Saravanan666/pypi-org huggingface dataset:
Package: paitest
PAITest📦 版本v1.1.1✨支持大/小端格式输出🛠️ 使用配置帧及对应测试输入帧，以实现硬件通路的简单测试，后续将芯片实际测试输出帧与预期结果进行对比即可。⚠️ 由于配置帧/测试帧I型需要配合串口配置使用，因此目前仅采用配置/测试帧II型方案，且CHIP_ADDR与CORE*_ADDR均固定为(0, 0)。实例化PAITest。在这个版本下，参数test_chip_coord的优先级大于direction；当仅配置dirction时通过此计算test_chip_coord，否则使用test_chip_coord参数frompaitestimportpaitest# Set the address of chip under test and the test chip address.PAITestManager=paitest("EAST",(0,0),test_chip_coord=(1,0))Get1GroupForNCoresWithNParams，产生1组针对N个核的配置-测试帧，每个核配置不同参数。可以指定单个需要屏蔽的核坐标groups=10# Generate 10 groupssave_to_dir="./test"# Save frames into ./test directory# Generate configuration frames, testin & testout framescf,ti,to=PAITestManager.Get1GroupForNCoresWithNParams(groups,save_dir=save_to_dir,verbose=True)# Mask a cord coordinate so that avoid generating the same coordinate.cf,ti,to=PAITestManager.Get1GroupForNCoresWithNParams(groups,save_dir=save_to_dir,masked_core_coord=(12,16),gen_txt=True)⚠️ 指定verbose=True以开启日志显示，默认关闭Get1GroupForNCoresWith1Param，产生1组针对N个核的配置-测试帧，每个核配置相同参数。可以指定单个需要屏蔽的核坐标# Same as Get1GroupForNCoresWithNParamscf,ti,to=PAITestManager.Get1GroupForNCoresWith1Param(10,save_dir="./test")GetNGroupsFor1CoreWithNParams，产生N组针对1个核的配置-测试帧，每个核配置不同参数。可以指定单个需要屏蔽的核坐标# Same as Get1GroupForNCoresWithNParamscf,ti,to=PAITestManager.GetNGroupsFor1CoreWithNParams(1,save_dir="./test")ReplaceCoreCoord，替换单个或一组帧中的CORE_ADDR为指定坐标# Replaced core coordinate with (9, 9)replaced=PAITestManager.ReplaceCoreCoord(original_frames,(9,9))⚠️ 一组指一组完整的配置帧，包含3帧。对于测试输入帧，即为单帧。SaveFrames，保存帧数据至指定文件，支持.bin或.txt格式，支持指定大/小端输出# Save into binary files with big-edian format(default)PAITestManager.SaveFrames("./test/config.bin",replaced,byteorder="big")# Or text filesPAITestManager.SaveFrames("./test/config.txt",replaced)⚠️ 指定byteorder="big"/"little"以大/小端格式储存帧数据，默认大端🗓️ TODO上板验证参数检验配置/测试帧III/IV型
Package: paithon
🧠 PaithonPRE-ALPHA STATEMake your AI models interactive in no time. Easy to use examples and components for the Jupyter Notebook, your favorite Editor/ IDE and your next AI App.PaithonisPanel AI Tools with Humor ON. Get started now!Jupyter LabsApps🏁 BackgroundI believe that Panel is a very flexible and powerful tool compared to the other python ML app frameworks out there. I believe it can give you and your AI team super powers.So I started this project to help you and the Panel framework. As a bonus I will learn more about AI.⚖️ LicenseThepaithonpackage and repository is open source and free to use (MIT License).🏃 Getting StartedWithpippipinstallpaithonFrom within a Jupyter Notebookimportpaithonaspaimportpanelaspnpn.extension()👩‍🏫 Reference GuidesGuideNotebookJupyter LabsAppsDocStringViewerViewImageInputView💡 InspirationI find inspiration atHugging Face WidgetsGradioPanel🛣️ RoadmapWhen I get the time I would like toSupport common workflows in ML and DL.Implement Panel versions of the Hugging Face widgetsShow that Panel can easily do what Gradio can and so much more.Add badges for 100% test coverage etc.Distribute as conda package📰 Change Log0.0.3: Fix some broken links0.0.2: Add ImageInput and DocStringViewer0.0.1: First Version
Package: paiutils
Programming Artificial Intelligence Utilities is a package that aims to make
artificial intelligence and machine learning programming easier through
abstractions of extensive APIs, research paper implementations, and data
manipulation.Package FeaturesAnalyticsPlotting of data through embedding algorithms, such as Isomap and TSNEAudioRecording and playingVolume, speed, and pitch manipulationTrimming and SplittingSpectrogram, Fbanks, and MFCC creationAudio file conversionsImageSimplified OpenCV InterfaceAutoencoderTrainer and PredictorTrainer with extra decoderVAE TrainerEvolution AlgorithmOne dimensional evolution algorithmHyperparameter tunerGANGAN TrainerGANI Trainer (GAN which takes provided Inputs)Cycle GAN TrainerPredictorsNeural NetworkTrainer and PredictorDense layers that combine batch normConvolution layers that combine batch norm, max pooling, upsampling, and transposingReinforcementOpenAI Gym wrapperMulti-agent adverserial environmentGreedy, ascetic, and stochastic policiesNoise policiesExponential, linear, and constant decayNormal memory and efficient time distributed memory (for stacked states)AgentsQAgent: Q-learning with a tableDQNAgent Q-learning with a neural network modelPGAgent: State to action neural network model (Actor) trained with
policy gradientsDDPGAgent: State to continous action space neural network model trained
with deterministic policy gradientsReinforcement AgentsDQNPGAgent: Combination of a DQN and PG agent into one agentA2CAgent: Advantage Actor Critic agentPPOAgent: Proximal Policy Optimization agentTD3Agent: Twin Delayed DDPG AgentPGCAgent: Continuous variant of PGAgentA2CCAgent: Continuous variant of A2CAgent
Package: pajamas
pajamasFancy light clothes for jupyter notebooks to sleep in.MotivationWe lovejupyter notebooks: they allow visual, intuitive, and fast data analysis, code testing and development, as well as documentation and sharing of results.However, when sharing jupyter notebooks via repositories, two drawbacks become painfully apparent:jupyter notebooks can get extremely large because they often include all of their output.jupyter notebooks change whenever they are being worked with, even without changes to the code.Of course, the first drawback can be avoided by diligently cleaning the notebook before submitting to the repo, and the second by resetting the file before submitting. In practice, however, we find that this is rarely done consistently.SummaryIn a nutshell, the pajamas package simply strips the jupyter notebook of its output. Additionally, it provides an output-less format using the.pajamasextension, which facilitates working with templates from which jupyter notebooks are created. This enables us to block any.ipynbfiles from being committed to a repository (by including the extension to.gitignore), which keeps repositories smaller.We see jupyter notebooks as a worker having all tools, materials, and results in hand, while the pajamas format retains all of the knowledge and ability (the code) but removes all of the heavy tools, materials, and results (the output contained in the jupyter notebook). Just like the worker would not go to sleep with all of the tools, neither should a jupyter notebook. Both use pajamas.
Package: pajek-tools
Pajek ToolsTools for converting network data to pajek files.Pajekis a file format for networks, typically stored in files with a.netextension. It is used as input for software such asInfomap.InstallationInstall from PyPI:pip install pajek-toolsUsageUse thePajekWriterobject to convert a network from an edgelist in pandas DataFrame form, to a Pajek .net file:from pajek_tools import PajekWriter
writer = PajekWriter(dataframe)
writer.write("output.net")Examplefrom pajek_tools import PajekWriter
import pandas as pd

df = pd.DataFrame([["a", "b"], ["a", "c"], ["c", "a"], ["c", "d"]], columns=["source", "target"])
writer = PajekWriter(df,
                     directed=True,
                     citing_colname="source",
                     cited_colname="target")
writer.write("output.net")CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-02-12)First release on PyPI.
Package: paka.breadcrumbs
paka.breadcrumbsis a Python library with helpers (currently few data
structures) for breadcrumbs navigation building.FeaturesPython 2.7 and 3.5 are supportedPyPy (Python 2.7) is supporteddoes not depend on any web frameworkdoes not require use of WSGIExamples>>>frompakaimportbreadcrumbsCreate breadcrumbs container:>>>bcrumbs=breadcrumbs.Bread("Example Site")Add crumbs for parent (withurl_path) and current (withouturl_path,
as there is no need to link to yourself) pages:>>>bcrumbs.add("Some category",url_path="/some/")>>>bcrumbs.add("Some page")Now breadcrumbs container can be iterated over:>>>[crumb.labelforcrumbinbcrumbs]['Example Site', 'Some category', 'Some page']And value you can put into<title></title>may be constructed
(it is actually an instance ofmarkupsafe.Markup):>>>print(bcrumbs.get_title("<-"))Some page &lt;- Some category &lt;- Example SiteInstallationLibrary isavailable on PyPI,
you can usepipfor installation:$pipinstallpaka.breadcrumbsGetting documentationBuild HTML docs:$tox-edocsView built docs:$sensible-browser.tox/docs/tmp/docs_html/index.htmlRunning tests$toxGetting coverageCollect info:$tox-ecoverageView HTML report:$sensible-browser.tox/coverage/tmp/cov_html/index.htmlChecking code styleRun code checkers:$tox-echecks
Package: paka.cmark
paka.cmarkis a Python library that wraps subset ofcmarkC library
(that is one of reference implementations of CommonMark).FeaturesPython 2.7 and 3.6 are supportedPyPy (Python 2.7) is supported, as wrapping is made withCFFIno need to installlibcmark, it is bundled withpaka.cmark(and sources of the former are regularly updated according to upstream)supported output: HTML, XML, CommonMark, man, LaTeXsupported options:CMARK_OPT_UNSAFE,CMARK_OPT_NOBREAKS,CMARK_OPT_HARDBREAKS,CMARK_OPT_SOURCEPOS,CMARK_OPT_SMARTunlikelibcmark—underlying C library—paka.cmarkusesCMARK_OPT_NOBREAKSby default (breaksargument allows to control
line break rendering)safe HTML output is on by default (like inlibcmark)Examples>>>frompakaimportcmarkRender withCMARK_OPT_DEFAULT | CMARK_OPT_NOBREAKS:>>>print(cmark.to_html(u"<p>nope</p>"))<!-- raw HTML omitted -->Render withCMARK_OPT_DEFAULT | CMARK_OPT_NOBREAKS | CMARK_OPT_UNSAFE:>>>print(cmark.to_html(u"Hello,\n*World*!",safe=False))<p>Hello, <em>World</em>!</p>Render withCMARK_OPT_DEFAULT:>>>print(cmark.to_html(u"Hello,\n*World*!",breaks=True))<p>Hello,
<em>World</em>!</p>Render withCMARK_OPT_DEFAULT | CMARK_OPT_HARDBREAKS:>>>print(cmark.to_html(u"Hello,\n*World*!",breaks="hard"))<p>Hello,<br />
<em>World</em>!</p>Render CommonMark withCMARK_OPT_DEFAULT | CMARK_OPT_NOBREAKS:>>>print(cmark.to_commonmark(u"_Hello_"))*Hello*InstallationLibrary isavailable on PyPI,
you can usepipfor installation:$pipinstallpaka.cmarkGetting documentationBuild HTML docs:$tox-edocsView built docs:$sensible-browser.tox/docs/tmp/docs_html/index.htmlRunning tests$toxGetting coverageCollect info:$tox-ecoverageView HTML report:$sensible-browser.tox/coverage/tmp/cov_html/index.htmlChecking code styleRun code checkers:$tox-echecks
Package: paka.funcreg
Migration from older versions of this libraryThere are two deprecated versions of this library (funcreg from orico-libs,
and libreg). Though internally they differ frompaka.funcreg, migration
is just a matter of changing imports and registry factories:Imports likeimportlibregorimportfuncregbecomeimportpaka.funcregCalls likelibreg.make()orfuncreg.make()becomepaka.funcreg.NameRegistry()Calls likelibreg.make_type_dispatcher()becomepaka.funcreg.TypeRegistry()
Package: pakages
PakagesPakages is a framework for building community packages and containers 📦️⭐️Documentation⭐️You can see trusted packages under thepakagesorganization. Trusted means
that they are built, tested, and deployed from modular repositories, and can be
installed into consistent container bases that Pakages provides.importantrecent updates to spack have broken pakages, and I don't have the patience with spack to fix it again, so I'm pursuring developing this toward other kinds of general artifact builds. If you have ideas to save spack please open an issue. The install -> cache functionality is not consistent enough imho for this to reliably work, and (as I've learned before) maintaining anything with spack as a dependency is a really bad idea.GoalsWe want a framework that is optimized to help people build packages from source,
and distribute the binaries via GitHub packages and also provide robust metadata
and an organization scheme that works well for containers. We want a focus on that
and then testing and automatically updating the individual packages provided.
Pakages provides this functionality by wrapping spack to perform builds, and then
providing tooling to release to a GitHub packages build cache, and to run base
containers that will reliably hit the cache.🚧️under development🚧️
Package: pakala
Pakala"ilo Pakala li pakala e mani sona"Pakala is a tool to search for exploitable bugs in Ethereum smart contracts.Pakala is a symbolic execution engine for the Ethereum Virtual Machine.The intended public for the tool are security researchers interested by Ethereum / the EVM.Installationpip3 install pakalaIt works only with python 3.UsageLet's look at0xeBE6c7a839A660a0F04BdF6816e2eA182F5d542C:
it has atransfer(address _to, uint256 _value)function. It is supposedly protected by arequire(call.value - _value) >= 0but that condition always holds because we are substracting two unsigned integers, so the result is also an unsigned integer.Let's scan it:pakala 0xeBE6c7a839A660a0F04BdF6816e2eA182F5d542C --force-balance="1 ether"The contract balance being 0, we won't be able to have it send us some ethers.
So we override the balance to be 1 ETH: then it has some "virtual" money to send us.The tool with tell you a bug was found, and dump you a path of "states". Each
state corresponds to a transaction, with constraints that needs to be respected
for that code path to be taken, storage that has been read/written...Advice: look atcalldata[0]in the constraints to see the function signature for each transaction.Seepakala helpfor more complete usage information.How does it works? What does it do?See theintroductory articlefor more information and a demo.In a nutshell:It's very good at finding simple bugs in simple contracts.The false-positive rate is very low. If it flags your contract it's likely people can drain it.It can exploit non-trivial bugs requiring to overwrite some storage keys with others (array size underflow...), has a good
modeling of cryptographic hashes, and support chaining multiple transactions.However, It only implements an "interesting" subset of the EVM. It doesn't handle:gas,precompiles,or a contract interacting with other contracts (DELEGATECALL, STATICCALL...).This means that CALL support is limited to sending ethers. Other tools like Manticore can do that much better, and the focus for
Pakala was offensive vulnerability scanning of contractsen masse.
Package: paka.sitemaps
paka.sitemapsis a Python library that helps generate XML files according
tositemaps.org protocol.FeaturesPython 2.7 and 3.5 are supportedPyPy (Python 2.7) is supporteddoes not depend on any web frameworkautomatically splits all added URLs into sitemaps and sitemap indexeslazily writes to file systemExamples>>>frompakaimportsitemapsCreate directory for resulting XML files (here it is a temporary directory):>>>importtempfile>>>fs_root=tempfile.mkdtemp()Create sitemap building context, add few URLs to it, and close the context:>>>ctx=sitemaps.Context(...fs_root=fs_root,base_url=u"http://example.org")>>>ctx.add(u"/some/path-here/",priority=0.1)>>>ctx.add(u"/other")>>>ctx.close()Nowfs_rootcontains one sitemap and one sitemap index:>>>importos>>>sorted(os.listdir(fs_root))['i1.xml', 's1-1.xml']Results can be checked with XML parser (herelxmlis used):>>>fromlxmlimportetree>>>doc=etree.parse(os.path.join(fs_root,"s1-1.xml"))>>>ns={"s":sitemaps.XMLNS}>>>url_els=doc.xpath("//s:url",namespaces=ns)>>>sorted([el.findtext("s:loc",namespaces=ns)forelinurl_els])['http://example.org/other', 'http://example.org/some/path-here/']Remove directory and files created for demonstration:>>>importshutil>>>shutil.rmtree(fs_root)InstallationLibrary isavailable on PyPI,
you can usepipfor installation:$pipinstallpaka.sitemapsGetting documentationBuild HTML docs:$tox-edocsView built docs:$sensible-browser.tox/docs/tmp/docs_html/index.htmlRunning tests$toxGetting coverageCollect info:$tox-ecoverageView HTML report:$sensible-browser.tox/coverage/tmp/cov_html/index.htmlChecking code styleRun code checkers:$tox-echecks
Package: pakettikauppa
PakettikauppaClient python modules for Pakettikauppa integrationFree software: MIT licenseDocumentation:https://pakettikauppa.readthedocs.io.FeaturesFor merchantsSearch pickup pointsCreate shipment included shipping labelGet shipping labelGet shipping statusGet list of additional servicesGet list of shipping methodsFor re-sellerCreate customerGet list of customersUpdate customer dataCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.2 (2019-07-01)Fix Pip files and requirement files for fixing security issue in pyyaml module0.1.1 (2018-11-02)Fix related Pip files for requests moduleFixed test data generating function in merchant.pyFixed tracking code for testing in test_get_shipping_label.py0.1.0 (2018-01-22)First release on PyPI.
Package: pakiet-szkoleniowy
Struktura folderówpakiet_Mariusza
├── LICENSE
├── README.md
├── example_pkg
│	├──moj_modul1.py
│	├──moj_modul2.py
│   └──init.py
└── setup.pyProcedura tworzenia pakietuUtwórz pliki o strukturze jak powyżejw pliku setup.py dodaj kod:import pathlib
from setuptools import setup, find_packagesHERE = pathlib.Path(file).parentVERSION = '0.1.0'
PACKAGE_NAME = 'nazwa_pakietu'
AUTHOR ='Ty'
AUTHOR_EMAIL = 'mail'
URL = 'Twój github'LICENSE = 'MIT'
DESCRIPTION = 'Kilka przydatnych funkcji zwracających czas w różnych postaciach'
LONG_DESCRIPTION = (HERE / "README.md").read_text()
LONG_DESC_TYPE = "text/markdown"INSTALL_REQUIRES = []setup(name=PACKAGE_NAME,
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
long_description_content_type=LONG_DESC_TYPE,
author=AUTHOR,
license=LICENSE,
author_email=AUTHOR_EMAIL,
url=URL,
install_requires=INSTALL_REQUIRES,
packages=find_packages()
)Wypełnij swoje moduły kodemSprawdz czy pakiet działa odwołując się do niego z innego pliku (poza pakietem)zainstaluj twine     pip install twinezbuduj instalatora   python setup.py sdist bdist_wheelsprawdź builda       twine check dist/*opublikuj pakiet     twine upload dist/*podczas publikacji jako użytkownika podajtokenHasło jest = wartości tokena
Package: pakkanpdf
pakkan-pdfPDF 内の text や image へのアクセスをコンテキストマネージャーを使ってシンプルに行える。pdfminer/pdfminer.sixの Wrapper ライブラリです。installpip install pakkanpdf使い方PdfExtractor の pdf_path に pdf のパスを与え、work_dir に存在するディレクトリを指定するwork_dir に image を書き出すための一時ディレクトリが作成さえるextractor.text を使うと、PDF の text を取得できるextractor.image_file_paths を使うと、PDF の image (file path) を取得できるfrompakkanpdfimportPdfExtractordeftest_sample():withPdfExtractor(pdf_path="data/example.pdf",work_dir="demo_work_dir")asextractor:assert"これはサンプルのPDFです"inextractor.textassertextractor.image_file_paths==["demo_work_dir/work_images/X8.jpg"]
Package: pakker
pakker is a Python library that helps dealing with data that was
serialized in language-specific serialization formats. In its current
state, it only implements (part of) PHP’sserializeandunserializefunctions.Supported Python versionsWorks with Python 2.6 up to Python 3.3.NoteAs it makes use of the CPython C API, pakker is probably only
useful when used on top of CPython.DevelopmentFound an issue or just want to get the source? Check out
pakker’sGithub page.Licensepakker is distributed under a 3-clause BSD license. SeeLICENSEfor
details.pakker is abuffer.ioproject.
Package: pakk-flask
pakk flaskUtilities for working with pakk files in a flask web application.Getting StartedInstall pakk and pakk_flask:$python3-mpipinstallpakkpakk_flaskThen you can use pakk_flask to send pakked assets as static files in a flask web application:frompakk_flaskimportsend_from_directory@APP.route("/static/<path:path>")defget_static_file(path):withopen("./files.pakk","rb")asin_file:unpakked=unpakk(KEY,in_file)ifunpakked.has_blob(path):returnsend_from_directory(unpakked,path)else:returnsend_from_directory("./static",path)You can also use pakk_flask to render templates from pakk files:frompakk_flaskimportrender_template,PakkTemplate@APP.route("/home")defget_home_page():withopen("./files.pakk","rb")asin_file:unpakked=unpakk(KEY,in_file)render_template(PakkTemplate(unpakked,"index.html"),some_value="Hello, ",another_value="World!")
Package: pakkr
PAKKRPython pipeline utility libraryDescriptionIn the process of building machine learning things at Zendesk, we have noticed that a lot of the steps are sequential where later steps rely on outputs of previous steps. Because Python functions only return a single value (returnwith multiple values are returned as a tuple), deconstructing and keeping track of return values becomes tedious for long sequences of steps, especially when inputs are not returned from the immediately previous step.PAKKR is an utility created to remediate these pain points; it provides the user with a way to specify how return values should be interpreted and optionally caches results and injects them in later steps automatically.Install from PyPipipinstallpakkrInstall from sourcegitclonegit@github.com:zendesk/pakkr.gitcdpakkr
pythonsetup.pyinstallUsagefrompakkrimportPipeline,returns@returns(int,original_num_as_string=str)# this function returns an integer and insert original_num_as_string into the meta cachedeftimes_two(n):returnn*2,{'original_num_as_string':str(n)}@returns(int,int)# this functions returns two integers and will be passed on as two argumentsdefplus_five_and_three(n):returnn+5,n+3@returns(str)defsummary(a,b,original_num_as_string):# a and b are passed in as positional arguments,# but original_num_as_string would be injected from the meta cachereturnf'Original input was{original_num_as_string}and it became{str(a)}and{str(b)}after processing'pipeline=Pipeline(times_two,plus_five_and_three,summary,_name='process_int')print(pipeline(3))Running the above code should print:Original input was 3 and it became 11 and 9 after processingWhat's going on?returnsis used to indicate how the return values should be interpreted;@returns(int, str, x=bool)means theCallableshould be returning something likereturn 10, 'hello', {'x': True}and the10and'hello'will be passed as two positional arguments into the nextCallablewhilexwould be cached in the meta space and be injected if any followingCallables requirexbut not being given as positional argument from the previousCallable.DevelopmentThis project usestoxto manage testing on multiple Python versions assuming the required Python versions are available.git clone git@github.com:zendesk/pakkr.git
cd pakkr
pip install tox
toxOptionally, usespyenvandpipenvto manage Python installation and development dependencies.git clone git@github.com:zendesk/pakkr.git
cd pakkr

# Install pyenv, see instructions in https://github.com/pyenv/pyenv
# Install Python versions supported by pakkr if not available locally
# pyenv install 3.6.10
# pyenv install 3.7.6
# pyenv install 3.8.1

# Set available Python verions
pyenv local 3.6.10 3.7.6 3.8.1

# Install pipenv
pip install pipenv
pipenv sync --dev

# Run tests
pipenv run toxReporting BugsPleaseraise an issesin GitHub.ContributingImprovements are always welcome. Please follow these steps to contributeSubmit a Pull Request with a detailed explanation of changesReceive approval from maintainersMaintainers will merge your changesLicenseUse of this software is subject to important terms and conditions as set forth in theLICENSEfile.