Saravanan666/pypi-org huggingface dataset:
Package: deepdirect
<!-- README.md is generated from README.Rmd. Please edit that file --># deepdirect<!-- badges: start --><!-- badges: end -->Deepdirect is an in silico approach to generate mutations for proteincomplexes towards a specified direction (increase/decrease) in bindingaffinity.## System requirements and dependencies### Hardware Requirements`deepdirect` model is able to be trained and perform its operations on astandard computer.### OS RequirementsThe `deepdirect` model should be compatible with Windows, Mac, and Linuxoperating systems. The package has been tested on the following systems:- Linux 3.10.0- Windows 10### Dependencies`deepdirect` framework is built and trained on the `Tensorflow 2.4.0`and `Keras 2.4.0`.## Framework constructionThe python file including all required deepdirect framework builtfunction is able to be downloaded from GitHub:`deepdirect_framework/model_function.py`## Data structure- `data` folder contains the original datasets used for building thetraining datasets.- `deepdirect_framework` folder contains the trained model weights andthe model constructing functions.- `deepdirect_paper` folder contains codes for building and trainingmodels, and performing analysis in the deepdirect manuscript. The file`ab_bind_data_extract.py` and `skempi_data_extract.py` contains codefor constructing training datasets for Deepdirect framework.`train_step_1.py` contains code for training step 1 for the mutationmutator. `final_model.py` contains code for training step 2 (final)for the mutation mutator. `model_function.py` contains code forconstructing the Deepdirect framework.`model_evaluation_application.py` contains code for model evaluation,and teh application on Novavax-vaccine. `evolution_analysis.py`contains code for performing evolution analysis.## File sourceFor files that are required as input in the code but not generated fromother codes, please refer to the data availability section in theoriginal paper.## InstallationClone repository:git clone https://github.com/tianlt/deepdirect.gitCreate virtual environment:conda create --name deepdirect python=3.6.8Activate virtual environment:conda activate deepdirectInstall dependencies:pip install tensorflow==2.4.0pip install keras==2.4.0## Running deepdirect### data processingData to be input to deepdirect include sequence to be mutated `pre`, RBDsite `rbd`, ligand-receptor index `same`, protein tertiary structureinformation `x`, `y` and `z`, and random noise `input_noi`. All inputhas to be `tf.float32` type.### Build deepdirect mutator with trained weightsaa_mutator = build_aa_mutator()aa_mutator.load_weights('deepdirect_framework/model_i_weights.h5')### Binding affinity-guided mutationaa_mutator.predict([pre, rbd, same, x, y, z, input_noi])### Additional informationExpected outputs: mutated amino acid sequenceExpected runtime for mutation: ~1 mintue## Issues and bug reportsPlease use <https://github.com/tianlt/deepdirect/issues> to submitissues, bug reports, and comments.## Licensedeepdirect is distributed under the [GNU General Public License version2 (GPLv2)](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html).
Package: deep-doors-2-labelled
DeepDoors2 LabeledTo download the new version of DeepDoors2, clickhere.This repository contains the necessary code to manage the relabeled version of theDeepDoors2dataset. The new dataset version we provide is built to perform object detection over 2D and 3D doors data. Each examples is composed by an 480x640 RGB image, the depth data, and the bounding boxes coordinates with their label. Each doors can assume 3 different statuses: open, semi-open, and closed. The dataset provided in this repo is managed using thegeneric-datasetpackage.
Package: deepdraken
Deep DrakenDeep Draken aims to provide a framework for developing Deep Learning Models in Image Domain. It currently supports Pytorch and have implementations for image generation algorithems like:GANCGANWGANWGANGPInstallation1. Basic InstallationYou can install DeepDraken's latest release by usingpip.
The command is as follows:pip3 install deepdrakenThe experimental features have tensorflow dependencies and needs to be installed seperately.pip install tensorflow>=2.0.0
pip install tensorflow-hub==0.12.02. Installation from SourceIf you plan to be using more advanced features that currently not available in the python library, you need to install a DeepDraken from github.
The following command will install the latest version of DeepDraken from the main branch.git clone https://github.com/DevavratSinghBisht/deepdraken.git
pip install --upgrade pip
cd deepdraken
pip install ./3. Learn MoreYou can find out more about our PyPi package on ourPyPi page.TutorialsTutorials will be added soon.
Package: deepdraw
<!–
SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>SPDX-License-Identifier: GPL-3.0-or-later
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[![repository](https://img.shields.io/badge/gitlab-project-0000c0.svg)](https://gitlab.idiap.ch/biosignal/software/deepdraw)# Semantic Segmentation Benchmarks on Public DatasetsPackage to benchmark and evaluate a range of neural network architectures for
semantic segmentation tasks. For installation and usage instructions, check-out
our documentation.
Package: deepdrive-api
Deepdrive APIPython implementation of Deepdrive API used to run agents within theDeepdrive simover the network.Theserveraccepts messages over ZMQ serialized with
Apache Arrow, allowing agents to be written in any language.
This will run locally alongside the simulation (Unreal).A reference client implementation in python can be found inclient.py.To install the package, runpip3 install deepdrive-apiExample usagehttps://github.com/crizcraig/forward-agentDevelopmentPyPi upload./pypi_upload.shLegalCopyright © 2019,Deepdrive.
Licensed under the MIT License, see the fileLICENSEfor details.
Package: deepee
deepeedeepeeis a library for differentially private deep learning in PyTorch. More precisely,deepeeimplements the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm originally described byAbadi et al.. Despite the name,deepeeworks with any (first order) optimizer, including Adam, AdaGrad, etc.It wraps a regularPyTorchmodel and takes care of calculating per-sample gradients, clipping, noising and accumulating gradients with an API which closely mimics thePyTorchAPI of the original model.Check out the documentationhereFor paper readersIf you would like to reproduce the results from our paper, please gohere
Package: deepend
DeependDeepend is an advanced end-to-end machine learning framework. It offers manytools, andlibrarieswith aplatformthat helps novices gain experience fast and offers researchers and community members all around the globe to publish their work and show their talents.Explore the docs »View Demo·Report Bug·Request FeatureTable of ContentsAbout The ProjectBuilt WithGetting StartedPrerequisitesInstallationUsageRoadmapContributingLicenseContactAcknowledgmentsAbout The ProjectHere's a blank template to get started: To avoid retyping too much info. Do a search and replace with your text editor for the following:Letsmoe,deepend,Continu13798401,linkedin_username,email,email_client,Deepend,project_description(back to top)Getting StartedTo start off, open a terminal and add Deepend from your preferred package manager, this will get you up and running in seconds. Deepend comes with some smalldatasetsalready preinstalled, we offer you to download many morefrom our websitethough and we entice you to take a look atKaggledatasets.DependenciesTo run Deepend it is required that you haveNumPyinstalled. The utilities module requires you to haveOpenCVandosinstalled.pippipinstalldeependFirst ContactDeepend is both a backend and frontend for creating machine learning models.
Importing it is as simple as any other package. After you installed it from pip you can runimportdeependThis will get you everything deepend comes with, to be more specific you can import each module separately. Deepend comes with the following modules:importdeepend.utilsasutilitiesimportdeepend.activationsasactivationsimportdeepend.modelsasmodelsimportdeepend.lossesaslossesimportdeepend.metricsasmetrics(back to top)Roadmap[] LSTM Layers and GRU Cells[] N-Dimensional Convolutional LayersSee theopen issuesfor a full list of proposed features (and known issues).(back to top)ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request(back to top)LicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.(back to top)ContactYour Name -@Continu13798401-info@continuum-ai.deProject Link:https://github.com/Letsmoe/deepend(back to top)Acknowledgments(back to top)
Package: deeper-rpg
Deeper :skull:Dungeon RPG usingThe Python Arcade LibraryTiles courtesy ofDragoshaLinks:skull_and_crossbones:Discord:notebook:Documentation:package:PackageDevelopmentYou need to haveHatchinstalledYou need to haveRustinstalledgitclonehttps://github.com/kfields/deeper.gitcddeeper
hatchshell
hatchrundevelop
python-mdeeperResources:link:PyO3:link:Maturin:link:Maturin Action:link:ParryScreen Shots
Package: deeperwin
DeepErwinDeepErwin is python package that implements and optimizes wave function models for numerical solutions to the multi-electron Schrödinger equation.DeepErwin is based on JAX and supports:Optimizing a wavefunction for a single nuclear geometryOptimizing wavefunctions for multiple nuclear geometries in parallel, while sharing neural network weights across these wavefunctions to speed-up optimizationUsing pre-trained weights of a network to speed-up optimization for entirely new wavefunctionsUsing second-order optimizers such as KFAC or L-BFGSA detailed description of our method and the corresponding results can be found in our recentarxiv publication. When you use DeepErwin in your work, please cite:M. Scherbela, R. Reisenhofer, L. Gerard, P. Marquetand, and P. Grohs.Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks.arXiv preprintarXiv:2105.08351(2021).Getting StartedThe quickest way to get started with DeepErwin is to have a look at ourdocumentation. It contains a detailed description of our python codebase and atutorialwhich should help you to quickly get up-and-running using DeepErwin.AboutDeepErwin is a collaborative effort of Michael Scherbela, Rafael Reisenhofer, Leon Gerard, Philipp Marquetand, and Philipp Grohs.The code was written by Michael Scherbela, Leon Gerard, and Rafael Reisenhofer.If you have any questions, freel free to reach out viae-mail.
Package: deepeval-haystack
deepeval-haystackTable of Contentsdeepeval-haystackInstallationTestingExamplesLicenseInstallationpip install deepeval-haystackFor more information about the deepeval evaluation framework, please refer to theirdocumentation.Testinghatch run testExamplesYou can find a code example showing how to use the Evaluator under theexample/folder of this repo.Licensedeepeval-haystackis distributed under the terms of theApache-2.0license.
Package: deepevent
DeepEvent Version 0.4We propose a new application (DeepEvent) of long short term memory recurrent neural network to the automatic detection of gait events.
The 3D position and velocity of the markers on the heel, toe and lateral malleolus were used by the network to determine Foot Strike (FS) and Foot Off (FO).
The method was developed from  10526 FS and 9375 FO from 226 children.DeepEventpredictedFSwithin5.5 msandFOwithin10.7 msof the gold standard (automatic determination using force platform data) and was more accurate than common heuristic marker trajectory-based methods proposed in the literature and another deep learning method.RequirementWindows 64 bits only, python3.7, Keras, Tensorflow, pyBtk, Numpy, Scipy, GoogleDriveDownloaderAll depedancies are available in pipyInstallationpipinstalldeepevent==0.4RunningDeepEventdeepevent-iFilenameIn.c3d-oFilenameOut.c3d
deepevent--inputFilenameIn.c3d--outputFilenameOut.c3d
deepevent--inputFilenameIn.c3dwhere FilenameIn.c3d is the c3d file to identify gait events, FilenameOut.c3d is the new file with the gait events.In the last case, filenameIn.c3d is overwritten with gait eventsBibliographyLempereur M., Rousseau F., Rémy-Néris O., Pons C., Houx L., Quellec G., Brochard S. (2019).A new deep learning-based method for the detection of gait events in children with gait disorders: Proof-of-concept and concurrent validity. Journal of Biomechanics. Volume 98, 2 January 2020, 109490.ContactsOriginal developermathieu.lempereur@univ-brest.frMathieu LEMPEREURCHRU de Brest - Hopital MorvanService MPR2 avenue Foch29609 BREST cedexFRANCEForked developerfabien.leboeuf@gmail.comFabien LeboeufIngénieur "analyste du mouvement" du Pole 10, CHU Nantes, FranceChercheur associé de l'Université de Salford, Manchester, Royaume uniLaboraratoire d'analyse du mouvement85 rue saint Jacques44093 NantesFRANCE
Package: deep-expressions
DeepExpressions: A Deep Learning tookit for Facial Expressions RecognitionAboutDeepExpressions is a Deep Learning toolkit for Facial Expressions Recognition (FER) which aims to enable an easy way to implement projects based on human emotions.For more details, clickhere.InstallationBefore installing DeepExpression, please installTensorflow 2.0. We recommend the GPU version.Install DeepExpressions from PyPI (recommend):pip install deep-expressionsExamplesFER in imagesFER in videos
Package: deepextract
deepextract 🔥A Python library to parse JSON, YAML files and extract deeply nested keys.Usage 🎯See examples.Installation ⚠️pipinstalldeepextractRequirements 🌌Support for Python 3.6 and greater.Development 🛎️condacreate-ndeepextract_envpython=3.7# do it oncecondainstall-rrequirements.txtSource code is in deepextract. Start editing and Happy contributing! 🌟Deployment to PyPI 💎Based on descriptions fromhere, whenever project owner pushes a tagged commit to this Git repository remote on GitHub, GH workflow will publish it to PyPI.And it'll publish any push to TestPyPI which is useful for providing test builds to repo's alpha users as well as making sure that your release pipeline remains healthy.Manual deploy to TestPyPI:pythonsetup.pysdistbdist_wheel
twineupload-rtestpypidist/*Running tests 🔥python-munittestLicence ✅MIT. See LICENSE.mdVersionFollows syntax vM.M.P
First is major and means not backwards compatible changes. Second is minor and means backwards compatible changes.
Third is patch and means small backwards compatible changes.The manual place of source of truth is atdeepextract/__init__.pySource:https://packaging.python.org/guides/single-sourcing-package-version/#single-sourcing-the-versionAuthors ✏️deepextractwas written byYigit Alparslan <alparslanyigitcan@gmail.com>.
Package: deepeye-pack
deepeye_packUpdate - 2018/10/22 v0.0.2read_csv_handld_changedate revise to pandas methodboth the import methods requireMandatorytable_info to specify the tablemysql_handle now change to pandas dataframe verisonfrom_mysql also changes to para-method, pass port/user/db... instead of query and MySQLdb connDescriptionThis is a Python package for DeepEye API,can easily visualize data without too much effort. And provide with really simple usagethe DeepEye system:https://github.com/TsinghuaDatabaseGroup/DeepEye/tree/master/APIs_DeepeyeInstallationPython 2.7MySQL 5.7Packagesmysqldb binary packages for windows: link1:https://www.lfd.uci.edu/~gohlke/pythonlibs/link2:https://sourceforge.net/projects/mysql-python/Download 'MySQL-python' and choose the right version for itInstall the .whl by wheel installthere is a back up version in this repository under 'mysqldb' foldernumpy(latest version)pandas(latest version) above ver 0.23.0UsageInitialexample code:importdeepeye_pack#create a deepeye_pack class that wraps everythingdp=deepeye_pack.deepeye('demo')# the name here doesnt actually matter# then user needs to input table info# as in table_info(table_name,column_names,column_types)dp.table_info('electricity',['city','date','electricity(kWh)'],['varchar','date','float'])the column_types that supported by deepeye_pack are specified as below:numerical:int,float,doubletemporal:date,datetime,yearcategorical:char,varcharImportfrom_mysql()# call the from_mysql() functiondp.from_mysql(host='localhost',port=3306,user='root',passwd='ppww',db='deepeye',query='SELECT * FROM `table_name`')from_csv()path="file.csv"# the path where the file locateddp.from_csv(path)Visualization# choose one from threedp.learning_to_rank()dp.partial_order()dp.diversified_ranking()Output# can use several different methods at the same timedp.to_print_out()dp.to_single_json()dp.to_multiple_jsons()dp.to_multiple_htmls()dp.to_single_html()
Package: deepfake-ecg
deepfake-ecgPaper|GitHub|Pre-generated ECGs (150k)Generate unlimited realistic deepfake ECGs using the deep generative model:Pulse2pulse introduced in our full paper here:https://doi.org/10.1101/2021.04.27.21256189(DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine)InstallationUse the package managerpipto install deepfake-ecg generator.pipinstalldeepfake-ecgUsageThe generator functions can generate DeepFake ECGs with 8-lead values  [lead names from first coloum to eighth colum:'I','II','V1','V2','V3','V4','V5','V6'] for 10s (5000 values per lead). These 8-leads format can be converted to 12-leads format using the following equations.lead III value = (lead II value) - (lead I value)
lead aVR value = -0.5*(lead I value + lead II value)
lead aVL value = lead I value - 0.5 * lead II value
lead aVF value = lead II value - 0.5 * lead I valueRun on CPU (default setting)importdeepfakeecg#deepfakeecg.generate("number of ECG to generate", "Path to generate", "start file ids from this number", "device to run")deepfakeecg.generate(5,".",start_id=0,run_device="cpu")# Generate 5 ECGs to the current folder starting from id=0Run on GPUimportdeepfakeecg#deepfakeecg.generate("number of ECG to generate", "Path to generate", "start file ids from this number", "device to run")deepfakeecg.generate(5,".",start_id=0,run_device="cuda")# Generate 5 ECGs to the current folder starting from id=0Pre-generated DeepFake ECGs and corresponding MUSE reports are here:https://osf.io/6hved/- In this repository, there are two DeepFake datasets:
    1. 150k dataset - Randomly generated 150k DeepFakeECGs
    2. Filtered all normals dataset - Only "Normal" ECGs filtered using the MUSE analysis reportA real ECG vs a DeepFake ECG (from left to right):A sample DeepFake ECG:ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.Citation:@article{ecg-pulse2pulse,
	author ={Thambawita, Vajira Lasantha and Isaksen, Jonas L and Hicks, Steven and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Olesen, Morten Salling and Hansen, Torben and Graff, Claus and Holstein-Rathlou, Niels-Henrik and Str{\"u}mke, Inga and Hammer, Hugo L. and Maleckar, Mary M and Halvorsen, P{\aa}l and Riegler, Michael A. and Kanters, J{\o}rgen K.},
	doi ={10.1101/2021.04.27.21256189},
	elocation-id ={2021.04.27.21256189},
	journal ={medRxiv},
	publisher ={Cold Spring Harbor Laboratory Press},
	title ={DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine},
	url ={https://doi.org/10.1101/2021.04.27.21256189},
	year ={2021}}LicenseMITFor more details:Please contact:vajira@simula.no,michael@simula.no
Package: deepfake-gitract
deepfake-gitractGenerate deepfake-gitract images.InstallationUse the package managerpipto install deepfake-gitract.pipinstalldeepfake-gitractUsageRun on CPU (default setting)TBA-YouhavetohaveaNVIDIAGPUtorunthispackage.WeareworkingonCPUversion.Run on GPU - generation processimportdeepfake_gitractdeepfake_gitract.generate(name,result_dir,checkpoint_dir,num_img_per_tile,num_of_outputs,trunc_psi=0.75):""" Generate deepfake Gastrointestinal tract images.Keyword arguments:name -- Any name to keep trac of generationsresult_dir -- A directory to save outputcheckpoint_dir -- A directory to download pre-trained checkpointsnum_img_per_tile -- Number of images per dimenstion of the gridnum_of_outputs -- Number of outputs to generatetrunc_psi -- value between 0.5 and 1.0 (default 0.75)"""Run on GPU - to generate interpolations between random pointsdeepfake_gitract.generate_interpolation(name,result_dir,checkpoint_dir,num_img_per_tile,num_of_outputs,num_of_steps_to_interpolate,save_frames,trunc_psi=0.75):""" Generate deepfake Gastrointestinal tract images.Keyword arguments:name -- Any name to keep trac of generationsresult_dir -- A directory to save outputcheckpoint_dir -- A directory to download pre-trained checkpointsnum_img_per_tile -- Number of images per dimenstion of the gridnum_of_outputs -- Number of outputs to generatenum_of_steps_to_interpolate -- Number of step between two random pointssave_frames -- True if you want frame by frame, otherwise .gif will be generatedtrunc_psi -- value between 0.5 and 1.0 (default 0.75)"""ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.For more details:Please contact:vajira@simula.no,michael@simula.noLicenseMIT
Package: deep-filter
deep-filterA simple package that filters out values from dicts/lists, including all dicts/lists nested within it.Usagefromdeep_filterimportdeep_filterx={'nope':69,'yep':[69,{'maybe':None},99]}deffilter_func:returnvalue!=69result=deep_filter(x,filter_func)print(result)# {'yep': [{}, 99]}deep_filter(dict_or_list, filter_func=default_filter_func)dict_or_list: A dictionary or listfilter_func: An optional callback function. It will take a value as an argument, and returnTrueif the value will be kept andFalseif not. If omitted,Nonevalues will be filtered out.Returns your dict or list, filtered.Dev instructionsGet startedInstall Python (Python 3.7 works, probably other versions too)InstallPoetry. Poetry is used to manage dependencies, the virtual environment and publishing to PyPIRunpoetry installto install Python package dependenciesI recommend runningpoetry config virtualenvs.in-project true, which makes Poetry store your Python virtual environment inside the project folder. Additionally, it lets VSCode's Python extension detect the virtual environment if you set thepython.pythonPathsetting to${workspaceFolder}/.venv/bin/pythonin your settings.RunningTo test if things work, you can run the following command to open the Python REPL. Then you can write Python, such as the usage examples:poetry run pythonReleasing a new versionConsider updating the lockfile by runningpoetry update, then check if thing still workBump the version number:poetry version <version>UpdateCHANGELOG.mdBuild:poetry buildCommit and create git tagCreate GitHub release with release notes and attach the build filesPublish to PyPi:poetry publish