Saravanan666/pypi-org huggingface dataset:
Package: slideshow-screensaver
IntroductionSlideshow Screensaver is a screensaver for Gnome which displays a new photo
every 10 seconds from$HOME/Pictures.  If you want to customize the photo
directory, the time between photos or the screensaver delay you will need to
edit the source code for now.This is a very simple app which, for now, might not play nicely with
xscreensaver, gnome-screensaver, etc.InstallationInstall the system-wide dependencies:python 2.7dbus-pythonxautolockInstall the python package:sudo pip install slideshow-screensaverConfigure the screensaver to run automatically:Disable any other screensavers.Makeslidesaver-daemonrun at startup.PlansShort-termGUI ConfigurationDistribution packagesSimple image viewer appLong-termWork with other (or no) desktop environmentsPython 3Cool Transitions
Package: slidetemplater
SlideTemplater.com Python APIThis is a simple client API for SlideTemplater.comPlease see documentation there for how to prepare a presentation for templatingYou can use it as the following:import slidetemplater.api

result = slidetemplater.api.convert(open(
    'covid.pptx', 'rb'), "https://api.coronavirus.data.gov.uk/v2/data?areaType=overview&metric=cumPeopleVaccinatedFirstDoseByVaccinationDate&format=json")

f = open('result.pptx', 'w+b')
f.write(result)
f.close()
Package: slidge
Slidge 🛷Home|Source|Issues|Patches|ChatTurn any XMPP client into that fancy multiprotocol chat app that every cool kid want.Slidge is a general purpose XMPP (puppeteer) gateway framework in python.
It's a work in progress, but it should makewriting gateways to other chat networks(plugins) as frictionless as possible.It comes with a few plugins included, implementing at least basic direct messaging and often more "advanced"
instant messaging features:Presences¹Typing²Marks³Upload⁴Edit⁵React⁶Retract⁷Reply⁸Groups⁹SignalN/A✅✅✅N/A✅✅✅~Telegram✅✅✅✅✅✅✅✅~Discord❌✅N/A✅✅~✅✅❌Steam✅✅N/A❌N/A~N/AN/A❌Mattermost✅✅~✅✅✅✅❌❌Facebook❌✅✅✅✅✅✅✅❌Skype✅✅❌✅✅❌✅❌❌WhatsApp✅✅✅✅N/A✅✅✅❌This table may not be entirely accurate, butin theory, stuff marked ✅ works.
N/A means that the legacy network does not have an equivalent of this XMPP feature
(because XMPP is better, what did you think?).WARNING: you may break the terms of use of a legacy network and end up getting your account locked
by using slidge. Refer to thekeeping a low profiledocumentation page for more info.StatusSlidge isbeta-grade software for 1:1 chats.
Group chat support isexperimental.Try slidge and give us some
feedback, through theMUC, theissue trackeror in thepublic inbox.
Don't be shy!InstallationcontainersContainers for arm64 and amd64 are available ondocker hub.debianDebian packages forbullseye(amd64 only for now, help welcome
to support other architectures)
are built on each push to master as artifacts ofthis build job.A repo is maintained by IGImonster. To use it do this (as root):# trust the repo's keywget-O-http://deb.slidge.im/repo/slidge.gpg.key\|gpg--dearmor\|tee/usr/share/keyrings/slidge.gpg>/dev/null# add the repo, replace 'release' with 'nightly' if you're feeling adventurousecho"deb [signed-by=/usr/share/keyrings/slidge.gpg] http://deb.slidge.im/repo/debian release main"\>/etc/apt/sources.list.d/slidge.list# installaptupdate&&aptinstallslidge-yRefer tothe docsfor information about how to use the provided systemd service files.pipTagged releases are uploaded topypi.pipinstallslidge[signal]# you can replace signal with any network listed in the table abovepython-mslidge--legacy-module=slidge.plugins.signalIf you're looking for the bleeding edge, download an artifacthere.About privacySlidge (and most if not all XMPP gateway that I know of) will break
end-to-end encryption, or more precisely one of the 'ends' become the
gateway itself. If privacy is a major concern for you, my advice would
be to:use XMPP + OMEMOself-host your gatewayshave your gateways hosted by someone you know AFK and trustRelated projectsSpectrumBitfrostMautrixmatterbridgeXMPP-discord-bridge
Package: slidge-style-parser
GithubPyPiLicense: AGPL-3.0-or-laterA parsing library forSlidge. Supports parsing these attributes:"_underline_""*bold*""~strikethrough~""`code span`""```languagecode block```"">quote""||spoiler||""\_escape style_"Most of them correspond toXEP-0393: Message Styling.Methods:format_for_telegram(body: String, mentions: Optional<(_, start, end_index_exclusive)>)
-> (body: String, Vec<(format: String, offset: usize, length: usize, language: String)>)format_for_matrix(body: String, mentions: Optional<(mxid, start, end_index_exclusive)>) -> body: Stringformat_body(body: String, new_tags: HashMap<String, (String, String)>) -> Stringnew_tags = {"_": ("<em>", "</em>"),

"*": ("<strong>", "</strong>"),

"~": ("<del>", "</del>"),

"`": ("<code>", "</code>"),

"```": ("<pre><code>", "</code></pre>"),

"```language": ('<pre><code class="language-{}">', "</code></pre>"),

">": ("<blockquote>", "</blockquote>"),

"||": ("<span data-mx-spoiler>", "</span>"),

"\n": ("<br>", "")}To build:maturin build
Package: sliding-rate-limiter
sliding-rate-limiterSliding rate limiter using memory or a distributed Redis backend.Free software: Apache Software License 2.0Documentation:https://sliding-rate-limiter.readthedocs.io.FeaturesPluggable rate limiting backend (threadsafe in-memory or distributed Redis backend)Usagefromsliding_rate_limiter.regionimportRateLimiterRegionfromsliding_rate_limiter.backends.memoryimportMemoryBackendregion=RateLimiterRegion('default')region.configure(MemoryBackend())@region.rate_limit_on_arguments()deffoo(user_id):passCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.API heavily inspired bydogpile.cacheHistory0.1.0 (2018-05-10)First release on PyPI.
Package: slidingRP
slidingRefractoryCode to perform a new test of whether neurons have contaminated refractory periods, with a sliding windowPythonInstallationInstall using pip:pip install slidingRPInstall using sources in development mode. First clone the repository.cd slidingRefractory
pip install -e .Minimal working examplefrompathlibimportPathimportnumpyasnpimportpandasaspdfromslidingRPimportmetricsTEST_DATA_PATH=Path(slidingRP.__file__).parent.parent.joinpath("test-data","integration")params={'sampleRate':30000,'binSizeCorr':1/30000}spikes=pd.read_parquet(TEST_DATA_PATH.joinpath('spikes.pqt'))table=slidingRP.slidingRP_all(spikes.times,spikes.clusters,**params)assertnp.allclose(pd.read_parquet(TEST_DATA_PATH.joinpath("rp_table.pqt")),pd.DataFrame(table),equal_nan=True)ContributeRun unit testspytest python/test_*Upload packagerm -fR dist
rm -fR build
python setup.py sdist bdist_wheel
twine upload dist/*
Package: sliding-window-counter
Sliding Window Countersliding-window-counterprovides time based sliding window counters. These are useful for tracking things like throughput metrics for long running applications.InstallationInstall via pippip3installsliding-window-counterQuick Start## Creating Counters## -----------------------------------------------------------------------------importdatetimeasdtfromsliding_window_counterimportSlidingWindowCounter# Count across 1 minute with 5 second resolutionsmall_counter=SlidingWindowCounter(60,5)# Count across 1 hour with 1 minute resolutionlong_counter=SlidingWindowCounter(dt.timedelta(hours=1),dt.timedelta(minutes=1))# Count across 12 hours with 10 second resolution (not a good idea to be honest)long_and_precise_counter=SlidingWindowCounter(dt.timdelta(hours=12),10)## Using Counters## -----------------------------------------------------------------------------importrandomimporttimemini_counter=SlidingWindowCounter(10,1)# fill it up and check it's stateforiinrange(20):add=random.randint(1,20)mini_counter.increment(add)print(f"{i:>3}s Added:{add:<3}Window:{mini_counter.current_total:<6}Total:{mini_counter.grand_total:<6}Avg:{mini_counter.current_throughput:>5.2f}/s")time.sleep(1)# Watch it drainforiinrange(12):time.sleep(1)print(f"Window:{mini_counter.current_total:<5}Total:{mini_counter.grand_total:<5}Avg:{mini_counter.current_throughput:>5.2f}/s")Bugs, Feature Requests etcTLDR: Pleasesubmit an issue on github.In the case of bug reports, please help me help you by following best practices12.In the case of feature requests, please provide background to the problem you are trying to solve so to help find a solution that makes the most sense for the library as well as your usecase.DevelopmentThe only development dependency is bash and docker. All actions are run within docker for ease of use. See./dev.sh helpfor commands. Typical commands areformat,lint,test,repl,build.I am still working through open source licencing and contributing, so not taking PRs at this point in time. Instead raise and issue and I'll try get to it as soon a feasible.LicenceThis project is licenced under the MIT Licence - seeLICENCE.This project may include other open source licenced software - seeNOTICE.AuthorsA project by Nicholas Hairs -www.nicholashairs.com.
Package: sliding-window-generator
SlidingWindowGeneratorbased on tensorflow v2.3.0usetimeseries_dataset_from_array functionwhich was introduced in tf v2.3.0This module converts time series data from dataframe type to sliding window typeto use as input in RNN based layerThis module was based ontensorflow official docs, just aggregate some functions and add small tuning to use it more efficiently.to make it possible to control batch_size, sequence_stride_size and shuffle more freely.Module is in./src/sliding_window_generator.pyand the example of how to use it is in./src/example.ipynb
Package: slidingwindow-generator
SlidingWindowGeneratorbased on tensorflow v2.3.0usetimeseries_dataset_from_array functionwhich was introduced in tf v2.3.0This module converts time series data from dataframe type to sliding window typeto use as input in RNN based layerThis module was based ontensorflow official docs, just aggregate some functions and add small tuning to use it more efficiently.to make it possible to control batch_size, sequence_stride_size and shuffle more freely.Module is in./src/sliding_window_generator.pyand the example of how to use it is in./src/example.ipynb
Package: slidl
SliDL: a Python library of pre- and post-processing tools for applying deep learning to whole-slide imagesSliDLis a Python library for performing deep learning image analysis on whole-slide images (WSIs), including deep tissue, artefact, and background filtering, tile extraction, model inference, model evaluation and more.Please see ourtutorial repositoryto learn how to useSliDLon an example problem from start to finish.Installing SliDL and its depedenciesInstall SliDL by cloning its repository:git clone https://github.com/markowetzlab/slidlSliDL is best run inside an Anaconda environment. Once you haveinstalled Anaconda, you can createslidl-env, a conda environment containing all of SliDL's dependencies, then activate that environment. Make sure to adjust the path to your local path to the slidl repository:conda env create -f /path/to/slidl/slidl-environment.yml
conda activate slidl-envNote thatslidl-environment.ymlinstalls Python version 3.7, PyTorch version 1.4, Torchvision version 0.5, and CUDA version 10.0. Stable versions above these should also work as long as the versions are cross-compatible. Be sure that the CUDA version matches the version installed on your GPU; if not, either update your GPU's CUDA or change thecudatoolkitline ofslidl-environment.ymlto match your GPU's version before creatingslidl-env.Some users have run into an error message saying that something from libvips is missing whenSliDLtries to import pyvips. This is because on some operating systems, the pip install of pyvips performed in theconda env createcommand leads to a flawed pyvips build. To solve this issue, also install pyvips using conda inslidl-env:conda install -c conda-forge pyvipsFor users who don't wish to use conda,SliDLcan also be installed via pip. To do so, navigate to to theslidldirectory containingsetup.py, and run the following command:pip install -e .Learning to use SliDLSee our extensive tutorialhere.DocumentationThe complete documentation forSliDLincluding its API reference can be foundhere.DisclaimerNote that this is prerelease software. Please use accordingly.
Package: slightly-better-types
slightly-better-types... arguablyImproved abstraction for dealing with union and named types.Installationpipinstallslightly-better-types==1.2.1UsageUsing theParameterclass directlyfrominspectimportsignaturefromtypingimportget_type_hintsfromslightly_better_types.parameterimportParameterclassFoo:passdefaccepts_string(a:Foo):passsignature=signature(accepts_string)type_hints=get_type_hints(get_type_hints(accepts_string))parameter=list((signature.parameters.values()))[0]parameter=Parameter(parameter=parameter,type_hint=type_hints.get(parameter.name))parameter.accepts(1)# Falseparameter.accepts(Foo())# TrueUsing theFunctionclass:fromslightly_better_types.functionimportFunctiondefaccepts_string(a:str,b:int):passsb_function=Function(accepts_string)sb_function.accepts('string',1)# Truesb_function.accepts(1,'string')# Falsesb_function.accepts('string')# FalseUsingHandlersto determine which methods accept a given set of input:fromslightly_better_types.handlersimportHandlersclassBar:defaccepts_string(self,a:str):passdefaccepts_string_too(self,a:str):passdefaccepts_int(self,a:int):passhandlers=Handlers(Bar)list(handlers.accepts('a string').all().keys())# ['accepts_string', 'accepts_string_too']list(handlers.accepts(1).first().get_name())# 'accepts_int'Testspython3-munittestLicenseThe MIT License (MIT). Please seeLicense Filefor more information.
Package: slim
A Web framework provides instant restful api for your database.Based on ASGI.Instant restful api for your database model.Automatic api document (rendered byredoc).Form validate byschematicsAccess control supportedCLI to create projectTest friendlySDKTypeScript/JavaScriptLinksHomepageDocumentContributorsfyleexuanhe0xn0nehlx70375CYY666666ProjectsIcarusA opensource forum project write with python3 and vue.js
Package: slim4py
Slim4Py - Slim Templates for PythonWhat is Slim?Slim is a fast, lightweight templating engine whose goal is reduce html syntax to the essential parts without becoming cryptic.Check on the official site:http://slim-lang.com/What is slim4py?Slim4Py is a simple script that allows you to use all the functions of Slim (Ruby)
in any python framework with the addition that you can also use the
mako syntax (a popular and fast template engine for python).Here is a small example of usage:fromslim4py.slimimportSlimslim=Slim("slim")defsay_hello():return"Hello World"slim.render("example.slim",say_hello=say_hello,year="2019",author="https://github.com/multiversecoder/slim4py")About Magic CommentsIf you need to use python inside the templates, you can define at the beginning of the file a magic comment containing all the variables that will be rendered by mako using the logic written in python.To define a magic comment use:/ mako_vars = [name_var_1, name_var_2, ...]This comment will be detected by the engine and rendered using mako and python.Combining Python With Ruby/ mako_vars = [list_]doctype5htmlheadtitleSlim4Py Example - Basicbody#contents|% for a in list_:% if a.startswith("t"):itstwo or three% elif a.startswith("f"):four/five% else:one% endif% endforfooter| Copyright - #{author} - #{year}RequirementsYou need Python 3.7 or later to run slim4py, ruby and the slim gemIn Ubuntu, Mint and Debian you can install Requirements like this:$ apt-get install python3 python3-pip
$ apt-get install ruby
$ gem install slimFor RHEL, Fedora and CentOS:$ dnf install python3 python3-pip
$ dnf install ruby
$ gem install slimFor other systems- Install Python3
- Install Ruby
- gem install slimQuick startslim4py can be installed using pip:$ python3 -m pip install -U slim4pyor:$ pip install slim4pyfor install slim4py from source:$ git clone https://github.com/multiversecoder/slim4py
$ cd ./slim4py
$ pip install .Development statusslim4py is beta software, but it has already been used in production and it has an extensive test suite.
Package: slim-compose
slim-composeSimple management tool for podman pods, pure Python, no third-party dependencies.requirementsPodman 4.x (network backend: netavark)Python 3.10installpipx install slim-composeusage# create config fileslim-composetemplate# edit config filevimslim-compose-template.json# rename config filemvslim-compose-template.jsonslim-compose.json# deploy podslim-composeup# stop podslim-composestop# destroy podslim-composedown# helpslim-compose-h
Package: slimDNS
slimDNSA simple DNS server written in vanilla Python.slimDNSdocumentationslimDNSdiscordserverslimDNS supportsA,NS,SOAandSRVrecords(as of v1.0.0rc2).Installationpip install slimDNSor simplygit clonethis repository.Minimal exampleimportslimDNSdns=slimDNS.server(slimDNS.UDP)dns.run()This would host a DNS server without any records.There's two ways you can add records:Swap out all records via annotation@dns.recordsdefrecords(server):return{"example.com":{"A":{"target":"264.30.198.2","ttl":60},"SOA":{"target":"example.com","ttl":60},"NS":{"target":"example.com","ttl":60,"priority":10}},"nas.example.com":{"A":{"target":"264.30.198.2","type":"A","ttl":60}},"_matrix._tcp.riot.example.com":{"SRV":{"ttl":60,"priority":10,"port":8448,"target":"nas.example.com"}}}Which would swap out all current records for the defined set of records.Add, delete and update recordsdns.remove('example.com','A')dns.add('example.com','A','264.30.198.1')dns.update('example.com','A','264.30.198.5')Which would remove theArecordexample.com,Then add a new similar one with a new UP andfinally update that new record with a newIP.NoteRequires Python 3.8+ &Linux(not tested on other platforms).
Package: slimebones-autofiles
autofilesDoes file generation.UsageGenerationTo generate a file:fromautoimportAutoUtilsAutoUtils.generate(name="testing",extension=FileExtension.Python,author="testco",dir=Path(Path.cwd(),"var"),content="print(1 + 1)\n",)Which will result in a file with content like this:"""Do not modify, auto-generated by testco"""print(1+1)CleaningTo clean all auto-generated file in given directory, call:autofilesclean.
Package: slimelearnpy
slimelearn.pyA very easy to use API wrapper for SlimeLearn written in Python.InstallingPython 3.9.1 or higher is requiredTo install the library:pipinstallslimelearnpyClient ExampleThe following code recives updates from the server every 3 seconds, and executes a jump if the player is ready to do so.fromslimelearnpyimportSlimeLearnsl=SlimeLearn()conf={"req":"config","payload":{"mode":"sec","delay":3,}}defmy_agent(input_data):ifinput_data["player"]["state"]=='idle':sl.jump()sl.run("//localhost:8080",config=conf,function=my_agent)More examples can be found in the examples folder.
Package: slimer
SlimerDirectory and File Content Visualisation CLI ToolSlimer is a command line tool that helps you digest a folder structure and its file contents. It was designed to help with prompt engineering.FeaturesDisplay directory structures in a tree-like format.Show the content of files in the structure with an optional character limit.Exclude or forcefully include specific files or directories.Recognize and tag binary files, with an option to include/exclude them.Limit the depth of directory exploration.Copy the result to the clipboard or output to a file.Filter the displayed files based on their modification time.Include specific files based on their extension.InstallationEnsure you have Python installed.Install necessary dependencies:pipinstallslimerDependenciespyperclip: Used for copying output to clipboard.UsageTo use Slimer, execute theslimercommand and provide the necessary arguments:slimer/path/to/directory-c-l500-e__pycache__tempArgumentDescription-h, --helpshow this help message and exit-c, --copyCopy the output to the clipboard.-l LIMIT, --limit LIMITMaximum number of characters to display from each file. No limit by default.-d DEPTH, --depth DEPTHMaximum depth to explore in the directory structure.-e [EXCLUDE ...], --exclude [EXCLUDE ...]List of files or directories to exclude.-i [INCLUDE ...], --include [INCLUDE ...]List of files or directories to forcefully include even if they are in the exclude list.-b, --binaryInclude binary files with a [Binary File] flag.-t, --treeOnly display the folder structure without file content.-p PREPEND, --prepend PREPENDString to prepend at the beginning of the output.-a APPEND, --append APPENDString to append at the end of the output.-o OUTPUT, --output OUTPUTPath to a file where the output will be written. If not provided, prints to console.-r RECENT, --recent RECENTOnly display files modified within the last N minutes. Defaults to 10 minutes when no value is provided to the argument.-f [FILE_EXTENSIONS ...], --file-extensions [FILE_EXTENSIONS ...]List of file extensions to exclusively display (e.g. .py .ts).-v, --versionshow program's version number and exit-s, --strip-commentsStrip comments from the code in the output.AuthorBen VilliereContributingIf you'd like to contribute to the development of Slimer, please create an issue or pull request in the project repository.LicenseThis project is licensed under the MIT License.
Package: slimfit
SlimFitSymFit's little brotherDocumentation:https://jhsmit.github.io/slimfit/This project is inspired bySymFitand is functional, to some degree, but in currently in BETAFree software: MIT licenseAimsInspiration for a potential SymFit 2.0Expectation-Maximization likelihood maximizationQuick StartfromsympyimportsymbolsfromslimfitimportModel,Fit,Parameterimportnumpyasnpy,a,x,b=symbols('y a x b')model=Model({y:a*x+b})parameters=[Parameter(a,guess=2.5),Parameter(b,guess=1,lower_bound=0.)]xdata=np.linspace(0,11,25)ydata=0.5*xdata+2.5ydata+=np.random.normal(0,scale=ydata/10.0+0.2)data={'x':xdata,'y':ydata}fit=Fit(model,parameters,data)result=fit.execute()print(result.parameters)>>>{'a':array(0.47572707),'b':array(2.6199133)}Installation$pipinstallslimfit