Saravanan666/pypi-org huggingface dataset:
Package: libra_py_001_04
Citing libra_py_001_02=============The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justifiedby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry `citation.bib <http://jmlr.org/papers/v17/15-355.bib>`_).Bayer, I. "fastFM: A Library for Factorization Machines" Journal of Machine Learning Research 17, pp. 1-5 (2016)libra_py: A Package for sparsity problem============================================Supported Operating Systems---------------------------fastFM has a continuous integration / testing servers (Travis) for **Linux (Ubuntu 14.04 LTS)**and **OS X Mavericks**. Other OS are not actively supported.Usage-----.. code-block:: pythonfrom fastFM import alsfm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=2, l2_reg_w=0.1, l2_reg_V=0.5)fm.fit(X_train, y_train)y_pred = fm.predict(X_test)Tutorials and other information are available `here <http://arxiv.org/abs/1505.00641>`_.The C code is available as `subrepository <https://github.com/ibayer/fastFM-core>`_ and providesa stand alone command line interface. If you have still **questions** after reading the documentation please open a issue at GitHub.+----------------+------------------+-----------------------------+| Family         | Solver           | Loss                        |+================+==================+=============================+| Gaussian       | LBI_Linear       | Square Loss                 |+----------------+------------------+-----------------------------+| Binomial       | LBI_Logit        | Logit Model                 |+----------------+------------------+-----------------------------+*Supported solvers and tasks*Installation------------**binary install**``pip install libra_py``Tests-----
Package: libra_py_001_05
Citing libra_py_001_05=============The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justifiedby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry 'citation.bib <http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity/bibtex>' ).Huang, Chendi and Sun, Xinwei and Xiong, Jiechao and Yao, Yuan. "Split LBI: An Iterative Regularization Path with Structural Sparsity" Advances in Neural Information Processing Systems 29, pp. 3369--3377 (2016)libra_py_001_05: A Package for sparsity problem============================================Usage-----.. code-block:: pythonfrom libra_py_001_05 import lbiobj = lbi.LB(X,y,family='gaussian')obj.predict(X)Tutorials and other information are available 'here <https://arxiv.org/abs/1604.05910>' and'here <https://www.sciencedirect.com/science/article/pii/S1063520316000038>'.The R code is available as 'subrepository <https://cran.r-project.org/web/packages/Libra/index.html>'; the Matlab code is available as 'subrepository <https://github.com/yuany-pku/split-lbi>'.If you have still **questions** after reading the documentation please open a issue at GitHub.+----------------+------------------+-----------------------------+| Family         | Solver           | Loss                        |+================+==================+=============================+| Gaussian       | LBI_Linear       | Square Loss                 |+----------------+------------------+-----------------------------+| Binomial       | LBI_Logit        | Logit Model                 |+----------------+------------------+-----------------------------+*Supported solvers and tasks*Installation------------**binary install**``pip install libra_py_001_05``Tests-----import libra_py_001_05
Package: libra_py_001_06
Citing libra_py_001_05=============The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justifiedby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry 'citation.bib <http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity/bibtex>' ).Huang, Chendi and Sun, Xinwei and Xiong, Jiechao and Yao, Yuan. "Split LBI: An Iterative Regularization Path with Structural Sparsity" Advances in Neural Information Processing Systems 29, pp. 3369--3377 (2016)libra_py_001_05: A Package for sparsity problem============================================Usage-----.. code-block:: pythonfrom libra_py_001_05 import lbiobj = lbi.LB(X,y,family='gaussian')obj.predict(X)Tutorials and other information are available 'here <https://arxiv.org/abs/1604.05910>' and'here <https://www.sciencedirect.com/science/article/pii/S1063520316000038>'.The R code is available as 'subrepository <https://cran.r-project.org/web/packages/Libra/index.html>'; the Matlab code is available as 'subrepository <https://github.com/yuany-pku/split-lbi>'.If you have still **questions** after reading the documentation please open a issue at GitHub.+----------------+------------------+-----------------------------+| Family         | Solver           | Loss                        |+================+==================+=============================+| Gaussian       | LBI_Linear       | Square Loss                 |+----------------+------------------+-----------------------------+| Binomial       | LBI_Logit        | Logit Model                 |+----------------+------------------+-----------------------------+*Supported solvers and tasks*Installation------------**binary install**``pip install libra_py_001_05``Tests-----import libra_py_001_05
Package: libra_py_001_07
Citing libra_py_001_07=============The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justifiedby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry 'citation.bib <http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity/bibtex>' ).Huang, Chendi and Sun, Xinwei and Xiong, Jiechao and Yao, Yuan. "Split LBI: An Iterative Regularization Path with Structural Sparsity" Advances in Neural Information Processing Systems 29, pp. 3369--3377 (2016)libra_py_001_07: A Package for sparsity problem============================================Usage-----.. code-block:: pythonfrom libra_py_001_07 import lbiobj = lbi.LB(X,y,family='gaussian')obj.predict(X)Tutorials and other information are available 'here <https://arxiv.org/abs/1604.05910>' and'here <https://www.sciencedirect.com/science/article/pii/S1063520316000038>'.The R code is available as 'subrepository <https://cran.r-project.org/web/packages/Libra/index.html>'; the Matlab code is available as 'subrepository <https://github.com/yuany-pku/split-lbi>'.If you have still **questions** after reading the documentation please open a issue at GitHub.+----------------+------------------+-----------------------------+| Family         | Solver           | Loss                        |+================+==================+=============================+| Gaussian       | LBI_Linear       | Square Loss                 |+----------------+------------------+-----------------------------+| Binomial       | LBI_Logit        | Logit Model                 |+----------------+------------------+-----------------------------+*Supported solvers and tasks*Installation------------**binary install**``pip install libra_py_001_07``Tests-----import libra_py_001_05
Package: librapythonpro
librapythonproModulo para exemplificar construÃ§ao de projetos python no curso PytoolsNesse curso Ã© ensinado como contribuir com projetos de codigo abertoLink do cursoPython ProSuportado Python 3Para instalar:''' Console
python3 -m venv .venvsource .venv/bin/activatepip install -r requirements-dev.txt
'''Para conferir qualidade de codigo:''' Consoleflake8
'''Topicos a serem abordados:GitVirtualenvPip
Package: libraria
LibrariaA python library used for searching libraries for books. Currently, only the San Francisco
Bay Area libraries are supported.Upcoming FeaturesMore comprehensive testsimplement support for usingadvanced searchInstallingpip3 install libraria
libraria 'byzantine empire'
S76C2550363,The Culture of the Byzantine Empire,BK,['9781508150015', '150815001X', '9781508150060', '1508150060']
S76C1793574,The Byzantine Empire,BK,['9781410305862', '1410305864']
S76C1672035,Daily Life in the Byzantine Empire,BK,['9780313324376', '0313324379']
S76C2047443,History of the Byzantine Empire,EBOOK,[]
S76C1340752,The Byzantine Empire,BK,['9780684166520', '0684166526']
S76C2034317,The End of the Byzantine Empire,EBOOK,[]
S76C1443729,The Byzantine Empire,BK,['9781560063070', '1560063076']
S76C1718171,The Byzantine Empire,BK,['9781590188378', '1590188373']
S76C2636299,The Byzantine Empire,BK,['9781680487800', '1680487809', '9781680488616', '1680488619']
S76C1618227,The Byzantine Empire,BK,['9780761414957', '0761414959']Running the testsTo execute tests, runpoetry run pytest testsLicenseMIT License
Package: librarian-curator
curatorCLI library designer for theLibrarianAPI.Curatorallows for simple card design and storage in aLibrarianlibrary
database. To enable highly general useCuratorwill do nothing more then allow
creation and storage of simple card information and does nothing to interpreted
this data or design how cards may interact with each other.This installs acuratorcommand line tool for manipulatingLibrariancard databases.LinksCurator Github:https://github.com/Nekroze/curatorLibrarian PyPi:https://pypi.python.org/pypi/librarian
Package: library-analyzer
Library AnalyzerAnalysis of Python libraries and code that uses them.DocumentationYou can find the full documentationhere.InstallationGet the latest version fromPyPI:pipinstalllibrary-analyzerExample usageAnalyze the API of a library:analyze-libraryapi-psklearn-ooutAnalyze client code of a library:analyze-libraryusages-psklearn-c"Kaggle Kernels"-ooutGenerate annotations for the library:analyze-libraryannotations-adata/api/sklearn__api.json-udata/usages/sklearn__usage_counts.json-oout/annotations.jsonMigrate annotations for a new version of the library:analyze-librarymigrate-a1data/api/scikit-learn_v0.24.2_api.json-a2data/api/sklearn__apiv2.json-adata/annotations/annotations.json-oout
Package: libraryArsein
How to import Arsin’s library is as follows:<< from libraryArsein.Arsein import Robot_Rubika >>An example:from libraryArsein.Arsein import Robot_Rubikabot = Robot_Rubika(“Your Auth Account”)Made by Team ArianBotAddress of our team’s GitHub :https://github.com/Arseinlibrary/Arsein__library.git
Package: librarycreator
LibraryCreator is a Python library that provides a user-friendly way to generate setup files, build wheel files, and upload Python libraries to PyPI (Python Package Index). It offers a graphical user interface (GUI) powered by tkinter, making it easy for developers to create and distribute their Python libraries.Key Features:- Generate Setup: Generate a setup.py file for a Python library by providing package details such as name, version, description, author, and author email.- Build Wheel: Build a wheel file for your Python library, simplifying the distribution and installation process.- Upload Library: Upload your Python library to PyPI with ease, prompting you for your PyPI credentials and handling the upload process for you.Installation:You can install LibraryCreator using pip:pip install LibraryCreatorUsage:Here's an example of how to use LibraryCreator:from LibraryCreator import LibraryCreator# Create an instance of LibraryCreatorcreator = LibraryCreator()# Run the GUI event loopcreator.run()This will open the Library Creator GUI, where you can interactively generate setup files, build wheel files, and upload your Python libraries to PyPI.Documentation:For detailed documentation and usage examples, please refer to the LibraryCreator Documentation.License:LibraryCreator is licensed under the MIT License.Contributing:Contributions are welcome! Please see the Contributing Guidelines for more information.Issues:If you encounter any issues or have suggestions, please feel free to open an issue on the GitHub repository.Credits:LibraryCreator is developed and maintained by TheUnkownHacker.Release History:0.0.1-beta (2023-06-03): Initial beta release.Support:For support or any inquiries, please contact jhamb.aarav@gmail.com.
Package: librarykelompok-2
import awalfrom librarykelompok2.mylibrary import deskripsitambahan pada kode programclass AttendanceApp:
definit(self, root):
self.btn_tampilkan_deskripsi = tk.Button(root, text='Tampilkan Deskripsi', command=self.tampilkan_deskripsi, font=("Arial", 15))
self.btn_tampilkan_deskripsi.place(x=1100, y=590)
self.btn_tampilkan_deskripsi.configure(width=20, height=3)def tampilkan_deskripsi(self):
    # Memanggil fungsi untuk menampilkan deskripsi dari mylibrary
    deskripsi()
Package: librarylink
Library.Link
Tools for processing data from the Library.Link projectUche Ogbuji <uche@ogbuji.net>InstallRequires Python 3.5+pip install LibraryLinkliblink_resource_summaryCommand Tool to parse RDFa 1.1 Lite (from the Library.Link pages or other HTML). Example:liblink_resource_summary "http://link.houstonlibrary.org/portal/Half-of-a-yellow-sun-Chimamanda-Ngozi/n7KqqbZFJuM/"
Package: library-nsxt
Library NSX-TEsta librería implementa métodos y servicios para interactuar con un cluster de NSX-T a tavés de su API.RequisitosLos requisitos para instalar y ejecutar el proyecto son:Un IDE o un editor de código:IDE:PyCharmEditores de Código:Visual Studio Code,Notepad++,VIMUn entorno dePython 3.6.8mínimo con las siguientes librerías:PyYAMLrequestsparamikoInstalaciónPara instalar el programa localmente o en un Linux remoto:Descargar o clonar el proyecto desde el repositorio.Asegurarse de tener un entorno de Python funcional con la versión requerida y los paquetes necesarios.Activar el entorno de Python que fue creado.Instalar el módulo ejecutando este comando en la ruta del proyecto:pythonsetup.pyinstallConfiguraciónEste proyecto no tiene ficheros de configuración o ninguna configuración adicional.
Package: librarypaste
UsageLaunch with thelibrarypastecommand or withpython-mlibrarypaste. The library will host the service
on[::0]:8080by default. Pass cherrypy config files on the command line
to customize behaivor.By default, the server saves pastes to the file system  in./repousing the
JSON store, but there is support for a MongoDB backend as well.See alsolpastefor a Python-based
client (including a clipboard helper) and Mac OS X App.
Package: librarypy
LibraryPyLibraryPy is a Python library built for Python 3.11.0.InstallationTo install LibraryPy, you can run the following command in your terminal or command line:
pip install librarypyUsageHere's an example of how you can use LibraryPy in your own Python project:
import librarypy
result = librarypy.do_something()
print(result)DependenciesLibraryPy requires that Python 3.11.0 or later is installed on your system.CollaboratorsLibraryPy was created by Felipe and Edwin as a collaboration to create a library for their projects.ContributingIf you would like to contribute to the development of LibraryPy, please follow the guidelines in our CONTRIBUTING.md file.ProjektnavnDette prosjektet er tilgjengelig som en Python-pakke på Python Package Index (PyPI). Du kan installere den ved hjelp av pip:
pip install librarypiFor å publisere nye versjoner av prosjektet til PyPI, kan du bruke verktøyet twine. Du kan finne mer informasjon om hvordan du publiserer til PyPI på denne siden:https://pypi.org/Vi håper du finner dette prosjektet nyttig i ditt eget arbeid. Hvis du har noen spørsmål eller forslag til forbedringer, vennligst åpne en issue på GitHub.
Package: librarypython
librarypythonMódulo para exemplicar a construção de um projeto profissional em PythonNesse curso é ensinado com contribuir com projetos de código aberto.Link do cursoPython ProSuporta versão 3 de PythonPara instalar utilizando pip:python3 -m venv .venvsource .venv/bin/activatepip install -r requirements.txtPara conferir qualidade do código:flake8Para instalar utilizando pipenv:pip install -q pipenv codecovpipenv sync --devPara conferir qualidade do código:pipenv run flake8Tópicos a serem abordados:1. Git
2. Virtualenv
3. Pip
4. Pipenv
Package: libraryshad
An example:fromlibraryshadimportBotimportasynciobot=Bot(auth="Auth Account",key="Key Account")gap="guids"asyncdefmain():data=awaitbot.sendMessage(gap,"mamadcodrr")print(data)asyncio.run(main())An example:fromlibraryshadimportasyncio,Botbot=Bot(session="session")asyncdefmain():data=awaitbot.getChats()print(data)asyncio.run(main())How to import the shad's libraryfromlibraryshadimportBotHow to install the librarypipinstalllibraryshad==1.1.3My ID in Rubika@professor_102And My ID Channel in Rubika@python_java_source
Package: library-simulator
Library simulator is a python package for generating random mutagenesis
libraries given a nucleotide sequence and error-prone polymerase.ExamplesCreate a libraryfromlibrary_simulatorimportLibrarySimulatorlib=LibrarySimulator("example.fasta",mutation_spectrum="published")lib.simulate(num_samples=10,mutation_rate=2)lib.clonesaa: amino acid changes (*: new stop codon)base: base changes (-and+are insertions and deletions)num: total number of amino acid changes (could be a huge number if there is
an early stop or indel)indel: whether or not there is an indelstop: whether or not there is new stop codonstart: whether or not the real start codon was messed upFind the frequencies of classes of mutants versus mutation ratefromlibrary_simulatorimportLibrarySimulator,utillib=LibrarySimulator("example.fasta",mutation_spectrum="published")f_v_r=util.freq_vs_mutation_rate(lib,num_samples=10000)util.plot_freq_vs_mutation_rate(f_v_r)Seeexample/examples.ipynbfor more functionality.Installationpipinstalllibrary_simulatorAssumptionsThe number of mutations per clone is determined by a Poisson process.The sites mutated are independent within a clone and between clonesThe probability of each possible mutation (A->T, G->C, etc.) is determined
by the enzyme, not the sequence.  The profiles for different enzymes
are found in:library_simulator/mutation_spectra
Package: librarywatch
LibraryWatch makes checking for updates to the packages you have installed easy, with a handy Django management command wrapper.Install it, add 'librarywatch' to settings.INSTALLED_APPS and run python manage.py check_for_updates to see if there are any newer libraries available.Output will look similar to:celery (3.0.5 < 3.0.9)django-celery (3.0.4 < 3.0.9)django-haystack (2.0.0-beta < 1.2.7)httplib2 (0.7.4 < 0.7.5)kombu (2.3.2 < 2.4.5)python-dateutil (1.5 < 2.1)requests (0.13.5 < 0.14.0)PIL (1.1.7 < 1.1.6)It's a simple app / function, but will save me (and hopefully you) time and be easily installable on everything you do.If you want to ignore versions (for instance you're tracking django-haystack on git, or want to make sure python-dateutil doesn't recommend you go to the 2.x branch with your app on Python 2.x) you can add LIBRARYWATCH_IGNORE_VERSIONS to your settings file.LIBRARYWATCH_IGNORE_VERSIONS = {'python-dateutil': '*',  # 2.x series it recommends is not for Python 2.x'django-haystack': '*',  # running from git MASTER'PIL': '1.1.6',  # running 1.1.7 which doesn't appear to be on PyPI}Future ideas:- Track versions to allow ignoring when newer versions aren't backwards compatible so you absolutely DON'T want to upgrade.- Handle projects with multiple versions availabe better (i.e.) python-dateutil with 2.0 for Py3k only, 1.x series for Python 2.x)- Detect things not from PyPi but pulled from repositories (i.e. GitHub / Bitbucket) and use their respective APIs to determine if the SHA installed is the most recent.
Package: libras
Libras基于Flask框架的Web服务框架简单封装了Flask框架，并提供了如下功能：提供了yaml | yml格式文件配置，且配置文件可与工程分离，达到不同环境的配置更加灵活，重写了Flask类，定义了统一异常处理、配置类、请求日志输出等。实现对blueprint的包路径扫描机制，开发只需在对应包中，按规定格式实现 api接口，框架会自动注册到flask blueprint中。使用外观模式，简化程序启动入口，只需一行代码即可启动。使用方式# 入口

from Libras import Application

if __name__ == '__main__':
    Application(packages='app.api', profile='/').run(host='0.0.0.0', port=9003)其中参数说明：packages: 指定blueprint的包路径，启动时会自动扫描并注册profile: 指定配置文件所有目录
Package: librato_bg
Enables submitting of Librato events in a background thread. Heavily
inspired by segment.io’s python library which does the same.Usagefromlibrato_bgimportClient# initialize with Librato API tokensclient=Client(username,token)# track as your normally would, params are event, value and source.# This is non-blocking, submission will take place in other threadclient.gauge('user_clicked',1,'prod')# when exiting, flush to join threads and make sure everything is sentclient.join()
Package: librator
DocumentationThe full documentation is athttp://librator.rtfd.org.QuickstartInstall Librator:pip install libratorThen start creating cards with:libratorcard cards/mycardWhen you have your directory of card files simple pack then up:librator cards/ library.lbrFeaturesTurns a collection of yml based card files into a librarian card database.Can turn a librarian card database into a directory of yml based card files.Provides a tool for creating an empty example card file.Historyv0.1.3Switched unpacking to use library generator retrieve_allv0.1.2Fix for creating a folder for new card templates.v0.1.1Added guards to ensure directories exist before use.Packing now starts the library anew if it already exists.v0.1.0First PyPi release.
Package: libravatar-py
A libravatar client for Python thats built on modern TechnologyIntroduction :Friendly fork ofpyLibravatar.Why did I write this library:While working on the Libravatar client project I came across the well builtpyLibravatarlibrary. The last update was at Jun 28, 2015 ( Thats over 6 years ago when I was writing this ). So thats why I decided to write this library. It mimics the API ( and functionality ) ofpyLibravatarbut withasynciospice.Why use "libravatar-py" ?Provides API for different kinds of usage.Built with modern techknology.No unnecessary codes. ( My goal was to keep the library as small as
possible )Is very minimal. ( Only provides 4 functions )Fully Fedarated and feature rich.Why shouldn't you use "libravatar-py" ?I donno ? If you do please submit a Pull RequestUser guide :Installation :Install with pip from pypi (No extra dependencies):$python-mpipinstalllibravatar-pyInstall with pip from github ( Development | Not Recommended for Production ):$python-mpipinstallhttps://codeload.github.com/baseplate-admin/libravatar-py/zip/refs/heads/mainUsage:Call any of these 4 methods.libravatar_urlfunction ( This will essentially return the base url of
the image ) :fromlibravatarimportlibravatar_urlurl=libravatar_url(email="someone@example.com")print(url)# https://seccdn.libravatar.org/avatar/16d113840f999444259f73bac9ab8b10libravatar_img_tagfunction ( This will wrap the libravatar url in a <img /> tag. You can also customize _alt text ) :fromlibravatarimportlibravatar_img_tagurl=libravatar_img_tag(email="someone@example.com")print(url)# <img src=https://seccdn.libravatar.org/avatar/16d113840f999444259f73bac9ab8b10 alt='Avatar for someone@example.com' />libravatar_raw_imagefunction ( This will return the Libravatar image in a binary form ) :fromlibravatarimportlibravatar_raw_imageres=libravatar_raw_image(email="someone@example.com")print(res)# Large binary string.# To load it.fromPILimportImagefromioimportBytesIOres=Image.open(BytesIO(res))libravatar_raw_queryfunction ( Essentially passes the {args, kwargs} to httpx_get_avatar ):fromlibravatarimportlibravatar_raw_image# Note this this must be called from an async functionres=awaitlibravatar_raw_query(email="someone@example.com",{})# All httpx variables available in resContributing :If you like this project add a star. If you have problems or suggestions please put them in theIssue Tracker. If
you like to add features. Fork this repo and submit a Pull Request. 😛Updates :The library is feature complete ( in my opinion ).