Saravanan666/pypi-org huggingface dataset:
Package: pornhub-api
Key Featuresresponse are fully-annotated withpydanticrest without parsingInstalling$pipinstallpornhub-apior with aiohttp support$pipinstallpornhub-api[aiohttp-backend]or with httpx support$pipinstallpornhub-api[httpx-backend]Supported versionsPython 3.7+Getting startedInitiate Api clientfrompornhub_apiimportPornhubApiapi=PornhubApi()Initiate with AioHttp backendimportasynciofrompornhub_api.backends.aiohttpimportAioHttpBackendasyncdefexecute():asyncwithAioHttpBackend()asbackend:api=PornhubApi(backend=backend)video=awaitapi.video.get_by_id("ph560b93077ddae")print(video.title)asyncio.run(execute())Search Videosvideos=api.search_videos.search_videos("chechick",ordering="mostviewed",period="weekly",tags=["black"],)forvidinvideos:print(vid.title,vid.video_id)Get Starsapi.stars.all()orapi.stats.all_detailed()Get single Video detailsvideo=api.video.get_by_id("ph560b93077ddae")print(video.title)Get all videos tags or categoriescategories=api.video.categories()tags=api.video.tags("a")Check Video availabilityresponse=api.video.is_active("ph560b93077ddae")print(response.is_active)Search video by random tag and categoryimportrandomapi=PornhubApi()tags=random.sample(api.video.tags("f").tags,5)category=random.choice(api.video.categories().categories)result=api.search.search_videos(ordering="mostviewed",tags=tags,category=category)print(result.size())forvidinresult:print(vid.title,vid.url)
Package: pornhub-api-sskender
PornHub Unofficial APIUnofficial API for pornhub.com in PythonUsageCreate clientimportpornhubclient=pornhub.PornHub()Create client with proxyimportpornhubclient=pornhub.PornHub("5.135.164.72",3128)#With proxy, given a Proxy IP and Port. For the countries with restricted access like Turkey, etc.Grab starsforstarinclient.getStars(10):print(star)print(star["name"])Create client with search keywordskeywords=["word1","word2"]client=pornhub.PornHub(keywords)forvideoinclient.getVideos(10,page=2):print(video)print(video["url"])LicenseMIT license
Package: pornhubSpider
pornhun爬虫可以获取pornhub的视频信息getVideoList 任意页面的视频列表传入一个url如www.pornhub.com/video?c=111&page=1该函数会返回一个PornhubVideo类列表，格式如下：PornhubVideo类(Class)用于获取并保存视频信息函数(methdos)函数解释PornhubVideo(url_or_viewkey, ifGetInfo=False)构造函数-url_or_viewkey: str- 视频页面链接或viewkey-ifGetInfo: bool- 是否获取信息,默认为False-True: 生成对象时获取视频信息。-False: 生成对象时不获取视频信息，之后若需获取，可用 GetInfo() 函数。GetInfo()获取视频信息GetRelated(page=1, ifGetInfo=False)利用视频ID,获取该视频ID的全部相似推荐（共10个），返回格式与getVideoList相同page: 选择要获取第几页，默认为1。ifGetInfo: bool- 是否获取信息,默认为False-True: 生成对象时获取视频信息。-False: 生成对象时不获取视频信息，之后若需获取，可用 GetInfo() 函数。RandomPorn()获取随机视频属性(properties)属性解释pageUrl: str原始网页链接地址viewkey: str视频viewkeyvideoID: str视频ID,用于获取相关视频title: str视频标题imgUrl: str视频封面图片链接地址duration: str视频时长，格式为 hh:mm:ssdefinitions: dict视频所有格式、分辨率及链接isPremium: bool是否为会员视频
Package: pororo-ocr
pororo-ocrpororo-ocrinspired bykakaobrain/pororo, rebuilding only the OCR task of the pororo package for lighteweight and fast performance.Installationpororo-ocris based ontorch>=1.6(cuda 10.1)andpython>=3.6You can install a package through the command below:pip install pororo-ocrUsagepororo-ocrcan be used as follows:First, in order to importpororo-ocr, you must execute the following snippet>>>importprrocrAfter the import, you can check languages currently supported by thepororo-ocrthrough the following commands>>>importprrocr>>>prrocr.ocr.get_available_langs()"Available lanugages are ['en', 'ko']"To check which models are supported by each task, you can go through the following process>>>importprrocr>>>prrocr.ocr.get_available_models()"Available models are {'en': ['brainocr'], 'ko': ['brainocr']}"If you want to perform in specific language, you can put the language name in thelangargument>>>importprrocr>>>ocr=prrocr.ocr(lang="en")After object construction, it can be used in a way that passes the input value as follows:>>>ocr("sample.jpg")['MAKE TODAY','TOLERABLE']If you want to get position information for each string, you can turn ondetailargument as follows:>>>ocr("sample.jpg",detail=True){'description':['MAKE TODAY','TOLERABLE'],'bounding_poly':[{'description':'MAKE TODAY','vertices':[{'x':585,'y':397},{'x':730,'y':397},{'x':730,'y':520},{'x':585,'y':520}]},{'description':'TOLERABLE','vertices':[{'x':588,'y':558},{'x':884,'y':558},{'x':884,'y':612},{'x':588,'y':612}]}]}Licensepororo-ocrproject is licensed under the terms ofthe Apache License 2.0.
Package: poros
This is a project for later lazy work!
Only support for python3, ☹️, but maybe you can try in python2Install命令行直接安装pipinstallporos从代码库安装gitclonehttps://github.com/diqiuzhuanzhuan/poros.gitcdporos
pythonsetupinstallSome code is created by myself, and some code is inspired by others,  such as allennlp etc.poros_charsProvide a set of small functionsusage:convert Chinese words into Arabic numbers:fromporos.poros_charsimportchinese_to_arabic>>>print(chinese_to_arabic.NumberAdapter.convert("四千三百万"))43000000poros_lossProvide some loss functions, such as gravity loss, and dice loss usage:fromporos.poros_lossimportGravityLoss>>>gl=GravityLoss()# [1, 2]>>>input_a=torch.tensor([[1.0,1]],requires_grad=True)>>>input_b=torch.tensor([[1.0,1]],requires_grad=True)>>>target=torch.tensor([[4.0]])>>>output=gl(input_a,input_b,target)>>>torch.testing.assert_close(output,target)clusteringfromporos.poros_clusterimport*ThanksPyCharm, Mircosoft Visual Studio Code
Package: porous-media
porous_media: python utilities for porous media analysis and visualizationporous_media is a collection of python utilities for working with porous media simulation results and meshes with source code available fromhttps://github.com/matthiaskoenig/porous_media.Features include among othersVisualization of VTK resultsImage processingMesh manipulationFEBio helpersIf you have any questions or issues pleaseopen an issue.How to citeInstallationporous_mediais available frompypiand
can be installed via:pip install porous_mediaRequired dependenciesporous_mediadepends onffmpegfor creation of the videos
which can be installed via on linux via:sudo apt-get install ffmpegMake sure you can execute the following successfully:ffmpegDevelop versionThe latest develop version can be installed via:pip install git+https://github.com/matthiaskoenig/porous_media.git@developOr via cloning the repository and installing via:git clone https://github.com/matthiaskoenig/porous_media.git
cd porous_media
pip install -e .To install for development use:pip install -e .[development]ContributingContributions are always welcome! Please read thecontributing guidelinesto
get started.LicenseSource Code:LGPLv3Documentation:CC BY-SA 4.0The porous_media source is released under both the GPL and LGPL licenses version 2 or
later. You may choose which license you choose to use the software under.This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License or the GNU Lesser General Public
License as published by the Free Software Foundation, either version 2 of the
License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.FundingMatthias König is supported by the Federal Ministry of Education and Research (BMBF, Germany)
within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054)
and by the German Research Foundation (DFG) within the Research Unit Programme FOR 5151
“QuaLiPerF(Quantifying Liver Perfusion-Function Relationship in Complex Resection -
A Systems Medicine Approach)” by grant number 436883643 and by grant number 465194077 (Priority Programme SPP 2311, Subproject SimLivA).© 2023 Matthias König
Package: porphyry
PorphyryThe library for convenient exploratory data analysis, completing, tuning and deployment machine learning models in working environment using PI-system data storage. Applying of basic algorithms for gas wells current flow prediction based on indirect technological parameters.flexible extraction of historical data;receiving current values;machine learning models prediction based on current or historical data;automatic updating of machine learning models;tracking of machine learning models metrics;gas flow distribution along the wells;sending notifications.
Package: portable-es
Portable ESPortable ES is a distributed gradient-less optimization framework built on PyTorch & Numpy.
It's main focus is autoregressive reinforcement learning scenarios.
A lot of the code from PyTorch has been replaced as they are not intended for this purpose.
It was originally made as an Evolutionary Strategies (ES) implementation.FeaturesDynamic distributionAdd/remove worker nodes over networksNo additional code requiredInfinitely DistributableCPU & GPU possibleEasy setupGet going in less time than a regular PyTorch setupReuse PyTorch model/architecturesOptimized for quick trainingVery simplistic implementationExtendable grid searchCaveatsHeterogeneous computing isn't always deterministicWhile this hasn't been an issue so far, it could end up degrading training performanceConfig isn't very intuitive because of performance optimization & distributed-nessGetting StartedCreating an enviromentFirst thing you'll need is an enviroment, ideally these are deterministic but at larger minibatches/populations this doesn't matter as much.
It uses a similar interface as OpenAI's Gym project, however it is somewhat simplified.classEnvInterface:def__init__(self,**kwargs);# note: no args# step through enviroment, maps: action -> newstate, reward, donedefstep(self,action)->Tuple[torch.Tensor,float,bool];# will change the seed of the current batch; called per epochdefrandomize(self,np_randomstate:np.random.RandomState);# reset internal parameters, called per episodedefreset(self)->torch.Tensor;# Get stats/dist from a single run; can be found in tensorboard# Format {'scalars': {[str]: float}, 'images': {[str]: numpy.ndarray}}defeval(self)->dict;Configuring your trainingCheck outexamples/train.pyfor a full example with configuration.Grid SeachCheck outexamples/train_grid.pyfor the full script.
It's a very minimal implementation (about 14 loc), but also very powerful.
It will run through all possible permutations of the specified super-config and log them as usual to the tensorboard.Note that currently remote workers don't auto-rejoin so this only works on a local instance.An example output:the tensorboardFAQWhy the_args/_kwargs?These are the standard way to refer to pythonic arguments, and is done partially for optimization and partially for compatibility.
This allows you to use other enviroments (like OpenAI Gyms), and other models without the overhead of copying/transfering large memory-blocks/tensors over the network.We might change this later on, but for now this works.Why can't I use the standard PyTorch optimizers?While it is possible to use regular optimizers with custom gradients in pytorch, they might cause some overhead/flexibility issues down the line.
An additional consideration is that traditional pytorch optimizers are not as easily modifiable/readable.We may add support for the regular PyTorch optimizers later on.RoadmapImplement and test ESAC (ES+SAC)Make it more modularMakereward_normextendableCreate a hyperparameter schedulerFurther Profile portable_esWrite-up on zeroth-order optimizationRecurrent wrapperAuto-rejoining workers
Package: portable-fdsnws-dataselect
This is a portable fdsnws-dataselect/1 implementation which allows users to
run a standardized web service to serve their own, arbitrary seismological
data sets in miniSEED format.  Users can then take advantage of existing data
selection and collection tools to access their own data repository.The system uses a miniSEED data index stored in SQLite.  The index is created
with themseedindexprogram.The specification for thefdsnws-dataselectweb service is published by
theInternational Federation of Digital Seismograph Networks.This software is a product of theIRIS Data Management Center.
Package: portablemc-archives
Archives add-onThe archives addon allows you to install and run old archived versions that are not officially
listed by Mojang. It is backed by theomniarchivework.pip install --user portablemc-archivesUsageThis add-on extends the syntax accepted by thestartsub-command, by
prepending the version witharc:. Every version starting with this prefix will be resolved from
archive repositories hosted onarchives.org. This addon also add a-a(--archives) flag to thesearchsub-command. You can use it to
list all archived versions or search for some specific ones before actually trying to run them.The following repositories are used to resolve your versions:Pre-Classic (Rubydung)ClassicIndevInfdevAlphaBetaExamplesportablemcsearch-a# List all archived versions.portablemcsearch-aa1.2.0# List all archived versions that contains the string 'a1.2.0'.portablemcstartarc:a1.2.0# Start the archived version of alpha 1.2.0.portablemcstart--dryarc:a1.2.0# Install the archived version of alpha 1.2.0 if it's not already the case.CreditsOmniarchive community
Package: portablemc-console
Console add-onThis addon provides an interactive console for Minecraft process' output. this is useful to debug
the game when it crashes multiple times, or simply if you can to track what's going on.pip install --user portablemc-consoleUsageThis addon requires you to install theprompt_toolkitpython
library.This addon is enabled by default when launching the game with thestartsub-command. To disable
it and fall back to the default process' output, you can add the--no-consoleflag to the command
line. By default, when the game ends, you need to do Ctrl+C again to close the terminal, you
can disable it using the--single-exitflag in the command, this will cause your interactive
console to close with the game's process.Examplesportablemcstartmy_version# Starts the game and open the interactive console.portablemcstart--no-consolemy_version# Starts the game and don't open the interactive console.CreditsPyPI page of prompt_toolkit
Package: portablemc-fabric
Fabric add-onThe fabric add-on allows you to install and run Minecraft with fabric mod loader in a single command
line!pip install --user portablemc-fabricUsageThis add-on extends the syntax accepted by thestartsub-command, by
prepending the version withfabric:. Almost all releases since 1.14 are supported by fabric,
you can find more information onfabric website, note the snapshots
are currently not supported by this addon, but this could be the case in the future because fabric
provides support for them. You can also use version aliases likereleaseor equivalent empty version
(justfabric:). This addon also provides a way of specifying the loader version, you just have to
add:<loader_version>after the game version (the game version is still allowed to be aliases
or empty, the following syntax is valid:fabric::<loader_version>).This addon requires external HTTP accesses if:the game version is an alias.if the loader version is unspecified.if the specified version is not installed.Examplesportablemcstartfabric:# Start latest fabric loader version for latest releaseportablemcstartfabric:release# Same as aboveportablemcstartfabric:1.19# Start latest fabric loader version for 1.19portablemcstartfabric:1.19:0.14.8# Start fabric loader 0.14.8 for game version 1.19portablemcstartfabric::0.14.8# Start fabric loader 0.14.8 for the latest releaseportablemcstart--dryfabric:# Install (and exit) the latest fabric loader version for latest releaseCreditsFabric Website
Package: portablemc-forge
Forge add-onThe forge add-on allows you to install and run Minecraft with forge mod loader in a single command
line!pip install --user portablemc-forgeUsageThis add-on extends the syntax accepted by thestartsub-command, by
prepending the version withforge:. Almost all releases are supported by forge, the latest
releases are often supported, if not please refer to forge website. You can also append either-recommendedor-latestto the version to take the corresponding version according to the
forge public information, this is reflecting the "Download Latest" and "Download Recommended" on
the forge website. You can also use version aliases likereleaseor equivalent empty version
(justforge:). You can also give the exact forge version like1.18.1-39.0.7, in such cases,
no HTTP request is made if the version is already installed.Note that this add-on uses the same JVM used to start the game (see--jvmargument).Examplesportablemcstartforge:# Start recommended forge version for latest releaseportablemcstartforge:release# Same as aboveportablemcstartforge:1.18.1# Start recommended forge for 1.18.1portablemcstartforge:1.18.1-39.0.7# Start the exact forge version 1.18.1-39.0.7portablemcstart--dryforge:# Install (and exit) recommended forge version for latest releaseCreditsForge WebsiteConsider supportingLexManos
Package: portablemc-quilt
Quilt add-onThe quilt add-on allows you to install and run Minecraft with quilt mod loader in a single command
line!pip install --user portablemc-quiltUsageThis add-on extends the syntax accepted by thestartsub-command, by
prepending the version withquilt:. Almost all releases since 1.14 are supported by quilt,
you can find more information onquilt website, note the snapshots
are currently not supported by this addon, but this could be the case in the future because quilt
provides support for them. You can also use version aliases likereleaseor equivalent empty version
(justquilt:). This addon also provides a way of specifying the loader version, you just have to
add:<loader_version>after the game version (the game version is still allowed to be aliases
or empty, the following syntax is valid:quilt::<loader_version>).This addon requires external HTTP accesses if:the game version is an alias.if the loader version is unspecified.if the specified version is not installed.Examplesportablemcstartquilt:# Start latest quilt loader version for latest releaseportablemcstartquilt:release# Same as aboveportablemcstartquilt:1.19# Start latest quilt loader version for 1.19portablemcstartquilt:1.19:0.14.8# Start quilt loader 0.14.8 for game version 1.19portablemcstartquilt::0.14.8# Start quilt loader 0.14.8 for the latest releaseportablemcstart--dryquilt:# Install (and exit) the latest quilt loader version for latest releaseCreditsQuilt Website
Package: portabletab
PortableTabPortableTabis a Python library that allows for serialization of
typed tables into a set of files, as well as deserialization of
specific rows extracted from the files.FeaturesThe serialized files are independent of OS and CPU architecture, so it can
be used to create portable table which is useful when working with large
datasets that need to be shared between different systems or environments.It also allows fast deserialization of only specified rows without loading
the entire table into memory, so it does not take time to load and
deserialize the table on the first access, nor consume memory during execution.Capn' Protois used for serialization,
making the file format portable.SincePortableTabuses mmap for file access, it does not consume
much memory even when handling large tables.Indexes on strings are created usingMarisa-trie,
the output files are also portable and accessible using mmap.LimitationsThe tables are serialized into compact files so they cannot be dynamically
modified.Rows can only be retrieved at their specified position. If you want to
access by an attribute such asid, you must create an index on that attribute.Updating records in serialized files is possible but very slow.It is not possible to insert rows in the middle of a serialized file. If you
want to insert rows in the middle, the only way is to deserialize
the entire table and recreate another table.How to usePlease refer to the documentation atPortableTab Document.Development statusUnstable alpha version.LicenseThis package is available according to the MIT license.AuthorTakeshi SAGARAsagara@info-proto.com
Package: portable-wisdom
Portable WisdomPortable Wisdom is a command-line tool to generate an EPUB file from your unread articles inInstapaper. You can then copy this file to your ereader.TechnologiesPython 3FeaturesRetrieves unread articles from InstapaperFinds and downloads articles' images from the web, downsizes them, converts them to greyscale, and embeds them into the fileCaches articles and images, runs fast for frequent usersCreates well-formatted EPUB files tailored for your ereaderQuick StartBefore you begin, you'll need torequest an Instapaper API keyor copy one from a friend.PythonDownload and install Portable Wisdomfrom PyPI:$ pip install portable-wisdomRun Portable Wisdom from the command line:$ portable-wisdom \
 	--instapaper-api-key KEY \
 	--instapaper-api-secret SECRET \
 	--instapaper-login USER \
 	--instapaper-password PASSOn success, the script will print the output filename. To view all of the options, run$ portable-wisdom -h.DockerAlternatively, usingDocker, to download and execute Portable Wisdomfrom Docker Hub, run:$ docker pull jacobbudin/portable-wisdom:latest
$ docker run jacobbudin/portable-wisdom:latest \
	--instapaper-api-key KEY \
	--instapaper-api-secret SECRET \
	--instapaper-login USER \
	--instapaper-password PASSEnvironmentAlternatively, you can supply the Instapaper credentials via environment variables:INSTAPAPER_API_KEY,INSTAPAPER_API_SECRET,INSTAPAPER_LOGIN, andINSTAPAPER_PASSWORD.TransformersTransformers are functions that modify the EPUB before writing the file to disk. There are many built-in transformers including:beautify_hr— converts lines of asterisks to horizontal rulesremove_duplicative_blockquotes— removes magazine-style "pull quotes"strip_emojis— replaces emojis with shortcodesstrip_links— removesaelementsembed_images— embeds remote web imagesCompatibilityPortable Wisdom usesEbookLibto create EPUB files. These files are compatible with most ereaders—including Nook, Kobo, and Sony—as well as most ebook software. Kindle owners can usePandocor a similar tool to convert from EPUB to MOBI.StylesPortable Wisdom supports styles (--style) to create EPUB files optimized for your ereader's rendering engine. These styles are regular CSS files. They specify header sizes, image layout, quote formatting, etc. Use the default style or create your own.ContributingTo report a bug or request a feaure,create an issue on GitHub. Developers are welcome and encouraged to submit pull requests, but contributors should strongly consider creating an issue and requesting comments before starting work.SourceYou can run Portable Wisdom from its source like so:$ python3 -m portable_wisdom.wisdomGuidelinesComply withPEP 8(useFlake8to confirm,autopep8can help)Run and pass all testsCreate new tests or refine existing ones, if necessaryLicenseMIT License
Package: portafilter
PortafilterPowerful and simple data validation functionality.Portafilterprovides several different approaches to validate your application's incoming data.  It'szero dependenciesand easy to use.HelpSeedocumentationfor more details.InstallationIt's onPyPIand all you need to do is:pip install portafilterA Simple ExamplefromportafilterimportValidatorvalidator=Validator({'name':'Flat White','description':'A Flat White is a coffee drink consisting of espresso with microfoam ','ingredients':[{'name':'Espresso',},{'name':'Steamed Milk',}],},{'name':'required','description':'required|max:255','ingredients':'required|list','ingredients.*.name':'required|string',})ifvalidator.fails():# The data is not validprint(validator.errors())# The data is validTo see all the features, visitportafilter.dev