Saravanan666/pypi-org huggingface dataset:
Package: dnngior
A novel way to gapfill metabolic modelsDescription of the package##DependenciesTensorflow(or throughconda)cobrapyTo run the "Going from a fasta file to a curated model" tutorial, that includes the reconstruction generation:[ModelSEEDp]y(https://github.com/ModelSEED/ModelSEEDpy)Installation instructionsExamples of usagehow to use cobrapy and gapfill modelshow to use NN-weights to gapfill modelsGoing from a fasta file to a curated model
Package: dnn-inference
🔬 dnn-inf: significance tests of feature relevance for a black-box modeldnn-inferenceis a Python module for hypothesis testing based on black-box models, includingdeep neural networks.GitHub repo:https://github.com/statmlben/dnn-inferenceDocumentation:https://dnn-inference.readthedocs.ioPyPi:https://pypi.org/project/dnn-inferenceOpen Source:MIT licensePaper:arXiv:2103.04985InstallationDependenciesdnn-inferencerequires:Python>=3.8+requirements.txtpipinstall-rrequirements.txtUser installationInstalldnn-inferenceusingpippipinstalldnn_inferencepipinstallgit+https://github.com/statmlben/dnn-inference.gitReferenceIf you use this code please star the repository and cite the following paper:@misc{dai2021significance,title={Significance tests of feature relevance for a blackbox learner},author={Ben Dai and Xiaotong Shen and Wei Pan},year={2021},eprint={2103.04985},archivePrefix={arXiv},primaryClass={stat.ML}}NotebookMNIST dataset:Notebook1Boston house prices dataset:Notebook2
Package: dnnlab
DnnLabDnnlab is a small framework for deep learning models based on TensorFlow.It provides custom training loops for:Generative Models (GAN, cGan, cycleGAN)Image Detection (custom YOLO)Additonaly custom Keras Layer:Non-Local-Blocks (Self-Attention)Squeeze and Excitation Blocks (SEBlocks)YOLO-Decoding LayerInput pipeline functionality:YOLO (Tfrecords to Datasets)YOLO data augmentationGenerative Models (Tfrecords to Datasets)TensorBoard output:YOLO coco metrics (Precision (mAP) & Recall)YOLO loss (loss_class, loss_conf, loss_xywh, total_loss)YOLO bounding boxesGenerative Models (Loss & Images)RequirementsTensorFlow 2.3.0InstallationRun the following to install:pipinstalldnnlab
Package: dnnlib-util
dnnlib-utilStyleGAN2's dnnlib/util.py functionality as a standalone libraryWARNING: This repo is in development. It was automatically generated withmkpylib. If you're reading this message, it means that I use this repo for my own purposes right now. It might not do anything at all; the default functionality isprint('TODO').If you really want to try it out, feel free. I recommend reading through the code and commit history to see if it does what you need, orask mefor status updates.Stay tuned!Installpython3 -m pip install -U dnnlib-utilUsageimportdnnlib_utilprint('TODO')LicenseMITContactA library byShawn Presser. If you found it useful, please considerjoining my patreon!My Twitter DMs are always open; you shouldsend me one! It's the best way to reach me, and I'm always happy to hear from you.Twitter:@theshawwnPatreon:https://www.patreon.com/shawwnHN:sillysaurusxWebsite:shawwn.com
Package: dnnocr
==========Python Package for OCR.### Install```$ pip install dnnocr```### Usage```python#!/usr/bin/env python# coding=utf-8import dnnocrnet = dnnocr.Net('chars');image_file = "./10.png";ocr_text = net.ocr(image_file);print "ocr_text:" + ocr_text```Home-page: https://github.com/dnnocr/dnnocrAuthor: UNKNOWNAuthor-email: dnnocr@outlook.comLicense: UNKNOWNDescription: UNKNOWNPlatform: UNKNOWN
Package: dnnpy
dnnpy FrameworkInstallationpipinstalldnnpyExample usagefromdnnpy.activationsimportReLUfromdnnpy.dataimportmake_regression_datafromdnnpy.layersimportSequential,Dense,Dropoutfromdnnpy.loss_functionsimportMAELossfromdnnpy.optimizersimportAdamfromdnnpy.trainimporttrainfromdnnpy.utilsimportsplit_dataimportmatplotlib.pyplotaspltn_inputs=10hidden_units=32n_outputs=1x,y=make_regression_data(n_samples=1000,n_features=n_inputs,n_labels=1)(x_train,y_train),(x_test,y_test)=split_data(x,y,ratio=0.7)model=Sequential(Dense(in_features=n_inputs,out_features=hidden_units,activation=ReLU()),Dropout(0.3),Dense(in_features=hidden_units,out_features=n_outputs))opt=Adam(model.parameters(),lr=1e-3)loss_func=MAELoss()train_loss,valid_loss=train(data=(x_train,y_train),network=model,loss=loss_func,optimiser=opt,epochs=30,batch_size=16)plt.plot(train_loss,label='train')plt.plot(valid_loss,label='val')plt.legend()plt.show()
Package: dnnr
DNNR: Differential Nearest Neighbors Regression[Paper]
[Documentation]The Python package ofdifferential nearest neighbors regression (DNNR):Raising KNN-regression to levels of gradient boosting methods.Whereas KNN regression only uses the averaged value, DNNR also uses the gradient or even higher-order derivatives:Our implementation usesnumpy,sklearn, and theannoyapproximate nearest neighbor index. Usingannoyis optional, as you can also usesklearn's KDTree as index. We support Python 3.7 to 3.10.🚀 QuickstartTo install this project, run:pipinstalldnnr🎉 ExampleimportnumpyasnpfromdnnrimportDNNRX=np.array([[0],[1],[2],[3]])y=np.array([0,0,1,1])model=DNNR(n_neighbors=1,n_derivative_neighbors=3)model.fit(X,y)model.predict([[1.5]])# Will output: 0.25Also check out ourJupiter Notebookon how to use DNNR.📊 HyperparametersDNNR has three main hyperparameters:n_neighbors: number of nearest neighbors to use. The default value of3is usually a good choice.n_derivative_neighbors: number of neighbors used in approximating the
derivatives. As a default value, we choose3 * dim, wheredimis
the input dimension.order: Taylor approximation order, one of1,2,2diag,3diag.
The preferable option here is1. Sometimes2diagcan deliver
small improvements.2and3diagare implemented but usually do
not yield significant improvements.We recommend a hyperparameter search over then_derivative_neighborsvariable to archive the best performance.For all options, see thedocumentation of the DNNR class.🛠 Development Installationpython3-mvenvvenv# create a virtual environmentsourcevenv/bin/activate# and load itgitclonehttps://github.com/younader/dnnr.gitcddnnr
pipinstall-Upipwheelpoetry
poetryinstall
maketest# to run the tests📄 CitationIf you use this library for a scientific publication, please use the following BibTex entry to cite our work:@InProceedings{pmlr-v162-nader22a,title={{DNNR}: Differential Nearest Neighbors Regression},author={Nader, Youssef and Sixt, Leon and Landgraf, Tim},booktitle={Proceedings of the 39th International Conference on Machine Learning},pages={16296--16317},year={2022},editor={Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},volume={162},series={Proceedings of Machine Learning Research},month={17--23 Jul},publisher={PMLR},pdf={https://proceedings.mlr.press/v162/nader22a/nader22a.pdf},url={https://proceedings.mlr.press/v162/nader22a.html},}
Package: dnntime
Deep Neural Networks (DNN) for time-series dataTurnkey and modular deep learning predictive modeling package for time-series data. It allows for univariate and multivariate time-series as well as single and multi-step forecasts. DNN models include RNNs, LSTMs, GRUs, CNNs, hybrids, and more.Quick startStep 1)Create and activate new env usingpipenvorcondawith Python 3.6 or higher. Here, the env is nameddts.conda create -n dts python=3.6
conda activate dtsStep 2)Pip installdnntimepackage. It will automatically install or update the dependent packages.pip install dnntimeStep 3)In your working directory, download the example directory from this repo andcdinto it.svn export https://github.com/Kevin-Chen0/dnn-time-series.git/trunk/example
cd exampleStep 4)To run it locally, openlocal_run.ipynband proceed to run all. It will uselocal_config.yamlas parameters to customize the procedures at runtime. It double-checks whether you installed the latestdnntime(v0.3.9.3) and will install for you if not. Make sure that you have set the dataset file path in local_config.yaml and the local_config.yaml path in local_run.ipynb.NOTE:It is highly recommended to run this package using a GPU. Although CPU may work on small-scale datasets of < 10,000 samples, it may encounter performance issues on any dataset larger than that, including the example datasets found here. If you do not have a GPU, you can skip Step 4) and move to Step 5) Google Colab.Step 5)To run it on Google Colab, first make sure that you have a Google account and is logged in. Then go to the Colabpageand uploadcolab_run.ipynbcolab_config.yaml, and an time-series exampledataset. Those files will actually be stored onto your Google Drive upon uploading into Colab. Next, copy the sharable links and the colab_config.yaml and given dataset files and add them onto colab_run.ipynb. Finally, in the colab_run.ipynb notebook, set the runtime type asGPUand run all.NOTE:You can copy this notebook to run on any cloud notebook as long as you can customize how to store and extract the files from that cloud instance.
Package: dnn-tip
DNN-TIP: Common Test Input Prioritizers LibraryImplemented ApproachesSurprise AdequaciesDistance-based Surprise Adequacy (DSA)Likelihood-based Surprise Adequacy (LSA)MultiModal-Likelihood-based Surprise Adequacy (MLSA)Mahalanobis-based Surprise Adequacy (MDSA)abstractMultiModal Surprise AdequacySurprise CoverageNeuron-Activation Coverage (NAC)K-Multisection Neuron Coverage (KMNC)Neuron Boundary Coverage (NBC)Strong Neuron Activation Coverage (SNAC)Top-k Neuron Coverage (TKNC)UtilitiesAPFD calculationCoverage-Added and Coverage-Total Prioritization Methods (CAM and CTM)If you are looking for the uncertainty metrics we also tested (including DeepGini),
head over to the sister repositoryuncertainty-wizard.If you want to reproduce our exact experiments, there's a reproduction package and docker stuff available attestingautomated-usi/simple-tip.InstallationIt's as easy aspip install dnn-tip.DocumentationFind the documentation athttps://testingautomated-usi.github.io/dnn-tip/.CitationHere's the reference to the paper as part of which this library was release:@inproceedings{10.1145/3533767.3534375,
author = {Weiss, Michael and Tonella, Paolo},
title = {Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)},
year = {2022},
isbn = {9781450393799},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3533767.3534375},
doi = {10.1145/3533767.3534375},
booktitle = {Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis},
pages = {139–150},
numpages = {12},
keywords = {neural networks, Test prioritization, uncertainty quantification},
location = {Virtual, South Korea},
series = {ISSTA 2022}
}
Package: dnode
dnode$ pip install dnodefromdnodeimport*print('============== load data ===============')data={'a':1,'b':{'b1':3},'c':{'c1':1,'c2':{'c22':22}},'d':['d1','d2','d3'],'e':[{'ee':1},{'ee':2},{'ee':3}],'f':[['f11','f12'],['f21','f22']],'g':[[{'gg':11},{'gg':12}],[{'gg':21},{'gg':22}]],}obj=DNode(data)print(obj.to_dict())assertobj.to_dict()==dataprint('=========== print object ===============')print(obj)obj.pprint()print('============= print json ===============')print(obj.json)# or print(obj.dumps(indent=4))print('=========== test getattr ===============')assertobj.a==1assertobj.b.b1==3assertobj.c.c2=={'c22':22}==DNode({'c22':22})assertobj.d[1]=='d2'assertobj.e[1].ee==2assertobj.f[0][0]=='f11'assertobj.g[0][0].gg==11assertobj.h==Noneprint('=========== test setattr ===============')obj.a='change_a'obj.b.b1='change_b'obj.c.c2.c22='change_c'obj.d[1]='change_d'obj.e[1].ee='change_e'obj.f[0][0]='change_f'obj.g[0][0].gg='change_g'data=obj.to_dict()assertdata['a']=='change_a'assertdata['b']['b1']=='change_b'assertdata['c']['c2']['c22']=='change_c'assertdata['d'][1]=='change_d'assertdata['e'][1]['ee']=='change_e'assertdata['f'][0][0]=='change_f'assertdata['g'][0][0]['gg']=='change_g'print('======== test set non-json type =========')obj.a={1,2,3}data=obj.to_dict()assertdata['a']==Noneprint('============== test clear ===============')obj.clear()obj.pprint()print('============== list init ================')data='[ {"a": 1}, [{"b": 2}, {"c": 3}] ]'rs=DNode(data)print(rs)assertisinstance(rs,list)assertrs[0].a==1assertrs[1][0].b==2assertrs[1][1].c==3print('=========================================')print('\n\n-------------- test SMNode ---------------')print('========= test STRUCT_FIELDS ============')obj=SMNode({})SMNode.STRUCT_FIELDS=set(['aaa','bbb'])obj.aaa=123obj.ccc=456print(obj.json)print('`ccc` should not in the output')print('=========================================')
Package: dnoticias-backoffice
=====
dnoticias_backofficednoticias_backoffice is a Django app to make the authentication in the DNOTICIAS PLATFORMS.Detailed documentation is in the "docs" directory.Quick startAdd "dnoticias_backoffice" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'dnoticias_backoffice',
]Include the dnoticias_backoffice URLconf in your project urls.py like this::path('auth/', include('dnoticias_backoffice.urls')),Runpython manage.py migrateto create the polls models.Add the necessary settings variables
Package: dnpak.py
dnpak.pyA python package to manipulate Dragon Nest pak file.Based on data definitions fromvincentzhang96/DragonNestFileFormatsInstallationGetting StartedWrite a new PAK and add files into itWrite a new PAK and add all files inside a folderRead PAK and extract files insideDevelopingInstall package locallyBuild packageInstallationMake sure you have pip installed, and run this command:$pipinstalldnpak.pyGetting StartedWith this package you can write and read to pak fileWrite a new PAK and add files into itimportdnpakpak=dnpak.EtFileSystem.write("filename.pak")pak.add_file("path/to/file","file/location/in/pak")pak.add_file("another/file","another/file/location/in/pak")pak.close_file_system()# Make sure to close fileWrite a new PAK and add all files inside a folderpak=dnpak.EtFileSystem.write("filename.pak")pak.add_files("path/to/folder")pak.close_file_system()Read PAK and extract files insidepak=dnpak.EtFileSystem.read("filename.pak")pak.extract()pak.close_file_system()DevelopingGuide for developing, if you're interested in developing this feel free to make a pull requestInstall package locally$pipinstall-e.Build packageMake sure you have the latest version of PyPA’sbuildinstalled:$py-mpipinstall--upgradebuildNow run this command from the same directory wherepyproject.tomlis located:$py-mbuild
Package: dnplab
DNPLab - Bringing the Power of Python to DNP-NMR SpectroscopyDNPLab is a collaborative project created by:Bridge12 Technologies, Inc.Han Lab UCSBFranck Lab Syracuse UniversityAuthors:
Timothy Keller, Thomas Casey, Yanxian Lin, Yen-Chun Huang, Karl Rieger, John Franck, Thorsten Maly, Songi HanDNPLab is an object-oriented Open Source Python-based package for importing, processing, and analyzing data determined in aDynamic Nuclear Polarization (DNP)experiment.FeaturesImport many different NMR data formats (Topspin, VnmrJ, Prospa, ...)Create N-dimensional data objectsEasy processing of NMR data (apodization, zero-filling, Fourier transformations, alignment, ...)Analyze data using the specific modulesInstallationDNPLab can be easily installed via pip:pip install dnplabDocumenationFind the online documentation at:http://docs.dnplab.netDevelopementClone the git repository fromhttps://github.com/DNPLab/dnplab
Package: dn-plans
Django-plans is a pluggable app for managing pricing plans with quotas and accounts expiration.Features currently supported:Multiple plans,Support for user custom plans.Flexible model for parametrizing plans (quota).Customizable billing periods (plan pricing),Order total calculation using customizable taxation policy (e.g. in EU calculating VAT based on seller/buyer countries and VIES)Invoicing,Account expiratons + E-mail remainders.Documentation:https://django-plans.readthedocs.org/Master branch: Support for Django 2.0, 2.1, support for Python 3.6LicenseDjango Plans is licensed and distributed under MIT licesne..
Package: dnptools
DENOPTIM's Python tools (dnptools)Repository for python utilities related with DENOPTIM: the software for De Novo OPTimization of In/organic Molecules (seehttps://github.com/denoptim-project/DENOPTIM).ToolsHere is an overview of the tools included here:Socket server running a scoring servicescoringservicerun a socket server that provides scores to DENOPTIM'sSocketProvidedDescriptor. The sever allows low-latency communication between DENOPTIM and scoring functions written in python. In particular, it allows to by-pass the overhead needed to startup a python process from within DENOPTIM'sfitness providers.
Example of usage:from dnptools import scoringservice

def scoring_function(json_msg):
    smiles_string = json_msg[scoringservice.JSON_KEY_SMILES]
    score = ...do something to get the score from processing smiles_string...
    return score
    
scoringservice.start(scoring_function, 'localhost', 3863)Note thatlocalhostand port number 3863 are just parameters that can be choosen freely, but should be consistent with the settings of any client that wants to communicate with such server. The server is a threading server that can deal with multiple clients, like parallel threads running a fitness providing task each.
When you do not need the scoring service any more, use the following to stop the server:scoringservice.stop('localhost',3863)Logging can be controlled as follows:scoringservice.logger.setLevel(logging.DEBUG)InstallThe package is available onpypiandanaconda, so install it withpip install dnptoolsorconda install -c denoptim-project dnptoolsLicenseGNU Affero General Public License v3 or later (AGPLv3+)AcknowledgmentsThe Research Council of Norway is acknowledge for funding.
Package: dnres
dnres: data n resultsdnresis a python package for managing and sharing data and results generated from any type of data analysis. It is a tagging system that facilitates modular type of analysis by allowing easy tagging, storing and loading of python objects or files across different python scripts.UsageConfiguration filednresrequires a configuration file. In this file, two sections should be specified:PATHS, andINFO. In the sectionPATHSthe keysstructureanddatabaseare mandatory. In the sectionINFOthe keydescriptionis mandatory.Example of configuration file with filenameconfig.ini:[PATHS]structure=foo/bardatabase=foo/foo/bar[INFO]description="This is the description of the analysis related to the data and results."The configuration file is passed as argument upon instantiation of theDnResfromdnresimportDnResres=DnRes('config.ini')Storing and loading of python objectsExample of storing a list in analytical scriptscript_01.pyfromdnresimportDnResres=DnRes('config.ini')# Create some datax=[1,2,3]# Store data to use in another analytical scriptres.store(data=x,tag='variousLists',path='dir1/mylist.pickle',description='List with three numbers.',source='script_01.py')Valid serializations arejsonorpickle. Serialization if taken from the extension of the path (.jsonor.pickle).Example of loading stored data fromscript_01.pyinscript_02.py:fromdnresimportDnResres=DnRes('config.ini')# Show available tagged dataprint(res)# Load tagged datax=res.load('dir1/mylist.pickle')Tagging of files or directoriesTagging is only available for files or directories that are inside thestructure.
Example of tagging a.csvfile in analytical scriptscript_01.pyfromdnresimportDnResres=DnRes('config.ini')filepath='dir1/foo.csv'res.tag('someTag',filepath)Load inscript_02.pythe.csvfilepath that was tagged inscript_01.py.fromdnresimportDnResimportpandasaspdres=DnRes('config.ini')# load() method returns absolute filepath when stored data is not python object.filepath=res.load('dir1/foo.csv')df=pd.read_csv(filepath,sep='\t')DocumentationFor complete package reference visitdnres.readthedocs.InstallationpipinstalldnresCLI utilitydnres-clican be foundhere.LicenseBSD 3
Package: dnres-cli
dnres-cliCommand line utility to be used along withdnrespython package.UsageParse config$dnresconfig.iniPrint contents of stored/tagged data$dnresconfig.inicat--helpFor files, the following filetypes are supported for printing:.csv.tsv.xls.xlsxFor python objects, the following classes are supported:dictlisttuplesetstrIf filetype or class is not supported, the filepath of the stored data gets printed.Print absolute filepath of stored/tagged data$dnresconfig.inils--helpOther commandsThe followingdnresmethods are exposed as cli commands:infotagremove-from-dbremove-tagInformation on these commands/methods can be foundhere.To get help on these commands run:$dnresconfig.iniCOMMAND--helpInstallationpip install dnres-cliLicenseBSD 3
Package: dnry-config
DNRY-ConfigA multi-source configuration library.The goal of DNRY-Config is to simplify configuration
loading and overriding.  With DNRY-Config you can
easily specify several configuration sources and use them
from your application without dealing with the details.DNRY-Config resolve conflicts and provides namespaced
access to keys to support well organized configuration files.Quick StartInstall DNRY-Configpipinstalldnry_configRead a Yaml file in your program.fromdnry.configimportConfigFactoryfromdnry.config.yamlimportYamlSourceconf=ConfigurationFactory([YamlSource("./config1.yaml")]).build()config_value=conf.get("app:message")There are many examples in thesamples/directory.
Package: dnry-srvhost-builder
DNRY-SrvHost-BuilderA library for building a long running service in python.This library is inspired by ASPNETCORE. This package provides a set of classes and interfaces that
simplify the initialization of a service by integrating configuration management fromdnry.configand container based dependency injection usingpyioc3.This library was intended as a platform on which to create long-running
services and reduce boiler plate code. You can create your own service host
use one from the DNRY.SrvHost library.Quick StartInstall dnry-srvhost-builderpipinstalldnry-srvhost-builderCreate your own own service hostfromdnry.srvhost.builderimportSrvHostBaseclassAppHost(SrvHostBase):defrun(self,*args,**kwargs):print('Do something cool!')Build your programfromdnry.configimportIConfigFactoryfromdnry.srvhost.builderimportSrvHostBuilder,ISrvHostContext,ISrvHostfrompyioc3importStaticContainerBuilderdefsetup_config(ctx:ISrvHostContext,conf:IConfigFactory):# Add configuration files herepassdefsetup_services(ctx:ISrvHostContext,services:StaticContainerBuilder):services.bind(annotation=ISrvHost,implementation=AppHost)if__name__=="__main__":SrvHostBuilder("App")\.config_configuration(setup_config)\.config_services(setup_services)\.build()\.run()That's it! You are ready to build something cool. You can do much moreHow What?in setup_service and setup_config. For information on how to use theIConfigFactory, see the documentation aten0/dnry-config.
For more information on how to useStaticContainerBuilder, see the
documentation aten0/pyioc3.
Package: dnsadmin53
[![Build Status](https://travis-ci.org/huit/python-dnsadmin53.png?branch=master)](https://travis-ci.org/huit/python-dnsadmin53)Manage access to zones in Route 53There are a couple of limitations on IAM Objectshttp://docs.aws.amazon.com/IAM/latest/UserGuide/LimitationsOnEntities.htmlBased on this it seems like roles will be the best way to goTwo types of cross account access both grant access by giving the external AWS account the ARN of the role and the user then makes api calls to AssumeRole with the ARN of the role to get temp credentials allowing access to the role. (in our case updating DNS entries within a zone)The first allows you to delegate access with just Account ID putting the trust on the repote account to create a group which has approriate access to the Role ARNhttp://docs.aws.amazon.com/IAM/latest/UserGuide/cross-acct-access.htmlThe second type of delegation requires both an Account ID AND a External ID, which prevents the “Confused Deputy” problem (http://en.wikipedia.org/wiki/Confused_deputy_problem)http://docs.aws.amazon.com/STS/latest/UsingSTS/sts-delegating-externalid.htmlhttp://docs.aws.amazon.com/STS/latest/UsingSTS/Welcome.html