Saravanan666/pypi-org huggingface dataset:
Package: lazywf
#lazywfThe laziest web framework ever.[![CircleCI](https://circleci.com/gh/cleberzavadniak/lazywf/tree/master.svg?style=svg)](https://circleci.com/gh/cleberzavadniak/lazywf/tree/master)## Why I started this projectI really enjoy working withDjango. It’s a very nice
framework if you want to do some serious business without
worrying too much about little details that won’t help you
at all to make your deliveries in time. It’s opinionated
and I think it’s good. Except when I don’t want to do
anything “serious”.Really, it’s too difficult to get a Django project up and
running. You have to deal with your “settings.py”,
configure where your templates are, enable static files
serving, write your models upfront, create migrations,
migrate, write the views, then write the templates then
run the server and finally see nothing really happening
because all you did was simply the skeleton of your
application andnowyou can start really making the
things happen as you wanted at first.What was that, again?Man, I don’t have the patience to deal with so much only
to put into life something very simple I want to play with.I am too lazy!## Django REST -ANYTHING- make everything even worse!Add to all that the decision to have a REST API. Now I have
to write serializers or specialized views, too.Oh, no, God!, no!I can’t do that anymore. Really. Maybe when I was 20 years
old, but not on my age…## Save your laziness on a DBMSSo, I decided to create a project using Bottle. But, where
should I save my data? Planning to run the app on Heroku, I
couldn’t save things on the filesystem. So I found the
“dataset” project and loved it. That wasexactlywhat I
needed.## Models are still niceI like to design models. What I don’t like is to write the
models as code. Yes, you heard it. There should be a better
way.
Since “dataset” is very loose about data and I wanted some
way of validating what was coming from the REST API, I
found it suitable to validate data using “cerberus”. So I
needed some place to save the validation schemata, and
decided to useYAMLfor that, since JSON is too verbose
and I am too lazy to comply with JSON strict rules (and
type all that innumerous ‘{’ and ‘}’ and ‘,’…).Also, model defining should be very simple. Your project
must have a “models.yaml” file on the root directory, from
where models definition, some constraints and validation
rules are going to be loaded.And that is it.## Example project using lazywfThere is an example project included on this repository.
You can copy it to use in your own project. It’s as simple
as it seems. ;-)
Package: lazyxml
BriefA simple xml parse and build library.InstallationpipinstalllazyxmlExamplesxml to python object>>>importlazyxml
>>>xml='<demo><foo>foo</foo><bar>bar</bar></demo>'>>>lazyxml.loads(xml){'bar':'bar','foo':'foo'}>>>lazyxml.loads(xml,strip_root=False){'demo':{'bar':'bar','foo':'foo'}}>>>xml='<demo><foo>foo</foo><bar>1</bar><bar>2</bar></demo>'>>>lazyxml.loads(xml){'bar':['1','2'],'foo':'foo'}python object to xml>>>data={'demo':{'bar':['1','2'],'foo':'<foo>'}}>>>lazyxml.dumps(data)'<?xml version="1.0" encoding="utf-8"?><demo><foo><![CDATA[<foo>]]></foo><bar><![CDATA[1]]></bar><bar><![CDATA[2]]></bar></demo>'>>>lazyxml.dumps(data,cdata=False)'<?xml version="1.0" encoding="utf-8"?><demo><foo>&lt;foo&gt;</foo><bar>1</bar><bar>2</bar></demo>'>>>printlazyxml.dumps(data,indent=' '*4)<?xmlversion="1.0"encoding="utf-8"?>
<demo><foo><![CDATA[<foo>]]></foo><bar><![CDATA[1]]></bar><bar><![CDATA[2]]></bar>
</demo>DocumentationSeedocs in readthedocsChangelogSeechangelog
Package: lbcollage
lbcollageCreate a collage of album art from your ListenBrainz listening history.OverviewThis is a simple pure-python command line tool which uses the ListenBrainz and MusicBrainz APIs to compile a collage of your scrobbled listening history.InstallationThis is available onPyPI. It can be installed as follows:> pip install lbcollageIf your path is configured correctly, the tool should be available to use from the command line immediately.UsageThe default behaviour of the script is to retrieve the last month of listening history. This is what happens when only a username is passed.> lbcollage <username>Different timespans and larger collages can be created using different specifiers. For example:> lbcollage thumc -t year -s 5Other options are available which can be listed using:> lbcollage -h
Package: lb-controller
# What’s that thing?lb-controlleris kubernetes operator that can handle dynamic reconfiguration
of an external load balancer,e.ghaproxy, it aims to allow the use of theLoadBalancerorNodePortservice type with on premise clusters.This operator is not deployed in the K8s cluster but on the load balancers and
is a subscriber of the cluster events to know when it has to wake up and do its
thing.We thought of 3 scenarios while building this app:HAProxy + keepalivedEnvoy + keepalivedkeepalived onlyThe advantage of this implementation compared to [MetalLB](https://metallb.universe.tf/)
is that you gain the ability to completely master the LB configuration, for
example ipv6 to ipv4 or specific SSL configurations.LB-controller doesn’t allocate IP addresses for LB. They must be specified:forLoadBalancerservices, usingspec.loadBalancerIPfieldforNodePortservices, using an annotation nammedexternal-lb/ip-address# How should I integratelb-controller?Install haproxy and keepalivedpip install lb-controlleradd the configurations in /etc/lb-controller/Define a dedicated service account to consume only the appropriate resources
from the cluster API serverset this identity in the/root/.kube/configfile on the HAProxy hosts.You can find an ansible role at this address… as an example.# MetricsThis app exposes two metrics in the openmetrics format to be scrape by
prometheus for example:
- number of valid managed services
- number of invalid managed services# DevelopersIn order to contribute to lb-controller, you should havePipenvinstalled
Install the virtualenv and all dependencies withpipenv install –dev, then run unit tests withpytest
Package: lbdrabbit
Welcome tolbdrabbitDocumentationDocumentation forlbdrabbit.设计思路Lambda Function 是 Serverless Architect 中的核心部件. 而架构中常用的中间件有:Api Gateway, 用于向外部提供调用接口.SQS, Kinesis Stream, DynamoDB Stream, 通过流中间件触发 Lambda FunctionEvent Rule, 根据 Cron Job 的规则, 定时触发 Lambda.S3 Put Object Event, 由存储桶中的数据更新的事件触发.那么我就会开始想, 能不能开发一个框架, 让我们专注于 Lambda Function 的核心处理逻辑实现, 以及事件驱动的规则定义, 然后自动的生成那些与部署相关的代码呢?在对开源社区进行了一番调查之后发现, 目前已有 Serverless Framework 和 AWS Sam 两个框架. 这两个框架都可以简化 Lambda Function 的部署, 但是需要用户自行维护除 AWS Lambda 以外的模块的部署, 例如 API Gateway, S3, SQS, Kinesis, DynamoDB Stream. 另一个问题是, 他们都使用了 YML 格式的配置文件, 但是当 Lambda Function 数量很多时, 仍然需要手动的一个一个指定每个 Lambda Function 的具体配置.于是, 我萌生了一个想法, 能不能 Lambda Function 的配置 像 Python 中的 类继承 一样简单和灵活呢? 能不能由 Lambda Function 的配置, 自动推导出其他系统的配置, 并自动帮我们部署这些系统呢?这就是 lbdrabbit 诞生的原因.Installlbdrabbitis released on PyPI, so all you need is:$pipinstalllbdrabbitTo upgrade to latest version:$pipinstall--upgradelbdrabbit
Package: lbfi
lbfilbfiorLinux Bangla Font Installeris a useful tool to help you acquire useful bangla fonts for your linux machine.
It is tested or maintained only for Debian based distributions like Ubuntu, Debian, Linux Mint, Deepin etc.InstallationLinux - Debian BasedUse the package managerpipto installlbfi.$pip3installlbfisuggested command to install$pip3install--upgradelbfiUsage$lbfi--installyesIt will install the fonts in your home directory.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.For developersMake a fork and clone the source locally. Make sure you have -Installed poetry build toolProperly wrote the changes and did testGive a good branch name and commit messageGive a Pull requestDependencyclickpoetry (Development Dependency)requestsLicenseMIT
Package: lbh15
Author:Daniele Panico, Daniele TomatisContributor:Gabriele Ottino, Lucie Kerleau, Chloé LargeronDate:08/01/2024Introductionlbh15(LeadBismuthHandbook 2015) is a Python package that implements the
thermo-physical and the thermo-chemical properties of lead, bismuth and lead-bismuth eutectic (lbe) metal alloy available from
the handbook edited byOECD/NEAlbh15is released under theGNU Lesser General Public License 3.lbh15is listed among the Open-source Nuclear Codes for Reactor Analysis (ONCORE) by IAEA.Project StructureThe project is organized according to the following folder structure:<lbh15 parent folder>
  ├── docs/
  ├── lbh15/
  ├── tests/
  ├── tutorials/
  ├── CHANGELOG.rst
  ├── LICENSE
  ├── MANIFEST.in
  ├── README.rst
  ├── pyproject.toml
  └── setup.pylbh15: contains all modules, classes and methods implemented inlbh15;docs: contains files for the generation of the documentation by Sphinx;tests: collection of tests used to verify the correct implementation;tutorials: collection of tutorials and examples, each one into a dedicated sub-folder.DependenciesTo run the code, the following dependencies must be satisfied:Python\(>= 3.8.10\)SciPy\(>= 1.8.1\)NumPy\(>= 1.22.3\)To build the documentation in bothhtmlandLaTeXformats, the following dependencies must be satisfied:sphinx\(>= 6.2.1\)sphinx-rtd-theme\(>= 1.3.0\)myst-parser\(>= 1.0.0\)sphinxcontrib-bibtex\(>= 2.5.0\)InstallationTo install thelbh15package, please type the following command:pipinstalllbh15Or usehttps://github.com/newcleo-dev-team/lbh15.gitto clone the package.
After cloning the package, execute the following command inside the base folder:pipinstall.To upgrade thelbh15package, please type theinstallcommand along with the--upgradeor-Uflag:pipinstall--upgradelbh15DocumentationThe Sphinx documentation can be built inhtmlandLaTeXformats by executing
the following command in the folderdocs/:makehtmlmakelatexpdfThehtmldocumentation is available on GitHub Pages atnewcleo-dev-team.github.io/lbh15.To see the available templates for generating the documentation inPDFformat and to choose among them, please
look at thedocs/conf.pyfile.How to Cite@inproceedings{NURETH20lbh15,
  author ={Panico, Daniele and Tomatis, Daniele},
  title ={{lbh15: a Python package implementing lead, bismuth, and lead-bismuth eutectic thermophysical properties for fast reactor applications}},
  booktitle ={Proc. of 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20), Washington DC, USA},
  pages ={1--12},
  year ={2023},
  month ={Aug 20--25},
  editor ={ANS}}@article{lbh15JOSS2024,
  author ={Ottino, G.M., Panico, D., Tomatis, D. and Pantel, P.A.},
  title ={{lbh15: a Python package for standard use and implementation of physical data of heavy liquid metals used in nuclear reactors}},
  editor ={Journal of Open-Source Scientific Software}note={submitted}}
Package: lbibhelper
lbibhelperPython helper for easy manage LBIBCell simulationFree software: MIT licenseDocumentation:https://lbibhelper.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.1 (2020-11-20)Remove unused packages0.1.0 (2020-11-20)First release on PyPI.
Package: lbjobmonitor
lbjobmonitor招聘网站信息监控工具，监控招聘网站工作岗位更新情况并发送通知。目的招聘网站的问题招聘网站每天都会显示大量的岗位更新，但大多岗位都是常年发布，要从这些岗位里过滤出真正更新的岗位并不容易。招聘网站的搜索功能还不够完善，做不了高度个性化的定制化搜索条件。专业论坛的招聘版块，几乎没有搜索功能。这个工具可以做什么支持定制招聘网站搜索条件，并对网站提供的标准搜索功能进行少量增强。对检索到的工作岗位进行过滤，如果该岗位之前已发布过，自动忽略。可部署在服务器上，设置定时任务方式定时推送岗位更新，支持多种消息推送方式。新工作岗位通知方式支持：显示到控制台、保存到文件、发送到Slack（强烈推荐Slack）。注：如果想支持微信、邮件的通知，需要自行扩展。内置了51JOB和V2EX的支持。注：如需要支持其他招聘网站，需要自行进行扩展。使用范例初始化项目$ mkdir jobs
$ cd jobs
$ pip install pipenv --upgrade
$ pipenv --python 3.6
$ pipenv shell
$ pipenv install lbjobmonitor创建jobs.py。使用python jobs.py执行查询。可在服务器上使用crontab设置定时任务，定期检查# jobs.pyimportosfromlbjobmonitor.messageimportCLIMessageBackendfromlbjobmonitor.messageimportFileMessageBackendfromlbjobmonitor.monitorimportQCWYJobMonitorfromlbjobmonitor.storageimportJobMonitorJsonStorageBASE_DIR=os.path.dirname(os.path.abspath(__file__))DATA_DIR=BASE_DIRdefqcwy():params={# 51job的查询参数。51job设置好查询条件后发起查询，通过chrome的调试功能查看请求的具体参数。'saltype':'',# 薪资范围'keyword':'python',# 关键词'postchannel':'0000','keywordtype':'2','jobarea':'080200',# 地区编码'pagesize':'5',# 每页记录数'':''}storage=JobMonitorJsonStorage(base_path=DATA_DIR)# 使用JSON方式将工作列表保存到当前目录message_backend_list=[# 显示的推送方式CLIMessageBackend(),# 显示到控制台FileMessageBackend(fn=os.path.join(DATA_DIR,'jobs.txt'))# 保存到文件]monitor=QCWYJobMonitor(storage=storage,message_backend_list=message_backend_list)# monitor.max_page_idx = 1  # 最多查询页数，设置成1方便调试skip_words=['AI']monitor.monitor_jobs(params=params,skip_words=skip_words)# 执行查询if__name__=="__main__":qcwy()构架说明JobMonitor # 工作岗位监控类，需要根据网站给出具体实现。job_classJob  # 将通过API查询返回的工作信息转换为标准的Job对象storageJobMonitorStorage  # 历史工作列表的存储实现类message_backend_list[BaseMessageBackend]  # 消息发送的后端实现代码导航monitor.pyJobMonitor  # 工作岗位监控基础类QCWYJobMonitor  # 51JOB岗位监控实现V2exJobMonitor  # V2EX岗位监控实现storage.pyJobMonitorStorage  # 存储区基础类JobMonitorJsonStorage  # 将信息以json方式保存到文件的存储区实现message.pyBaseMessageBackend 消息发送处理后端基础类IMMessageBackend IM类消息的后端基础类CLIMessageBackend 将消息发送到控制台FileMessageBackend 将消息保存到文件SlackMessageBackend 将消息发送到SlackTelegramMessageBackend 将消息发送到Telegrammodels.pyJob 岗位信息基础数据类QCWYJob 51JOB的岗位信息解析类V2exJob V2EX的岗位信息解析类
Package: lb-matching-tools
ListenBrainz matching toolsIn the ListenBrainz project we've created a number of tools and techniques that allowed us to
create the MBID Mapping sub-project. This project takes an artist name and a recording (track)
name and attempts to find the best matching recording in MusicBrainz.This library will contain code to facilitate matching non-MusicBrainz metadata to MusicBrainz
(tag a music collection) and from MusicBrainz to non-MusicBrainz metadata (content resolution).Metadata cleanerOne component in this toolkit is the metadata cleaner, which removes (often) useless gunk
from a metadata recording string:"Tabula Rasa (feat. Lorraine Weiss)" -> "Tabula Rasa""Don't Give up - 2001 remaster" -> "Don't Give up"This library also supports artist name cleaning:"Amy Winhouse, Lorraine Weiss" -> "Amy Winehouse"When a piece of metadata fails to be identified the metadata should be run through the
metadata cleaner in an effort to remove this useless gunk that might be preventing a match.
If the cleaner sucessfully removes gunk, the cleaned metadata should be looked up again, hoping
that a match will be found this time.To use this class, instantiate the MetadataCleaner class and call the clean_recording or
clean_artist methods:from lb_matching_tools.cleaner import MetadataCleaner

mc = MetadataCleaner()
mc.clean_recording("Tabula Rasa (feat. Lorraine Weiss)")
mc.clean_artist("Amy Winhouse, Lorraine Weiss")InstallationTo install this python module, simply do:pip install .DevelopmentCreate virtual environmentpython -m venv .venvInstall dependenciespip install .Running testsTo run the internal unit tests, run:pip install .
./test.py
Package: lbmpy
lbmpyRun fast fluid simulations based on the lattice Boltzmann method in Python on CPUs and GPUs.
lbmpy creates highly optimized LB compute kernels in C or CUDA, for a wide variety of different collision operators, including MRT,
entropic, and cumulant schemes.All collision operators can be easily adapted, for example, to integrate turbulence models, custom force terms, or multi-phase models.
It even comes with an integrated Chapman Enskog analysis based on sympy!Common test scenarios can be set up quickly:frompystencilsimportTargetfromlbmpy.sessionimport*ch=create_channel(domain_size=(300,100,100),force=1e-7,method=Method.TRT,equilibrium_order=2,compressible=True,relaxation_rates=[1.97,1.6],optimization={'target':Target.GPU})To find out more, check out the interactivetutorial notebooks online with binder.InstallationFor local installation use pip:pipinstalllbmpy[interactive]Without[interactive]you get a minimal version with very little dependencies.All options:gpu: use this if a NVIDIA GPU is available and CUDA is installedopencl: use this to enable the targetopencl(execution using OpenCL)alltrafos: pulls in additional dependencies for loop simplification e.g. libislinteractive: installs dependencies to work in Jupyter including image I/O, plotting etc.Options can be combined e.g.pipinstalllbmpy[interactive,gpu,doc]DocumentationRead the docshereand
check out the Jupyter notebooks indoc/notebooks.ContributingTo see how to open issues, submit bug reports, create feature requests or submit your additions to lbmpy please refer tocontribution documentationof pystencils since lbmpy is heavily build on pystencils.Many thanks go to thecontributorsof lbmpy.Please cite usIf you use lbmpy in a publication, please cite the following articles:Overview:F. Hennig et al, Advanced Automatic Code Generation for Multiple Relaxation-Time Lattice Boltzmann Methods. SIAM Journal on Scientific Computing, 2023.https://doi.org/10.1137/22M1531348(Preprint)M. Bauer et al, lbmpy: Automatic code generation for efficient parallel lattice Boltzmann methods. Journal of Computational Science, 2021.https://doi.org/10.1016/j.jocs.2020.101269(Preprint)Multiphase:M. Holzer et al, Highly efficient lattice Boltzmann multiphase simulations of immiscible fluids at high-density ratios on CPUs and GPUs through code generation. The International Journal of High Performance Computing Applications, 2021.https://doi.org/10.1177/10943420211016525Further ReadingF. Hennig et al, Automatic Code Generation for the Cumulant Lattice Boltzmann Method. ICMMES, 2021.Poster Link
Package: lbmpy-walberla
lbmpy-walberlaFormulate lattice Boltzmann methods in lbmpy and generate optimized C++ Code for waLBerla from it.
Look at the waLBerla documentation on how to use it.Install withpipinstalllbmpy-walberlaand enable WALBERLA_BUILD_WITH_CODEGEN in waLBerla's CMake.
Package: lbn
Lorentz Boost Network (LBN)TensorFlow implementation of the Lorentz Boost Network fromarXiv:1812.09722 [hep-ex].Usage exampleimporttensorflowastffromlbnimportLBN# initialize the LBN, set 10 combinations and pairwise boostinglbn=LBN(10,boost_mode=LBN.PAIRS)# create a feature tensor based on input four-vectorsfeatures=lbn(four_vectors)# use the features as input for a subsequent, application-specific network...Or with TensorFlow 2 and Keras:importtensorflowastffromlbnimportLBN,LBNLayer# start a sequential modelmodel=tf.keras.models.Sequential()# add the LBN layerinput_shape=(6,4)model.add(LBNLayer(input_shape,10,boost_mode=LBN.PAIRS))# add a dense layermodel.add(tf.keras.layers.Dense(1024))# continue builing and training the model...For more examples on how to set up the LBN with TensorFlow (eager mode and autograph /tf.function) and Keras, seethis gist.Installation and dependenciesViapip:pipinstalllbnNumPy and TensorFlow are the only dependencies. Both TensorFlow v1 and v2 are supported.TestingTests should be run for Python 2 and 3 and for TensorFlow 1 and 2. The following commands assume you are in the root directory of the LBN respository:python-munittesttest# or via dockerfortagin1.15.21.15.2-py32.1.02.2.0;dodockerrun--rm-v`pwd`:/root/lbn-w/root/lbntensorflow/tensorflow:$tagpython-munittesttestdoneContributingIf you like to contribute, we are happy to receive pull requests. Just make sure to add new test cases and run the tests. Also, please use a coding style that is compatible with our.flake8config.DevelopmentOriginal source hosted onGitHubReport issues, questions, feature requests onGitHub Issues
Package: lbnotes
## lbnotes
—lbnotes is a lightweight web app for quick note taking in the markdown format with tags support.lbnotes is based on flask and sqlite3.lbnotes is licenced under GNU GPLv3.—### install:` pip install lbnotes `### database initialization:` # database initialization needs to be done before usage. export FLASK_APP=lbnotes flaskinit-db`### config:` main config file "config.py" should be put in the instance folder. details can be found in "config.py.example" file. `### run:` flask run `### deploy:` - lbnotes is os independent. - lbnotes can be deployed with any webserver that flask supports. - choose your webserver of choice and see its details for deployment. - [deployreferences](https://flask.palletsprojects.com/en/1.1.x/deploying/)`
Package: lboxd
lboxdAn unofficial letterboxd.com APIGet reviews from letterboxd users. Done with pure HTML parsing.Development EnvironmentUbuntu 20.04 ltsPython 3.8.5This has not been tested on Windows, there may be encoding problems.Installationpip install lboxdUsage'''Pretty printing reviews with a generator.=> Generators are good for when there are requests to many different URLs.=> A new requests session is created for the duration of the generator.'''importlboxdfrombs4importBeautifulSoupasbsfromrichimportprintasrprintforreviewinlboxd.reviews(user='redlettermedia',count=5):title=review['title']review=review['review']htmlPretty=bs.prettify(bs(review,'html.parser'))rprint(f'[yellow]Title:[/yellow] [red]{title}[/red]\n{htmlPretty}')fromlboxdimportlboxdlistfromrichimportprintasrprintformovieinlboxdlist(user='daqoon'):title=movie['title']rating=movie['rating']richTitle=f'[yellow]Title:[/yellow] [red]{title}[/red]'ifrating:rprint(f'{richTitle}rating={rating}')else:rprint(richTitle)CLIExampleArguments--user USER-u USERletterboxd.com user--reviews-rGets reviews--testing-tTesting flag - for development only--save-html-wSaves an HTML document for easily viewing reviews--browser-open-bOpens saved HTML document in the browser--search SEARCH [SEARCH ...]-s SEARCH [SEARCH ...]Will only get search terms, currently needs to match exactly with letterboxd notation. Replace spaces with dashes.
Package: lbpextract
LBP ExtractCet outil permet de convertir vos relevés de compte PDF La Banque Postale en fichiers CSV lisibles dans des logiciels de type tableur comme Excel et LibreOffice Calc.
Il vous suffit d'installer l'outil, rassembler vos PDFs dans un dossier et executer l'outil, vous aurez alors un ensemble de fichiers CSV qui apparaitront dans le dossier contenant vos relevés.Assembler vos relevés de compteCréez un dossierAllez sur votre espace client -> Documents et suivi -> Relevé et documents -> Recherche avancéeChoisissez un compte et une année puis cliquez sur RechercherTéléchargez les fichiers un par un (attention à ne pas en ouvrir plusieurs à la fois au risque de voir certains fichiers ne pas correspondre au bon mois)Déplacez tous ces fichiers dans le dossier initialement crééInstaller LBPExtractSur LinuxOuvrez un terminal en appuyant sur Ctrl + Alt + T ou en cherchant "Terminal" dans votre menu.Assurez-vous d'avoir pip installé. Si ce n'est pas le cas, vous pouvez l'installer en utilisant la commande sudo apt install python3-pip.Utilisez la commande suivante pour installer le package "lbpextract" depuis PyPI :pip install lbpextractSur WindowsSi vous n'avez pas installé Python,suivez ces instructionsd'abord.Ouvrez l'invite de commande en appuyant sur Win + R, tapez "cmd", puis appuyez sur Entrée.Utilisez la commande suivante pour installer le package "lbpextract" depuis PyPI :pip install lbpextractUtilisation de "lbpextract"Maintenant que vous avez installé le package "lbpextract", voici comment l'utiliser pour convertir les PDFs en fichiers CSV.Ouvrez un terminal (sous Linux) ou l'invite de commande (sous Windows).Naviguez vers le répertoire où se trouvent vos fichiers PDF que vous souhaitez traiter. Vous pouvez utiliser la commandecdpour vous déplacer dans le répertoire souhaité. Par exemple :cd /chemin/vers/votre/dossierAlternativement, dans l'explorateur de fichiers, vous pouvez maintenir la touche "Maj" enfoncée tout en cliquant avec le bouton droit de la souris dans le répertoire, puis choisir "Ouvrir une fenêtre de commande ici" pour ouvrir l'invite de commande.Une fois dans le répertoire cible, vous pouvez exécuter la commande suivante pour utiliser "lbpextract" et extraire des données à partir de tous les fichiers PDF présents :lbpextract *.pdfLes fichiers CSV seront générés dans le même répertoire. Attention, si vous voulez recommencer l'opération (après avoir ajouté de nouveau relevés par exemple), vous devez déplacer ou supprimer les anciens fichiers CSV.