Saravanan666/pypi-org huggingface dataset:
Package: pyTempico
PyTempicolibrary and examples to use Tausand Tempico TP1000 devices with PythonWritten in Python3, pyTempico relies on the following modules:pyserialLibrary version:       1.0.0Original release date: 02/12/2024 (mm/dd/yyyy)Current release date:  02/12/2024Supported models:      TP1004.About Tausand Tempico TP1000This is a family of time-to-digital converters, ideal to measure time differences between electrical pulses in particle detection, microscopy, and quantum optics experiments.To learn more about them, visit our websitewww.tausand.comTo obtain a Tausand's Tempico time-to-digital converter, visit ouronline shopor contact us atsales@tausand.comInstallationpyTempicocan be installed usingpipas:pip install pyTempicoOr from GitHubpip install git+https://github.com/Tausand-dev/PyTempico.gitExamples and documentationFor details on how to run this library, read the PDF documentation located atdocs/build/latex/pytempico.pdf, or navigate the HTML version located atdocs/build/html/index.html.For developersClone the GitHub repository and then follow the next steps:Creating a virtual environmentRun the following code to create a virtual environment called.venvpython -m venv .venvActivateOn Unix systems:source .venv/bin/activateOn Windows:.venv\Scripts\activateDeactivatedeactivateInstalling packagesAfter the virtual environment has been activated, install required packages by using:python -m pip install -r requirements.txtEditing version numberWhen a new version is created, the new numbering should be updated in the following files:pyTempico/__init__.pyBuilding docsGo to thedocsfolder and runmake <command>Where<command>is one of the following:latexpdfhtmlTo run thelatexpdfcommand you will need a working installation of Latex.Generating distribution archivesAfter the virtual environment has been activated, and the packages has been installed, run the commandpython -m buildOnce completed, this should generate two files in the\distdirectory: a.tar.gzand a.whlfile. These files may be published in the TestPyPI or the PyPI repositories.
Package: py-template-creator
Py Template CreatorPy Template Creator is a pip package providing an easy way to jump-start a project or a micro-service.InstallationThe project requires cookiecutter installed.Install with pippip install py_template_generatorTemplatesFastAPI monolith skeletonService skeletonUsageTemplate generator will create the template in the current directory.py_template_generatorAuthors@ivke-99ContributingContributions are always welcome! :)
Package: py-templater
py-templaterAbout this ProjectPython utility tool that generates code files from user-defined templates. Save files/directories and user-defined templates, and load them on the fly whenever you need access to your boilerplate templates.Getting StartedPrerequisitesYou will needtyperandrichin order to usepy-templater. These should install as dependencies by default.pipinstallpy-templatertyperrich⚠️ Warningpy-templateronly works on Linux and macOS machinesUsagetemplater--helpUsage:templater[OPTIONS]COMMAND[ARGS]...Options--install-completionInstallcompletionforthecurrentshell.--show-completionShowcompletionforthecurrentshell,tocopyitorcustomizetheinstallation.--helpShowthismessageandexit.

CommandsaddAddanewtemplategenGeneratesfile(s)fromtemplateslistListcurrentcurrenttemplatesrmDeleteatemplateviewViewtemplateheadaddAllows user to define a file/directory as new templatetemplateradd--helpUsage:templateradd[OPTIONS]NAMEPATH

Argumentsnametemplatenamepathpathtooriginalfile/directorygenGenerates any user defined templates in the current working directorytemplatergen--helpUsage:templatergen[OPTIONS]NAME

ArgumentsnametemplatenamelistLists all user-defined templates and corresponding metadatatemplaterlist--helpUsage:templaterlist[OPTIONS]rmAllows user to delete any user-defined templatestemplaterrm--helpUsage:templaterrm[OPTIONS]viewViews head snippet of templatetemplaterview--helpUsage:templaterview[OPTIONS]NAME[N]Argumentsnametemplatenamen(default:5)numbersoflinestoshow
Package: pytemplate-reverse
pytemplate-reverseA package to "reverse-enginner" simple strings based on a template.Reverse-engineer a string based on a template, can be usefull to extract information from a generated string when you know how the information will be formated in said string, like the a file name.Usage sample:fromtemplate_reverseimportReverseTemplatesegments=["shrek3_0_600.avi","shrek3_1_560.avi","shrek3_2_780.avi"]rt=ReverseTemplate("{video_name}_{segment_id}_{segment_duration_in_secs}.avi")total_duration=0forsegmentinsegments:values=rt.reverse(segment)print("Checking out movie",values["video_name"],"part ",values["segment_id"])total_duration+=int(values["segment_duration_in_secs"])print("Total video duration so far",total_duration)Build locallyRequires Python 3.This package is simple enough that it should work with any version. I recommend you to install python usingpyenvwithpyenv-virtualenv. If you are on windows checkpyenv-winout.Installcd/path/to/pytemplate-reverse
pipinstall.# Thats it :)If you want to develop on it and run tests use the following:pipinstall-rtest-requirements.txt-e.# `-r test-requirements.txt` Will install the dependencies required to run the tests# `-e .` Install in editable mode, so you can edit the contents of /src/pytemplate_reverse without having to install the package again to see changespytestTo get coverage and lint run pytest with the following plugins:pytest--black--cov=template_reverse
Package: pytemplatize
TemplatizerTemplatizer allows for sprinkling in some imperative logic to your declarative configuration files.Why Imperative?While a fully declarative configuration file is often nice to be able to describe theintendedstate of the world in a single place, these can also becomequiteverbose over time.For Kubernetes, for example, you might be configuring a number of monitoring jobs. These will have different storage, CPU, and memory requirements. They also have unique configuration requirements for each one. I might be holding it wrong, but I find this very hard to make work in Helm and Kustomize without repeating myself over and over again.What I want is something that runsbeforeHelm, Kustomize, and whatever other tools you're using. I want something to generate files with the ability to do imperative logic first.ExampleimporttemplatizerclassSimple(templatizer.Templatable):prop=12345classImperative(templatizer.Templatable):tick=0defprop(self):return12345+self.ticksimple=Simple()assertsimple.propval('prop')==12345imperative=Imperative()assertimperative.propval('prop')==12345imperative.tick=1assertimperative.propval('prop')==12346Thetemplatizer.runfunction takes a list ofTemplatableobjects, generates them into strings, and adds separators in between. By default the---separator is used, which is useful for generating YAML documents. This can be changed to suit your use case.KubernetesThetemplatizer.k8smodule includes generated definitions from theKubernetes OpenAPI Spec.This allows for generating Kubernetes manifests with imperative hooks. Examples of this are provided in theexamples/directory.
Package: pyTemplePriest
# pyTemplePriestAPI* Установка с репозитория:```$ git clone https://github.com/painca/pyTemplePriestAPI.git$ cd pyTemplePriestAPi$ python setup.py install```* Запуск:```import templepriestapp_id = "app_id"app_token = "app_token"user_id = 123456789api = templepriest.api(app_id, app_token)api.get_user_code(user_id)user_token = api.get_user_token(user_id, input("Enter code: ")) #Код придет в лс с ботомuser = api.get_user(user_token)print("Уровень:", user.lvl, "("+str(user.xp)+"/"+str(user.next_lvl_xp)+")")```Для получения app_id и app_token обратится к @serogaq
Package: pytemppack
This is a template for Python packages withpoetrywith additional tools for development, such as autoformatting, type checking, and more.Thepytemppackfolder contains the python module, and we have someexamples.As best practices are always changing, and people have different experiences, we encourage you tocontributeto this project!FeaturesArranging imports withisort.Autoformatting withblack.BoilerplateREADMEwithshields.Documentation withSphinxandRead the Docs.Issue and pull requesttemplates.Templates forchangelog,code of conductandcontribute.Testing withpytestand coverage withpytest-cov.Static type-checking withmypy.InstallationUse the package managerpipto installpytemppackfrom PyPi.pipinstallpytemppackFor development install, seecontribute.UsageTransform anp.ndarrayinto apd.DataFrame.frompytemppackimportPyPackfrompytemppack.utilsimportgenerate_random_arraydata=generate_random_array((5,5))columns=['a','b','c','d','e']pypack=PyPack(data)pypack.transform_results(columns)pypack.results.head()abcde00.9767000.1180910.4410060.6598740.06013910.3801960.2417660.6098710.7357580.68368920.9232460.3185340.8636210.2227540.67123830.2616920.9640790.8637580.1720660.61101840.3190970.2636500.6748810.8704150.060137CommunityIf you are interested in cross-disciplinary research related to machine learning, you can:Follow DataLab onTwitter.Check thewebsite.Read ourpapers.DisclaimerThe package and the code is provided “as-is” and there is NO WARRANTY of any kind.
Use it only if the content and output files make sense to you.AcknowledgementsThis README contains many embedded links from which inspiration was drawn. We suggest to check them out!
Especially, thisrepowas a great help for autosummary recurssion to document the API.
Package: pytempscsp
pytempscsp : Temporal Scale Space Toolbox for PythonFor performing temporal smoothing with the time-causal limit kernel and
for computing discrete temporal derivative approximations by applying
temporal difference operators to the smoothed data.This code is the result of porting a subset of the routines in the Matlab
package tempscsp to Python, however, with different interfaces for the functions.For examples of how to apply these functions for smoothing temporal signals
to different temporal scales in a fully time-causal manner, please see the
enclosed Jupyter notebooktempscspdemo.ipynb.For more technical descriptions about the respective functions, please see
the documentation strings for the respective functions in the source code
in tempscsp.py.InstallationThis package is available
through pip and can installed bypipinstallpytempscspThis package can also be downloaded directly from GitHub:gitclonegit@github.com:tonylindeberg/pytempscsp.gitReferencesLindeberg (2023) "A time-causal and time-recursive temporal scale-space representation
of temporal signals and past time", Biological Cybernetics 117 (1-2): 21-59.
(Open Access)Lindeberg (2016) "Time-causal and time-recursive spatio-temporal receptive fields",
Journal of Mathematical Imaging and Vision 55(1): 50-88.
(Open Access)The time-causal limit kernel was first defined in Lindeberg (2016), however,
then also in combination with a spatial domain, and experimentally tested on
video data. The later overview paper (Lindeberg 2023) gives a dedicated treatment
for a purely temporal domain, and also with relations to Koenderink's scale-time
kernels and the ex-Gaussian kernel.
Package: pyTenable
pyTenable is intended to be a pythonic interface into the Tenable application
APIs.  Further by providing a common interface and a common structure between
all of the various applications, we can ease the transition from the vastly
different APIs between some of the products.Issue Tracker:https://github.com/tenable/pyTenable/issuesGithub Repository:https://github.com/tenable/pyTenableInstallationTo install the most recent published version to pypi, its simply a matter of
installing via pip:pipinstallpytenableIf you’re looking for bleeding-edge, then feel free to install directly from the
github repository like so:pipinstallgit+git://github.com/tenable/pytenable.git#egg=pytenableGetting StartedLets assume that we want to get the list of scans that have been run on our
Tenable.io application.  Performing this action is as simple as the following:fromtenable.ioimportTenableIOtio=TenableIO('TIO_ACCESS_KEY','TIO_SECRET_KEY')forscanintio.scans.list():print('{status}:{id}/{uuid}-{name}'.format(**scan))Getting started with Tenable.sc is equally as easy:fromtenable.scimportTenableSCsc=TenableSC('SECURITYCENTER_NETWORK_ADDRESS')sc.login('SC_USERNAME','SC_PASSWORD')forvulninsc.analysis.vulns():print('{ip}:{pluginID}:{pluginName}'.format(**vuln))For more detailed information on whats available, please refer to thepyTenable DocumentationLoggingEnabling logging for pyTenable is a simple matter of enabling debug logs through
the python logging package.  An easy example is detailed here:importlogginglogging.basicConfig(level=logging.DEBUG)LicenseThe project is licensed under the MIT license.
Package: pytennis
PytennisInstallpip install pytennisHow to useDatapath=untar_data(URLs.ATP_2019_AUSTRALIAN_OPEN_SINGLES_FINAL)pathPath('/Users/robertseidl/.pytennis/data/ATP_2019_AustralianOpen_Singles_Final')path.ls()(#4) [Path('/Users/robertseidl/.pytennis/data/ATP_2019_AustralianOpen_Singles_Final/rallies.csv'),Path('/Users/robertseidl/.pytennis/data/ATP_2019_AustralianOpen_Singles_Final/points.csv'),Path('/Users/robertseidl/.pytennis/data/ATP_2019_AustralianOpen_Singles_Final/events.csv'),Path('/Users/robertseidl/.pytennis/data/ATP_2019_AustralianOpen_Singles_Final/serves.csv')]pd.read_csv(path/'rallies.csv',index_col=0).head()<style scoped>
    .dataframe tbody tr th:only-of-type {
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    }.dataframe tbody tr th {
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.dataframe thead th {
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}</style>rallyidserverreturnerwinnerreasonservestrokestotaltimexy01DjokovicNadalDjokovicwinnerfirst30.921.9221.9612DjokovicNadal__undefined__second_servefirst10.007.4212.1023DjokovicNadalDjokovicoutsecond44.163.33-0.3934DjokovicNadal__undefined__second_servefirst10.004.6417.6945DjokovicNadalDjokovicacesecond20.401.6217.18
Package: pytenno
PyTennoPyTenno is an asynchronous wrapper for thewarframe.marketAPI, written in Python.Read DocumentationDisclaimerPyTenno is not associated with warframe.market or any of its affiliates.
PyTenno is not associated with Digital Extremes or any of its affiliates.InstallationGitUse your favorite spice of the following:py-mpipinstallgit+https://github.com/ShadowMagic896/pytenno.gitGit-SCMis required for direct installationPyPipy-mpipinstallpytennoRequirementsThe project's only requirement is aiohttp, which is available on PyPi.aiohttp>= 3.8.1Skill-Set RequirementsPyTenno Requires Fundamental understanding of the following:Python datatypes, attributes, etc.Asyncrhonous programming in Python (Coroutines,async,await)Fundamental understanding of context managers in Python (async with)Howwarframe.marketworksWarframemechanicsExamplesThe Following Code Will Be Used in the Examplesimportasyncio# To use asynchronous programmingimportpytennofrompytenno.models.enumsimportPlatform# To specify platforms for requestsasyncdefmain():# PyTenno is asynchronous, so it must be done in an asynchronous contextdefault_language="en"# Set default response language to Englishdefault_platform=Platform.pc# Set default platform to PC# Create a client with the default language and platform# This must be done in an asynchronous context manager (async with ... [as ...])asyncwithpytenno.PyTenno(default_language,default_platform)astenno:...# Example code goes hereif__name__=="__main__":asyncio.run(main())# Create a new asyncio loop and run the coroutineLog into warframe.marketemail="you@example.com"# Email to accountpassword="password123"# Password to accountcurrent_user=awaittenno.Auth.login(email=email,password=password)# Log in to warframe.market# Note: the API stores absoloutely zero information about data passed. All code# is open-source and available at https://github.com/ShadowMagic896/pytennoprint(current_user.ingame_name)# Print the ingame name of the user that was logged in
Package: pytenor
PyTenorA simple API wrapper for tenor's gif api that is built with asyncio.PyTenor's code is small and fairly documented as well using docstrings. There are plans to add an actual documentation in this README in the future..Installationpython -m pip install pytenorExamplesimportasynciofrompytenorimportTenorapi=Tenor(key="api key here")asyncdefmain():# Search for gifs. This returns a GIF object.gifs=awaitapi.search("linux users opening a new tab")print("Here are the gifs I found")forgifingifs:print(f"URL:{gif.url}| Available Formats:{gif.available_formats}")if__name__=="__main__":asyncio.run(main())Covered API Endpoints/search/trending/categories/search_suggestions/autocomplete/trending_terms/registershare/gifsFor more help on what parameters you can pass onto each method. Please redTenor's API documentation.
Package: pytensor
PyTensor is a fork ofAesara– a Python library that allows one to define, optimize, and
efficiently evaluate mathematical expressions involving multi-dimensional arrays.FeaturesA hackable, pure-Python codebaseExtensible graph framework suitable for rapid development of custom operators and symbolic optimizationsImplements an extensible graph transpilation framework that currently provides
compilation via C,JAX, andNumbaBased on one of the most widely-used Python tensor libraries:TheanoGetting startedimportpytensorfrompytensorimporttensoraspt# Declare two symbolic floating-point scalarsa=pt.dscalar("a")b=pt.dscalar("b")# Create a simple example expressionc=a+b# Convert the expression into a callable object that takes `(a, b)`# values as input and computes the value of `c`.f_c=pytensor.function([a,b],c)assertf_c(1.5,2.5)==4.0# Compute the gradient of the example expression with respect to `a`dc=pytensor.grad(c,a)f_dc=pytensor.function([a,b],dc)assertf_dc(1.5,2.5)==1.0# Compiling functions with `pytensor.function` also optimizes# expression graphs by removing unnecessary operations and# replacing computations with more efficient ones.v=pt.vector("v")M=pt.matrix("M")d=a/a+(M+a).dot(v)pytensor.dprint(d)#  Add [id A]#  ├─ ExpandDims{axis=0} [id B]#  │  └─ True_div [id C]#  │     ├─ a [id D]#  │     └─ a [id D]#  └─ dot [id E]#     ├─ Add [id F]#     │  ├─ M [id G]#     │  └─ ExpandDims{axes=[0, 1]} [id H]#     │     └─ a [id D]#     └─ v [id I]f_d=pytensor.function([a,v,M],d)# `a/a` -> `1` and the dot product is replaced with a BLAS function# (i.e. CGemv)pytensor.dprint(f_d)# Add [id A] 5#  ├─ [1.] [id B]#  └─ CGemv{inplace} [id C] 4#     ├─ AllocEmpty{dtype='float64'} [id D] 3#     │  └─ Shape_i{0} [id E] 2#     │     └─ M [id F]#     ├─ 1.0 [id G]#     ├─ Add [id H] 1#     │  ├─ M [id F]#     │  └─ ExpandDims{axes=[0, 1]} [id I] 0#     │     └─ a [id J]#     ├─ v [id K]#     └─ 0.0 [id L]Seethe PyTensor documentationfor in-depth tutorials.InstallationThe latest release of PyTensor can be installed from PyPI usingpip:pip install pytensorOr via conda-forge:conda install -c conda-forge pytensorThe current development branch of PyTensor can be installed from GitHub, also usingpip:pip install git+https://github.com/pymc-devs/pytensorContributingWe welcome bug reports and fixes and improvements to the documentation.For more information on contributing, please see thecontributing guide.A good place to start contributing is by looking through the issueshere.
Package: pytensordb
Tensordb is a vector database software independently designed and developed by Shanghai Actionsky Technology Co., Ltd.
The product realizes the efficient organization of super large-scale vector data, designs an easy to expand index structure, and effectively supports the rapid comparison of vector data in time-varying environment.
For entity analysis and relationship inference in complex scenarios, tensordb overcomes the difficulties of unstructured data management, processing and analysis faced by diversified applications in AI field, and improves the heterogeneous integration ability of database.
Tensordb has extremely high concurrent retrieval performance, supports excellent horizontal expansion ability, and provides high availability requirements in diversified business scenarios.
Tensordb is committed to providing users with one-stop unstructured data retrieval and storage solutions.More information:Refer to the TensorDB API(Python) documentation for more information
Package: pytensordock
Pytensordock - Python wrapper for the Tensordock APIA Python wrapper library for the TensorDock Marketplace API.You can find the documentation to the wrapper atPytensordock DocumentationReferencesAPI Endpoint Documentation:https://documenter.getpostman.com/view/20973002/2s8YzMYRDcAPI Keys:https://marketplace.tensordock.com/api
Package: pytent
PytentPackage for collecting, processing and visualizing patent data for data scienceGetting StartedDependenciesYou need Python 3.10 or later to usepytent. You can find it atpython.org.
You also need setuptools, wheel and twine packages, which is available fromPyPI. If you have pip, just run:pip install setuptools
pip install wheel
pip install twineInstallationClone this repo to your local machine using:git clone https://github.com/raulivan/pytentFeaturesCollection of patent data on Espacenet
Package: pytential
pytential helps you accurately evaluate layer
potentials (and, sooner or later, volume potentials).
It also knows how to set up meshes and solve integral
equations.It relies onnumpyfor arraysboxtreefor FMM tree buildingsumpyfor expansions and analytical routinesmodepyfor modes and nodes on simplicesloopyfor fast array operationspytestfor automated testingand, indirectly,PyOpenCLas computational infrastructurePyOpenCL is likely the only one you’ll have to install
by hand, all the other ones will automatically be installed.Resources:documentationwiki home pagesource code via git
Package: pytenzir
Tenzir PythonThe Python package of Tenzir provides a flexible control plane to integrate Tenzir
with other security tools.NoteThe Python effort is still highly experimental and subject to rapid change.
Please do not consider it for production use.UsageTo get started, clone the Tenzir repository and install the Python package viaPoetry:gitclonehttps://github.com/tenzir/tenzir.gitcdtenzir/python
poetryinstallDevelopmentWe recommend that you work with an editable installation, which is the default
forpoetry install.Unit TestsRun the unit tests via pytest:poetryrunpytestIntegration TestsRun the integrations tests via Docker Compose and pytest:./docker-poetry-run.shpytest-vPackagingThe following instructions concern maintainers who want to publish the Python
package to PyPI.NoteOur releasing scripts and CI run these steps automatically. You do not need to
intervene anywhere. The instructions below merely document the steps taken.Bump the versionPrior to releasing a new version, bump the version, e.g.:poetryversion2.3.1This updates thepyproject.tomlfile.Publish to Test PyPIAdd a Test PyPi repository:poetryconfigrepositories.test-pypihttps://test.pypi.org/legacy/Get the token fromhttps://test.pypi.org/manage/account/token/.Store the token:poetryconfigpypi-token.test-pypipypi-XXXXXXXXPublish:poetrypublish--build-rtest-pypiPublish to PyPIGet the token fromhttps://pypi.org/manage/account/token/.Store the token:poetryconfigpypi-token.pypipypi-XXXXXXXXPublishpoetrypublish--build