Saravanan666/pypi-org huggingface dataset:
Package: laff
Lightcurve and Flare Fitter - LAFFA python package for automatic lightcurve and flare fitting in GRB lightcurves.DescriptionThis program looks to provide automatic and consistent fitting of GRB lightcurves, ultimately intended for statistical studies of a large collection of GRBs.
Initially it looks for any 'significant' rises in flux which can be marked as a potential flare. Each potential flare is then refined and cut down until a set
of start, peak and end times for flares are designated. This flare data is temporarily removed in order to fit a broken powerlaw to the continuum data - the best
solution between 0 and up to 5 breaks is used. The flare data can then be readded and the flares fitted with either a simple gaussian, or more appropriately, a
fast-rise slow-decay (FRED) curve.With a fully fitted model, the program will output useful information, either printed to terminal or into a csv table. Such information includes flare timings,
durations, number of breaks and fluence of the flares and continuum.To run the program, the user simply needs to point LAFF towards an appropriate lightcurve file (current and planned formats are shown below). See below for full commands and optional instructions.Getting StartedDependenciesPython 3lmfit (v1.0.3 or newer)astropy (v5.1 or newer)pandas (v1.4 or newer)matplotlib (v3.5 or newer)scipy (v1.8 or newer)numpy (v1.23 or newer)All available through standard python package installation methods (e.g. pip). Earlier versions of these may work but have not been explicility tested.InstallingTo download as pip package:pip install laffExecuting programHow to run the programStep-by-step bulletscode blocks for commandsHelpAny advise for common problems or issues.command to run if program contains helper infoAuthorsContributors names and contact infoex. Dominique Pizzieex.@DomPizzieVersion History0.2Various bug fixes and optimizationsSeecommit changeor Seerelease history0.1Initial ReleaseLicenseThis project is licensed under the [NAME HERE] License - see the LICENSE.md file for detailsAcknowledgmentsInspiration, code snippets, etc.awesome-readmePurpleBoothdbaderzenorochafvcproductions
Package: laf-fabric
Tools to read LAF resources (Linguistic Annotation Framework ISO 24612:2012) and analyse them efficiently.
With additions for the Hebrew Text Database of the ETCBC (Eep Talstra Centre for Bible and Computing).laf: processor for LAF resources, specialized in handling resources with millions of nodes and edges.etcbc: modules on top of laf that make use of the characteristics of the Text Database of the Hebrew Bible by the ETCBC.emdros2laf: conversion from the format in which the ETCBC delivers its data (http://emdros.org) to LAF.More info onhttps://shebanq.ancient-data.org
Package: lafhterlearn
Label-Free Handwritten Text Recognition LearnerIt is a toolkit providing
a set of tools for training a neural net to recognize handwritten words.
No labeled data is needed. The toolkit achieves this in two phases:pre-train the model on synthetic handwriting imagesfine-tune the model on a dataset of real unlabeled handwriting images via a self-training technique.This implementation is based on the following paper:Self-Training of Handwritten Word Recognition for
Synthetic-to-Real Adaptation.Possible use casesOffline Handwritten Word Recognition converts an image that contains a handwritten word
into a machine-readable representation (text). So it is Image-to-Text technology.
A pre-trained handwritten word/line recognizer usually gets used as a part of a complete
pipeline. For example, the full-page recognition system will apply handwritten text
segmentation to split the page image into lines and individual words. Then, a
handwritten word recognition neural net will transcribe these tightly cropped word images into text.Why use this toolkitTraining a neural net for handwriting recognition requires large amounts of handwriting data.
Collecting the data is a laborious and time-consuming process. On the other hand, generating
synthetic handwritten images is cheap due to the abundance of handwritten fonts.It turns out that training the model on purely synthetic images already gives a decent performance.
A follow-up synthetic-to-real adaptation (fine-tuning) can significantly improve the accuracy of
the final system. It can also help the model adapt to the style of a particular writer.The toolkit enables one to build a handwriting recognizer using only unlabeled data. Moreover,
pretraining on purely synthetic images requires no data at all.What can it doConcretely, the toolkit implements the following tasks:extracting probability distribution from text corporagenerating synthetic handwritten word imagestraining on synthetic imagesfine-tuning on real (handwritten word) imagesevaluating a pretrained model by computing metrics (loss, CER, WER, etc.)using the pretrained model to recognize handwritten wordssaving/retrieving model checkpointsWhat is neededTo make it work, the toolkit expects a developer to provide:handwritten fontstext corporareal unlabeled images of handwritten wordsoptionally, real labeled images of handwritten words (see the note)Note: although transcriptions aren't required, a small subset of transcribed/labeled
images will help estimate metrics.DocumentationYou can find a documentation athereSupportIf you find this repository useful, consider starring it by clicking at the ★ button. It will be much appreciated.
Package: laflem
LaFlemDescriptionLaFlem is a simple, easy to use, and lightweight Python binary for creating and managing recuring task in the life of an IT guy. It is designed to be easy to use and easy to understand. It is also designed to be lightweight and fast. It is not designed to be a full fledged task manager, but rather a simple tool to help you manage your day to day tasks.InstallationTo install LaFlem, simply run the following command:pipinstall--userlaflemor with an isolated environment:python-mvenv~/bin/laflem_venv
~/bin/laflem_venv/bin/pipinstalllaflem

ln-s~/bin/laflem_venv/bin/laflem~/bin/laflem
ln-s~/bin/laflem_venv/bin/lf~/bin/lfUsageTo use LaFlem, simply run the following command :laflem--helpor with the alias :lf--helpContributingTo contribute to LaFlem, simply fork the repository and create a pull request. Please make sure to include a detailed description of your changes. Here are the things I will check during the review :Is CHANGELOG.md have been updated (required)Is the lint score did not decrease (required)Is the test coverage did not decrease (required)Is the documentation have been updated (if required)If tests have been added (optional)DevelopmentThis repository usesTaskfileto manage the development tasks. To see the available tasks, run the following command:task--list
Package: laforge
laforge is a low-key build system for working with data.💻 IntroductionYou know, I've always thought technology could solve almost any problem.$ python -m pip install laforge -q
...

$ laforge create
Creating /home/matvan/science/build.ini
? Creating a new laforge INI at:  /home/matvan/science/build.ini

Creating /home/matvan/science/build.ini

? Default read directory, relative to /home/matvan/science/:  ./data
? Default write directory, relative to /home/matvan/science/:  ./output
? Default execute directory, relative to /home/matvan/science/:  ./
? SQL Distribution:  SQLite
?     Database:  :memory:
New laforge INI written at: /home/matvan/science/build.ini
Enjoy!There's theory and then there's application. They don't always jibe.$ vim ./build.ini...$ cat ./build.ini[DEFAULT]read_dir:./datawrite_dir:./outputexecute_dir:./distro:sqlitedatabase::memory:# Write the contents of an Excel sheet as a SQL table[load_individual]read:2019_indiv_data.xlsxwrite:raw_grp# Write the contents of a CSV as a SQL table[load_group]read:2019_grp_data.csvwrite:raw_indiv# Execute a standalone SQL script; read a SQL table and save as CSV[do_some_things]execute:do_stuff.sqlread:laforge.demo.aggregatewrite:aggregate.csv# Read the result of an ad-hoc SQL query; write to an HTML table[peek]read:"select top 50 *from demo.aggregate aggleft join demo.raw_indiv rion agg.v1=r1.v2order by newid();"write:results_peek.htmlYeah, but that's imposs—yes, sir.$laforgebuild‖-)📓 Documentationhttps://laforge.readthedocs.io/en/latest/⚗️ DevelopmentCaptain, we can do it... It'll take fifteen years and a research team of a hundred.🤖 Automation🚀 Downstream🧙‍ Author
Package: la-forge
La ForgePulsar Timing Array Bayesian Data VisualizationGraphic Credit: Stewart Vernon, via Deviant ArtPython package for conveniently plotting results from pulsar timing array bayesian analyses. Many of the functions are best used withenterpriseoutputs.La Forgeis available on PyPI:pipinstallla-forgeFree software: MIT licenseDocumentation:https://la-forge.readthedocs.io.FeaturesSweep up Bayesian analysis MCMC chains along with sampling info.Allow easy retrieval of various samples from chains.Support for saving chains asHDF5files.Call chains with parameter names.Plot posteriors easily.Reconstruct Gaussian process realizations using posterior chains.Plot red noise power spectral density.Separate consituent models of a hypermodel analysis.This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.1.0 (2023-10-22) Adds Bayes factor calculations.1.0.2 (2022-07-19) Fixes a delimiter bug for pulling in priors.txt.1.0.1 (2021-10-21) Fixes a bug that did not allow parameter dictionary I/O in HyperModelCores1.0.0 (2021-10-18) Capabilities to save as HDF5 files. Added full documentation and filled out the testing suite.0.4.0 (2021-09-24) Added CI testing suite and cleaned up functions.0.3.0 (2020-10-28) New docs.0.2.0 (2020-02-13) Cleaned up various functionality and added more docs.0.1.0 (2018-09-21)*First release on PyPI.
Package: laftel
LaftelUnofficial Python Laftel.net API WrapperInstallationRequires Python 3.6 or upperpython3-mpipinstalllaftelUsageimportlaftel# Synchronousdefmain():laftel.sync.searchAnime("전생슬")# -> List[SearchResult]laftel.sync.getAnimeInfo(SerachResult.id)# -> AnimeInfo# Asynchronousasyncdefmain():awaitlaftel.searchAnime("전생슬")# -> List[SearchResult]awaitlaftel.getAnimeInfo(SerachResult.id)# -> AnimeInfoModelsSearchResult:id:int# Anime ID (애니 아이디)name:str# Anime Title (애니 제목)url:str# Anime Link (애니 링크)image:str# Cover image URL (커버 사진 URL)adultonly:bool# Adult Content (성인인증 필요 여부)genres:List[str]# Genres in korean string (장르 태그 목록)get_data:Callable# Function returns AnimeInfo for this anime (AnimeInfo 가져오는 함수)AnimeInfo:id:int# Anime ID (애니 아이디)name:str# Anime Title (애니 제목)url:str# Anime Link (애니 링크)image:str# Cover image URL (커버 사진 URL)content:str# Summary of anime (애니 줄거리)ended:bool# Anime complete or not (애니 완결 여부)awards:List[str]# Arards that granted to anime (애니가 받은 상 목록)content_rating:str# Content Rating in korean (콘텐츠 등급 - 00세 이용가)adultonly:bool# Adult Content (성인인증 필요 여부)viewable:bool# Available in Laftel (라프텔 시청 가능 여부)genres:List[str]# Genres in korean string (장르 태그 목록)tags:List[str]# Anime tags from Laftel (라프텔이 붙인 태그)air_year_quarter:str# Airing quarter (방영분기 - 2020년 1분기)air_day:str# Airing day (방영 요일)avg_rating:float# Average User Rating out of 5 (5점 만점 중 평균 별점)series_id:Optional[int]# Series ID (시리즈 아이디)production:str# Production company (제작사)Discord Bot ExampleExampleView example.py
Package: lagan
lagan介绍基于python语言的日志库.lagan取名来自于宜家的水龙头"拉根"。本软件包已上传到pypi，可输入命令直接安装。pipinstalllagan功能支持日志在终端实时打印支持日志保存在文件支持日志不保存文件,仅终端打印支持日志文件自动分割支持终端交互控制日志输出级别等功能支持二进制流打印支持带颜色的日志打印示例# 日志模块载入.全局载入一次,参数是分割文件大小,默认是10Mlagan.load()# 默认输出界别是info,本行不会打印lagan.debug("case4","debug test print")lagan.info("case4","info test print")lagan.warn("case4","warn test print")lagan.error("case4","error test print")输出：2021-03-20 11:59:35,196 - lagan.py:114 - I/case4: info test print
2021-03-20 11:59:35,196 - lagan.py:114 - W/case4: warn test print
2021-03-20 11:59:35,197 - lagan.py:114 - E/case4: error test print在本地会新建log文件夹，并新建日志文件。二进制流打印lagan.load()s=bytearray()foriinrange(100):s.append(i)lagan.print_hex('case2',lagan.LEVEL_ERROR,s)输出：2021-03-20 12:25:04,629 - lagan.py:207 - E/case2: 
****00 01 02 03 04 05 06 07 08 09 0a 0b 0c 0d 0e 0f 
---- : -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 
0000 : 00 01 02 03 04 05 06 07 08 09 0a 0b 0c 0d 0e 0f 
0010 : 10 11 12 13 14 15 16 17 18 19 1a 1b 1c 1d 1e 1f 
0020 : 20 21 22 23 24 25 26 27 28 29 2a 2b 2c 2d 2e 2f 
0030 : 30 31 32 33 34 35 36 37 38 39 3a 3b 3c 3d 3e 3f 
0040 : 40 41 42 43 44 45 46 47 48 49 4a 4b 4c 4d 4e 4f 
0050 : 50 51 52 53 54 55 56 57 58 59 5a 5b 5c 5d 5e 5f 
0060 : 60 61 62 63终端交互*******************************************
            lagan help shell             
current level:I,is pause:false
help:print help
filter_error:filter error level
filter_warn:filter warn level
filter_info:filter info level
filter_debug:filter debug level
filter_off:filter off level
pause:pause log
resume:resume log
*******************************************可以在终端敲对应的命令控制日志功能。颜色控制可以使用EnableColor函数开控制打开或者关闭日志颜色，默认关闭颜色。lagan.load()lagan.set_filter_level(lagan.LEVEL_DEBUG)lagan.enable_color(True)lagan.println('case1',lagan.LEVEL_OFF,'TestPrintOut1:%d',100)lagan.println('case1',lagan.LEVEL_DEBUG,'TestPrintOut1:%d',100)lagan.println('case1',lagan.LEVEL_INFO,'TestPrintOut1:%d',100)lagan.println('case1',lagan.LEVEL_WARN,'TestPrintOut1:%d',100)lagan.println('case1',lagan.LEVEL_ERROR,'TestPrintOut1:%d',100)s=bytearray()foriinrange(100):s.append(i)lagan.print_hex('case2',lagan.LEVEL_INFO,s)日志文件控制文件分割大小设置为0,则不会保存到日志文件,仅终端打印：lagan.load(0)
Package: lagent
English |简体中文|日本語|हिंदी|বাংলা|한국어👋 join us on𝕏 (Twitter),DiscordandWeChathttps://github.com/InternLM/lagent/assets/24622904/3242f9bf-32d2-4907-8815-e16a75a4ac0eGetting StartedPlease see theoverviewfor the general introduction of Lagent. Meanwhile, we provide extremely simple code for quick start. You may refer toexamplesfor more details.InstallationInstall with pip (Recommended).pipinstalllagentRun a Web DemoYou need to install Streamlit first.# pip install streamlitstreamlitrunexamples/internlm2_agent_web_demo.pyWhat's Lagent?Lagent is a lightweight open-source framework that allows users to efficiently build large language model(LLM)-based agents. It also provides some typical tools to augment LLM. The overview of our framework is shown below:Major FeaturesStream Output: Provides thestream_chatinterface for streaming output, allowing cool streaming demos right at your local setup.Interfacing is unified, with a comprehensive design upgrade for enhanced extensibility, including:Model: Whether it's the OpenAI API, Transformers, or LMDeploy inference acceleration framework, you can seamlessly switch between models.Action: Simple inheritance and decoration allow you to create your own personal toolkit, adaptable to both InternLM and GPT.Agent: Consistent with the Model's input interface, the transformation from model to intelligent agent only takes one step, facilitating the exploration and implementation of various agents.Documentation has been thoroughly upgraded with full API documentation coverage.💻Tech StackAll Thanks To Our Contributors:CitationIf you find this project useful in your research, please consider cite:@misc{lagent2023,
    title={{Lagent: InternLM}a lightweight open-source framework that allows users to efficiently build large language model(LLM)-based agents},
    author={Lagent Developer Team},
    howpublished ={\url{https://github.com/InternLM/lagent}},
    year={2023}}LicenseThis project is released under theApache 2.0 license.🔼 Back to top
Package: lager
Lager :beer:Install:pip install lagerLogging library based off of loguru (pip install loguru).Why not just use loguru?Lager is a better punLager is really a utility pack for loguruBTW: Loguru is an amazing lib. Check it out:https://github.com/Delgan/loguruUsage:fromlagerimportLOG,lager,LAGER,log,logger# All the same objectLOG.info("info")2022-07-21 08:38:20.263 | INFO     | __main__:<cell line: 3>:3 - info
Package: lager-trio-websocket
Trio WebSocketThis library implementsthe WebSocket
protocol, striving for safety,
correctness, and ergonomics. It is based on thewsproto
project, which is aSans-IOstate machine that implements the
majority of the WebSocket protocol, including framing, codecs, and events. This
library handles I/O usingthe Trio
framework. This library passes theAutobahn Test Suite.This README contains a brief introduction to the project. Full documentationis
available here.InstallationThis library requires Python 3.5 or greater. To install from PyPI:pip install trio-websocketClient ExampleThis example demonstrates how to open a WebSocket URL:importtriofromsysimportstderrfromtrio_websocketimportopen_websocket_urlasyncdefmain():try:asyncwithopen_websocket_url('wss://echo.websocket.org')asws:awaitws.send_message('hello world!')message=awaitws.get_message()print('Received message:%s'%message)exceptOSErrorasose:print('Connection attempt failed:%s'%ose,file=stderr)trio.run(main)The WebSocket context manager connects automatically before entering the block
and disconnects automatically before exiting the block. The full API offers a
lot of flexibility and additional options.Server ExampleA WebSocket server requires a bind address, a port, and a coroutine to handle
incoming connections. This example demonstrates an "echo server" that replies to
each incoming message with an identical outgoing message.importtriofromtrio_websocketimportserve_websocket,ConnectionClosedasyncdefecho_server(request):ws=awaitrequest.accept()whileTrue:try:message=awaitws.get_message()awaitws.send_message(message)exceptConnectionClosed:breakasyncdefmain():awaitserve_websocket(echo_server,'127.0.0.1',8000,ssl_context=None)trio.run(main)The server's handlerecho_server(…)receives a connection request object.
This object can be used to inspect the client's request and modify the
handshake, then it can be exchanged for an actual WebSocket objectws.
Again, the full API offers a lot of flexibility and additional options.
Package: lagmat
lagmatLagmatrix. Create array with time-lagged copies of the features.InstallationThelagmatgit repois available
asPyPi packagepipinstalllagmatUsageimportnumpyasnpA=(np.random.rand(7,3)*10+10).round(1)fromlagmatimportlagmatB=lagmat(A,lags=[0,1,2])# 0: copy itself, 1: one time-lag, 2: two time-lagsCheck theexamplesfolder
for notebooks.AppendixInstall a virtual environmentpython3-mvenv.venvsource.venv/bin/activatepip3install--upgradepippip3install-rrequirements.txtpip3install-rrequirements-dev.txtpip3install-rrequirements-demo.txt(If your git repo is stored in a folder with whitespaces, then don’t use
the subfolder.venv. Use an absolute path without whitespaces.)Python commandsJupyter for the examples:jupyter labCheck syntax:flake8--ignore=F401--exclude=$(grep-v'^#'.gitignore | xargs | sed-e's//,/g')Run Unit Tests:python-Wignore-munittest discoverPublishpandocREADME.md--frommarkdown--torst-s-oREADME.rstpythonsetup.pysdisttwineupload-rpypidist/*Clean upfind.-typef-name"*.pyc"|xargsrmfind.-typed-name"__pycache__"|xargsrm-rrm-r.pytest_cacherm-r.venvSupportPleaseopen an issuefor
support.ContributingPlease contribute usingGithub
Flow. Create a branch,
add commits, andopen a pull
request.
Package: lago-billing
Python SDKOpen-source metering and usage-based billingSDK Installationpipinstalllago-billingSDK Example Usageimportlagofromlago.modelsimportoperations,shareds=lago.Lago(security=shared.Security(bearer_auth="Bearer YOUR_BEARER_TOKEN_HERE",),)req=operations.FindInvoiceRequest(id="1a901a90-1a90-1a90-1a90-1a901a901a90",)res=s.invoices.find(req)ifres.invoiceisnotNone:# handle responseAvailable Resources and Operationsadd_onsapply- Apply an add-on to a customercreate- Create a new add-ondestroy- Delete an add-onfind- Find add-on by codefind_all- Find add-onsupdate- Update an existing add-onbillable_metricscreate- Create a new billable metricdestroy- Delete a billable metricfind- Find billable metric by codefind_all- Find Billable metricsfind_all_groups- Find Billable metric groupsupdate- Update an existing billable metriccouponsapplied_coupons- Find Applied Couponsapply- Apply a coupon to a customercreate- Create a new coupondestroy- Delete a couponfind- Find coupon by codefind_all- Find Couponsupdate- Update an existing couponcredit_notescreate- Create a new Credit notedownload- Download an existing credit notefind- Find credit notefind_all- Find Credit notesupdate- Update an existing credit notevoid- Void existing credit notecustomerscreate- Create a customercurrent_usage- Find customer current usagedelete_applied_coupon- Delete customer's appplied coupondestroy- Delete a customerfind- Find customer by external IDfind_all- Find customersportal_url- Get customer portal URLeventsestimate_fees- Estimate fees for an instant chargebatch_create- Create batch eventscreate- Create a new eventfind- Find event by transaction IDfeesfind- Find fee by IDfind_all- Find all feesupdate- Update an existing feeinvoicesdownload- Download an existing invoicefinalize- Finalize a draft invoicefind- Find invoice by IDfind_all- Find all invoicesretry- Retry invoice paymentupdate- Update an existing invoice statusvoid- Refresh a draft invoiceorganizationsupdate- Update an existing Organizationplanscreate- Create a new plandestroy- Delete a planfind- Fin plan by codefind_all- Find plansupdate- Update an existing plansubscriptionscreate- Assign a plan to a customerdestroy- Terminate a subscriptionfind_all- Find subscriptionsupdate- Update an existing subscriptionwalletscreate- Create a new walletcreate_transaction- Create a new wallet transactiondestroy- Delete a walletfind- Find walletfind_all- Find walletsfind_all_transactions- Find wallet transactionsupdate- Update an existing walletwebhooksfetch_public_key- Fetch webhook public keyMaturityThis SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage
to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally
looking for the latest version.ContributionsWhile we value open-source contributions to this SDK, this library is generated programmatically.
Feel free to open a PR or a Github issue as a proof of concept and we'll do our best to include it in a future release !SDK Created bySpeakeasy