Saravanan666/pypi-org huggingface dataset:
Package: simantha
SimanthaSimantha is a package for simulating discrete manufacturing systems. It is designed to model asynchronous production systems with finite buffers.The package provides classes for the following core manufacturing objects that are used to create a system:Source: Introduces raw, unprocessed parts to the system.Machine: Continuously retrieves, processes, and relinquishes parts. May also be subject to periodic degradation, failure, and repair.Buffer: Stores parts awaiting processing at a machine.Sink: Collects finished parts that exit the system.Maintainer: Repairs degrading machines according to the specified maintenance policy.For the complete Simantha documentation, seehttps://simantha.readthedocs.io/en/latest/.InstallationSimantha can be installed viapipusing:pip install simanthaQuickstartThe following is an example of the creation and simulation of a simple two-machine one-buffer line.fromsimanthaimportSource,Machine,Buffer,Sink,System# Create objectssource=Source()M1=Machine(name='M1',cycle_time=1)B1=Buffer(name='B1',capacity=5)M2=Machine(name='M2',cycle_time=1)sink=Sink()# Specify routingsource.define_routing(downstream=[M1])M1.define_routing(upstream=[source],downstream=[B1])B1.define_routing(upstream=[M1],downstream=[M2])M2.define_routing(upstream=[B1],downstream=[sink])sink.define_routing(upstream=[M2])# Create systemsystem=System(objects=[source,M1,B1,M2,sink])# Simulatesystem.simulate(simulation_time=100)# Output:# Simulation finished in 0.00s# Parts produced: 99For additional examples, seesimantha/examples.
Package: simaple
simaple: Simulation library for Maplestorysimaple은 메이플스토리 내 전투 환경을 분석히기 위한 라이브러리입니다.simaple은 클라이언트 리소스를 바탕으로 메이플스토리 내 직업의 시뮬레이션 환경을 구성하고, 적절한 전투 시나리오를 설계하여 스킬별 딜 비중 및 기대 DPM을 계산할 수 있도록 합니다.실제 메이플스토리 내 게임 설정을 바탕으로 한 realistic한 시뮬레이션 환경 제공제시된 캐릭터의 기대 DPM 을 포함한 전투결과 생성 및 분석게임 내 유저 정보를 바탕으로 한 시뮬레이션 환경 제공환산 주스텟을 포함한, 캐릭터 스펙에 대한 다차원적 지표 계산Web Clientsimaple은 시뮬레이션을 쉽게 진행하기 위한 웹 인터페이스 또한 제공합니다.웹 클라이언트는simaple/web에서 설치할 수 있습니다.Installpip install simapleDocumentationhttps://simaple.readthedocs.io/en/latest/CommunityDiscordSlackDevelopments & ContributionSeeCONTRIBUTING지원되는 기능인게임 시뮬레이션interactive한 시뮬레이션 환경 생성쿨타임 감소 효과, 버프 지속시간 증가, 코어 강화 등 인 게임내 존재하는 모든 변수를 적용한 시뮬레이션 환경을 구축 가능시뮬레이션 진행 결과에 대한 분석DPM 계산스킬 별 점유율 계산전체 시뮬레이션 결과를 human-readable format으로 출력하여 custom 분석아이템 관련스타포스 및 주문서 강화 적용 시 기대되는 아이템 성능 계산GearBlueprint를 통해, 환산 주스텟 등에 사용되는 기준 캐릭터 스펙의 성능 계산환산 주스텟 계산, 스텟별 효율 계산홈페이지 연동홈페이지로부터 정보 공개에 동의한 캐릭터를 simaple object로 로드 기능
Package: simapy
SIMAPY - Python utilities for SIMAThis Python library can be used to interact withSIMAParts of the library is generated from DMT models using a DMT Python code generatorPlease examine the tests for ways to interactAlso check out the examples insimapy-examplesDependenciesDMTPYAuthorsLasse Bjermeland-lassebjeLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: simar-co6-101703543
This is a simple package to use topsis(technique for Order of preference by Similarity to Ideal Solution)
approach to select best among many things based on different attributes, such as selecting the best
Machine Learning algorithms based on correlation,R-square,root mean square error and accuracy.
You can use this simple package to get ranks on basis of topsis approach, ypu simply need to pass
NumPy array after pre-processing your data along with weights between 0 and 1 and all weights should
sum to 1 to get correct results and you need to pass impacts as well in form of '+' or '-'
where '+' indicates that you need to maximise value of that particular column and '-' indicates that
you need to minimise value of that particular column.
After passing all these parameters you will get your rank row.
Package: simba
simba :lion:Similarity measures from Babylon Health.Installation$pipinstallsimbaYou can also checkout this repository and install from the root folder:$pipinstall.Many of the similarity measures in simba rely on pre-trained embeddings.
If you don't have your own encoding logic already, you can register your
embedding files to use them easily with simba, as long as they're in the
standard text format for word vectors (as describedhere).
For example, if you want to use fastText vectors that you've saved to/path/to/fasttext,
you can just run$simbaembsregister--namefasttext--path/path/to/fasttextand simba will recognise them under the namefasttext.You can do something similar for frequencies files (likethese):$simbafreqsregister--namewiki--path/path/to/wiki/countsUsagefromsimba.similaritiesimportdynamax_jaccardfromsimba.coreimportembedsentences=('The king has returned','Change is good')# Assuming you've registered fasttext embeddings as described abovex,y=embed([s.split()forsinsentences],embedding='fasttext')sim=dynamax_jaccard(x,y)There are more examples, including comparing different similarity metrics on a dataset
of pairs, in theexamplesdirectory.Similarity MeasuresThis library contains implementations of the following methods insimba.similarities.
Please consider citing the corresponding papers in your work if you find them useful.MethodDescriptionPaperavg_cosineAverage vector compared with cosine similarity-batch_avg_pcaAverage vector with principal component removal[1]fbow_jaccard_factoryFactory method for general fuzzy bag-of-words given a universe matrix[2]max_jaccardMax-pooled vectors compared with Jaccard coefficient[2]dynamax_{jaccard, otsuka, dice}DynaMax using Jaccard, Otsuka-Ochiai, and Dice coefficients[2]gaussian_correction_{tic, aic}Takeuchi and Akaike Information Criteria (TIC and AIC) for Gaussian likelihood[3]spherical_gaussian_correction_{tic, aic}TIC and AIC for spherical Gaussian likelihood[3]von_mises_correction_{tic, aic}TIC and AIC for von Mises Fisher likelihood[3]avg_{pearson, spearman, kendall}Average vector compared with Pearson, Spearman, and Kendall correlation[4]max_spearmanMax-pooled vectors compared with Spearman correlation[5]cka_factoryFactory method for general Centered Kernel Alignment (CKA)[5]cka_{linear, gaussian}CKA with linear and Gaussian kernels[5]dcorrCKA with distance kernel (distance correlation)[5]Papers:Arora et al., ICLR 2017.A Simple but Tough-to-Beat Baseline for Sentence EmbeddingsZhelezniak et al., ICLR 2019.Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word VectorsVargas et al., ICML 2019.Model Comparison for Semantic GroupingZhelezniak et al., NAACL-HLT 2019.Correlation Coefficients and Semantic Textual SimilarityZhelezniak et al., EMNLP-IJCNLP 2019.Correlations between Word Vector SetsContactApril ShenSasho SavkovVitalii Zhelezniak
Package: simba-lang
Simba DocumentationSimba is a Clojure-like, interpreted programming language that easily interoperates with all Python libraries.
Features:multimethodspersistent immutable data structures via Pyrsistenteasy interoperabilitymacrosClojure-like standard library based on similar abstractionsnamed argumentsInstall:
You can use Simba from the command line by callingsrc/simba.pyfrom the main directory.
You can execute some files with:src/simba.py <file-name> <eventual-other-file-names>*; at which point the execution will always start in the main namespace.Build:
Package: simba-ml
SimbaMLSimbaML is an all-in-one framework for integrating prior knowledge of ODE models into the ML process by synthetic data augmentation. It allows for the convenient generation of realistic synthetic data by sparsifying and adding noise. Furthermore, our framework provides customizable pipelines for various ML experiments, such as identifying needs for data collection and transfer learning.InstallationSimbaML requires Python 3.10 or newer and can be installed via pip:pip install simba_mlTo be lightweight, SimbaML does not install PyTorch and TensorFlow per default. Both packages need to be installed manually by the user.pip install pytorch-lightning>=1.9.0pip install tensorflow>=2.10.0; platform_machine != 'arm64'For further details on how to install Tensorflow on ARM-based MacOS devices, see:https://developer.apple.com/metal/tensorflow-plugin/DocumentationWe provide detailed documentation for SimbaML here:https://simbaml.readthedocs.io/.
Package: simbench
SimBench (www.simbench.net) is a research project to create a “simulation database for uniform comparison of innovative solutions in the field of network analysis, network planning and operation”, which was conducted for three and a half years from 1.11.2015 to 30.04.2019. It was part of the German Federal Government’s 6th Energy Research Program “Research for an Environmentally Friendly, Reliable and Affordable Energy Supply”. The project was carried out by the University of Kassel, the Fraunhofer IEE, the RWTH Aachen University and the Technical University of Dortmund in accordance with the authors mentioned above. The project, coordinated by the University of Kassel, was supported by the professional advisory from six German distribution network operators: DREWAG NETZ GmbH, Energie Netz Mitte GmbH, ENSO NETZ GmbH, Netze BW GmbH, Syna GmbH and Westnetz GmbH.The objective of the research project SimBench is the development of a benchmark dataset to support research in grid planning and operation. SimBench grids differ from existing grids in the following key aspects:Consideration of a wide range of use cases during the development of datasetsProvision of grid data for low voltage (LV), medium voltage (MV), high voltage (HV), extra-high voltage (EHV) as well as design of data for a suitable interconnection of a grid among different voltage levels for cross-level simulationsEnsuring high reproducibility and comparability by providing clearly assigned load and generation time seriesValidation of the suitability of the datasets with simulation, deliberately determined grid states including suitable dimensioning of grid assetsThis repository provides data and code to use SimBench within the software pandapower (www.pandapower.org).
Package: simber
Simple, minimal and powerful logger for PythonWhy Simber?Simber was merely built because of some restrictions that the Python's default logging library has. This library is aimed as a simple, minimal yet powerful logging library for all Python apps. This logging library aims to take away all the disadvantages of using the default logging library and at the same time be feature filled so that the end user feels powerful using it.[Installation] [Get Started] [Docs] [Contribute]
Package: simbind
Simbind CLIGenerate Python Wheel package from Simulink model to enhance your Software-in-Loop (SiL) tests development workflow!
Please refer toWikiandExamplesto explore the project's
capabilities and limitations.RequirementsWe highly recommend to useDockerizedversion of Simbind,
since collecting the correct versions of the necessary requirements can be challenging. If you opt for local
installation, the following is required:Linux x86-64 machine with installed Matlab R2023b.Python 3.10 or 3.11:Simbind Architectrequires
Python 3.10+ andSimulink Exporterneeds Python
3.9, 3.10 or 3.11.From Matlab side you need Simulink suite with Embedded Coder.You also need pip, gcc, cmake, make and swig. Under Debian-based distros you can install it withsudoapt-getinstallpython3-pipbuild-essentialcmakeswigTo run output model you need only Linux x86-64 machine and Python 3.8+. We're open to extending support to Linux ARM and
Windows x86-64 if there's sufficient interest!InstallationTo install from PyPi run$pipinstallsimbindTo clone project you will also need to pull submodules with:$gitclone--recurse-submoduleshttps://github.com/swag-engineering/simbind-cli.gitUsageSimbind CLI is a wrapper aroundSimulink ExporterandSimbind Architectprojects and intended to be used as a
standalone tool, not as an importable module. If you want to use its functionality programmatically, please refer to
subprojects.$simbind--help
usage:simbind[-h]--slx-pathSLX_PATH[--pkg-namePKG_NAME][--wheel-out-dirWHEEL_OUT_DIR][--solver{ode1,ode2,ode3,ode4,ode5,ode8,ode14x,ode1be}][--stepSTEP_SIZE][--license-textLICENSE_TEXT][-v]TooltogeneratePythonwheelpackagefromSimulinkmodel.

options:-h,--helpshowthishelpmessageandexit--slx-pathSLX_PATHPathtoSimulink.slxfile.--pkg-namePKG_NAMENameoftheoutputPythonpackage.(default:'model')--wheel-out-dirWHEEL_OUT_DIRPathtofolderwherewheelpackagewillbestored.(default:'.')--solver{ode1,ode2,ode3,ode4,ode5,ode8,ode14x,ode1be}Fixed-stepsolver.(default:'ode5')--stepSTEP_SIZEFixedstepsizeinseconds.(default:'0.001')--license-textLICENSE_TEXTLicensetextthatwillbeincludedinoutputPythonwheelpackage.(default:'')-vSpecifiesthelevelofverbosity.Example:-vvvFor detailed instructions on the structure of the output Python package, please refer to
ourWiki.IssuesIf you run into any issues or believe additional functionality is needed, please don't hesitate to open an issue or
email us atcontact@swag-engineering.io. We're always ready to assist!
Package: simbolotokenizer
We would like to introduce Multilingual Partial Syllable Tokenization—a novel rule-based tokenization method that avoids breaking into complete syllables. Through experimentation, its utility has been uncovered in keyword detection, effectively minimizing False Positive errors and helping a lot in Burmese’s rules-based+machine learning name recognition. Notably, this tokenization method is designed to align with the linguistic nuances of languages, but without requiring an exhaustive understanding of each specific language. Now it is integrated with a frequency-based approach to generate tokens.
Package: simbols
SimbolsNameSImilarity Measures for biOLogical Systems - short SiMBolsDescriptionSiMBols is meant to supply a fairly easy to use tool to apply similarity measures on trajectory data obtained, while utilizing existing measures from different libraries.
SiMBols includes:Discrete Fréchet Distance (DFD)Discrete Weak Fréchet Distance (DWFD)Dynamic Time Warping (DTW)Hausdorff Distance (HD)Longest Common SubSequence (LCSS)Difference Distance Matrix (DDM)Wasserstein distance (WD)Kullback-Leibler Divergence (KLD)Additionally, SiMBols includes two routines to read out protein simulation trajectories and align them for further analyses, for instance, employing the similarity measures.Two working examples are supplied including input files, one dealing with the protein routines and one exclusively calculating the similarity measures. Files can be found atExample/example.py (Similarity Measure Example)ProteinPreprocessing/Example/example.pyThe package is part of a publication available inPLOS ONE.Minimum RequirementSiMBols requires at least python 3.6 to run and some of the dependencies listed below, depending on the needed parts.InstallationAt this time, the package can be cloned to a working directory and imported to the code. The repository is locatedhere. It is also registered in PyPI and can be installed usingpip install simbols. The most recent version will always be the one on gitlab.DependenciesThe following python packages are loaded:
mdtraj, numpy, time, rmsd, concurrent.futures, scipy.stats, scipy.spatial.distance, tqdmUsageThe Usage is explained in the two examples, a quick overview however is the following workflowLoad a trajectory into a Trajectory class objectLoad a second trajectory into a Trajectory class objectCreate a Comparer object from the two Trajectory objectsCalculate the needed similarity measures, the results are saved to the comparer objectSave the results, plot the resultsSupportFor support, send an email tofabian.schuhmann@nbi.ku.dkor open an issue.Authors and acknowledgmentThe package is written byFabian Schuhmann,  University of CopenhagenLeonie Ryvkin,  Technische Universiteit EindhovenJames D. McLaren,  Carl-von-Ossietzky Universität OldenburgLuca Gerhards,  Carl-von-Ossietzky Universität OldenburgIlia A. Solov'yov,  Carl-von-Ossietzky Universität Oldenburgwith acknowledgements detailed below:Programming help was received from Jeróme Urhausen and Georg Manthey. The authors would like to thank Volkswagen Foundation (Lichtenberg Professorship to IAS), the DFG, German Research Foundation, (GRK1885 - Molecular Basis of Sensory Biology and SFB 1372 – Magnetoreception and Navigation in Vertebrates), Ministry for science and culture of Lower Saxony (Simulations meet experiments on the nanoscale: Opening up the quantum world to artificial intelligence (SMART)). Computational resources for the simulations were provided by the CARL Cluster at the Carl-von-Ossietzky Universität Oldenburg, which is supported by the DFG and the ministry for science and culture of Lower Saxony. The work was also supported by the North-German Supercomputing Alliance (HLRN).LicenseMIT
Package: simbots
Simple bots or Simbot's is a library designed to create simple chat bots using python.Its Basic functions :-Uses Machine learning to create Intents .Supports multiple ways of creating entities and relations .Also provides helper functions to query context stack .Provides a framework to create text based chatbots .Provides a framework to define test conversation of the bot and test it .Support saving and loading existing conversation .Support saving and loading trained Chatbot .Supports Sub Conversation management and creation also supports subconversation trace.Supports mongoDb integration to save bot logs.Supports other database integration(relational) to save bot logs.if you are unfamiliar with any of these terms check out thedocumentation.To install simply usepip install simbotsLinksDocumentationpypicodeCheck out thedocumentationpage for chatbot terminology and how to solve some common problems like :SrBotDocumentationCode Only1)How to make simple conversation .step by stepcode only2)Convert site FAQs (Frequently asked questions) to a chatbot .step by stepcode only3)Detect and handle person names from chat texts .step by stepcode only4)Create and Place restaurant orders from chat .step by stepcode only
Package: sim-bug-tools
sim-bug-toolssim_bug_toolsis a python toolkit for exploring bugs in software simulations. This module consists of python classes for describing, navigating, and interacting with software simulations to find bugs or other scenario outcomes based on geometric analysis of known information.This module is developed for research in scenario-based testing for the validation and verification (V&V) of autonomous vehicles (AV). We were frustrated by the limitations imposed by time and resource cost of the AV V&V testing process given the many configurations of parameters for AV Testing. This module contains the tools we use to understand and navigate these high-dimensional spaces.Research TeamThis research is conducted by Embry-Riddle Aeronautical University of Daytona Beach, Florida. Dept. of Electrical Engineering and Computer Science.Faculty Team LeadDr. Mustafa İlhan Akbaşakbasm@erau.eduPhD StudentsQuentin Gossgossq@my.erau.eduJohn Thompsonthomj130@my.erau.eduUndergraduate StudentsAnnamaria Summersummera@my.erau.eduCiting This WorkIf you would like to cite this work. Please cite our latest paper about the topic:J. M. Thompson, Q. Goss, and M. I. Akbas, “A Strategy for Boundary Adherence and Exploration in Black-Box Testing of Autonomous Vehicles,” in2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST), pp. 17–19, IEEE.@incollection{Thompson2023Strategy,
	author = {Thompson, John M. and Goss, Quentin and Akba{\ifmmode\mbox{\c{s}}\else\c{s}\fi}, Mustafa {\ifmmode\dot{I}\else\.{I}\fi}lhan},
	title = {{A Strategy for Boundary Adherence and Exploration in Black-Box Testing of Autonomous Vehicles}},
	booktitle = {{2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)}},
	journal = {Published in: 2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)},
	pages = {17--19},
	publisher = {IEEE},
	doi = {10.1109/MOST57249.2023.00028}
}
Package: simc
sim-CWhat is sim-C?Often people have trouble programming in C (especially beginners) due to its low level syntax and unavailability of stable third party libraries. We present sim-C a high-level front end for C which creates a dynamically typed syntax for C. User can write code in this high level syntax and then compile it to optimized C code. sim-C does not process the code and simply translates it to C thus, there is no possibility of the code running slowly. So, with sim-C users can write code faster using the high level syntax and at the same time be able to harness the power and speed of a C program. Let us make C cool again.PipelineNew DevelopersNew developers can join simC New Devs slack channel where they can get help from core developers on their issues, link to the slack channel -simC New Devs SlackLicensesim-C is licensed under GNU General Public License (GPL) v3.0.LICENSEThe TeamSiddhartha Dhar ChoudhuryDhairya JainMathias Fernandes Duarte CoelhoOSS ProgramsKWoC (Kharagpur Winter of Code) 2020SWoC (Script Winter of Code) 2021Contributor blogsFollow thislinkto read blogs written by contributors after completing various OSS programs.
Package: simcardems
Simula Cardiac Electro-Mechanics Solversimcardemsis a FEniCS-based cardiac electro-mechanics solver and is developed as a part of theSimCardio Test project. The solver depends onpulseandcbcbeat.InstallationSeeInstallation instructionsGetting startedSeethe demosDocumentationDocumentation is hosted athttp://computationalphysiology.github.io/simcardems.Automated testTests are provided in the foldertests. You can run the tests with pytestpython3 -m pytest tests -vvContributingSeethe contributing sectionCitingIf you usesimcardemsin your research, please cite it as follows@article{Finsberg2023,
  doi = {10.21105/joss.04753},
  url = {https://doi.org/10.21105/joss.04753},
  year = {2023},
  publisher = {The Open Journal},
  volume = {8},
  number = {81},
  pages = {4753},
  author = {Henrik Nicolay Topnes Finsberg and Ilsbeth Gerarda Maria van Herck and Cécile Daversin-Catty and Hermenegild Arevalo and Samuel Wall},
  title = {simcardems: A FEniCS-based cardiac electro-mechanics solver},
  journal = {Journal of Open Source Software}
 }Known issuesIssue with h5py, seehttps://github.com/ComputationalPhysiology/pulse#known-issuesAuthorsHenrik Finsberg (henriknf@simula.no)Ilsbeth van Herck (ilse@simula.no)Cécile Daversin-Catty (cecile@simula.no)