Saravanan666/pypi-org huggingface dataset:
Package: deepview-datasets
DeepView Dataset LibraryThis project contains python and rust binding for reading and exporting datasets.
The library also includes Dataset iterators for both GPU (Tensorflow/Pytorch) and
iterators for running validation on embedded devices where ML framewors are not
available.The labrary is distributed by Au-Zone Technologies with the aim of outperforming data processing pipelines
while running on embedded devices or even when training a model. In order to generalize as much as possible,
several tasks were included on it:Object DetectionDistance EstimationInstance Segmentation and Semantic SegmentationRadar and Camera FusionPCL classificationData augmentation is another key feature included into this library. Augmenting data
is higly recommended when training ML models. The larger the model, the more data needs.Python BindingThe python binding can be installed by calling:pipinstall--upgradedeepview-datasetsRust BindingComing Soon!
Package: deepvission-nightly
DeepvisionPython Library for Deep Neural Netrwork Pre-trained Model testing. Using this library anyone can test any image just give an image as an input arguments and only the single line of code and it will give the prediction so here in this library is only for testing Deep CNN models.Pretrained ModelModelCodeTop-5 AccuracyVgg16Code89.8ResNet50Code92.2MobileNet_v2_1.4Code92.5Tutorials
Package: deepvog
DeepVOGDeepVOG is a framework for pupil segmentation and gaze estimation based on a fully convolutional neural network.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.PrerequisitesTo run DeepVOG, you need to have a Python distribution (we recommendAnaconda) and the following Python packages:scikit-image
Tensorflow
etc.InstallingA step by step series of examples that tell you how to get DeepVOG running.Publication and CitationIf you plan to use this work in your research or product, please cite this repository and our publication pre-print onarXiv.AuthorsYiu Yuk Hoi-Implementation and validationSeyed-Ahmad Ahmadi-Research study conceptMoustafa Aboulatta-Initial workLicenseThis project is licensed under the GNU General Public License v3.0 (GNU GPLv3) License - see theLICENSEfile for detailsAcknowledgmentsWe thank our fellow researchers at the German Center for Vertigo and Balance Disorders for help in acquiring data for training and validation of pupil segmentation and gaze estimation. In particular, we would like to thank Theresa Raiser, Dr. Virginia Flanagin and Prof. Dr. Peter zu Eulenburg.
Package: deepvoxnet2
DeepVoxNet2DeepVoxNet2 (DVN2) is a Python library to make it easier to implement deep learning pipelines for medical applications using convolutional neural networks (CNNs).
It is lightly based on the privateDeepVoxNetlibrary.
In essence, the library can be used as an add-on to Tensorflow/Keras, Pytorch or any other deep learning framework in Python.
Currently, the use with Tensorflow/Keras is simplest, with readily available CNN architectures, metrics, losses and the DvnModel class to group your entire pipeline and, e.g., bypass the use of Keras' fit function, etc.DVN2 provides:Utility functions such as resampling, registration, Dicom loading, etc.Objects for data organization (Mirc, Dataset, Case, Record, Modality).Objects for data sampling (Sampler).Objects for building pre- to postprocessing pipelines (Transformer, Creator) that keep track of the spatial origin of the data inherently and that you build just like you work in Keras.InstallationThe library can be used as a Python package that can be added to your active Python 3.9 environment via:First downloading/cloning/forking a specific version of the repository to your local machine and then via:pip install -e "/path/to/deepvoxnet2[sitk]"Installing a specific version directly from GitHub via:pip install "git+https://github.com/JeroenBertels/deepvoxnet2@deepvoxnet2-2.13.21#egg=deepvoxnet2[sitk]"Installing a specific versiondirectly from PyPIvia (only for official releases):pip install "deepvoxnet2[sitk]==2.13.21"To upgrade your installation using the first method just download another version and repeat the process orgit pullanother version if possible. When using the second or third method simply repeat the command but add the--upgradeflag.
The[sitk]flag will install the SimpleITK and SimpleElastix software packages, but this is optional (for wider compatibility).Additionally, of the official releases there are also Docker containersavailable on DockerHub. These can be ran via:Docker:docker run --rm -it --gpus="device=0" -v /path/on/local/machine/a:/path/in/container/a -v /path/on/local/machine/b:/path/in/container/b jeroenbertels/deepvoxnet2:latestSingularity:cd /path/to/pulled/images
singularity pull docker://jeroenbertels/deepvoxnet2:latest
SINGULARITYENV_CUDA_VISIBLE_DEVICES=0 singularity run -B /path/on/local/machine/a:/path/in/container/a,/path/on/local/machine/b:/path/in/container/b  --cleanenv --nv deepvoxnet2_latest.sifTutorialsA Jupyter Notebook-style tutorial can be foundhere, which guides you through some of the basic design ideas behind DeepVoxNet2.Other real-world examples are:A notebook with allexperiments and codeaccompanyingthis articleabout the effect of $\Phi$ and $\epsilon$ when using the Dice loss in tasks with missing or empty labels.Cite asBertels, J., Robben, D., Lemmens, R., & Vandermeulen, D. (2022). DeepVoxNet2: Yet another CNN framework. ArXiv, 1–15.http://arxiv.org/abs/2211.09569
Package: deepwave
DeepwaveDeepwave provides wave propagation modules for PyTorch, for applications such as seismic imaging/inversion. You can use it to perform forward modelling and backpropagation, so it can simulate wave propagation to generate synthetic data, invert for the scattering potential (RTM/LSRTM), other model parameters (FWI), initial wavefields, or source wavelets. You can use it to integrate wave propagation into a larger chain of operations with end-to-end forward and backpropagation. Deepwave enables you to easily experiment with your own objective functions or functions that generate the inputs to the propagator, letting PyTorch's automatic differentiation do the hard work of calculating how to backpropagate through them.To install it, I recommend first installing PyTorch using the instructions on thePyTorch website. Deepwave can then be installed usingpip install deepwaveThe documentationcontains examples and instructions on how to install and use Deepwave. You might also like to watcha video summary of Deepwave.FeaturesSupports the 2D constant density acoustic / scalar wave equation (regular and Born modelling) and 2D elastic wave equation (P-SV)Runs on CPUs and appropriate GPUsThe gradient of all outputs (final wavefields and receiver data) can be calculated with respect to the model parameters (wavespeed, scattering potential, etc.), initial wavefields, and source amplitudesUses thePasalic and McGarryPML for accurate absorbing boundaries in the scalar wave propagatorUsesC-PMLwith theW-AFDAfree-surface method for the elastic wave propagatorThe PML width for each edge can be set independently, allowing a free surface (no PML) on any sideFinite difference accuracy can be set by the userA region of the model around the sources and receivers currently being propagated can be automatically extracted to avoid the unnecessary computation of propagation in distant parts of the modelDouble backpropagation through the regular scalar propagator, includingcalculating the HessianQuick ExampleIn a few lines you can make a velocity model, propagate a wave from a source in the top left corner to a receiver in the top right, calculate an objective function, and backpropagate to obtain its gradient with respect to the velocity.importtorchimportdeepwaveimportmatplotlib.pyplotaspltv=1500*torch.ones(100,100)v[50:]=2000v.requires_grad_()out=deepwave.scalar(v,grid_spacing=4,dt=0.004,source_amplitudes=deepwave.wavelets.ricker(25,200,0.004,0.06).reshape(1,1,-1),source_locations=torch.tensor([[[0,0]]]),receiver_locations=torch.tensor([[[0,99]]]))(out[-1]**2).sum().backward()_,ax=plt.subplots(1,3,figsize=(9,3))ax[0].imshow(v.detach())ax[0].set_title("Velocity model")ax[1].plot(out[-1].detach().flatten())ax[1].set_title("Receiver data")ax[2].imshow(v.grad.detach(),vmin=-1e-5,vmax=1e-5)ax[2].set_title("Gradient")There are more examples inthe documentation.CitingIf you would like to cite Deepwave, I suggest:@software{richardson_alan_2023,author={Richardson, Alan},title={Deepwave},month=sep,year=2023,publisher={Zenodo},version={v0.0.20},doi={10.5281/zenodo.8189232},url={https://doi.org/10.5281/zenodo.8189232}}
Package: deepzoomup
Welcome to deepzoomupEasily share your high resolution photos as deep zoom images with the world!The ability to create and share web based high resolution interactive deep zoom images is a basic need in almost any heritage science project. Unfortunately this used to be a technically demanding job, out of reach for mortals with limited coding skills. Until now.With the python packagedeepzoomupyou can share your beautiful high resolution images via the web with just a few lines of code. Within a few minutes, convert your high resolution image files into deep zoom image tiles, upload them to yourBackblazecloud storage, and share with the world!Here is an example of a cloud stored image that was created withdeepzoomup:One of the Three Kings Adoring the Virgin and Child, Rembrandt van Rijn, c.1635 - c.1640; Rijksmuseum Amsterdam catalog numberRP-T-1930-22For installation instructions, documentation and code see:https://fligt.github.io/deepzoomup/
Package: deeq
DeeqQuantum Computing SDKVersionTutorialhttps://github.com/Deeq/Deeq-tutorialsNoticeThe back end has been changed to tensor network. The previous backend environment can still be used with .run(backend="numpy").Installgit clone https://github.com/Deeq/Deeq
cd Deeq
pip3 install -e .orpip3 install deeqCircuitfromdeeqimportCircuitimportmathc=Circuit()c=Circuit(50)Method ChainCircuit().h[0].x[0].z[0]c=Circuit().h[0]c.x[0].z[0]SliceCircuit().z[1:3]# Zgate on 1,2Circuit().x[:3]# Xgate on (0, 1, 2)Circuit().h[:]# Hgate on all qubitsCircuit().x[1,2]# 1qubit gate with commaRotation GateCircuit().rz(math.pi/4)[0]RunfromdeeqimportCircuitCircuit(50).h[:].run()Run(shots=n)Circuit(100).x[:].run(shots=100)# => Counter({'1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111': 100})Single AmplitudeCircuit(4).h[:].run(amplitude="0101")Expectation value of hamiltonianfromdeeq.pauliimportZhamiltonian=1*Z[0]+1*Z[1]Circuit(4).x[:].run(hamiltonian=hamiltonian)# => -2.0Deeq to QASMCircuit().h[0].to_qasm()Hamiltonianfromdeeq.pauliimport*hamiltonian1=(1.23*Z[0]+4.56*X[1]*Z[2])**2hamiltonian2=(2.46*Y[0]+5.55*Z[1]*X[2]*X[1])**2hamiltonian=hamiltonian1+hamiltonian2print(hamiltonian)Simplify the Hamiltonianhamiltonian=hamiltonian.simplify()print(hamiltonian)QUBO Hamiltonianfromdeeq.pauliimportqubo_bitasqhamiltonian=-3*q(0)-3*q(1)-3*q(2)-3*q(3)-3*q(4)+2*q(0)*q(1)+2*q(0)*q(2)+2*q(0)*q(3)+2*q(0)*q(4)print(hamiltonian)Time Evolutionhamiltonian=[1.0*Z(0),1.0*X[0]]a=[term.get_time_evolution()forterminhamiltonian]time_evolution=Circuit().h[0]forevoina:evo(time_evolution,np.random.rand())print(time_evolution)QAOAfromdeeqimportCircuitfromdeeq.utilsimportqaoafromdeeq.pauliimportqubo_bitasqfromdeeq.pauliimportX,Y,Z,Ihamiltonian=q(0)-q(1)step=1result=qaoa(hamiltonian,step)result.circuit.run(shots=100)Circuit Drawing Backendfromdeeqimportvqefromdeeq.pauliimport*fromdeeq.pauliimportqubo_bitasqhamiltonian=Z[0]-3*Z[1]+2*Z[0]*Z[1]+3*Z[2]*Z[3]+Z[4]step=8result=vqe.Vqe(vqe.QaoaAnsatz(hamiltonian,step)).run()result.circuit.run(backend='draw')Documenthttps://deeq.readthedocs.io/en/latest/ContributorsContributorsDisclaimerCopyright 2022 The DeepRecommend Developers.Deeq
Package: deeqnlpy
What is this?deeqnlpyis the python 3 library for deeq NLP.Deeq(pronounce as deeque) NLP is a Korean NLP,
which provides tokenizing, POS tagging for Korean.How to installpip3installdeeqnlpyHow to get deeq NLPClickthis formFill it.Get emailed download link, a license file.Or use docker image.dockerpullbaikalai/deeq-nlp:v1.4.2Caution: You should use deeq NLP v1.4.2 or later.How to useimportsysimportgoogle.protobuf.text_formatastffromdeeqnlpyimportTaggermy_tagger=Tagger('localhost')# If you have your own local deeq NLP.# ortagger=Tagger()# With smaller public cloud instance, it may be slow.# print results.res=tagger.tags(["안녕하세요.","반가워요!"])# get protobuf message.m=res.msg()tf.PrintMessage(m,out=sys.stdout,as_utf8=True)print(tf.MessageToString(m,as_utf8=True))print(f'length of sentences is{len(m.sentences)}')## output : 2print(f'length of tokens in sentences[0] is{len(m.sentences[0].tokens)}')print(f'length of morphemes of first token in sentences[0] is{len(m.sentences[0].tokens[0].morphemes)}')print(f'lemma of first token in sentences[0] is{m.sentences[0].tokens[0].lemma}')print(f'first morph of first token in sentences[0] is{m.sentences[0].tokens[0].morphemes[0]}')print(f'tag of first morph of first token in sentences[0] is{m.sentences[0].tokens[0].morphemes[0].tag}')# print number# get json objectjo=res.as_json()print(jo)# get tuple of pos tagging.pa=res.pos()print(pa)# another methodsma=res.morphs()print(ma)na=res.nouns()print(na)va=res.verbs()print(va)# custom dictionarycust_dic=tagger.custom_dict("my")cust_dic.copy_np_set({'내고유명사','우리집고유명사'})cust_dic.copy_cp_set({'코로나19'})cust_dic.copy_cp_caret_set({'코로나^백신','"독감^백신'})cust_dic.update()tagger.set_domain('my')tagger.pos('코로나19는 언제 끝날까요?')
Package: deer
DeeRDeeR is a python library for Deep Reinforcement. It is build with modularity in mind so that it can easily be adapted to any need. It provides many possibilities out of the box such as Double Q-learning, prioritized Experience Replay, Deep deterministic policy gradient (DDPG), Combined Reinforcement via Abstract Representations (CRAR). Many different environment examples are also provided (some of them using OpenAI gym).DependenciesThis framework is tested to work under Python 3.6.The required dependencies are NumPy >= 1.10, joblib >= 0.9. You also need Keras>=2.1.For running the examples, Matplotlib >= 1.1.1 is required.
For running the atari games environment, you need to install ALE >= 0.4.Full DocumentationThe documentation is available at :http://deer.readthedocs.io/
Package: deeru-qiniu
DeerU七牛插件安装pipinstalldeeru-qiniu使用运行初始化命令pythonmanage.pyinit_qiniu根据提示进行配置，
你也可以跳过此步，在admin的 “配置” 中添加名为 “七牛配置” 的配置，配置内容为：{'access_key':'access_key','secret_key':'secret_key','bucket_name':'空间名','media_pre':'媒体文件前缀（可为空）','static_pre':'静态文件前缀（可为空）'}或是在settings_local.py中添加如下配置：QINIU={'access_key':'access_key','secret_key':'secret_key','bucket_name':'空间名','media_pre':'媒体文件前缀（可为空）','static_pre':'静态文件前缀（可为空）'}Note运行时优先读取settings_local.py中的配置，没有再读取admin中的修改settings在settings_local.py的CUSTOM_APPS中添加app，
修改或添加STATIC_URL、MEDIA_URL内容为七牛的域名，如果你设置了前缀，需要加上前缀CUSTOM_APPS=['deeru_qiniu.apps.DeeruQiniuConfig']STATIC_URL='http://xx.bkt.clouddn.com/你的前缀/'MEDIA_URL='http://xx.bkt.clouddn.com/你的前缀/'上传文件运行命令上传文件# 上传媒体文件pythonmanage.pyupload_qiniu--typemedia# 上传静态文件，上传静态文件前先运行collectstatic命令pythonmanage.pycollectstaticpythonmanage.pyupload_qiniu--typestatic若有相同名字的文件会上传失败，可用删除命令删除之前上传的文件命令删除命令会删除所有媒体文件或静态文件，不支持单个文件删除，删除单个文件在七牛后台中自行删除# 上传媒体文件pythonmanage.pydelete_qiniu[--type(media|static)]配置说明media_pre ,static_preurl的前缀，可为空，建议设置防止与空间中的旧文件冲突其他如果文章中已经插入了图片，代理媒体文件会导致文章中的图片失效，需要重新编辑文章licenseGUN V3.0
Package: deescalate
Using linux capabilities to drop privileges in python.Github project:https://github.com/stephane-martin/deescalateDocumentation:http://deescalate.readthedocs.org/License: GNU LESSER GENERAL PUBLIC LICENSE v3History0.1.2 (2019-02-26)python3 compatibility0.1.1 (2019-02-26)explicitly relative imports (seehttps://github.com/stephane-martin/deescalate/issues/2)0.1.0 (2015-08-16)init on github
Package: deescovery
DeescoveryDeescoveryis a Python package to find and initialize modules of your Python projects on startup.Find and register blueprints in a Flask project.Automatically initialize Flask extensions.Find all SQLAlchemy models to make alembic happy.Find all FastAPI endpoints.Collect all Celery tasks.Initially designed to initialize Flask applications, it was made generic enough to work with any micro-framework or no framework at all.Micro-framework initialization problemMicro-framework-based projects are clean while they're small. Every micro-framework codebase I've seen, has a mess in the project initialization. With time,create_app()becomes filled with ad-hoc settings, imports-within-functions, and plug-in initializations.The Application Factory Pattern, proposed, for example, in theofficial Flask documentation, and theFlask Mega-Tutorial, legitimize this approach.The nature ofcreate_app()leaves no place for theopen-closed principle. We update this module every time we add a new plug-in, a new blueprint, or a new package.# myproject/__init__.pydefcreate_app(config_class=Config):app=Flask(__name__)app.config.from_object(config_class)db.init_app(app)migrate.init_app(app,db)login.init_app(app)mail.init_app(app)bootstrap.init_app(app)moment.init_app(app)babel.init_app(app)frommyproject.errorsimportbpaserrors_bpapp.register_blueprint(errors_bp)frommyproject.authimportbpasauth_bpapp.register_blueprint(auth_bp,url_prefix='/auth')returnappA common Flask application. The code is based on the Flask Mega-Tutorial.Withdeescovery, you can make the same code shorter, and remove the dependencies from implementation details.# file: myproject/app.pyfromflaskimportFlaskfromdeescoveryimportdiscoverfromdeescovery.flaskimportget_flask_rulesdefcreate_app():flask_app=Flask(__name__)flask_app.config.from_object("myproject.config")discover("myproject",get_flask_rules("myproject",flask_app))returnflask_appRead moreUsage with FlaskUsage with anything elseAPI
Package: deeSOM
DeeSOMSelf-organized map based classifier, developed to deal with large and highly imbalanced data.The methods automatically build several layers of SOM. Data is clustered and samples that
are not likely to be positive class member are discarded at each level.The elastic-deepSOM (elasticSOM) is a deep architecture of SOM layers where the map
size is automatically set in each layer according to the data filtered in each previous
map. The ensemble-elasticSOM (eeSOM) uses several SOMs in ensemble layers to
face the high imbalance challenges. These new models are particularly suited
to handle problems where there is a labeled class of interest (positive
class) that is significantly under-represented with respect to a higher number
of unlabeled data.This code can be used, modified or distributed for academic purposes under GNU
GPL. Please feel free to contact with any issue, comment or suggestion.This code was used in:"Deep neural architectures for highly imbalanced data in bioinformatics"
L. A. Bugnon, C. Yones, D. H. Milone and G. Stegmayer, IEEE Transactions on Neural Networks and Learning Systems,
Special Issue on Recent Advances in Theory, Methodology and Applications of Imbalanced Learning (in press).sinc(i) -http://sinc.unl.edu.arInstalationjust do:python-mpipinstall--user-UdeeSOMRunning the demoYou'll find a Jupyter notebook with a small tutorial to train a deeSOM model and use it for predictions.
Package: deet
DeetDeetis a universal data mapper and SQL-Database engine, that implements
high-performance and security requirements for large-scale enterprise analytical
applications.The primary goal of Deet is to separate data integration and data analysis
into independent tasks, by providing a universal data interface for machine
learning- and data analysis applications. To achieve this goal, Deet
implements the two fundamental layers of a data warehouse:Theintegration layerof Deet utilizesSQLAlchemyto allow it's connection to a variety
of SQL-Databases (e.g. IBM DB2, Oracle, SAP, MS-SQL, MS-Access, Firebird,
Sybase, MySQL, Postgresql, SQLite, etc.). Thereupon it provides native support
for flat file databases (e.g. CSV-Tables, R-Table exports), laboratory
measurements and data generators.Thestaging layerof Deet is implemented as a native SQL-Database engine,
featuring a DB-API 2.0 interface with full SQL:2016 support, a vertical data
storage manager and real-time encryption. This allows the data analysis
application to integrate a variety of different data sources, by using a unified
data interface (and SQL dialect).Deet is open source, based on thePythonprogramming language and actively developed as
part of theLiquid Codeframework
atFrootlab. Deet is developed as a generic
data interface, which can be integrated into data analysis applications, to
facilitate the integration of data.Current Development StatusDeet currently is inPre-Alphadevelopment stage, which immediately follows
thePlanningstage. This means, that at least some essential requirements of
Deet are not yet implemented. A comprehensive list of all currently supported
data back-ends is given in theonline
manual.InstallationComprehensive information and installation support is provided within theonline manual. If you already have
a Python environment configured on your computer, you can install the latest
distributed version by using pip:$ pip install deetDocumentationThe latest Deet documentation is available as anonline
manualand for download in the
formatsPDF,EPUBandHTML.ContributeContributors are very welcome! Feel free to report bugs and feature requests to
theissue trackerprovided by
GitHub. Currently, as the Frootlab Developers team still is growing, we do not
provide any Contribution Guide Lines to collaboration partners. However, if you
are interested to join the team, we would be glad, to receive an informalapplication.LicenseDeet isopen sourceand available free
for any use under theGPLv3 license:Â© 2019 Frootlab Developers:
  Patrick Michl <patrick.michl@frootlab.org>
Â© 2018-2019 Patrick Michl
Package: deethon
DeethonDeethon is a lightweight Python library for downloading high quality music from Deezer.Getting startedInstallationpipinstalldeethonUsageimportdeethondeezer=deethon.Session("YOUR ARL TOKEN")deezer.download("https://www.deezer.com/track/1234567",bitrate="FLAC"# or MP3_320 / MP3_256 / MP3_128 (optional))
Package: deetlist
Deetlsit ScraperModule: ScraperSubmodule: DragonsFunctions:scrape_all_dragons(): Get data from all dragons page;scrape_new_dragons(): Get data from new dragons page;scrape_dragon_page(): Get data from a page specific to a dragon;Submodule: IslandsFunctions:scrape_fog_islands(): Get data from Fog Islands page;scrape_grid_islands(): Get data from Grid Islands page;scrape_heroic_races(): Get data from Heroic Races page;scrape_maze_islands(): Get data from Maze Islands page;scrape_puzzle_islands(): Get data from Puzzle Islands page;scrape_runner_islands(): Get data from Runner Islands page;scrape_tower_islands(): Get data from Tower Islands page;