.04 and in an HPC environment running under Red Hat Enterprise 7.1.Thank youClassifiersDevelopment Status :: 3 - AlphaIntended Audience :: Science/ResearchTopic :: Scientific/Engineering :: Bio-InformaticsLicense :: OSI Approved :: GNU General Public License v3 (GPLv3)Programming Language :: Python :: 3citationAdrien Leger. a-slide/pyBioTools. Zenodo.http://doi.org/10.5281/zenodo.3235625licenceGPLv3 (https://www.gnu.org/licenses/gpl-3.0.en.html)Copyright © 2020 Adrien LegerAuthorsAdrien Leger /aleg@ebi.ac.uk/https://adrienleger.com
Package: pybiouml
pybioumlBioUML- an integrated environment
for systems biology and collaborative analysis of biomedical data.
This library is a python interface which gives an opportunity to use BioUML
through python scripts.DocumentationDocumentation and use cases for pybiouml are presented available here:Documentation and use casesInstalationFor library instalation you can use the package
managerpip.pipinstallpybioumlLicenseMIT
Package: pybip39
pybip39pybip39is a fast Python library forBIP39Bitcoin HD wallet mnemonic phrases. It supports multiple languages
and allows for seed phrases of 12 to 24 words.pybip39calls the Rust librarytiny-bip39under the hood, thus benefitting from Rust's speed and safety.Online Docs.Installationpip install pybip39Note: requires Python >= 3.7.Usage:frompybip39importMnemonic,Seedmnemonic=Mnemonic()# Get the phrasephrase=mnemonic.phraseprint(f"phrase:{phrase}")# Get the HD wallet seedseed=Seed(mnemonic,"")# get the HD wallet seed as raw bytesseed_bytes=bytes(seed)print(seed_bytes)Documentation creditMost of this documentation is copied from
thetiny-bip39 docs.DevelopmentSetupInstallpoetryInstall dev dependencies:poetry installActivate the poetry shell:poetryshellTestingRunmaturin developto compile the Rust code.Runmake fmt,make lint, andmake test.
Package: py-bip39-bindings
py-bip39-bindingsPython bindings for the tiny-bip39 library:https://crates.io/crates/tiny-bip39Reference to Reference tohttps://github.com/LocalCoinSwap/kusama-reference-implementation/tree/improve-trading-tests/bindingsfor the initial workDocumentationhttps://docs.rs/py-bip39-bindings/InstallationInstall from PyPIpipinstallpy-bip39-bindingsCompile for local developmentpipinstall-rrequirements.txt
maturindevelopBuild wheelspipinstall-rrequirements.txt# Build local OS wheelmaturinbuild# Build manylinux1 wheeldockerbuild.--tagpolkasource/maturin
dockerrun--rm-i-v$(pwd):/iopolkasource/maturinbuildExamplesimportbinasciifrombip39importbip39_to_mini_secret,bip39_generate,bip39_validatemnemonic=bip39_generate(12)bip39_validate(mnemonic)seed_array=bip39_to_mini_secret(mnemonic,"")seed_hex=binascii.hexlify(bytearray(seed_array)).decode("ascii")Multi-language supportThe following language codes are supported: 'en', 'zh-hans', 'zh-hant', 'fr', 'it', 'jap', 'ko', 'es'. Defaults to 'en'mnemonic=bip39_generate(12,'fr')# 'moufle veinard tronc magasin merle amour toboggan admettre biotype décembre régalien billard'bip39_validate(mnemonic,'fr')seed_array=bip39_to_mini_secret(mnemonic,"",'fr')mnemonic=bip39_generate(12,'zh-hans')# '观 敲 荣 硬 责 雪 专 宴 醇 飞 图 菌'Licensehttps://github.com/polkascan/py-bip39-bindings/blob/master/LICENSE
Package: py-bipartite-matching
Py Bipartite MatchingAlgorithms for Enumerating All Perfect, Maximum and Maximal Matchings in Bipartite Graphs.Algorithm described in “Algorithms for Enumerating All Perfect, Maximum and Maximal Matchings in Bipartite Graphs”
By Takeaki Uno in “Algorithms and Computation: 8th International Symposium, ISAAC ‘97 Singapore,
December 17-19, 1997 Proceedings”
Seehttp://dx.doi.org/10.1007/3-540-63890-3_11Free software: MIT licenseDocumentation:https://py-bipartite-matching.readthedocs.io.FeaturesFunctions available:enum_perfect_matchingsenum_maximum_matchingsusageTo use Py Bipartite Matching in a project thenetworkxpackage is needed as well>>>importpy_bipartite_matchingaspbm>>>importnetworkxasnxUseenum_perfect_matchingsto enumerate all perfect matchings>>>n=2>>>graph=nx.complete_bipartite_graph(n,n,nx.Graph)>>>formatchinginpbm.enum_perfect_matchings(graph):>>>print(matching){0:2,1:3}{0:3,1:2}Useenum_maximum_matchingsto enumerate all maximum matchings>>>n=2>>>m=3>>>graph=nx.complete_bipartite_graph(n,m,nx.Graph)>>>formatchinginenum_maximum_matchings(graph):>>>print(matching){0:3,1:4}{0:4,1:3}{0:3,2:4}{1:3,2:4}{0:4,2:3}{2:3,1:4}CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.0 (2021-04-25)Implemented brute force functionsIncreased testing of perfect and maximum matching enumeratorsFixed maximum matching enumeration (some results were missing)improve code cleanness0.1.16 (2021-04-04)Iterators extended: top_nodes, bottom_nodesMore graphics utilsRemoved plot_bipartite, only draw functions now0.1.15 (2021-04-04)Iterators implemented: top_nodes, bottom_nodesUtils to plot with matplotlib: draw_bipartite, plot_bipartite0.1.10 (2021-04-02)Functions already available: enum_perfect_matchings and enum_maximum_matchingsReadme files updated0.1.0 (2021-02-14)First release on PyPI.