Saravanan666/pypi-org huggingface dataset:
Package: dlomix
DLOmixDLOmixis a Python framework for Deep Learning in Proteomics. Initially built on top of TensorFlow/Keras, support for PyTorch can however be integrated once the main API is established.UsageExperiment a simple retention time prediction use-case using Google ColabA version that includes experiment tracking withWeights and Biasesis available hereResources RepositoryMore learning resources can be found in thedlomix-resourcesrepository.InstallationRun the following to install:$pipinstalldlomixIf you would like to useWeights & Biasesfor experiment tracking and use the available reports for Retention Time under/notebooks, please install the optionalwandbpython dependency withdlomixby running:$pipinstalldlomix[wandb]General Overviewdata: structures for modeling the input data, currently based ontf.Dataseteval: classes for evaluating models and reporting resultslayers: custom layers used for building models, based ontf.keras.layers.Layerlosses: custom losses to be used for training withmodel.fit()models: common model architectures for the relevant use-cases based ontf.keras.Modelto allow for using the Keras training APIpipelines: an exemplary high-level pipeline implementationreports: classes for generating reports related to the different tasksconstants.py: constants and configuration valuesutils.py: utility functionsUse-casesRetention Time Prediction:a regression problem where the retention time of a peptide sequence is to be predicted.To-DoFunctionality:integrate prositextend pipeline for different types of models and backbonesextend pipeline to allow for fine-tuning with custom datasetsadd residual plots to reporting, possibly other regression analysis toolsoutput reporting results as PDFextend data representation to include modificationsrefactor reporting module to use W&B Report API (Retention Time)Package structure:integratedeeplc.pyintomodels.py, preferably introduce a package structure (e.g.models.retention_time)add references for implemented models in the ReadMeintroduce a style guide and checking (e.g. PEP)plan documentation (sphinx and readthedocs)Developing DLOmixTo install dlomix, along with the tools needed to develop and run tests, run the following command in your virtualenv:$pipinstall-e.[dev]References:[Prosit][1] Gessulat, S., Schmidt, T., Zolg, D. P., Samaras, P., Schnatbaum, K., Zerweck, J., ... & Wilhelm, M. (2019). Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nature methods, 16(6), 509-518.[DeepLC][2] DeepLC can predict retention times for peptides that carry as-yet unseen modifications
Robbin Bouwmeester, Ralf Gabriels, Niels Hulstaert, Lennart Martens, Sven Degroeve
bioRxiv 2020.03.28.013003; doi: 10.1101/2020.03.28.013003[3] Bouwmeester, R., Gabriels, R., Hulstaert, N. et al. DeepLC can predict retention times for peptides that carry as-yet unseen modifications. Nat Methods 18, 1363–1369 (2021).https://doi.org/10.1038/s41592-021-01301-5
Package: dlong
dlongPrototype framework for projected socio-economic climate damagesThis is a prototype. It is likely to change in breaking ways. It might delete all your data so don't use it in production.Examplesimportdlongfromdlong.typesimportClimateDataimportxarrayasxr# First, let's make some demo data.# Say 3 years of annual temperature data across 3 regions:region=[1,2,3]year=[2019,2020,2021]input_temperature=xr.DataArray([[1.0,2.0,3.0],[2.0,3.0,4.0],[3.0,4.0,5.0]],coords=[region,year],dims=["region","year"],)# And here are coefficients for a quadratic damage function. Different for# each region.damage_coefficients=xr.Dataset(data_vars={"beta0":(["region"],[1,1,1]),"beta1":(["region"],[1,2,3]),"beta2":(["region"],[4,5,6]),},coords={"region":(["region"],region)},)# Put it all together to describe climate data, a damage function, and# a strategy for discounting damages.climate=dlong.types.ClimateData(temperature=input_temperature)damage_model=dlong.QuadraticDamageModel(coefs=damage_coefficients)discount_strategy=dlong.FractionalDiscount(rate=0.02,reference_year=2020)# We could use these individually, or combine them into a "recipe" to output# discounted damages. The idea is that these components are compositable# so components can be customized and run in large batches.recipe=dlong.ExampleRecipe(climate=climate,damage_function=damage_model,discount=discount_strategy)damages=recipe.discounted_damages()#<xarray.DataArray (region: 3, year: 3)>#array([[  6.12      ,  19.        ,  39.21568627],#       [ 25.5       ,  52.        ,  87.25490196],#       [ 65.28      , 109.        , 162.74509804]])#Coordinates:#* region   (region) int64 1 2 3#* year     (year) int64 2019 2020 2021Installationpipinstalldlongdlongcurrently requires Python > 3.8 and thexarraypackage.Install the unreleased bleeding-edge version of the package with:pipinstallgit+https://github.com/brews/dlongSupportSource code is available online athttps://github.com/brews/dlong/. This software is Open Source and available under the Apache License, Version 2.0.DevelopmentPlease file bugs in thebug tracker.Want to contribute? Great! Fork the main branch and file a pull request when you're ready. Please be sure to write unit tests and followpep8. Fork away!
Package: dlopes7-avro
Apache Avro™Apache Avro™ is a data serialization system. To learn more, please visit ourwebsite.DocumentationApache Avro documentation is maintained on ourwiki.ContributingTo contribute to Avro, please readHow to Contributeon the wiki.License, Credits and AcknowledgementsLicense, credits and acknowledgements are maintained in theLICENSE.txtandNOTICE.txtin the source code repository. Those files are also included with the installed package.
Package: dlordinal
Deep learning utilities librarydlordinalis an open-source Python toolkit focused on deep learning with ordinal methodologies. It is compatible withscikit-learn.The library includes various modules such as loss functions, models, layers, metrics, and an estimator.OverviewCI/CDCode⚙️ InstallationYou can installdlordinaldirectly from the GitHub repository using the following command in your terminal:pip install git+https://github.com/ayrna/dlordinal.git@mainAlso, you can clone the repository and then install the library from the local repository folder:git clone git@github.com:ayrna/dlordinal.git
pip install ./dlordinalCollaboratingCode contributions to the dlordinal project are welcomed via pull requests.
Please, contact the maintainers (maybe opening an issue) before doing any work to make sure that your contributions align with the project.Guidelines for code contributionsIn order to set up the environment for development, install the project in editable mode and include the optional dev requirements:pipinstall-e'.[dev]'Install the pre-commit hooks before starting to make any modifications:pre-commitinstallWrite code that is compatible with all supported versions of Python listed in thepyproject.tomlfile.Create tests that cover the common cases and the corner cases of the code.Preserve backwards-compatibility whenever possible, and make clear if something must change.Document any portions of the code that might be less clear to others, especially to new developers.Write API documentation as docstrings.
Package: dlorenc-requests
Python 3.7.4 (default, Sep  7 2019, 18:27:02)>>>import requests>>> r = requests.get('https://api.github.com/repos/psf/requests')>>> r.json()["description"]'A simple, yet elegant HTTP library.'This software has been designed for you, with much joy,
byKenneth Reitz& is protected by ThePython Software Foundation.Requestsis an elegant and simple HTTP library for Python, built with ♥.>>>importrequests>>>r=requests.get('https://api.github.com/user',auth=('user','pass'))>>>r.status_code200>>>r.headers['content-type']'application/json; charset=utf8'>>>r.encoding'utf-8'>>>r.text'{"type":"User"...'>>>r.json(){'disk_usage':368627,'private_gists':484,...}Requests allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode yourPUT&POSTdata — but nowadays, just use thejsonmethod!Requests is one of the most downloaded Python package today, pulling in around14M downloads / week— according to GitHub, Requests is currentlydepended uponby500,000+repositories. You may certainly put your trust in this code.Supported Features & Best–PracticesRequests is ready for the demands of building robust and reliable HTTP–speak applications, for the needs of today.+ International Domains and URLs       + Keep-Alive & Connection Pooling
         + Sessions with Cookie Persistence     + Browser-style SSL Verification
         + Basic & Digest Authentication        + Familiar `dict`–like Cookies
         + Automatic Decompression of Content   + Automatic Content Decoding
         + Automatic Connection Pooling         + Unicode Response Bodies*
         + Multi-part File Uploads              + SOCKS Proxy Support
         + Connection Timeouts                  + Streaming Downloads
         + Automatic honoring of `.netrc`       + Chunked HTTP Requests

                            &, of course, rock–solid stability!✨ 🍰 ✨Requests Module InstallationThe recommended way to install therequestsmodule is to simply usepipenv(orpip, of
course):$pipenvinstallrequestsAdding requests to Pipfile's [packages]…✔ Installation Succeeded…Requests officially supports Python 2.7 & 3.5+.P.S. — Documentation is available atrequests.readthedocs.io.
Package: dlotter
MasterDevelopLintdlotterPlot data from NWP output (BETA - in development).The "word"dlotteris a contraction ofDMIandPlotter.InstallInstallation should be relatively simple.Install using pip:pip install dlotterOne dependency is "cartopy". pip installation of cartopy seems to be currently broken, thus leads to a failed installation of dlotter. If this happens, either build cartopy from source or with conda (see section "Prerequisites").UsageSubmodules can be imported, but this software is designed to be used as a CLI (Command-Line-Interface), by calling the__main__module. See examples on the wiki page:https://github.com/dmidk/dlotter/wikipython -m dlotter: Prints a help messagepython -m dlotter plot: Calls the plot submodule and prints a help message (as flags are needed for this command to work)python -m dlotter plot -p t2m:w10m -d /path/to/grib2/files/ --filetype grib2 --verbose: Plots t2m and w10m from grib2 files in the directory specified.python -m dlotter plot -p t2m:w10m -d /path/to/grib2/files/ --prefix 0 --filetype grib2 --limit-files 4 --verbose: Limits to use the first 4 files sorted by name and only use files starting with "0" in their name.python -m dlotter plot -p t2m:w10m -d /path/to/netcdf/files/ --postfix .nc --filetype netcdf --verbose: Plots data from netcdf files with the postfix ".nc" in the directory specified.Flags and commands'-a'or'--area'dk,neu'-d'or'--directory'/path/to/files/'-p'or'--parameters't2m:w10m:precip:slp:td2m:tcc:lmhcareas:dk: Denmarkneu: Northern Europegl: Greenlanddisko: Disko Baysjalland: Zealandeurope: Europefaroes: Faroe Islandsscoresbysund: Scoresbysund (Greenland)Parameters:t2m: 2 Meter Temperaturew10m: 10 Meter Wind Speedprecip: Accumulated Precipitationslp: Sea Level Pressuretd2m: 2 Meter Dewpoint Temperaturetcc: Total Cloud Coverlmhc: Low, Medium, High cloudsImportant assumptionsAll files should be in the same projection. This is to speed up the plotting.All files will be plotted in the same domain. This is also to speed up plotting.All data at one time are in the same filePrerequisitesdlotteris a script tool based on python. Dependencies is defined indlotter.ymland a python environment can be made using (mini)conda:conda env create -f dlotter.ymlBuild packageTo build from the source code:pythonsetup.pysdistbdist_wheelTo install locally:pipinstalldist/dlotter-$VERSION-py3-none-any.whl--upgradeTo deploy to PyPi (Only users with access can do this):python-mtwineuploaddist/*
Package: dl-package-test
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.This is the README file for the project.The file should use UTF-8 encoding and be written using ReStructured Text. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: dlpacker
DLPackerThis repo contains the code from DLPacker paperDLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins.What can this code do?Restore full-atom protein structure from backbone (packing)Generate structures of point mutants (assumes the backbone has not changed)Pack or refine parts of protein structure (e.g. after you modelled backbone of a missing loop)Restore partially of fully missing side chains (to be implemented)probably moreInput may contain any protein/protein complex/RNA/DNA/small molecules etc. Only water molecules are removed by default and MSE residues are renamed into MET, the rest will stay the same (except side chains of course).Installationpip install git+https://github.com/nekitmm/DLPackerOr alternativelygit clone https://github.com/nekitmm/DLPacker
cd DLPacker
pip install .UsageAs easy as three lines of code:fromdlpackerimportDLPackerdlp=DLPacker('my_structure.pdb')dlp.reconstruct_protein(order='sequence',output_filename='my_structure_repacked.pdb')Input stricture might or might not contain side chains, existing side chains, if present, will be ignored.You can find more examples with explanations in the jupyter notebookDLPacker.ipynb.PerformanceThe table below shows validation RMSD (Å) for DLPacker as well as two other state of the art algorithms, SCWRL4 and Rosetta Packer (fixbb):AA NameSCWRL4Rosetta PackerDLPackerArg2.071.841.44Lys1.541.401.21Phe0.670.530.32Tyr0.830.680.38Trp1.270.960.46His1.181.050.81Glu1.341.261.02Gln1.431.241.09Met1.080.910.76Asn0.880.800.65Asp0.680.650.47Ser0.590.520.36Leu0.490.450.36Thr0.360.330.27Ile0.400.360.31Cys0.400.300.24Val0.240.230.19Pro0.210.190.14Citing our workIf you use our code in your work, please cite the DLPacker paper:@article {Misiura2021.05.23.445347,
	author = {Misiura, Mikita and Shroff, Raghav and Thyer, Ross and Kolomeisky, Anatoly},
	title = {DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins},
	elocation-id = {2021.05.23.445347},
	year = {2021},
	doi = {10.1101/2021.05.23.445347},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2021/05/25/2021.05.23.445347},
	eprint = {https://www.biorxiv.org/content/early/2021/05/25/2021.05.23.445347.full.pdf},
	journal = {bioRxiv}
}
Package: dl-pipeline
dl_pipelineA general deep learning pipeline (in construction) for kaggle competitions and other projects.InstallSetup with pip:pip install dl_pipelineClone and editable setup:gitclonehttps://github.com/mnpinto/dl_pipelinecddl_pipeline
pipinstall-e.Rainforest Connection Species Audio Detection#!/bin/basharch='densenet121'model_name='model_0'sample_rate=32000n_mels=128hop_length=640forfoldin01234doecho"Training$modelfor fold$fold"kaggle_rainforest2021--fold$fold--model_name$model_name\--model$arch--sample_rate$sample_rate--n_mels$n_mels\--hop_length$hop_length--bs32--head_ps0.8\--tile_width1024--mixuptrue>>log.traindonefortwin64128256doecho"Generate predictions for$modelwith tile_width of$tw"kaggle_rainforest2021--run_testtrue--model_name$model_name\--model$arch--sample_rate$sample_rate--n_mels$n_mels\--hop_length$hop_length--tile_width$tw\--save_predstrue>>log.predictdone
Package: dl-plus
dl-plusAyoutube-dlextension with pluggable extractorsDescriptiondl-plusis an extension and a drop-in replacement ofyoutube-dl(or any compatible fork, e.g.,yt-dlp). The main goal of the project is to add an easy-to-use extractor plugin system toyoutube-dlwhile maintaining full backward compatibility.dl-plusis not a fork ofyoutube-dland does not contain code fromyoutube-dl, it is a pure dynamic wrapper (thanks to Python dynamic nature) hacking someyoutube-dlinternals.InstallationInstalldl-plus:usingpipx:pipx install dl-plususing pip:pip install dl-plus(*nix) Alternatively, you can download a single file binary (zipapp) and put it somewhere in yourPATH:curl -L https://github.com/un-def/dl-plus/releases/latest/download/dl-plus -o dl-plus
chmod a+x dl-plusInstall a backend —youtube-dlor any compatible package (fork), e.g.,yt-dlp:usingdl-plusitself:dl-plus --cmd backend install yt-dlpusingpipx:pipx inject dl-plus yt-dlpusing pip:pip install yt-dlp(optional) Install some extractor plugins:usingdl-plusitself:dl-plus --cmd extractor install un1def/wasdtvPyPI package names are supported too:dl-plus --cmd extractor install dl-plus-extractor-un1def-wasdtvusingpipx:pipx inject dl-plus dl-plus-extractor-un1def-wasdtvusing pip:pip install dl-plus-extractor-un1def-wasdtv(optional) Createdl-plus→youtube-dlsymlink (for apps relying onyoutube-dlexecutable inPATH, e.g.,mpv):*nix:dlp=$(command-vdl-plus2>&1)&&ln-s"$dlp""$(dirname"$dlp")/youtube-dl"Useln -sfinstead ofln -sto overwrite an existingyoutube-dlexecutable.Windows(PowerShell, requires administrative privileges):$dlp=(Get-Command-ErrorAction:Stopdl-plus).Path;New-Item-ItemTypeSymbolicLink-Path((Get-Item$dlp).Directory.FullName+"\youtube-dl.exe")-Target$dlpExtractor Plugin Authoring GuideSeedocs/extractor-plugin-authoring-guide.md.Available Extractor PluginsSeedocs/available-extractor-plugins.md.LicenseTheMIT License.
Package: dl-plus-extractor-un1def-goodgame
dl-plus-extractor-un1def-goodgameAdl-plusextractor plugin forGoodGamestreaming platformIncluded extractorsun1def/goodgame:stream— live streams (https://goodgame/<username>andhttps://goodgame/player?<key>)un1def/goodgame:vod— VODs (https://goodgame/vods/<key>/<timestamp>)un1def/goodgame:clip— clips (https://goodgame/clip/<id>)
Package: dl-plus-extractor-un1def-rinsefm
dl-plus-extractor-un1def-rinsefmAdl-plusextractor plugin forRinse FMinternet radioIncluded extractorsun1def/rinsefm:channel— channels (https://rinse.fm/channels/<slug>/)un1def/rinsefm:episode— episodes (https://rinse.fm/episodes/<slug>/)
Package: dl-plus-extractor-un1def-wasdtv
dl-plus-extractor-un1def-wasdtvAdl-plusextractor plugin forWASD.TVstreaming platformIncluded extractorsun1def/wasdtv:stream— live streams (https://wasd.tv/<username>andhttps://wasd.tv/channel/<channel_id>)un1def/wasdtv:privatestream— private live streams (https://wasd.tv/private-stream/<private_stream_hash>)un1def/wasdtv:record— stream records (https://wasd.tv/<username>/videos?record=<record_id>andhttps://wasd.tv/channel/<channel_id>/videos/<record_id>)un1def/wasdtv:clip— clips (https://wasd.tv/<username>/clips?clip=<clip_id>)
Package: dlpoly-py
dlpoly-py package contains tools to read input and output for DL_POLY
it can also produce inputs and be mixed with other python packages
like ASE, MDAnalysis, MDAnse or pymatgeninstallYou need Python 3.6 or later to rundlpoly-py. You can have multiple Python
versions (2.x and 3.x) installed on the same system without problems.To install Python 3 for different Linux flavors, macOS and Windows, packages
are available athttps://www.python.org/getitUsing pippipis the most popular tool for installing Python packages, and the one
included with modern versions of Python.dlpoly-pycan be installed withpippipinstalldlpoly-pyNote:Depending on your Python installation, you may need to usepip3instead ofpip.pip3installdlpoly-pyDepending on your configuration, you may have to runpiplike this:python3-mpipinstalldlpoly-pyUsing pip (GIT Support)pipcurrently supports cloning overgitpipinstallgit+https://gitlab.com/drFaustroll/dlpoly-py.gitFor more information and examples, see thepip installreference.Using a virtual environment# create virtual envvirtualenv3venv/dlpolysourcevenv/dlpoly/bin/activatepip3installdlpoly-pyUsing condacondais the package management tool for Anaconda Python installations.Installingdlpoly-pyfrom theconda-forgechannel can be achieved by addingconda-forgeto your channels with:condaconfig--addchannelsconda-forgeOnce theconda-forgechannel has been enabled,dlpoly-pycan be installed
with:condainstalldlpoly-pyIt is possible to list all of the versions ofdlpoly-pyavailable on your
platform with:condasearchdlpoly-py--channelconda-forgeusageExamples can be found inhttps://gitlab.com/drFaustroll/dlpoly-py/-/tree/devel/examplessime run using Ar data from above folder.fromdlpolyimportDLPolydlp="/home/drFaustroll/playground/dlpoly/dl-poly-alin/build-yaml/bin/DLPOLY.Z"dlPoly=DLPoly(control="Ar.control",config="Ar.config",field="Ar.field",workdir="argon")dlPoly.run(executable=dlp,numProcs=4)# change temperature and rerun, from previous terminationdlPoly=DLPoly(control="Ar.control",config="argon/REVCON",destconfig="Ar.config",field="Ar.field",workdir="argon-T310")dlPoly.control.temp=310.0dlPoly.run(executable=dlp,numProcs=4)alternatively you can set the environment variable DLP_EXE to point to DL_POLY_4 executable and remove the executable parameter from
run.exportDLP_EXE="/home/drFaustroll/playground/dlpoly/dl-poly-alin/build-yaml/bin/DLPOLY.Z"fromdlpolyimportDLPolydlPoly=DLPoly(control="Ar.control",config="Ar.config",field="Ar.field",workdir="argon")dlPoly.run(numProcs=4)# change temperature and rerun, from previous terminationdlPoly=DLPoly(control="Ar.control",config="argon/REVCON",destconfig="Ar.config",field="Ar.field",workdir="argon-T310")dlPoly.control.temp=310.0dlPoly.run(numProcs=4)authorsAlin M Elena, Daresbury Laboratory, UKJacob Wilkins, University of Oxford, UKcontactplease report issues in thegitlab trackeravailable in thematrix room
Package: dlprepare
dlprepareA data preparation package for deep learning.
current version:0.0.2you can see the source codeherefor contribution plsopen an issueon the repo site.installation:pip install dlprepareClasses:csv_utils- Utility helper class for .csv files , to create a (test,train)  set.tpu- A set of Decorators to  facilitate the creation of TPU-supporting models  (kears only for now)
Package: dlproject
Deep Learning Project TemplateThe opinionated deep learning template.Descriptiondlprojectbelieves three things.All code should be documented.All experiments should be logged.Configs are better than constants.InstallationThese instructions assume you are using a linux machine with at least one GPU (CUDA 11.1).Create a new repository using this template and change to the root directory. For example,gitclonegit@github.com:benjamindkilleen/dlproject.gitcddlprojectInstall dependencies using eitherAnaconda(preferred) or Pip:Anaconda:modifyenvironment.ymlto suit your needs. Then run:condaenvcreate-fenvironment.yml
condaactivatedlprojectThis will create a new environment with the project installed as an edit-able package.Pip:InstallPytorchto ensure GPU available. Then:pipinstall-rrequirements.txt
pipinstall-e.UsageThe project is separated into "experiments," which are just differentmainfunctions. Use theexperimentgroup parameter to change which experiment is running. For example:pythonmain.pyexperiment=mnistThe results are then neatly sorted into the newly-createdresultsdirectory (ignored by default). This is important for reproduceability, utilizing Hydra's automatic logging and config storage.DocumentationDocumentation and tutorials fordlprojectare availablehere. You should document your code as you go. If you use Visual Studio Code,thisis an extension which will create Google style docstrings automatically.To build the docstrings you write into a local static web-page, runpipinstall-rdocs/requirements.txt
sphinx-apidoc-f-odocs/sourcedlprojectcddocs
makehtmlAnd open/docs/build/html/index.htmlin your browser.Citation@article{YourName,
  title={Your Title},
  author={Your team},
  journal={Location},
  year={Year}
}