Saravanan666/pypi-org huggingface dataset:
Package: dynnode2vec
dynnode2vecdynnode2vecis a package to embed dynamic graphs.It is the python implementation ofS. Mahdavi, S. Khoshraftar, A. An: dynnode2vec: Scalable Dynamic Network Embedding. IEEE BigData 2018Installationpipinstall-Udynnode2vecUsageimportpicklefromdynnode2vec.dynnode2vecimportDynNode2Vecfromdynnode2vec.utilsimportcreate_dynamic_graph# Create random graphsgraphs=create_dynamic_graph(n_base_nodes=100,n_steps=50,base_density=0.05)# Instantiate dynnode2vec objectdynnode2vec=DynNode2Vec(p=1.,q=1.,walk_length=10,n_walks_per_node=20,embedding_size=64)# Embed the dynamic graphsembeddings=dynnode2vec.compute_embeddings(graphs)# Save embeddings to diskwithopen('example_embeddings.pkl','wb')asf:pickle.dump(embeddings,f)ParametersDynNode2Vecclass:p: Return hyper parameter (default: 1)q: Inout parameter (default: 1)walk_length: Number of nodes in each walk (default: 80)n_walks_per_node: Number of walks per node (default: 10)embedding_size: Embedding dimensions (default: 128)seed: Number of workers for parallel execution (default: 1)parallel_processes: Number of workers for parallel execution (default: 1)use_delta_nodes: Number of workers for parallel execution (default: 1)DynNode2Vec.fitmethod:graphs: list of nx.Graph (ordered by time)TO DOremove pandas use in embeddings formattingcode examples of synthetic and real-life usesget rid of Stellar Graph dependencyReleasesYou can see the list of available releases on theGitHub Releasespage.LicenseThis project is licensed under the terms of theMITlicense. SeeLICENSEfor more details.Citation@misc{dynnode2vec,author={Paul-Emile Dugnat},title={dynnode2vec, a package to embed dynamic graphs},year={2022},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/pedugnat/dynnode2vec}}}This project was generated withpython-package-template
Package: dynochemy
dynochemy: Clever pythonic and async interface to Amazon DynamoDB=========================Yet another python inteface for Amazon Dynamo. The API is somewhat inspired by SQLAlchemy.Features--------- Synchronous and Async Support (using Tornado)- Full API abstraction (rather than needing to know internals of how Dynamo actually needs it's JSON formatted.- High-level constructs for dealing with provisioning throughput limits and maintaining secondary indexes.- SQL-backend for testingStatus------Under heavy development, just barely functioning. I would stay away except forexperimental use or you really want to get involved with development yourself.Really just useful for seeing how the API would work if this library was completed### Known TODOs* Solvent: Needs scan support* Views: Need the ability to regenerate views from scratch, or even partial rewrites to help with failure scenarios.* Solvent: Need throttling support rather than always hammer until provisioning error.
Package: dynoclick
## DescriptionSimple CLI commands loader for [click](http://click.pocoo.org)## Installpip install dyno-click## ExamplesStatic modules:```pythonfrom dynoclick import DynoClicli = DynoCli(['test', 'test1'])cli()```Dynamic discovery:```pythonimport pipfrom dynoclick import DynoClimodules = [x.key for x in pip.get_installed_distributions()if x.key.startswith('someprefix_')cli = DynoCli(modules)cli()```
Package: dynode
dynodeDynode solves equations of the formy' = f(t, y)using SciPy'sode solverbut allowsfto be modelled in a modular, object-oriented fashion using the notions of separateSystemsthat exposestatesand their corresponding derivatives,ders.fmay then be composed of an arbitraily complex collection of, connected or unconnected,Systems.Documentation is available here:https://freol35241.github.io/dynode/LicenseDistributed under the terms of the MIT license,dynodeis free and open source software
Package: dynonet
dynoNet: A neural network architecture for learning dynamical systemsThis repository contains the Python code to reproduce the results of the paperdynoNet: A neural network architecture for learning dynamical systemsby Marco Forgione and Dario Piga.In this work, we introduce the linear dynamical operator as a differentiable layer compatible with back-propagation-based training.
The operator is parametrized as a rational transfer function and thus can represent an infinite impulse response (IIR)
filtering operation, as opposed to the Convolutional layer of 1D-CNNs that is equivalent to finite impulse response (FIR) filtering.In the dynoNet architecture, linear dynamical operators are combined with static (i.e., memoryless) non-linearities which can be either elementary
activation functions applied channel-wise; fully connected feed-forward neural networks; or other differentiable operators.A 15-min presentation about dynoNet is availablehere.Folders:dynonet:  PyTorch implementation of the linear dynamical operator (aka G-block in the paper) used in dynoNetexamples: examples using dynoNet for system identificationutil: definition of metrics R-square, RMSE, fit indexdoc: paper & slidesThreeexamplesdiscussed in the paper are:WH2009: A circuit with Wiener-Hammerstein behavior. Experimental dataset fromhttp://www.nonlinearbenchmark.orgBW: Bouc-Wen. A nonlinear dynamical system describing hysteretic effects in mechanical engineering. Experimental dataset fromhttp://www.nonlinearbenchmark.orgEMPS: A controlled prismatic joint (Electro Mechanical Positioning System). Experimental dataset fromhttp://www.nonlinearbenchmark.orgFor theWH2009example, the main scripts are:WH2009_train.py: Training of the dynoNet modelWH2009_test.py: Evaluation of the dynoNet model on the test dataset,  computation of metrics.Similar scripts are provided for the other examples.NOTE: the original data sets are not included in this project. They have to be manually downloaded fromhttp://www.nonlinearbenchmark.organd copied in the data sub-folder of the example.Software requirements:Simulations were performed on a Python 3.7 conda environment withnumpyscipymatplotlibpandaspytorch (version 1.4)These dependencies may be installed through the commands:conda install numpy scipy pandas matplotlib
conda install pytorch torchvision cudatoolkit=10.2 -c pytorchLocal installation:From PyPIType in terminal:pip install dynonetThis will install the latest stable version packaged on PyPI:https://pypi.org/project/dynonet/From a local copy of this repositoryNavigate to a local copy of this repository, where setup.py and setup.cfg are located.
Then, type in terminal:pip install -e .CitingIf you find this project useful, we encourage you toStar this repository :star:Cite thepaper@article{forgione2021dyno,
  title={\textit{dyno{N}et}: A neural network architecture for learning dynamical systems},
  author={Forgione, M. and Piga, D.},
  journal={International Journal of Adaptive Control and Signal Processing},
  volume={35},
  number={4},
  pages={612--626},
  year={2021},
  publisher={Wiley}
}
Package: dynoptimdict
DynoptimdictA dynamic data dict class that inherits and overrides the built-in dict class for special purposes. That provides real-time access to dynamic data, while still allowing the option to get only the specified data without calculating all. It adheres to the concept of program optimization which avoids loading if not used, saving both memory and time.⚖️LicenseMIT📗DocumentationThe basic usage is similar to that of Python built-indict, please refer to the code example for specific differences:https://github.com/leoweyr/Python-Dynoptimdict/tree/main/examples
Package: dyn-o-tool
Dyn-O-ToolTools for easier living with AWS DynamoDB, it’s Dyn-O-Mite!FeaturesGet a quick informational summary of any tableQuickly grab 10 records from any tableExport all or a subset of any DynamoDB table to CSVwipe or truncate a table…and much, much more!Installpip install dyn-o-toolUsageCloudZero Dyn-O-Tool!Tools for making life with DynamoDB easierUsage:dynotool list [–profile <name>]
dynotool info <TABLE> [–profile <name>]
dynotool head <TABLE> [–profile <name>]
dynotool copy <SRC_TABLE> <DEST_TABLE> [–profile <name>]
dynotool export <TABLE> [–format <format> –file <file> –profile <name>]
dynotool import <TABLE> –file <file> [–profile <name>]
dynotool wipe <TABLE> [–profile <name>]
dynotool truncate <TABLE> [–filter <filter>] [–profile <name>]Options:-? –help               Usage help.
list                    List all DyanmoDB tables currently provisioned.
info                    Get info on the specified table.
head                    Get the first 20 records from the table.
copy                    Copy the data from SRC TABLE to DEST TABLE
export                  Export TABLE to JSON
import                  Import file into TABLE
wipe                    Wipe an existing table by recreating it (delete and create)
truncate                Wipe an existing table by deleting all records
–format <format>       JSON or CSV [default: json]
–file <file>           File to import or export data to, defaults to table name.
–profile <profile>     AWS Profile to use (optional) [default: default].
–filter <filter>       A filter to apply to the operation. The syntax depends on the operation.Copyright &copy; 2020 CloudZero, Inc.
Package: dynotree
DynotreeDynamic Kd-Tree in C++ and Python: Euclidean, SO(2), SO(3) and more!The underlying KD-tree implementation is based onbucket-pr-kdtree, but dynotree supports non-Euclidean spaces and custom compound spaces. State spaces can be defined at both compile and runtime for both efficiency and flexibility.The C++ library is header-only, with Eigen as single dependency, and we provide Python bindings for fast development and prototyping.Try it out!PYTHONPIP Packagepip3installdynotreeA first example:rrt_free.pyA second example:rrt.pyPython from sourceC++Test and examples are inmain.cppgitclone--recurse-submoduleshttps://github.com/quimortiz/dyn_kdtreecddyn_kdtree
mkdirbuildcdbuild
cmake..
make
./mainuse./main --run_test=NAME_OF_TESTto run only one example or testDevelopmentCreate Python Package (locally)Create wheelsCMAKE_ARGS="-DBUILD_PYTHON_BINDINGS=1" python3 -m buildWheels will be indist/directory. Install the wheels with
(adjust package and python version accordingly)pip3 install dist/dynotree-0.0.4-cp38-cp38-linux_x86_64.whlCreate Python Package and Upload to PYPI (locally)Create wheelsfor publishing in pypidockerpullquay.io/pypa/manylinux2014_x86_64
dockerrun-it-v$(pwd):/ioquay.io/pypa/manylinux2014_x86_64/io/build-wheels.sh
python3-mtwineuploadwheelhouse/*upload to  testpypipython3-mtwineupload--repositorytestpypiwheelhouse/*for creating local package:TODOWhy dyn_kdtree?Faster than OMPL and simpler than NIGHDynamic: Add points one by one -- Ideal for Motion PlanningSupport Euclidean, SO(2), SO(3) and any combinationPerformant and flexible C++ code based on Eigen.Single Header FilePython Bindings for easy integrationExtendable with custom spaces!Python BindingsOption 1Option 2Option 3DependenciesPython: No dependenciesC++ Code: EigenDevelop: Eigen and Boost TestingInterfaceSee this for examples.BenchmarkCheck this repo for benchmark againstbucket-pr-kdtreeOMPLNigh...RoadmapCode is Stable, currently used in ...
Package: dyno-viewer
Dyno-viewerDyno-viewer is dynamodb table viewer for your terminal build usingtextual.This came out from me being frustrated with how clunky and slow the dynamodb viewier is in the aws console and me finding no good free alternative, That works the way i want it to work.
Which is basically a spreadsheet with menus to change the table etc just like the aws console version and it being able to be used with just a keyboardNote:This is still in early alpha so some things to be expect things to be broken, I will fix them as i have time. Prs are welcomeInstallingprerequisitesYou need theaws clifully configured (seeherefor how to setup up auth)note about AWS IAM Identity Center(used to be called aws sso) aws profiles:if you are using sso profiles. Then you need to make sure that you have updated your config to use a sso-session profile otherwise the app won't work see thisdocon how to do that. See this issue:https://github.com/boto/botocore/issues/2374if you want to know why this is the caseright now this can be installed via flatpak and pip:installI recommend usingpipx:pipxinstalldyno-viewerDev notesPrerequisitesThis repo usespoetryfor package management and needs python 3.10+ installed either viapyenvorasdfusing theasdf-community/asdf-pythonaddone.g:envPYTHON_CONFIGURE_OPTS="--enable-shared"asdfinstallpython3.10.11or if using pyenv:envPYTHON_CONFIGURE_OPTS="--enable-shared"pyenvinstall3.10.11seehow to build cpython with --enable-sharedfor more infoLocal dev setupTo install locally run:poetryinstall# to go into a virtual env shellpoetryshell# or run app via scriptpoetryrundyno-viewerTesting textual notesSeetesting notes doc
Package: dynoxhost
Dynoxhost pythonRequirementsAPI Key-User API Key of Dynox HostHow to get API-KEYVisit thepanelLogin with your credentialsClick on the User-Icon in the upper right side of the panelChoose the Tab API CredentialsChoose a DescriptionLeave the IP field blank if you dont know what this meansClickCreateCopy the API-Key. Dont share the API-Key with anyone. Dynox Host will never ask you about the KeyUse the key for controlling your server and to get dataLinksMain WebsitePanelLicense© Copyright 2021, Dynox HostingMIT LicenseThis is a package made with ❤ and python
Package: dynprogstorage
Dynamic programming optimisation toolThis project implements the dynamic programming tool proposed inthis paperand
available in the R Software inthe ConConPiWiFun packageTable of Contents1. Installation2. Documentation2.1. Optimisation problem3. Examples3.1. Simplest example1. InstallationIf you want to install the package from source, you can dopip install git+https://github.com/robingirard/dynprogstorage#egg=dynprogstorageDocumentationoptimisation problemThis tool allows you to solve problems with the form## min_x  sum_i phi_i(x_i)   phi_i : convex piecewise linear function
## P_i^-<= x_i <=P_i^+
## C_i^-<= x_0 + sum_j=0^i x_j <= C_i^+ExamplesLet us give a few examples of usestorage operation exampleWhile participating in the market with a 100% efficiency storage you want to maximize the profit## min_x  sum_i Pi_i x_i  ( phi_i linear function)
## -p_max <= x_i <=p_max
## 0<= x_0 + sum_j=0^i x_j <= c_max
## x_i>0 : consumption from the network
## x_i<0 : producing (injection to network)
### --> phi_i(x_i) is a buying cost we want to minimizeThe code you need to use is  :## Definition of values    
x_0=0
nbTime=250
Prices=random.uniform(1, 1000, nbTime)
p_max=1.
c_max=10.*p_max

## Generation of a vector of cost functions 
cpl_func = GenCostFunctionFromMarketPrices(Prices.tolist())
cpl_func.vec_get(0).getBreakPoints() ## what does the first cost function look like
## now solve the optimisation problem
res = cpl_func.OptimMargInt([-p_max]*nbTime,[p_max]*nbTime,[-x_0]*nbTime,[c_max-x_0]*nbTime)
print(res)

## Visualisation of results (power) with prices            
period=100
plt.plot(res[:100])
plt.plot(-(Prices[:100]-Prices.mean())/Prices.max())
plt.ylabel("Puissance (MW)")
plt.xlabel("Index")
plt.show()

## Visualisation of Energy evolution 
energie=np.cumsum(res)
plt.plot(energie[:100], color='g')
plt.plot([0]*100, color='b')
plt.plot([c_max]*100, color='b')
plt.ylabel("Energie (MWh)")
plt.xlabel("Index")
plt.show()
Package: dynpy
dynpy [![Build Status](https://travis-ci.org/artemyk/dynpy.svg?branch=master)](https://travis-ci.org/artemyk/dynpy) [![Documentation Status](https://readthedocs.org/projects/dynpy/badge/)](https://readthedocs.org/projects/dynpy/)===================================================================================================================dynpy is a package for defining and running dynamical systems in Python.  Thegoal is to support a wide-variety of dynamical systems, both continuous anddiscrete time as well as continuous and discrete state.So far, it has basic support for linear dynamical systems, Markov chains,Boolean Networks, and Cellular Autamata.It supports both Python2 and Python3.For documentation, see http://dynpy.readthedocs.org/ .  The tutorial, athttp://dynpy.readthedocs.org/tutorial.html , may be particularly helpful.Requirements------------dynpy requires numpy, scipy, and six.Testing requires nose and coverage.Building the documentation requires matplotlib.
Package: dynrender
DynRender-skia使用skia渲染BiliBili动态
更新页面效果的优先级会很低注意Linux用户在导入skia-python包时可能会遇到以下报错libGL.so.1:cannotopensharedobjectfile:Nosuchfileordirectory解决方法ubuntu用户aptinstalllibgl1-mesa-glxArchLinux用户pacman-Slibglcentos用户yuminstallmesa-libGL-y使用方法安装必要依赖pipinstalldynamicadaptordynrender-skia测试importhttpximportasyncioimportskiafromdynrender.CoreimportDynRenderfromdynamicadaptor.DynamicConversionimportformate_message# 定义异步函数，用于执行Web测试asyncdefweb_test():dyn_id="440646043801479846"url=f"https://api.bilibili.com/x/polymer/web-dynamic/v1/detail?timezone_offset=-480&id={dyn_id}&features=itemOpusStyle"headers={"referer":f"https://t.bilibili.com/{dyn_id}","user-agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36"}# 发起HTTP请求并解析JSON响应message_json=httpx.get(url,headers=headers).json()# 格式化消息数据message_formate=awaitformate_message("web",message_json["data"]["item"])# 使用DynRender执行动态渲染img=awaitDynRender().run(message_formate)# 将渲染后的图像转换为Skia Image对象img=skia.Image.fromarray(img,colorType=skia.ColorType.kRGBA_8888_ColorType)# 保存图像为PNG文件img.save("1.png")# 当文件作为主程序执行时if__name__=="__main__":asyncio.run(web_test())效果展示
Package: dynrender-skia
DynRender-skia使用skia渲染BiliBili动态注意Linux用户在导入skia-python包时可能会遇到以下报错libGL.so.1:cannotopensharedobjectfile:Nosuchfileordirectory解决方法ubuntu用户aptinstalllibgl1-mesa-glxArchLinux用户pacman-Slibglcentos用户yuminstallmesa-libGL-y使用方法安装必要依赖pipinstalldynamicadaptordynrender-skia测试importhttpximportasyncioimportskiafromdynrender_skia.CoreimportDynRenderfromdynamicadaptor.DynamicConversionimportformate_messageasyncdefweb_test():dyn_id="440646043801479846"url=f"https://api.bilibili.com/x/polymer/web-dynamic/v1/detail?timezone_offset=-480&id={dyn_id}&features=itemOpusStyle"headers={"referer":f"https://t.bilibili.com/{dyn_id}","user-agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36"}message_json=httpx.get(url,headers=headers).json()message_formate=awaitformate_message("web",message_json["data"]["item"])img=awaitDynRender().run(message_formate)img=skia.Image.fromarray(img,colorType=skia.ColorType.kRGBA_8888_ColorType)img.save("1.png")if__name__=="__main__":asyncio.run(web_test())效果展示
Package: dynrender-skia-opt
DynRender-skia使用skia渲染BiliBili动态
更新页面效果的优先级会很低注意Linux用户在导入skia-python包时可能会遇到以下报错libGL.so.1:cannotopensharedobjectfile:Nosuchfileordirectory解决方法ubuntu用户aptinstalllibgl1-mesa-glxArchLinux用户pacman-Slibglcentos用户yuminstallmesa-libGL-y使用方法安装必要依赖pipinstalldynamicadaptordynrender-skia测试importhttpximportasyncioimportskiafromdynrender.CoreimportDynRenderfromdynamicadaptor.DynamicConversionimportformate_message# 定义异步函数，用于执行Web测试asyncdefweb_test():dyn_id="440646043801479846"url=f"https://api.bilibili.com/x/polymer/web-dynamic/v1/detail?timezone_offset=-480&id={dyn_id}&features=itemOpusStyle"headers={"referer":f"https://t.bilibili.com/{dyn_id}","user-agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36"}# 发起HTTP请求并解析JSON响应message_json=httpx.get(url,headers=headers).json()# 格式化消息数据message_formate=awaitformate_message("web",message_json["data"]["item"])# 使用DynRender执行动态渲染img=awaitDynRender().run(message_formate)# 将渲染后的图像转换为Skia Image对象img=skia.Image.fromarray(img,colorType=skia.ColorType.kRGBA_8888_ColorType)# 保存图像为PNG文件img.save("1.png")# 当文件作为主程序执行时if__name__=="__main__":asyncio.run(web_test())效果展示
Package: dyn-rl-benchmarks
Dynamic reinforcement learning benchmarksThis repository contains three open source reinforcement learning environments that require the agent to adapt its behavior to or make use of dynamic elements in the environment in order to solve the task. The environments follow OpenAI's gym interface.InstallationWith python3.7 or higher runpipinstalldyn_rl_benchmarksUsageAfter importing the packagedyn_rl_benchmarksthe environmentsPlatforms-v1Drawbridge-v1Tennis2D-v1are registered and can be instantiated viagym.make.The following example runs Platforms-v1 with randomly sampled actions:importgymimportdyn_rl_benchmarksenv=gym.make("Platforms-v1")obs=env.reset()done=Falsewhilenotdone:action=env.action_space.sample()obs,rew,done,info=env.step(action)env.render()How to cite@article{gurtler2021hierarchical,
  title={Hierarchical Reinforcement Learning with Timed Subgoals},
  author={G{\"u}rtler, Nico and B{\"u}chler, Dieter and Martius, Georg},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}
Package: dynrules
About dynrulesdynrules is a dynamic ruleset scripting package for Python. It uses the
Dynamic Scripting technique to create adaptive AI scripts automatically
from predefined rulesets.Installationdynrules is easy to install and integrate within your own projects. Just
follow Python’s standard procedure of installing packages or look for a
prebuilt package for your operating system or distribution.DocumentationYou can find the documentation atdoc/htmlor online athttp://dynrules.readthedocs.org.LicenseThis library is given to the public domain. There are no licensing
restrictions. Please seedoc/copying.rstfor further details.
Package: dynscope
-- restructuredtext --Dynamic Scope for PythonThis package implements simple dynamic scope syntactic sugar
for Python. It allows to (IMO elegantly) use dynamically scoped
variables where you need them with a scheme-like semantic.Install is quite standard:easy_install dynscopeor just download the sources and run setup.py install. There are testcases
you can run with setup.py test, too.I test and work with it under Python 2.6.How to use dynamically scoped variables>>> from dynscope import flet, fluid
>>> with flet(anton=5, berta=6):
>>>     print fluid.anton*fluid.berta
>>> print fluid.anton*fluid.bertaThe first print will print 30, the second one will raise an exception,
since those variables are not defined.There is a base-layer of fluid definitions (think of them as normal globals,
only that they are per-thread or per-coroutine if using for example gevent
with full patching) that you can use, too. But it’s often much better style
to restrict fluids to the dynamic scope that actually expects them. Unrestricted
globals are far more often the wrong choice than the right choice. But where
you can make use of them is to set defaults for dynamically scoped variables
so that functions can run outside flet scopes.
Package: dynsight
maintainers:andrewtarziadocumentation:https://dynsight.readthedocs.ioInstallationTo getdynsight, you can install it with pip:$ pip install dynsightExamplesThere are examples available in theexamples/directory of this repository.There are also examples available in thecpctoolsrepositoryhere <https://github.com/GMPavanLab/cpctools/tree/main/Examples>How To CiteIf you usedynsightplease citehttps://github.com/GMPavanLab/dynsightPublicationspeople need to add PRs to mention these.AcknowledgementsWe developed this code when working in the Pavan group,https://www.gmpavanlab.com/, whose members often provide very valuable
feedback, which we gratefully acknowledge.Much of the original code incpctoolswas written by Daniele Rapetti (Iximiel).The work was funded by the European Union and ERC under projects DYNAPOL and the
NextGenerationEU project, CAGEX.
Package: dynsimf
DyNSimF - Dynamic Network Simulation FrameworkThe Network Simulation Framework can be used to create any network-based simulation, where the nodes require continuous states.Read the documentation onReadTheDocs.InstallationTo install the library just download (or clone) the current project and copy the dynsimf folder in the root of your application.Alternatively use pip:sudopipinstalldynsimfDocumentationFor examples, tutorials and a complete reference visit the project documentation website onReadTheDocs.Collaborate with us!DyNSimFis an active open-source project, any contribution is welcome!If you like to include your model in DyNSimF feel free to fork the project, open an issue and contact us.Do you want to be part of the DyNSimF? Join the community to discuss enhancements, bugs, concepts or any other topic onSlack!