Saravanan666/pypi-org huggingface dataset:
Package: slow-start-rewatch
Slow Start RewatchMissionsMake cute things happen!Provide a command-line utility for hosting an awesome Slow Start Rewatch.FeaturesSchedule a submission of multiple Reddit postsThe templates for posts can be stored in Reddit's wiki or local filesEach post can include a navigation section with links to other posts which are automatically updated after the submission of new postsReddit authorization via OAuth2 using a local HTTP server with cute GIFsStoring the refresh token locally to keep the authorization activeSubmitting text posts with thumbnailsFully typed with annotations and checked with mypy,PEP561 compatibleInstallationpipinstallslow-start-rewatchUpgrade:pipinstall-Uslow-start-rewatchUsageWhen started for the first time the location of the schedule must be set.Using the schedule stored in Reddit's wiki:slow-start-rewatch-w/r/subreddit/wiki/wiki-pathUsing the schedule stored in the local YAML file:slow-start-rewatch-f/path/to/the/schedule.ymlAfter the location of the schedule is stored in the local config, the program can be launched without any parameters:slow-start-rewatchLicenseMITCreditsThis project was generated withwemake-python-package.GitHub avatar art byyunyunmaru.
Package: slowtestreporter
Slow Test ReporterSlow Test Reporter reads JUnit test result files and reports any slow running tests.Installationpip install slowtestreporterUsage$ slowtestreporter --help
usage: slowtestreporter [-h] [-t THRESHOLD] [-o OUTPUT_FILENAME] [-s] path

Reports slow tests based on the junit test input.

positional arguments:
  path                  Junit file path

optional arguments:
  -h, --help            show this help message and exit
  -t THRESHOLD, --threshold THRESHOLD
                        A float value threshold (in seconds) of when a test should fail when it exceeds this value
  -o OUTPUT_FILENAME, --output-filename OUTPUT_FILENAME
                        Filename of the generated junit results without any extensions. The file will be generated with a .xml extension.
  -s, --silent          Suppresses output to display
Package: slp-coroutine
slp_coroutine - Use Plain Old Functions as CoroutinesUsing Stackless Python tasklets it is possible to decorate any plain old
Python callable as a C-Python coroutine, that can be used with the
asyncio framework. It is also possible to "async" call a coroutine
from a callable decorated as coroutine.This Python module all sort of utility functions tocreate a coroutine from a callablecall a coroutine from a callablecreate an asynchronous generator/iterator form a normal generator/iteratorcreate a normal generator/iterator form an asynchronous normal generator/iteratorcreate an asynchronous context manager from a normal context managercreate a normal context manager from an asynchronous context managerThis code requires Stackless Python 3.7.6 or any later version.Documentation:https://slp-coroutine.readthedocs.io/Bug Tracker:https://github.com/akruis/slp_coroutine/issuesChangelog0.0.1: Initial release0.0.2: Add the documentation to the source archive
Package: sl_pip
ThePyPA recommendedtool for installing Python packages.InstallationDocumentationChangelogGithub PageIssue TrackingUser mailing listDev mailing listUser IRC: #pypa on Freenode.Dev IRC: #pypa-dev on Freenode.Code of ConductEveryone interacting in the pip project’s codebases, issue trackers, chat
rooms, and mailing lists is expected to follow thePyPA Code of Conduct.
Package: slpkg
AboutSlpkg is a software package manager that installs, updates and removes packages onSlackware-based systems.
It automatically calculates dependencies and figures out what things need to happen to install packages.
Slpkg makes it easier to manage groups of machines without the need for manual updates.
Slpkg works in accordance with the standards of theslackbuilds.orgorganization to build packages.
It also uses the Slackware Linux instructions for installing, upgrading or removing packages.HomepageVisit the project websitehere.SourceGitLabrepository.SlackBuilds.orgrepository.SourceForgerepository.PyPirepository.LicenseMIT LicenseDonateDid you know that we developers love coffee?SupportPlease support:Slackwareproject.SlackBuildsproject.AlienBobproject.Thank you all for your support!CopyrightsSlackware® is a Registered Trademark of Patrick Volkerding.
Linux is a Registered Trademark of Linus Torvalds.
Package: slprint11-pkg
This is simple module that objective is displays a animated text on the
terminal.The module has only one function calledslowprint ()that imposes
a fixed time interval between the display of each character. The
display can be vertically or horizontally.The text can also be displayed in theyoyomode where after the last
character is displayed the text is removed regressively by means of a
fixed interval of time between the deletion of each character.Use:>>>fromslprint.slowprintimportslowprint>>>>>>slowprint("Hello World my friend.",delay=0.05,yoyo=True,vertical=True)>>>slowprint("Hello World my friend.",delay=0.05,yoyo=True,vertical=False)>>>slowprint("Hello World my friend.",delay=0.05,yoyo=False,vertical=True)>>>slowprint("Hello World my friend.",delay=0.05,yoyo=False,vertical=False)Parameters:textThe text to be displayed on the terminal.delayThe time in seconds between the display of each character. Default is
0.2 seconds.yoyoIf True after displaying the last character, it begins to remove
character by character regressively. Default is False.verticalIf True displays the text vertically, if False displays the text
horizontally. Default is False.Copyright (c) 2020 Ronaldo Augusto Vasques de Souza
Package: slprint-pkg
Copyright (c) 2020 Ronaldo Augusto Vasques de SouzaPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Description: #SlowprintThis is simple module that objective is displays a animated text on the terminal.The module has only one function calledslowprint ()that imposes a fixed time interval between the display of each character.
The display can be vertically or horizontally.
The text can also be displayed in theyoyomode where after the last character is displayed the text is removed regressively by means of a fixed interval of time between the deletion of each character.##Use:slowprint (“Hello World my friend.”, delay = 0.05, yoyo = True, vertical = True)
slowprint (“Hello World my friend.”, delay = 0.05, yoyo = True, vertical = False)
slowprint (“Hello World my friend.”, delay = 0.05, yoyo = False, vertical = True)
slowprint (“Hello World my friend.”, delay = 0.05, yoyo = False, vertical = False)##Parameters:textThe text to be displayed on the terminal.delayThe time in seconds between the display of each character. Default is 0.2 seconds.yoyoIf True after displaying the last character, it begins to remove character by character regressively. Default is False.verticalIf True displays the text vertically, if False displays the text horizontally. Default is False.Copyright (c) 2020 Ronaldo Augusto Vasques de SouzaPlatform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Environment :: Console
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Topic :: Terminals
Classifier: Topic :: System :: Shells
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Desktop Environment
Requires-Python: >=3.0
Description-Content-Type: text/markdown
Package: slpso
SLPso - Social Learning Particle Swarm OptimizationSLPso is a Python library that implements the Social Learning Particle Swarm Optimization (SL-PSO) algorithm for scalable optimization problems.About the AlgorithmThis library used this article as a basis:A Social Learning Particle Swarm Optimization Algorithm for Scalable OptimizationAuthors: Ran Cheng and Yaochu JinJournal: Information Sciences, Volume 291, Pages 43-60, Year 2015DOI:10.1016/j.ins.2014.08.039URL to the Paper:Read the full paperIf you use the SLPso library in your research, please consider citing this library.Reveal quoteSLPso - Social Learning Particle Swarm Optimization[Software]. (2023).  Available at:https://github.com/vsg-root/slpso.About SL-PSOThe Social Learning Particle Swarm Optimization is a population-based optimization algorithm inspired by the behavior of a swarm of particles. It leverages social interactions to enhance exploration of the search space and convergence to optimal solutions in scalable optimization problems.Important NoteIt is crucial to highlight thatthis library's performance did not match the results reported in the referenced paper.The following results were obtained through extensive experiments, each run 30 times for different dimensions, with 100 particles, and a total of 200,000 fitness evaluations for each experiment.InstallationTo get started with SLPso, you can install it via pip:pipinstallslpsoTry your first SL-PSO program>>>importnumpyasnp>>>fromslpso.slpsoimportSLPSO>>>defcustom_objective_function(positions:np.ndarray)->np.ndarray:"""The custom objective function to be minimized.Args:positions (np.ndarray): An array of particle positions.Returns:np.ndarray: An array of fitness values.""">>>returnnp.sum(positions**2,axis=1)>>>lower_bound=-30.0# Set the lower bound>>>upper_bound=30.0# Set the upper bound>>>slpso_optimizer=SLPSO(custom_objective_function,seed=1,lower_bound=lower_bound,upper_bound=upper_bound,show_progress=False)>>>global_best_position,global_best_value=slpso_optimizer.optimize()>>>print("Global Best Position:",global_best_position)>>>print("Global Best Value:",global_best_value)Note:This library is not affiliated with or endorsed by the original researchers. It is an independent implementation of the SL-PSO algorithm for the convenience of users interested in applying it to their optimization problems. Please do not confuse this library with the work of the original authors.
Package: slrm
slrmPython library to submit slurm jobs.Installation:pip install git+https://github.com/vzhong/slrmUsage:importosimportslrmforcity,statein[('vancouver','bc'),('toronto','on'),('stanford','ca'),('seattle','wa')]:cmd='python myscript.py --city{}--state{}'.format(city,state)slurm_kwargs={'account':'cse','partition':'cse-gpu'}# you can see default args in slrm.launcher.default_argsslrm.launch(cmd,slurm_kwargs,dout=os.path.join('jobs','{}-{}'.format(city,state)),dry=False)Pull requests welcome!
Package: slsdebugger
serverlessdebugger-agent-pythonDebug your python lambda with ServerlessDebugger byThundra!ContentsConfigurationGetting HelpOpening IssuesConfigurationCheck out theVS CodeandPythonconfigurations of our docs for more detailed information.Getting HelpIf you have any issue around using the library or the product, please don't hesitate to:Use thedocumentation.Join ourdiscord channel.Opening IssuesFor any problem you encounter while using Serverless Debugger, please feel free to contact us.
Package: sls-detector
sls detectorThe sls detector python library. It is being used atALBA - BL04 MSPDbeamline to control a SLS Mythen II detector.It is built as a successor of the long lived C++ slsDetectorLibrary.It should be fairly easy to support other detectors of the SLS
family.The library provides complete remote control of a Mythen II detector,
a fairly complete simulator and aLimacamera plugin with a tango device class.
There is also an experimentalSardana1D controller which is not currently being used (theSardana Lima 2D controlleris being used instead).A simple GUI based onPyQt5andpyqtgraphis also provided. It
is intented for test purposes only.InstallationFrom within your favorite python environment type on the command line:$ pip install sls-detectorLibraryThe library can be used with:fromsls.clientimportDetectorfromsls.protocolimportRunStatusmythen=Detector('bl04mythen')print(mythen.energy_threshold)assertmythen.run_status==RunStatus.IDLEmythen.dynamic_range=32withmythen.acquisition(exposure_time=0.1,nb_frames=10)asacq:forevent_type,datainmythen.acquisition():ifevent_type=='frame':print(data)(more examples in theexamples/directory)SimulatorBefore using the simulator make sure all dependencies are installed with:$ pip install sls-detector[simulator]Write a simpleTOMLconfiguration file describing the detector(s) you
want to expose. Example:# mythen.toml[bl04mythen1]ctrl_port=1952stop_port=1953Run the simulator with:$ sls-simulator --log-level=DEBUG -c mythen.toml
INFO 2020-05-15 08:46:02,531 root: preparing to run...
INFO 2020-05-15 08:46:02,533 SLSServer.Detector(bl04mythen1): Ready to accept requestsYou will now be able to access the simulator in exactly the same way as a real detector:fromsls.clientimportDetectormythen=Detector('localhost')print(mythen.energy_threshold)LimaBefore using lima make sure lima is properly installed.From the command line$ sls-lima --host=bl04mythen -n 10 -e 0.25 -d /tmp/mythen --saving-format=EDF --saving-prefix=myth_
Last image Ready = 10/10
Took 2.5158393383026123sAs a libraryfromsls.clientimportDetectorfromsls.lima.cameraimportget_ctrlfromLima.CoreimportAcqRunningctrl=get_ctrl('bl04mythen')# a Lima.Core.CtControlacq=ctrl.acquisition()acq.setAcqExpoTime(0.1)acq.setAcqNbFrames(10)ctrl.prepareAcq()ctrl.startAcq()whilectrl.getStatus().AcquisitionStatus==AcqRunning:print('Running... Waiting to finish!')time.sleep(0.1)print('Finished!')As a lima tango serverFirst, register a lima mythen tango server.The LimaCCDs device should have theLimaCameraTypeproperty set toMythenSLSYou can start the Lima tango device server with the LimaCCDs script or with:$ sls-lima-tango-server <lima tango instance name>GUIA simple Qt GUI is provided. So far, it is intended for test purposes only.Before launching, make sure it is properly installed with:$ pip install sls-detector[gui]Launch it with:$ sls-gui --host=bl04mythenThat's all folks!
Package: slsdm
# Scikit-Learn SIMD DistanceMetrics (SLSDM)## Install from pipRun `pip install slsdm`. Note that the premade wheels are only built with support for `SSE2, SSE3` instructions.## Install From Source:1. Create a new environment with `xsimd`: `conda create -n <env_name> -c conda-forge python~=3.10.0 compilers`2. Activate the environment: `conda activate <env_name>`3. Run `pip install -e .`**Warning**: At this point, building from source only works with `gcc`. Support for `clang` and `MSVC` is in progress.## Specify SIMD Target ArchitecturesBy default, `slsdm` will be built with `{SSE3, SSE2}` support. The preferred architectures can be specified at build time using the `SLSDM_SIMD_ARCH` environment variable by passing a comma-separated list of architecture specification tokens. Currently, only `x86_64` CPU features are supported. Specifically, the following features are available:```Instruction Set | Specification Tokens======================================AVX512BW        |     avx512bwAVX512DQ        |     avx512dqAVX512CD        |     avx512cdAVX512F         |     avx512fFMA4            |     fma4FMA3 + AVX2     |     fma3, avx2AVX2            |     avx2FMA3 + AVX      |     fma3, avxAVX             |     avxFMA3 + SSE4.2   |     fma3, sse4_2SSE4.2          |     sse4_2SSE4.1          |     sse4_1SSSE3           |     ssse3SSE3            |     sse3SSE2            |     sse2```For example, to build for `{SSE3, AVX, FMA 3 + AVX, AVX2}` one would specify `SLSDM_SIMD_ARCH="sse3, avx, fma3<xs::avx>, avx2"`.You may also prefer to specify features *up to*, and optionally including, a certain instruction. For that, you may prepend a specification token with `<` (exclusive) or `<=` (inclusive). For example, to build for `{SSE2, SSE3, SSSE3, AVX2}` one would specify `SLSDM_SIMD_ARCH="<=ssse3, avx2"`.You can also disable specific features that would otherwise be enabled by your specification by prepending the `~` symbol to the specification token. For example, to build for `{SSE4_2, SSE4_1, SSE3, SSE2}`, one would specify `SLSDM_SIMD_ARCH="<=sse4_2, !ssse3"`. Note that the specification will be resolved in a left-to-right order, so `SLSDM_SIMD_ARCH="!ssse3, <=sse4_2"` would not produce equivalent results, and indeed still generates `SSSE3` instructions.To include `FMA3` combination instruction sets, include `fma3` in the `SLSDM_SIMD_ARCH` specification and any compatible instruction sets will automatically be enabled. For example, `SLSDM_SIMD_ARCH="fma3, <=avx"` will enable `{FMA3 + AVX, AVX, FMA3 + SSE4.2, SSE4.2, SSE4.1, SSSE3, SSE3, SSE2}`.**Warning**: Runtime dispatch is not yet correctly supported. When distributing to other machines, please build only with SIMD sets that all target machines support.
Package: slsdt
SLSDTStochastic Local Search Decision TreeThis repository is for my first scientific initiation project.AboutOblique Decision Tree is a algorithm for induction a machine learning method called decision tree using oblique approach.SLSDT is a method for induction oblique decision trees using stochastic local search method called Late Acceptance Hill-Climbing (LAHC).This project also provides a utility to read csv files and convert to the format accepted by the SLSDT method.How to useInstallpip3installslsdtread_csvfromslsdt.reader_csvimportread_csvX,y=read_csv("some_file.csv","class_column_name")slsdtfromslsdt.slsdtimportSLSDTclf=SLSDT()clf.fit(X,y)result=clf.predict(X)print(result)print(result==y)Iris example oblique splitfromsklearnimportdatasetsfromslsdt.slsdtimportSLSDTiris=datasets.load_iris()X=iris.data[:,:2]# we only take the sepal width and sepal length features.y=iris.targetmark=y!=2# we only take the 0 (Iris-setosa) and 1 (Iris-versicolor) class labelsX=X[mark]y=y[mark]clf=SLSDT()clf.fit(X,y)clf.print_tree()result=clf.predict(X)print(result)print(result==y)Plot iris oblique splitPlot with Matplotlib using the results obtained above.How to contributeLeave the :star: if you liked the projectFork this projectCloner your fork:git clone your-fork-url && cd slsdtCreate a branch with your features:git checkout -b my-featuresCommit your changes:git commit -m 'feat: My new features'Send the your branch:git push origin my-featuresLicenseThis project is licensed under the EPL 2.0 License - see theLICENSEfile for details.
Package: sls_enum
serverless-lambda-function-name-enum-pythonOverviewThis CLI tools provide code generation experience.If you have aserverless.ymlto host your lambda application, ptmpdy provides Python Enum objects likefromenumimportEnumclassLambdaHandlers(str,Enum):sample_handler="sample-service-sample-handler"sample_handler2="sample-service-sample-handler2"UsagePlease seeusage.md.Project ScopeServerless FrameworkAdding to Python code generation fromserverless.ymlAdding serverless pluginDevelopmentpoetryinstallContributionsThis project is welcome to contribute. When you want to contribute this project, please follow contribute guides.GuidesVote issuesCreate a PR to main branch in assigning to @hayata-yamamoto as a reviewer
Package: slsim
SLSim (Strong Lensing Simulation) pipelineLSST strong lensing simulation pipeline.This GitHub project originates from an intercollaboration agreement between
the two LSST Science collaborations:Strong Lensing Science Collaboration (SLSC)Dark Energy Science Collaboration (DESC)This repository provides the simulation tools for the purpose of conducting strong gravitational lensing science with the Vera C. Rubin Observatory.
All the tools are open-source available. Check out below how you can contribute and become part of the LSST strong lensing science.Free software: MIT licenseDocumentation:https://slsim.readthedocs.io.How to contribute to the repository?Check out our step-by-step instructions how you can contribute code to this projectContributingGuidelines.
Are you new to coding and contributing to a software development project? No worries! We help you out! Write us on the LSST Slack channel #sl-pipeline.How can I participate in LSST strong lensing projects?We have a dedicatedOnboardingpage with all the information about the collaboration structure and how to become part of this project.
We are excited to having you part of the LSST strong lensing effort!How can I give credit when using this package?We strive to acknowledge all contributors of this repository, be it direct code contributions or for the project at large.
See ourCitationguidelines.History0.1.0 (2022-02-01)First sceleton.
Package: sls-json-fast
阿里云日志服务 for JSON使用ALIYUN_LOG_ENDPOINT日志服务端点，默认根据环境自动配置内外网ALIYUN_LOG_ACCESSID阿里云 access idALIYUN_LOG_ACCESSKEY阿里云 access keyALIYUN_LOG_PROJECT阿里云日志项目名ALIYUN_LOG_STORE阿里云日志存储库名
Package: slskd-api
slskd-apiGoalThis project provides a python API forslskd.A comprehensive python API should help improve integration and increase slskd adoption by enabling rapid development of various software, such as:Desktop/Mobile Clientsbeetsplugin(s)headphonesintegrationInstallationThe package is availaible onpypi:pip install slskd-apiUsageCreate aslskdinstance with the following:import slskd_api
slskd = slskd_api.SlskdClient(host, api_key, url_base)Then you'll be able to access all API methods:app_status = slskd.application.state()
available_rooms = slskd.rooms.get_all()See thedocfor further details.