Saravanan666/pypi-org huggingface dataset:
Package: rsl.soap11
RSL - SOAP 1.1This module provides an implementation for the SOAP 1.1.Seerslfor more details.CHANGES0.2.2 (2009-06-18)small bugfix in rsl.soap11.wsdl1fixed tests for python 2.6updated minimal dependency versions to latest releases0.2.1 (2009-01-12)changed homepage urlfixed packaging0.2.0 (2008-12-03)Initial release
Package: rslsync
A Python client library and CLI of unofficial Resilio Sync (BTSync) API. No API Key is needed.InstallationInstall from pypipip install rslsyncInstall from repopip install .UsageAs a command line tool$ rsl --help
$ rsl general get-settings
$ rsl folder listAs a library$ from rslsync import RslClient
$ c = RslClient("http://localhost:8888/", "user", "pass")

# general commands
$ c.general.get_settings()     # get all settings

# folder commands
$ c.folder.list()  # list all shared folders

# file commands
$ c.file.list()  # list all shared files
$ share_id = c.file.share(path, days)   # share a single file
$ c.file.get_link(share_id)   # create a share link
$ c.file.unshare(share_id)   # unshare a file

# stat commands
$ c.stat.get_peers_stat()  # get the stats of peers

# fs (file system) commands
# c.fs.get_attr("/")Related Projectshttps://github.com/kevinjqiu/btsync.pyhttps://github.com/dlawregiets/btsync_statushttps://github.com/ywrac/btsync-api-pythonhttps://github.com/jminardi/python-btsynchttps://github.com/icy/btsynchttps://github.com/PythonNut/resilio-sync-clihttps://github.com/lxiange/ResilioSync-py
Package: rsl.wsdl1
RSL - WSDL 1This module provides an implementation for WSDL 1. It features it’s own
extension mechanism just likerslitself.Seerslfor more details.CHANGES0.2.2 (2009-01-12)changed homepage urlfixed packaging0.2.1 (2008-12-14)make error message a bit clearer when create binding fails0.2.0 (2008-12-03)Initial release
Package: rsl.xsd
RSL - XSDThis module provides an implementation to use XML-Schema as type system.Seerslfor more details.Status:work in progressCHANGES0.2.4 (2009-08-28)added missing built-in xml-schema data types. (but no parsing of
these new types, they are simply treated as strings or integers)0.2.3 (2009-01-27)added support for simpleType element under element definitionreplaced warnings with logging messages0.2.1 (2009-01-12)changed homepage urlfixed packaging0.2.0 (2008-12-03)Initial release
Package: rsm
Contents[TL;DR|Compatibility| [Usage] |About]Current version: 1.2.9TL;DRThis isnotreplacing the SiemplifySDK but a module which extends it.This is eitheroverridingoraddinga few methods or classes to be used with Siemplify.You are not obliged to choose between the SiemplifySDK and RSM, just use what you need. (Just don't get confused.)CompatibilityThis module supports Python>=3.7 and was tested with Siemplifyv5.5.1.XAboutAfter spending most of my days configuring, implementing Siemplify, a SOAR(Security Orchestration, Automation and Response) platform over the last months.
I decided to build something that would improve productivity as most of my time was spent in troubleshooting with problems that a customer should not encounter.Why did you name this repo 'realsiemplify' if it isn't an Official repository from Siemplify?This name was chosen to be as ironic as possible sinceSiemplifyis pronounced the same way asSimplifyand I do no consider this to be simple since it can be really confusing to work on something with the SiemplifySDK.
You know the way little repetitive things that bothers you? I'm sure you've experienced something in your life that made those little things turn into something heavy, well, I feel you.Do you still recommend Siemplify?Iabsolutelydo recommend it.Every products has their pros and cons.Here is what I value the most and to me, it really is making the difference:RelationshipSiemplify has anincrediblesupport team.Transparency is key with their relationship with customers.They are open to any feedback and they will be there for you if you need them.Are you against Siemplify?Not at all. Siemplify has significantly improved since I first started using the product and they keep evolving very quickly.My lack of patience is mostly what caused all of this.
Package: rsmine
RSMI-NErsmineis a Python package, implemented using Tensorflow, for optimising coarse-graining rules for real-space renormalisation group by maximising real-space mutual information.OverviewSystem requirementsInstallationCitationOverviewrsmineemploys state-of-the-art results for estimating mutual information (MI) by maximising its lower-bounds parametrised by deep neural networks [Poole et al. (2019),arXiv:1905.06922v1]. This allows it to overcome the severe limitations of the initial proposals for constructing real-space RG transformations by MI-maximization in [M. Koch-Janusz and Z. Ringel,Nature Phys. 14, 578-582 (2018), P.M. Lenggenhager et al.,Phys.Rev. X 10, 011037 (2020)], and to reconstruct the relevant operators of the theory, as detailed in the manuscripts accompanying this code [D.E. Gökmen, Z. Ringel, S.D. Huber and M. Koch-Janusz,Phys. Rev. Lett.127, 240603 (2021)andPhys. Rev. E104, 064106 (2021)].System requirementsHardware requirementsrsminecan be run on a standard personal computer. It has been tested on the following setup (without GPU):CPU: 2.3 GHz Quad-Core Intel Core i5, Memory: 8 GB 2133 MHz LPDDR3Software requirementsThis package has been tested on the following systems with Python 3.8.5:macOS:Catalina (10.15)Big Sur (11.1)Monterey (12.5.1)rsminemainly depends on the following Python packages:matplotlibnumpypandasscipyscikit-learntensorflow 2.0tensorflow-probabilityInstallationCloneRSMI-NEfrom GitHubgitclonehttps://github.com/RSMI-NE/RSMI-NEcdRSMI-NEand install thersminepackage viapipin editable modepipinstall-e.or create a virtual environment and install there:./install.shGetting startedThe package can be used by importing thersminemodule and its submodules:importrsmineimportrsmine.coarsegrainer.build_datasetasdsimportrsmine.coarsegrainer.cg_optimisersascg_optJupyter notebooks demonstrating the basic usage in simple examples are provided inhttps://github.com/RSMI-NE/RSMI-NE/tree/main/examples.CitationIf you use RSMI-NE in your work, please cite our publicationsPhys. Rev. Lett.127, 240603 (2021)andPhys. Rev. E104, 064106 (2021):@article{PhysRevLett.127.240603,
  title ={Statistical Physics through the Lens of Real-Space Mutual Information},
  author ={G\"okmen, Doruk Efe and Ringel, Zohar and Huber, Sebastian D. and Koch-Janusz, Maciej},
  journal ={Phys. Rev. Lett.},
  volume ={127},
  issue ={24},
  pages ={240603},
  numpages ={7},
  year ={2021},
  month ={Dec},
  publisher ={American Physical Society},
  doi ={10.1103/PhysRevLett.127.240603},
  url ={https://link.aps.org/doi/10.1103/PhysRevLett.127.240603}}@article{PhysRevE.104.064106,
  title ={Symmetries and phase diagrams with real-space mutual information neural estimation},
  author ={G\"okmen, Doruk Efe and Ringel, Zohar and Huber, Sebastian D. and Koch-Janusz, Maciej},
  journal ={Phys. Rev. E},
  volume ={104},
  issue ={6},
  pages ={064106},
  numpages ={17},
  year ={2021},
  month ={Dec},
  publisher ={American Physical Society},
  doi ={10.1103/PhysRevE.104.064106},
  url ={https://link.aps.org/doi/10.1103/PhysRevE.104.064106}}
Package: rsmorphy-lemmatizer
RSMorphy LemmatizerThis is simple package for lemmatize (word normal form) russian words.
Package based on rsmorphy:https://github.com/g-alexander/rsmorphyInstallationpipinstallrsmorphy_lemmatizer--upgradeUsagefromrsmorphy_lemmatizerimportRSMorphyTransformertransformer=RSMorphyTransformer()test_strings=["Мама мыла раму"]print(transformer.transform(test_strings))# Output: ['мама мыло рам']for multithread transform:transformer=RSMorphyTransformer(n_jobs=3)to back to single thread:transformer.to_single_thread()
Package: rsmtool
Rater Scoring Modeling ToolIntroductionAutomated scoring of written and spoken test responses is a growing field in educational natural language processing. Automated scoring engines employ machine learning models to predict scores for such responses based on features extracted from the text/audio of these responses. Examples of automated scoring engines includeProject Essay Gradefor written responses andSpeechRaterfor spoken responses.Rater Scoring Modeling Tool (RSMTool) is a python package which automates and combines in a single pipeline multiple analyses that are commonly conducted when building and evaluating such scoring models.  The output of RSMTool is a comprehensive, customizable HTML statistical report that contains the output of these multiple analyses. While RSMTool does make it really simple to run a set of standard analyses using a single command, it is also fully customizable and allows users to easily exclude unneeded analyses, modify the default analyses, and even include custom analyses in the report.We expect the primary users of RSMTool to be researchers working on developing new automated scoring engines or on improving existing ones. Note that RSMTool is not a scoring engine by itself but rather a tool for building and evaluating machine learning models that may be used in such engines.RSMTool is driven by a configuration file that users have to supply. Given the large number of available options, this can get complicated especially for new users. That’s why RSMTool can help users generate configuration files interactively via guided prompts! The video below demonstrates this feature.Getting StartedTo get started with RSMTool, please see the extensiveofficial documentation.  If you use theDashapp on macOS, you can also download the complete RSMTool documentation for offline use. Go to the Dash preferences, click on “Downloads”, then “User Contributed”, and search for “RSMTool”.Requirementspython>=3.8numpy<.1.25scipyscikit-learnstatsmodelsskllpandasipythonjupyternotebookseabornshapwandbContributingContributions to RSMTool are very welcome. Please refer to thedocumentationfor how to get started on developing new features or functionality for RSMTool.CitingIf you are using RSMTool in your work, you can cite it as follows:MLAMadnani, Nitin and Loukina, Anastassia. “RSMTool: A Collection of Tools for Building and Evaluating Automated Scoring Models”. Journal of Open Source Software 1(3), 2016.BibTex@article{MadnaniLoukina2016,doi={10.21105/joss.00033},url={http://dx.doi.org/10.21105/joss.00033},year={2016},month={jul},publisher={The Open Journal},volume={1},number={3},author={Nitin Madnani and Anastassia Loukina},title={{RSMTool}: A Collection of Tools for Building and Evaluating Automated Scoring Models},journal={{Journal of Open Source Software}}}ChangelogSeeGitHub Releases.
Package: rsnaped
rSNAPedrSNAPed : RNA Sequence to NAscent Protein Experiment Designer.Authors: Luis U. Aguilera, William Raymond, Brooke Silagy, Brian Munsky.:warning:This software is in a very early and experimental stage: at this point, it is intended to be used for testing and debugging purposes!DescriptionThis library is intended to quantify single-molecule gene expression experiments. Specifically, the code usesCellposeto segment the cell in the image. Then, it usesTrackpyto detect spots inside the mask. Finally, it uses the spot position to quantify the spot intensity. The code also generates simulated data usingrSNAPsim. If you userSNAPed, please make sure you properly citecellpose,trackpyandrSNAPsim.UsageTracking for single-molecule translation (RNA + nascent protein) spots.Tracking for single-molecule RNA spots.Simulating the single-molecule translation for any gene.Design of single-molecule gene expression experiments.Simulating translationThe code is intended to simulated single-molecule translation. A  video with the simulated cell and a data frame containing spot and intensity positions are generated. This simulation can be used to train new algorithms or for teaching new students.Local installation using PIPTo create a virtual environment using:condacreate-nrsnaped_envpython=3.8.5-ysourceactivatersnaped_envOpen the terminal and usepipfor the installation:pipinstallrsnapedLocal installation from the Github repositoryTo create a virtual environment navigate to the location of the requirements file, and use:condacreate-nrsnaped_envpython=3.8.5-ysourceactivatersnaped_envTo install GPU for Cellpose (Optional step). Only forLinux and Windows userscheck the specific version for your computer on thislink:conda install pytorch cudatoolkit=10.2 -c pytorch -yTo install CPU for Cellpose (Optional step). Only forMac userscheck the specific version for your computer on thislink:conda install pytorch -c pytorchTo include the rest of requirements use:pip install -r requirements.txtAdditional steps to deactivate or remove the environment from the computer:To deactivate the environment useconda deactivateTo remove the environment use:conda env remove -n rsnaped_envReferences for main dependenciesrSNAPsim:
Aguilera, Luis U., et al. "Computational design and interpretation of single-RNA translation experiments." PLoS computational biology 15.10 (2019): e1007425.Trackpy:
Dan Allan, et al. (2019, October 16). soft-matter/trackpy: Trackpy v0.4.2 (Version v0.4.2). Zenodo.http://doi.org/10.5281/zenodo.3492186Cellpose:
Stringer, Carsen, et al. "Cellpose: a generalist algorithm for cellular segmentation." Nature Methods 18.1 (2021): 100-106.Licenses for dependenciesFor a complete list containing the complete licenses for the dependencies, check file:Licenses_Dependencies.txt.License forrSNAPsim: MIT. Copyright © 2018 Dr. Luis Aguilera, William RaymondLicense forTrackpy: BSD-3-Clause. Copyright © 2013-2014 trackpy contributorshttps://github.com/soft-matter/trackpy. All rights reserved.License forCellpose: BSD 3-Clause. Copyright © 2020 Howard Hughes Medical Institute
Package: rsnapsim
Python 2.7 or 3.5+ Version of the Single Molecule Translation Simulator (MatLab) by Dr. Luis AguileraComputational Design and Interpretation of Single-RNA Translation ExperimentsTranslated by Will Raymond - 2018/2019rSNAPsim-RNASequence toNAscentProteinSimulationssa c++ libraryProject GoalProvide a Python module that takes nucleotide sequence as an input and does the following:Choose a file or pull a file from GeneBankAnalyzes the sequence and identifies proteinsDetects or adds fluorescent tagsSimulates translation trajectories and converts to intensity vectors of A.U. under various conditionsConstructs with Rare codons only or Common codons, FRAP or Harringtonite assaysProvides analyses of the trajectoriesAllows the user to save or export the dataCommandline / GUI implementationsDocumentationTutorials, Module Documentation, Installiation and more [LINK TO MUNSKY GROUP WEBSITE]Dependencies:NumPySciPyBioPythonmatplotlibpandasSnapGene ReaderDnaFeaturesViewerInstillationWithin a conda enviroment:conda install eigen 
pip install rsnapsim-ssa-cpp 
pip install rsnapsimWithin a Google Colab:!apt install libeigen3-dev
!ln -sf /usr/include/eigen3/Eigen /usr/include/Eigen
!pip install rsnapsim-ssa-cpp
!pip install rsnapsim
!pip install --upgrade rsnapsimCompilation of the C++The c++ model should attempt to compile when you pip install the ssa-cpp module, however in the event that it cannot here are some common errors:cannot include eigen3/Eigen/DenseThis means eigen was not installed correctly from the conda installiation, you may have to manually downloadeigenand pass the argument to the setup.py command.python setup.py build_ext --inplace -I[PATH TO EIGEN FOLDER]gcc not foundExample Colab NotebooksSimulating TranslationSimulating Constructs with Different codon usagesIntensity AnalysesHarringtonine / FRAP simulationsModel Maker/ DesignerMW/Diffusion CalculationsFuture workExample notebooks of all functions
Package: rsocket
RSocket-pyPython implementation ofRSocketInstallationThe latest version frompypican be installed using:pipinstallrsocketYou may also install using someextras:ExtraFunctionalityDocumentationrxReactiveX (v3) integrationTutorialreactivexReactiveX(v4) integrationTutorialaiohttpaiohttpWebsocket transport (server/client)TutorialquartQuartWebsocket transport (server only)quicQUICtransportwebsocketsWebsocketstransport (server only)asyncwebsocketsWebsocketstransport (client only)cliCommand lineTutorialoptimizedFrame parse/serialize optimizationscloudeventsCloudEventsintegrationgraphqlGraphQLintegrationTutorialFor example:pipinstallrsocket[reactivex]Alternatively, download the source code, build a package:python3setup.pybdist_wheelUse the resulting package from the./distfolder, or install locally:python3setup.pyinstallDocumentationDocumentationis available on the official rsocket.io site.ExamplesExamples can be found in the/examplesfolder. It contains various server and client usages. The following is a
table
denoting which client example is constructed to be run against which server example. Some examples
are in java to show compatibility with a different implementation. To run the java examples first build usingmvn
package.Theexamples/test_examples.pycan be used to execute the relevant example server/client pairs.client_springboot.py is set up to work against
theSpring RSocket demo.server (python)server (java)client (python)client(java)server.pyclient.pyserver_quic.pyclient_quic.pyserver_with_lease.pyClientWithLeaseserver_with_routing.pyclient_with_routing.pyClientserver_with_routing.pyclient_rx.pyserver_with_routing.pyclient_reconnect.pyServerrun_against_example_java_server.pyServerWithFragmentationclient_with_routing.pyserver_quart_websocket.pyclient_websocket.pyserver_aiohttp_websocket.pyclient_websocket.pyserver_graphql.pyServerWithGraphQLclient_graphql.pyBuild StatusProgressRequestsFire and forgetResponseStreamChannelMetadata pushFeaturesKeepalive / Max server lifeLeaseResumeFragmentationExtensionsComposite metadataPer Stream MimetypeRoutingAuthenticationTransportsTCPWebsocket (WS, WSS) - Quart and aiohttp integrationQUICHTTP/3HTTP/2AeronRxPy IntegrationStream ResponseChannel ResponseChannel Requester streamResponseOtherReconnectLoad balancingServer routing definition helper (Flask like syntax)Reactivex integration (v3, v4) server/client sideCommand line interface
Package: rsocks
RSOCKS is a reverse proxy server for transferring traffic of a specific host
through a SOCKS 4/5 proxy. It is useful for using SOCKS 4/5 in applications
that don’t have SOCKS proxy support.Installation$ pip install rsocksOr$ easy_install rsocksTo upgrade to latest version, you could execute:$ pip install -U rsocksResourcesOS Service TemplatesConfiguration SnippetsSoftware Integration GuidesUsageFirst, we need to create a config file with following format:# /path/to/rsocks.toml
[servers.foo]
proxy = "socks4://user:pass@192.168.1.10:1080"
listen_host = "0.0.0.0"
listen_port = 5025
upstream_host = "smtp.example.com"
upstream_port = 25
upstream_ssl = falseThere is an example for forwarding SMTP/IMAP of Gmail through a SOCKS5 proxy
server:[servers.imap]
proxy = "socks5://localhost:1080"
listen_port = 5993
upstream_host = "imap.gmail.com"
upstream_port = 993
upstream_ssl = true

[servers.smtp]
proxy = "socks5://localhost:1080"
listen_port = 5465
upstream_host = "smtp.gmail.com"
upstream_port = 465
upstream_ssl = trueNext, we start the rsocks server:$ rsocks --config=/path/to/rsocks.toml
[14:24:44] rsocks.servers.smtp       Using proxy server socks5://localhost:1080
[14:24:44] rsocks.servers.smtp       Listening 127.0.0.1:5465
[14:24:44] rsocks.servers.imap       Using proxy server socks5://localhost:1080
[14:24:44] rsocks.servers.imap       Listening 127.0.0.1:5993
[14:24:44] rsocks.pool               Prepared "smtp"
[14:24:44] rsocks.pool               Prepared "imap"
[14:24:44] rsocks.servers.smtp       Starting server...
[14:24:44] rsocks.servers.imap       Starting server...Don’t stop it now, open your application (such as Airmail in OS X) and set the
server option to local forwarding address:SMTP: 127.0.0.1:5465 without SSL
IMAP: 127.0.0.1:5993 without SSLNow the application’s traffic will be forwarded through the proxy server
(socks5://127.0.0.1:1080).If you wish to run the rsocks server in background, please check thePlatform Service Templates.
For example, OS X users can createcom.tonyseek.rsocks.plistfile in~/Library/LaunchAgentsand load it as a service:$ nano ~/.rsocks.toml  # create a config file
$ wget https://github.com/tonyseek/rsocks/blob/master/misc/platforms/osx-launchd/com.tonyseek.rsocks.plist \
      -O ~/Library/LaunchAgents/com.tonyseek.rsocks.plist
$ nano ~/Library/LaunchAgents/com.tonyseek.rsocks.plist  # edit the template
$ launchctl load -w ~/Library/LaunchAgents/com.tonyseek.rsocks.plistAPI OverviewThere is an example for using API to build a server:importosfromrsocks.poolimportServerPoolfromrsocks.serverimportReverseProxyServerproxy=os.environ.get('SOCKS_PROXY','socks5://localhost:1080')pool=ServerPool()withpool.new_server(name='IMAP',server_class=ReverseProxyServer,upstream=('imap.gmail.com',993),use_ssl=True)asserver:server.set_proxy(proxy)server.listen(('127.0.0.1',5993))withpool.new_server(name='SMTP',server_class=ReverseProxyServer,upstream=('smtp.gmail.com',465),use_ssl=True)asserver:server.set_proxy(proxy)server.listen(('127.0.0.1',5465))if__name__=='__main__':pool.loop()
Package: rsome
Robust Stochastic Optimization Made EasyWebsite:RSOME for PythonPyPI:RSOME 1.2.6RSOME (Robust Stochastic Optimization Made Easy) is an open-source Python package for generic modeling of optimization problems (subject to uncertainty). Models in RSOME are constructed by variables, constraints, and expressions that are formatted as N-dimensional arrays. These arrays are consistent with the NumPy library in terms of syntax and operations, including broadcasting, indexing, slicing, element-wise operations, and matrix calculation rules, among others. In short, RSOME provides a convenient platform to facilitate developments of robust optimization models and their applications.ContentInstallationSolver interfacesGetting startedTeamCitationInstallationThe RSOME package can be installed by using thepipcommand:pip install rsomeSolver interfacesThe RSOME package transforms robust or distributionally robust optimization models into deterministic linear or conic programming problems, and solved by external solvers. Details of compatible solvers and their interfaces are presented in the following table.SolverLicense  typeRequired versionRSOME interfaceSecond-order cone constraintsExponential cone constraintsSemidefiniteness constraintsscipy.optimizeOpen-source>= 1.9.0lpg_solverNoNoNoCyLPOpen-source>= 0.9.0clp_solverNoNoNoOR-ToolsOpen-source>= 7.5.7466ort_solverNoNoNoECOSOpen-source>= 2.0.10eco_solverYesYesNoGurobiCommercial>= 9.1.0grb_solverYesNoNoMosekCommercial>= 10.0.44msk_solverYesYesYesCPLEXCommercial>= 12.9.0.0cpx_solverYesNoNoCOPTCommercial>= 7.0.3cpt_solverYesNoYesGetting startedDocuments of RSOME are provided as follows:RSOME quick startRSOME users guideApplication examplesTeamRSOME is a software project supported by Singapore Ministry of Education Tier 3 GrantScience of Prescriptive Analytics. It is primarly developed and maintained byZhi Chen,Melvyn Sim, andPeng Xiong. Many other researchers, including Erick Delage, Zhaowei Hao, Long He, Zhenyu Hu, Jun Jiang, Brad Sturt, Qinshen Tang, as well as anonymous users and paper reviewers, have helped greatly in the way of developing RSOME.CitationIf you use RSOME in your research, please cite our papers:Chen, Zhi, and Peng Xiong. 2023.RSOME in Python: an open-source package for robust stochastic optimization made easy.INFORMS Journal on Computing35(4) 717-724.Chen, Zhi, Melvyn Sim, Peng Xiong. 2020.Robust stochastic optimization made easy with RSOME.Management Science66(8) 3329-3339.Bibtex entry:@article{chen2021rsome,
  title={{RSOME} in {Python}: An open-source package for robust stochastic optimization made easy},
  author={Chen, Zhi and Xiong, Peng},
  journal={INFORMS Journal of Computing},
  volume={35},
  number={4},
  pages={717--724},
  year = {2023},
  publisher={INFORMS}
}@article{chen2020robust,
  title={Robust stochastic optimization made easy with RSOME},
  author={Chen, Zhi and Sim, Melvyn and Xiong, Peng},
  journal={Management Science},
  volume={66},
  number={8},
  pages={3329--3339},
  year={2020},
  publisher={INFORMS}
}