Saravanan666/pypi-org huggingface dataset:
Package: protect-rm
protect-rmA modified version of the rm protectionbased onhttps://github.com/alanzchen/rm-protectionAdded ability to setup protection for multiple files and directories with one profile.useprotect + file_names...to protect files andprotect -R + pathsto protect all files under certain directorys.Change the /usr/bin/rm to rm_oldthen you could change the /usr/local/bin/rm-p to rm
Package: protectsql
PROTECTSQLTable of ContentsPROTECTSQLTable of ContentsInstallationInstalling the repository for contribution purposesInstalling the packageUsageInit commandAnalyze commandInspirationWhat it doesHow we built itTech StackChallenges we ran intoAccomplishments that we're proud ofWhat's next for ProtectsqlInstallationInstalling the repository for contribution purposesPlease refer toour CONTRIBUTING.md fileto setup protectsql locallyInstalling the packageOur package is publicly available onPyPi.
To install usingpip, run the command:pipinstallprotectsqlUsageInit commandprotectsqlinit# initialise the pysa configurationsAnalyze commandRuns the static analysis.protectsqlanalyze# analyze your appInspirationWe wanted to make a package to check for SQLi vulnerabilities for generic frameworks/specific to Flask as there are quite a few python applications that don't use ORM and are vulnerable to SQLi vulnerabilities.What it doesA CLI tool which will help you analyze your python/flask app, usingPysa(a static analysis tool by facebook), In case sqli are found, they're displayed at runtime after running theanalyzecommand.How we built itProtectsql is build on top ofpysa, a part of thepyre-checkproject package (see more aboutpysahere).
Additionally, since it's a CLI tool, we also make use ofclick(see morehere).Tech StackpythonpysaclickflaskChallenges we ran intoUnderstandingpysadocumentationComing up with target frameworks vulnerable to sqli injections and how can we usepysafor themAccomplishments that we're proud ofUsingpysafor static analysisUsage ofclick, the python CLI toolUploading our own package toPyPiWhat's next for ProtectsqlWe plan to add support to more lightweight framework which does not rely on ORM!As of now, Protectsql is published onPyPiand is ready for use. Anyone can contribute following our contributionrules and guidelines.
Package: protego-cli
Installation:$ pip install protego-cliRunning the cli tool :usage:protego_fsp_layer.py[-h]-m{get,add,reset,remove}[-pPROTEGO_ACCOUNT_ID][-tPROTEGO_ACCESS_TOKEN][-aAWS_PROFILE][-sFSP_VERSION][-q][-v][-rRUNTIME][-RREGION][-oOUTPUT][-fFUNCTION_ARN][--version]ProtegoFSPLayersInfoAdd/Get/Remove

optionalarguments:-h,--helpshowthishelpmessageandexit-m{get,add,reset,remove},--mode{get,add,reset,remove}ActionMode-pPROTEGO_ACCOUNT_ID,--protego-account-idPROTEGO_ACCOUNT_IDProtegoAccountID-tPROTEGO_ACCESS_TOKEN,--protego-access-tokenPROTEGO_ACCESS_TOKENProtegoToken-aAWS_PROFILE,--aws-profileAWS_PROFILE(Optional)AWSprofile-sFSP_VERSION,--fsp-versionFSP_VERSION(Optional)FSPVersion(default:latest)-q,--quiet(Optional)Quietmode:outputonlythejsonoutput-v,--verbose(Optional)Verbosedebuglogs-rRUNTIME,--runtimeRUNTIME(Optional)Environmentruntime[python2.7,python3.6,python3.7,nodejs6.10,nodejs8.10]-RREGION,--regionREGION(Optional)TheregionofwhichFSPlayer-oOUTPUT,--outputOUTPUT(Optional)Outputfilename(default:protego_layers.json)-fFUNCTION_ARN,--function-arnFUNCTION_ARN(Optional)FunctionArntoadd/remove/resetFsp.--versionDisplaytheprotegoclitoolversionprotego.yml file example:Account:
      ProtegoAccountId: <YourProtegoAccountId>
      ProtegoAccessToken: <YourProtegoAccessToken>
Package: protego-sqli
ProtegoDetect SQL Injection PayloadsExplore the docs »View Web Application·Report Bug·Request FeatureTable of ContentsAbout The ProjectBuilt WithGetting StartedInstallationRoadmapContributingLicenseContactAbout The ProjectProtego essentially bundles batch modeling algorithms from SciKit Learn with online modeling algorithms from River. It's purpose is to build machine learning models capable of detecting SQL injection payloads. The only items in this package restricted to SQL injection is a DataLoader class, which will fetch 115k samples that I have collected, and the FeatureExtractor class. The models themselves can be trained for any type of classification task.Built WithGetting StartedThis package can be downloaded using PyPI.Installationpip3installprotego-sqliRoadmapSee theopen issuesfor a list of proposed features (and known issues).ContributingContributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork the ProjectCreate your Feature Branch (git checkout -b feature/newFeature)Commit your Changes (git commit -m 'adding new feature xyz')Push to the Branch (git push origin feature/newFeature)Open a Pull RequestLicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactBradley Reeves -reevesbra@outlook.comProject Link:https://github.com/reevesba/protego
Package: proteincsm
# ProteinCSM## INTRODUCTIONThe ProteinCSM program calculates continuous symmetry and chirality measures of protein homomers in pdb format.Please note that pdb files should be cleaned prior to calculation. Our code pdb_prep can be used for this purpose (https://sagivba.github.io/pdb_prep).## USAGEAfter installation, the program can be called from the command line. For example:`proteincsm c2 inputmol.pdb output.pdb --optional-args`You can access a help menu with a list of all available options via `proteincsm -h`## INSTALLATIONproteincsm can be installed on Windows and Linux machines.### REQUIREMENTSBefore installing CSM, please install:* Openbabel (http://openbabel.org/wiki/Category:Installation),version 2.4.0 or later.* openbabel's python bindings (`pip install openbabel`)* numpy (`pip install numpy`)Because installing openbabel correctly is a delicate and bug-prone process, an alternative method of installingProteinCSM is available using Conda, and is described in the file conda_install_instructions.txt### INSTALLATIONProteinCSM can be installed using:`pip install proteincsm`You can also install ProteinCSM from the source.## CITATIONSPlease cite ProteinCSM using the following:proteincsm:> Tuvi-Arad I. and Alon G., "Improved Algorithms for Quantifying the Near Symmetry of Proteins: Complete Side Chains Analysis" (submitted).> Dryzun C., Zait A. and Avnir D., "Quantitative Symmetry and Chirality - A Fast Computational Algorithm for Large Structures: Proteins, Macromolecules, Nanotubes, and Unit Cells", J. Comput. Chem., 32, 2526-2538 (2011).Exact Algorithm for calculating the CSM of molecules:> Alon G. and Tuvi-Arad I., "Improved algorithms for symmetry analysis: Structure preserving permutations", J. Math. Chem., 56(1), 193-212 (2018).Original Code by Avnir and coworkers:> Pinsky M., Dryzun C., Casanova D., Alemany P., Avnir D., "Analytical Methods for Calculating Continuous Symmetry Measures and the Chirality Measure", Journal of Computational Chemistry 29(16): 2712-2721 (2008).> Zabrodsky H., Peleg S., Avnir D., "Continuous symmetry measures", Journal of the American Chemical Society 114(20): 7843-7851 (1992).## CREDITS**Science/Math:**Dr. Inbal Tuvi-Arad, Department of Natural Sciences, The Open University of IsraelDr. Gil Alon, Department of Mathematics and Computer Science, The Open University of IsraelProf. David Avnir, Institute of Chemistry, The Hebrew University of Jerusalem**Programming:**The Research Software Company (www.chelem.co.il)**Testing, scripts and additional technical support:**Sagiv Barhoom,The Open University of Israel**Intensive testing:**Yaffa Shalit, The Open University of IsraelThe code for the hungarian algorithm is copyright (c) 2012, Jacob Frelinger## CONTACTDr. Inbal Tuvi-Arad, Department of Natural Sciences, The Open University of Israel, E-mail: inbaltu@openu.ac.il
Package: protein-data-handler
Manejador de Datos de ProteínasDescripciónEste proyecto,Manejador de Datos de Proteínas, es una biblioteca de Python diseñada para facilitar el manejo y análisis de datos relacionados con proteínas. Ofrece herramientas para interactuar con bases de datos de proteínas, procesar datos y realizar diversas operaciones específicas del dominio.CaracterísticasInteracción con UniProt: Módulos para consultar y procesar datos desde UniProt.Herramientas de Base de Datos: Funcionalidades para manejar bases de datos relacionadas con proteínas.Configuración Flexible: Soporte para configuraciones personalizadas a través de archivos YAML.InstalaciónPara instalar esta biblioteca, ejecuta el siguiente comando en tu terminal:pipinstallprotein-data-handlerEjemplosPodemos encontrar ejemplos de muestra bajo el directorioexamples, configurar el directorio de trabajo donde se encuentra el script de muestra.fromsqlalchemyimportcreate_enginefromsqlalchemy.ormimportsessionmakerfromprotein_data_handler.helpers.config.yamlimportread_yaml_configfromprotein_data_handler.models.uniprotimportBasefromprotein_data_handler.uniprotimportcargar_codigos_acceso,extraer_entradasimportlogginglogging.basicConfig(level=logging.INFO,format='%(asctime)s-%(levelname)s-%(message)s')if__name__=="__main__":config=read_yaml_config("./config.yaml")DATABASE_URI=\(f"postgresql+psycopg2://{config['DB_USERNAME']}:"f"{config['DB_PASSWORD']}"f"@{config['DB_HOST']}:"f"{config['DB_PORT']}/"f"{config['DB_NAME']}")engine=create_engine(DATABASE_URI)Base.metadata.drop_all(engine)Base.metadata.create_all(engine)Session=sessionmaker(bind=engine)session=Session()cargar_codigos_acceso(criterio_busqueda=config['criterio_busqueda'],limite=config['limit'],session=session)extraer_entradas(session=session)
Package: protein-distribution
protein-distribution: protein variability of CYP, UGT, ABC and SLC in human liverThe objective of this project is to provide a systematic overview of protein variability of CYP450, UGT, ABC and SLC isoforms in the human liver. Relationships within protein isoforms (e.g., subgroups such as sex or ethnicity) and between CYP450 isoforms (e.g., correlations) will be studied. The project will provide an important resource to elucidate factors affecting individual drug metabolism and for computational modeling of drug detoxification by the liver.An interactive web application is available fromhttps://github.com/matthiaskoenig/protein-distribution-app.LicenseSource Code:LGPLv3Documentation:CC BY-SA 4.0The protein-distribution source is released under both the GPL and LGPL licenses version 2 or
later. You may choose which license you choose to use the software under.This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License or the GNU Lesser General Public
License as published by the Free Software Foundation, either version 2 of the
License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.FundingMatthias König is supported by the Federal Ministry of Education and Research (BMBF, Germany)
within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054)
and by the German Research Foundation (DFG) within the Research Unit Programme FOR 5151
“QuaLiPerF(Quantifying Liver Perfusion-Function Relationship in Complex Resection -
A Systems Medicine Approach)” by grant number 436883643 and by grant number
465194077 (Priority Programme SPP 2311, Subproject SimLivA).© 2022-2023 Afruja Hossain, Sophie Silberhorn & Matthias König
Package: proteingraph
protein-graphComputes a molecular graph for protein structures.why?Proteins fold into 3D structures, and have a natural graph representation: amino acids are nodes, and biochemical interactions are edges.I wrote this package as part of a larger effort to do graph convolutional neural networks on protein structures (represented as graphs). However, that's not the only thing I can foresee doing with this.One may be interested in the topology of proteins across species and over evolutionary time. This package can aid in answering this question.how do I install this package?Currently onlypip-installable:$pipinstallproteingraphhow do I use this package?This package assumes that you have a standard protein structure file
(e.g. a PDB file).
This may be a file generated after solving the NMR
or crystal structure of a protein,
or it may be generated from homology modelling.Once that has been generated, the molecular graph can be generated using Python code.fromproteingraphimportread_pdbp=read_pdb('my_model.pdb')The object that is returned is a NetworkX Graph object,
which means all of the graph theoretic methods in there are available.converting graphs to tensorsTo convert the graph into tensors for use as inputs to graph neural networks,
there are three functions provided.Here's how they are used,
starting first with converting node metadata to matrices:fromproteingraph.conversionimport(generate_feature_dataframe,format_adjacency,generate_adjacency_tensor)# You provide a collection of functions# that take in the node name and metadata dictionary,# and return a pandas Series:defmy_func(n,d):returnpd.Series({"key_name":d["key_name"]},name=n)defmy_func2(n,d):returnpd.Series(...,name=n)defmyfunc3(n,d):returnpd.Series(...,name=n)# If you have a function that depends on outside information,# be sure to scope the variables correctly# or use functools.partial to help:fromfunctoolsimportpartial@partial(argname=some_variable)defmyfunc4(n,d,argname):returnpd.Series(...,name=n)# seriously though, give the functions more informative names!funcs=[my_func,my_func2,my_func3,]# Now get a pandas DataFrame version of the tensorfeats=generate_feature_dataframe(p,funcs=funcs)# You can also return a NumPy array version:F=generate_feature_dataframe(p,funcs=funcs,return_array=True)# Same goes for adjacency matrices, or even adjacency tensors!# To facilitate return as XArray DataArrays (for inspection),# we provide a `format_adjacency` function.defmy_adj_func(G):adj=some_func(G)returnformat_adjacency(G,adj,"xarray_coord_name")defmy_adj_func2(G):adj=some_func2(G)returnformat_adjacency(G,adj,"another_xarray_coord_name")funcs=[my_adj_func,my_adj_func2,]# Now, generate the xarray adjacency tensoradj_da=generate_adjacency_tensor(G,funcs)# You can also generate a NumPy array version:A=generate_adjacency_tensor(G,funcs,return_array=True)
Package: protein-interaction
Project Description for protein-interactionThis is a Python package for getting interactions of proteinsInstallationYou can install the package usingpip:pipinstallprotein-interactionUsageImport the packageimportprotein_interaction
fromprotein_interactionimportget_interactionGenerate df from protein List and network typeget_interaction.get_interactions_df("physical",['P1','P2'])get_interaction.get_interactions_df("colocalization",['P1','P2'])Generate visualization from protein List and network typeget_interaction.get_visualizations("physical",['P1','P2'])get_interaction.get_visualizations("colocalization",['P1','P2'])
Package: proteinshake
QuickstartWebsiteDocumentationPaperContributeLeaderboardTutorialsProteinShake provides one-liner imports of large scale, preprocessed protein structure datasets and tasks for various model types and frameworks.We provide a collection of preprocessed and cleaned protein 3D structure datasets from RCSB and AlphaFoldDB, including annotations. Structures are easily converted to graphs, voxels, or point clouds and loaded natively into PyTorch, TensorFlow, NumPy, JAX, PyTorch Geometric, DGL and NetworkX. The task API enables standardized benchmarking on a variety of tasks on protein and residue level.Find more information on theWebsiteand theDocumentation, or check out theTutorials. The results of the paper and the baseline models can be found in theEvaluation Repository. If you would like to create your own release, see theRelease Repository.Installation:- This is a pre-release version. There may be unannounced changes to the API and datasets. -- We expect some bugs as well, please open an issue if you find one. -pip install proteinshakeCode in this repository is licensed underBSD-3, the dataset files on Zenodo are licensed underCC-BY-4.0.To build ProteinShake, we obtained and modified data from various sources. Please see the documentation of the respective dataset classes for a reference to the original data, license, and paper.
Package: protein-variant-nomenclature-parser
Protein Variant Nomenclature ParserThis repository contains a Python library for parsing and validating colloquial protein variant nomenclature
strings likeBRAF V600Ethat commonly appear in manuscripts.FeaturesParse protein variant nomenclature strings in the following formats:Single amino acid substitution, e.g.:BRAF V600E,BRAFV600E,BRAFᵛ⁶⁰⁰ᵉRange of amino acid substitutions:BRAFVK600_601>EExtract the components of the nomenclature string, such as gene name, prefix amino acid, position or range, and suffix amino acidValidate whether a given string conforms to the expected formatPure python with zero dependencies and no dependency on an internet connectionUsageFor parsing:fromprotein_variant_nomenclature_parser.parserimportparsemutation_string="BRAF V600E"parsed_components=parse(mutation_string)print(parsed_components)ProteinVariant(gene='BRAF', ref='V', position=NumberOrRange(start=600, end=None), alt='E')For validation:fromprotein_variant_nomenclature_parser.parserimportparsefromprotein_variant_nomenclature_parser.parserimportInvalidProteinVariantErrormutation_string="INVALID V600E"try:parse(mutation_string)exceptInvalidProteinVariantError:print(f"{mutation_string}is not valid")Supported NomenclatureThe parser supports all HUGO gene names.The parser supports the following amino acid single letter codes and stop codon (*).The parser supports situations where the variant has no space between the gene name in the substitution,
which unfortunately comes up sometimes.InstallationFrom PyPIpipinstallprotein-variant-nomenclature-parserFrom SourceTo install the library, clone the repository and install it usingpip:gitclonehttps://github.com/yourusername/protein-variant-nomenclature-parser.gitcdprotein-variant-nomenclature-parser
makeinstallDocker containerA docker container is available:docker pull jeffquinnmsk/protein-variant-nomenclature-parser:latestLicenseThis project is licensed under the MIT License. See the LICENSE file for more information.
Package: protenc
ProtEnc: generate protein embeddings the easy wayProtEncaims to simplify extraction of protein embeddings from various pre-trained models by providing simple APIs and bulk generation scripts for the ever-growing landscape of protein language models (pLMs). Currently, supported models are:ProtTransfamilyESMAlphaFold (coming soon™)OmegaPLM(coming soon™)UsageInstallationpipinstallprotencPython APIimportprotenc# List available modelsprint(protenc.list_models())# Load encoder modelencoder=protenc.get_encoder('esm2_t30_150M_UR50D',device='cuda')proteins=['MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG','KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE']forembedinencoder(proteins,return_format='numpy'):# Embeddings have shape [L, D] where L is the sequence length and D the  embedding dimensionality.print(embed.shape)# Derive a single per-protein embedding vector by averaging along the sequence dimensionembed.mean(0)Command-line interfaceAfter installation, use theprotencshell command for bulk generation and export of protein embeddings.protencsequences.fastaembeddings.lmdb--model_name=<name-of-model>By default, input and output formats are inferred from the file extensions.Runprotenc--helpfor a detailed usage description.ExampleGenerate protein embeddings using the ESM2 650M model for sequences provided in aFASTAfile and write embeddings to anLMDB:protencproteins.fastaembeddings.lmdb--model_name=esm2_t33_650M_UR50DThe generated embeddings will be stored in a lmdb key-value store and can be easily accessed using theread_from_lmdbutility function:fromprotenc.utilsimportread_from_lmdbforlabel,embedinread_from_lmdb('embeddings.lmdb'):print(label,embed)FeaturesInput formats:CSVJSONFASTAOutput format:LMDBHDF5(coming soon)General:Multi-GPU inference with (--data_parallel)FP16 inference (--amp)DevelopmentClone the repository:gitclonegit+https://github.com/kklemon/protenc.gitInstall dependencies viaPoetry:poetryinstallContributionHave feature ideas or found a bug? Love to see support for a new model? Feel free tocreate an issue.TodoSupport for more input formatsCSVParquetFASTAJSONSupport for more output formatsLMDBHDF5DataFramePickleSupport for large modelsModel offloadingShardingFlashAttention (via Kernl?)Support for more protein language modelsWhole ProtTrans familyWhole ESM familyAlphaFold (?)Implement all remaining TODOs in codeEvaluationDemosDistributed inferenceMaybe support some sort of optimized inference such as quantizationThis may be up to the model providersImprove documentationSupport translation of gene sequencesAdd tests. We need tests!!!
Package: proteo
ProteoA simple Python library for working with UniProt, theUniversal Protein resource.Installationpip install proteoUsagefromproteo.uniprotimportUniprotClient# Initialize a clientu=UniprotClient()# Use the get_protein method to retrieve a protein record by accession numberaccession_number="B5ZC00"response=u.get_protein(accession_number)# Protein data is returned as a dictionaryprint(response['gene'])#glyQSprint(response['recommended_name'])#Glycine--tRNA ligaseprint(response['sequence'])#MKNKFKTQEELVNHLKTVGFVFANSEIYNGLANAWDYGPLGVLLKNNLKNLWWKEFVTKQKDVVGLDSAIILNPLVWKASGHLDNFSDPLIDCKNCKARYRADKLIESFDENIHIAENSSNEEFAKVLNDYEISCPTCKQFNWTEIRHFNLMFKTYQGVIEDAKNVVYLRPETAQGIFVNFKNVQRSMRLHLPFGIAQIGKSFRNEITPGNFIFRTREFEQMEIEFFLKEESAYDIFDKYLNQIENWLVSACGLSLNNLRKHEHPKEELSHYSKKTIDFEYNFLHGFSELYGIAYRTNYDLSVHMNLSKKDLTYFDEQTKEKYVPHVIEPSVGVERLLYAILTEATFIEKLENDDERILMDLKYDLAPYKIAVMPLVNKLKDKAEEIYGKILDLNISATFDNSGSIGKRYRRQDAIGTIYCLTIDFDSLDDQQDPSFTIRERNSMAQKRIKLSELPLYLNQKAHEDFQRQCQKResponse DictionaryThe protein response dictionary contains the following items:KeyMeaninggeneThe gene coding the proteinnameThe protein's short nameorganismThe organism in which the protein is foundprimary_accession_numberThe protein's UniProt accession numberrecommended_nameThe protein's long/recommended namesequenceThe amino acid sequence defining the proteinExceptionsProteo defines the following exceptions:ExceptionMeaningproteo.exceptions.ProteinNotFoundErrorRaised when no protein can be found for the accession number that was provided.