Saravanan666/pypi-org huggingface dataset:
Package: deepcomparer
DeepcomparerDeep compare python objects and structures like dictionaries, lists and iterables.Getting StartedInstallationpipinstalldeepcomparerUsagefromdeepcomparerimportdeep_compareclassPokemon:def__init__(self,name):self.name=nameuser:dict={'name':'ash','links':{'pokehub':'@ash'},'pokemons':[Pokemon('pikachu')]}user2:dict={'name':'ash','links':{'pokehub':'@ash'},'pokemons':[Pokemon('geodude')]}print(deep_compare(user,user2))# output: Falseuser2['pokemons']=[Pokemon('pikachu')]print(deep_compare(user,user2))# output: Truefromdeepcomparerimportdeep_compareash_data:dict={'name':'ash','links':{'pokehub':'@ash'}}brock_data:dict={'name':'brock','links':{'pokehub':'@brock'}}# Over iterable structuresprint(deep_compare([ash_data,brock_data],[ash_data,brock_data]))# output: True# Over iterable structuresprint(deep_compare([ash_data,brock_data],[brock_data,ash_data]))# output: False# Over unsorted iterable structuresprint(deep_compare([ash_data,brock_data],[brock_data,ash_data],ignore_order=True))# output: True
Package: deepcon
Deepcon-packagePython package for the DEEPCON contact prediction tool athttps://github.com/ba-lab/DEEPCONHow to install DEEPCON package?pip3installdeepconHow to test DEEPCON package?wgethttps://raw.githubusercontent.com/ba-lab/deepcon-package/master/deepcon/test/1a0tP0.alnwgethttps://raw.githubusercontent.com/ba-lab/deepcon-package/master/deepcon/weights-rdd-covariance.hdf5python3importdeepconfromdeepconimportdeepconcovariancedeepconcovariance.main("1a0tP0.aln","1a0tP0.rr")To print the contact map as image, add the image name as the last parameter (optional)deepconcovariance.main("1a0tP0.aln","1a0tP0.rr", "1a0tP0.png")Example contact map as an image:Python package developed by:Chase RichardsEmail:CNRichards@gmail.com
Package: deepconpkg
DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with DropoutContact:Email:adhikarib@umsl.eduHomepage:https://badriadhikari.github.io/Paper:https://www.biorxiv.org/content/10.1101/590455v1DEEPCON using Covariance features as inputTrained and validated using the 3456 proteins in the DeepCov dataset with the covariance features (441 channels) as input.Installation Instructions:You need a deep learing backend that is Keras compatible:pip3 install -U tensorflow
pip3 install pyyamlInstall DEEPCON-Covariance packagepip3 install deepconIntstructions for User:Inside Python:import deepconPredictpython../deepcon-covariance.py--aln./16pkA0.aln--rr./16pkA0.rrEvaluate./coneva-lite.pl-pdb./16pkA.pdb-rr./16pkA0.rr
Package: deepconsensus
Important: Pip install is different for CPU versus GPUIf you're on a GPU machine:pipinstalldeepconsensus[gpu]==1.2.0# To make sure the `deepconsensus` CLI works, set the PATH:exportPATH="/home/${USER}/.local/bin:${PATH}"If you're on a CPU machine:pipinstalldeepconsensus[cpu]==1.2.0# To make sure the `deepconsensus` CLI works, set the PATH:exportPATH="/home/${USER}/.local/bin:${PATH}"Documentation, quick start, citationAll other documentation is on GitHub:https://github.com/google/deepconsensus.
Package: deep-copilot
pypi templateA minimal template for creating a pypi packageUse this template to more easily create a project project to submit to pypi and conda.How to use the template:Click hereto create a new repo (you need to be logged in to GitHub for this link to work), and follow the instructions to create a new repo from this templategit cloneyour new repoEditsettings.iniand fill out all the requested information (you can ignore parts starting with%)Installfastrelease,twine, andconda-buildRegister atpypiandAnacondaCreate a file called~/.pypircwith your pypi login details, as follows:[pypi]
username = your_pypi_username
password = your_pypi_passwordWrite your Python library!Add any package requirements to therequirementsline insettings.iniRunmake releasein the root of your repo. This will automatically:Publish to pypiPublish to AnacondaIncrement your version number
Package: deepcopyall
deepcopys almost everything (functions, lambda ... )pip install deepcopyallfromdeepcopyallimportdeepcopyclassbibi:def__init__(self,bebe=112):self.bebe=bebedefprintbebe(self):print(self.bebe)baba=lambda:print("baba")defbuax(x):print(x)returnxbaba2=deepcopy(o=baba)baba2()buax2=deepcopy(o=buax)q=buax2(444)print(q)bibi2=deepcopy(o=bibi)bibi2inst=bibi2(23323)bibi2inst.printbebe()#outputbaba44444423323
Package: deepcorrect
deepcorrectPre-trained models for punctuation correction (trained on google news, wikipedia and tatoeba) are available athttps://drive.google.com/open?id=1Yd8cJaqfQkrJMbRVWIWtuyo4obTDYu-eDemo of the punctuation model trained on google news corpus is available athttp://bpraneeth.com/projectsThis repo uses a seq2seq model written by me in keras with tensorflow backend. The multi-purpose seq2seq model can be found athttps://github.com/bedapudi6788/txt2txt/Usage:from deepcorrect import DeepCorrect
corrector = DeepCorrect('params_path', 'checkpoint_path')
corrector.correct('hey')
'Hey!'Installation:pip install deepcorrectPoints to Note:Max input and output lengths are 200Segment text into sentences usinghttps://github.com/bedapudi6788/deepsegmentand run punctuation correction on each sentence seperately.
Package: deepcrawl-robots
DeepCrawl Robots.txt live checker>caturls.txt

https://www.ebay.at/
https://www.ebay.at/adchoice
https://www.ebay.at/sl/sell
https://www.ebay.at/mye/myebay/watchlist
https://www.ebay.at/sch/ebayadvsearch
https://www.ebay.at/sch/Kleidung-Accessoires-/11450/i.html
https://www.ebay.at/sch/Auto-Tuning-Styling-/107059/i.html
https://www.ebay.at/sch/Modeschmuck-/10968/i.html
https://www.ebay.at/sch/Damenschuhe-/3034/i.html>catrobots.txt

User-agent:*
Disallow:/sch/pip install deepcrawl_robotsfromdeepcrawl_robotsimportProcessorurls_path="Path to urls file"robots_txt_path="Path to robots.txt file"processor=Processor(user_agent="User agent",urls_file_path=urls_path,robots_file_path=robots_txt_path)>catresult.txt

https://www.ebay.at/mye/myebay/watchlist,true
https://www.ebay.at/adchoice,true
https://www.ebay.at/,true
https://www.ebay.at/sl/sell,true
https://www.ebay.at/sch/Kleidung-Accessoires-/11450/i.html,false
https://www.ebay.at/sch/ebayadvsearch,true
https://www.ebay.at/sch/Modeschmuck-/10968/i.html,false
https://www.ebay.at/sch/Auto-Tuning-Styling-/107059/i.html,false
https://www.ebay.at/sch/Damenschuhe-/3034/i.html,false
Package: deepcs
Deepcs packageThe package can be installed with pip, see thepypi pageThis package is used within the deeplearning labs at CentraleSupélec you can access onthis page.It provides some handy high level functions :for the training and validation loopsfor displaying the summary of a model and progress during trainingfor easily getting unique log directories for logging the training with your tensorboard writerNote, though, that if you ended up on this page, you may still be interested in more professional libraries such astorch lightningwhich provides you with the high level pytorch scripting we need for easily experimenting.
Package: deepctr-torch
DeepCTR-TorchPyTorch version ofDeepCTR.DeepCTR is aEasy-to-use,ModularandExtendiblepackage of deep-learning based CTR models along with lots of core components layers which can be used to build your own custom model easily.You can use any complex model withmodel.fit()andmodel.predict().Install throughpip install -U deepctr-torch.Let'sGet Started!(Chinese Introduction)Models ListModelPaperConvolutional Click Prediction Model[CIKM 2015]A Convolutional Click Prediction ModelFactorization-supported Neural Network[ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response PredictionProduct-based Neural Network[ICDM 2016]Product-based neural networks for user response predictionWide & Deep[DLRS 2016]Wide & Deep Learning for Recommender SystemsDeepFM[IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR PredictionPiece-wise Linear Model[arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click PredictionDeep & Cross Network[ADKDD 2017]Deep & Cross Network for Ad Click PredictionsAttentional Factorization Machine[IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention NetworksNeural Factorization Machine[SIGIR 2017]Neural Factorization Machines for Sparse Predictive AnalyticsxDeepFM[KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender SystemsDeep Interest Network[KDD 2018]Deep Interest Network for Click-Through Rate PredictionDeep Interest Evolution Network[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate PredictionAutoInt[CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural NetworksONN[arxiv 2019]Operation-aware Neural Networks for User Response PredictionFiBiNET[RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate PredictionIFM[IJCAI 2019]An Input-aware Factorization Machine for Sparse PredictionDCN V2[arxiv 2020]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank SystemsDIFM[IJCAI 2020]A Dual Input-aware Factorization Machine for CTR PredictionAFN[AAAI 2020]Adaptive Factorization Network: Learning Adaptive-Order Feature InteractionsSharedBottom[arxiv 2017]An Overview of Multi-Task Learning in Deep Neural NetworksESMM[SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateMMOE[KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-ExpertsPLE[RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized RecommendationsDisscussionGroup & Related ProjectsGithub DiscussionsWechat Discussions公众号：浅梦学习笔记微信：deepctrbot学习小组加入主题集合Related ProjectsAlgoNotesDeepCTRDeepMatchGraphEmbeddingMain Contributors(welcome to join us!)​​Shen Weichen​Alibaba Group​​​Zan ShuxunAlibaba Group​Wang Ze​Meituan​​Zhang WutongTencent​​​Zhang YuefengPeking University​​​Huo JunyiUniversity of Southampton​​​Zeng Kai​SenseTime​​​Chen K​NetEase​​​Cheng Weiyu​Shanghai Jiao Tong University​​​TangTongji University​
Package: deepdanbooru-onnx
DeepDanbooru动漫图片分类模型安装pip install deepdanbooru-onnx使用fromdeepdanbooru_onnximportDeepDanbooru,process_imagefromPILimportImageimportnumpyasnpdanbooru=DeepDanbooru()#usage 1print(danbooru('test.jpg'))img=Image.open('test.jpg')print(danbooru(img))#usage 2img=process_image(img)# iamge to ndarrayprint(danbooru(img))#usage 3print(list(danbooru(['test1.jpg','test2.jpg'])))TODO提供轻量化模型结果缓存提供WEB接口模型量化精度更高的模型(slow)
Package: deep-dashboard
DEEP dashboardWelcome to the DEEP Dashboard, a web user interface to interact with theDEEP Platformresources.This is still work in progress, under very heavy development.ConfigurationPlease check the sample configuration file that is distributed with the
dashboard. You must pass one to the dashboard, otherwise it will refuse to
start.Minimal configurationThe minimal set of options that you must configure in order to run the
dashboard is the following:[iam]
base_url = <IAM endpoint>
client_id = <OpenID Connect Client ID>
client_secret = <OpenID Connect Client Secret>

[orchestrator]
url = <orchestrator_url>As you can see, you need to create an OpenID Connect Client in an IAM instance,
then add it to the configuration file. Assuming that you are using the DEEP
production infrastructure, the endpoints can be configured as follows:[iam]
base_url = "https://iam.deep-hybrid-datacloud.eu"
client_id = <OpenID Connect Client ID>
client_secret = <OpenID Connect Client Secret>

[orchestrator]
url = "https://deep-paas.cloud.ba.infn.it/orchestrator"The dashboard assumes that it has read and write permissions to the runtime
directory that is configured in theruntime_dirconfiguration option. By
defaul this is set to/var/run/deep-dashboard/.Running the dashboardThe simplest way is to use the Docker compose with the compose file that is
provided.First of all, you would need to define adocker/.envfile, containing
the aforementioned minimal configuration. Validate the docker-compose file
with:docker-compose -f docker/docker-compose.yml configThen you can build the containers needed, and start the Docker compose with:make docker-compose-build
make docker-compose-runIf you wish to use a different environment file, you can do so, indicating it
with theCOMPOSE_LOCAL_ENVenvironment variable:COMPOSE_LOCAL_ENV=foo.env make (...)Or, directly with Docker compose:docker-compose -f docker/docker-compose.yml build
docker-compose -f docker/docker-compose.yml --compatibility up --force-recreateAcknowledgementsThis software has been developed within the DEEP-Hybrid-DataCloud (Designing
and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud)
project that has received funding from the European Union's Horizon 2020
research and innovation programme under grant agreement No 777435.
Package: deep-data-profiler
The Deep Data Profiling (DDP) library provides tools for analyzing the internal decision structure of a trained deep neural network.
The library was inspired by the work of Qiu, et al. in Adversarial Defense Through
Network Profiling Based Path Extraction (2019), arXiv:1904.08089.
Full documentation may be found athttps://pnnl.github.io/DeepDataProfiler.DDP contains code for generating graphical representations and feature visualizations,
for VGG-like sequential and ResNet models implemented in PyTorch.
DDP also provides tools from topological data analysis for analysis of these representations.NoticeThe research used in this repository is part of the Mathematics of Artificial Reasoning in Science (MARS) Initiative at Pacific Northwest National Laboratory (PNNL).  It was conducted under the Laboratory Directed Research and Development Program at PNNL, a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy.Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights.
Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.PACIFIC NORTHWEST NATIONAL LABORATORY
operated by BATTELLE for the
UNITED STATES DEPARTMENT OF ENERGY
under Contract DE-AC05-76RL01830
Package: deepdefacer
DeepDefacer: Automatic Removal of Facial Features via Deep Learning.-------->DeepDefacer is a MRI anonymization tool written in Python, on top of Tensorflow and Keras, that was developed in partnership with the Poldrack Lab at Stanford University. It can be used to quickly deface 3D MRI images of any resolution and size on commercial CPUs and GPUs. Its goal is to provide the community with an easy to use and efficient tool for defacing medical images that require anonymization for compliance with federal privacy laws (e.g HIPAA).Referencing and citing DeepDefacerIf you use DeepDefacer in your work, please refer to this citation for the current version:@article{khazane2019state,
  title={DeepDefacer: Automatic Removal of Facial Features from MR Scans Via Deep Learning},
  author={Anish Khazane, Julien Hoachuck, Dr. Chris Gorgowelski, Dr. Russell Poldrack},
  journal={in proceedings, arXiv preprint},
  year={2019}
}If you use any of the architecture code from theARFF-CNN, please also use the citation above to comply with its authors' instructions on referencing.RequirementsAny Python version between 2.7 and 3.6.If you are using the GPU version of this library, please ensure that your GPU drivers are correctly installed and up to date. Please referenceGPU Support for Tensorflow-GPUfor further details on GPU setup.Input MRI images must have 3D structure and be saved in either .nii or .nii.gz format.InstallationDeepdefacer can be easily installed on any operating system via Pypi. There are two versions of this package; CPU or GPU support. Please enteroneof the following commands into your terminal window to begin installation, depending on your system specifications and desired python version.CPU Supportpip install deepdefacer[tf_cpu] / pip3 install deepdefacer[tf_cpu]GPU Supportpip install deepdefacer[tf_gpu] / pip3 install deepdefacer[tf_gpu]Note: If you are using a ZSH-type shell, you may need to wrap the package name in quotations in order to successfully initiate the Pip installation. (e.gpip install "deepdefacer[...]").Usage and DocumentationOnce installed, please enterdeepdefacer --helpinto your terminal window to see a list of available options for this program. Defacing a 3D MRI image is extremely simple, and can be done with the following command:deepdefacer --input_file <input filename>The program will output a defaced image in the same directory as the input file, unless--defaced_output_pathis specified.You can also save the predicted mask for any MRI image by specifying the desired output filename and directory with the--mask_output_pathoption.
Package: deepDegron
deepDegronWhile a few UPS substrate mutations can be implicated in cancer based on known degrons, systematic investigation requires better degron annotation. To address this challenge, we developed a protein sequence-based model, deepDegron, that leverages data from the recently published high throughput global protein stability (GPS, (Koren et al., 2018; Timms et al., 2019)) assay of the n-terminal and c-terminal proteome to predict degrons. GPS measures the protein stability impact of peptides when attached to proteins (Yen et al., 2008), as measured by FACS-sorting of cells based on a fluorescent reporter protein (GFP, green) compared to a control reporter with no peptide attached (dsRed, red). Because the peptides consisted of known sequences and could contain degrons, deepDegron can learn sequence-rules of degron impact on protein stability.DocumentationFor more documentation, please visit ourwebsite documentation.InstallationWe recommend that you use python 3.7 to run deepDegron.pipThe easiest way to install deepDegron is throughpip.$pipinstalldeepDegron
$pyensemblinstall--release75--specieshuman# download human hg19 reference data$pyensemblinstall--release95--specieshuman# download human hg38 reference dataFrom sourceAs a first step, please change to the top-level directory in the deepDegron source code.You can install the dependencies of deepDegron usingconda. Once you have conda installed, create an environment to run deep degron using the following commands:$condaenvcreate-fenvironment.yml# install dependencies$sourceactivatedeepDegron# activate environment$pyensemblinstall--release75--specieshuman# download human reference data$pythonsetup.pyinstall# install deepDegronAn alternative way to install the python dependencies is to use pip.$python-mpipinstall--upgradepip
$pipinstall-rrequirements.txt# install required packages$pyensemblinstall--release75--specieshuman# download human reference data$pythonsetup.pyinstall# install deepDegron
Package: deep-dict-update
deep-dict-updateA Python package for recursively updating nested dictionaries with the content of another dictionary.InstallationYou can installdeep-dict-updatevia pip:pipinstalldeep-dict-updateUsagefromdeep_dict_updateimportdeep_dict_updateorig_dict={'data':[{'id':1,'name':'Alice'},{'id':2,'name':'Bob'}]}new_dict={'data':[{'id':3,'name':'Charlie'},{'id':4,'name':'David'}]}updated_dict=deep_dict_update(orig_dict,new_dict)print(updated_dict)Output{'data':[{'id':3,'name':'Charlie'},{'id':4,'name':'David'}]}ContributingContributions are welcome! For major changes, please open an issue first to discuss what you would like to change.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: deep-diff
a tool to diff dict list set data output like the npm packagedeep_diffUSAGEpip install deep_diff
from deep_diff import diff
diff({'a':1},{'b':1})[{kind:'E',path:['name'],lhs:'myobject',rhs:'updatedobject'},{kind:'E',path:['details','with',2],lhs:'elements',rhs:'more'},{kind:'A',path:['details','with'],index:3,item:{kind:'N',rhs:'elements'}},{kind:'A',path:['details','with'],index:4,item:{kind:'N',rhs:{than:'before'}}}]DifferencesDifferences are reported as one or more change records. Change records have the following structure:kind- indicates the kind of change; will be one of the following:N- indicates a newly added property/elementD- indicates a property/element was deletedE- indicates a property/element was editedA- indicates a change occurred within an arraypath- the property path (from the left-hand-side root)lhs- the value on the left-hand-side of the comparison (undefined if kind == 'N')rhs- the value on the right-hand-side of the comparison (undefined if kind == 'D')index- when kind == 'A', indicates the array index where the change occurreditem- when kind == 'A', contains a nested change record indicating the change that occurred at the array index
Package: deepdiff-viewer
DeepDiff ViewerA simple utility to visualize DeepDiff in the terminal. Supportrichfor even prettier renders.Installpipinstalldeepdiff_viewerTo use theRichViewerclass:pipinstalldeepdiff_viewer[rich]UsageSimple usage:fromdeepdiffimportDeepDifffromdeepdiff_viewer.richimportRichViewert1={"a":1}t2={"a":3}ddiff=DeepDiff(t1,t2,view="tree")viewer=RichViewer(ddiff)print(viewer.render())Pydantic supportIf you usepydanticfor your models, theRichViewerwill use themodel_dumpfunction to render the model in ayamlformat when you add or delete new object. Otherwise the render would show you
the python class and not a pretty render of the object.Note thatpydanticis not a dependency of the project but will be imported if
it is found.ExamplesSee theexamples/folder.SimpleComplexWith PydanticConceptsTheDeepDiffTreeViewerclass wraps aDeepDiffobject that was built with
thetreeview mode. It
generates a tree of all the differences found in theDeepDiffobject so that
it can be rendered.Currently the only implemented viewer is theRichViewerbut you can
extend theDeepDiffTreeViewerbase class to make your own viewer if you want
to use something else thanrich.LicenseMIT