Saravanan666/pypi-org huggingface dataset:
Package: lampylib
Lampy:LaunchpadMinipython libraryRequirementsPython >= 3.7python-rtmidiTODOTODOInstallationFor usage onlyUnfortunately namelampywas already in use in PyPI.#viapipsi$pipsiinstalllampylib#viapip$pipinstall--userlampylibFor developmentYou needpoetryinstalled.
(It’s great tool! Check it out, please.)$poetryinstall--develop.TestingTODODocumentationTODO
Package: lamr
lamrlamris a content manager to organise and display markdown files and run Python code snippets.It is a programming manual with code and exercises to get you gradually started with Python projects.Documentationhttps://epogrebnyak.github.io/lamr/readme/Quickstartpip install lamrlamr --helpIn current version there are the following excercises and manuals available.The calendar utility>>> lamr show cal.py --all>>> lamr run cal.pyToday is 2024-02-04February 2024Mo Tu We Th Fr Sa Su1  2  3  45  6  7  8  9 10 1112 13 14 15 16 17 1819 20 21 22 23 24 2526 27 29Make an ASCII art logo>>> lamr show logo.py --all>>> lamr run logo.pySample font: slantSample text: Python 3ASCII art:____             __     __                           _____/ __ \   __  __  / /_   / /_   ____    ____          |__  // /_/ /  / / / / / __/  / __ \ / __ \  / __ \          /_ </ ____/  / /_/ / / /_   / / / // /_/ / / / / /        ___/ //_/       \__, /  \__/  /_/ /_/ \____/ /_/ /_/        /____//____/Learn about variable assignment:=,:=or<-?lamr learn variablesDevelopmentCheck out separateDevelopmentsection:>>> lamr about --dev
Package: lamzip
Install NotesTo install using pip:pipinstalllamzipTo update using pip:pipinstalllamzip--upgradeUsing LamZipInitial Project SetupCreate a defaults file in your project directory to simplify packaging:lamzip make-config [--config-name .altname]Just usinglamzip make-configwithout any arguments will create the default
config file.lamzip.yaml.  Contained in the default config is an example
setup that looks like the following:# Lambda-Zip (lamzip)## Defaults for packaging your AWS Lambda function#source-directory:./appsrcdestination-directory:./distpackage-prefix:foopackage-version:0.0.1Configure LamZipYour project structure should look something like the following:Main-Project-Folder\
   dist\
   appsrc\
      lib1\
      lib2\
      app.py
   .bumpversion
   .lamzip.yaml
   .gitignore
   LICENSE
   README
   requirements.txt
   setup.pyIn this example you would use the following settings:source-directoryto./appsrcdestination-directoryto./dist# the default is typically the case herepackage-prefixtoapp# zip file package name pre-pended to the versionpackage-versionto0.0.1# current app versionRun LamZip (Package your function)Once configured, you can just run LamZip in the following way:# lamzip use-config

LamZip using existing config file: .lamzip.yaml
  Source Dir: ./appsrc
  Destination Dir: ./dist
  Package Prefix: app
  Package Version: 0.0.1

Creating Archive: ./dist\app-0.0.1.zip
  adding: ./appsrc\app.pyPass all parameters using CLIIt is not required to use the LamZip config file.  All arguments can be passed
using the CLI in the following way:lamzipspecify--src-dir./appsrc--dest-dir./dist--zip-prefixapp--zip-version0.0.1IntegrationNow that you have the LamZip config deployed to your project folder it is
recommended to use a versioning utility like "Bumpversion" to easily maintain the version
configuration within the LamZip config file (.lamzip.yaml).https://github.com/peritus/bumpversionImportantEnsure the python "Scripts" directory is located in your system path, as this library
installs LamZip to that location for global use.Verify the path:Use the version response to verify the interpreter location on your system.# pip3 -Vpip20.0.2fromc:\programfiles\python37\lib\site-packages\pip(python3.7)Using BASH: "echo $PATH" to verify "<path-to-python-base>/scripts" exists
Using PowerShell: "$ENV:$PATH" to verify "<path-to-python-base>\Scripts" exists
Package: lan
introsuppose you want to make a comand line tool called lan.
it accept integer as numbers
ouput carrots we need to feedex:
	cmd> lan 2 
	ouput: 4we can get the output by two ways:command linelan2call python moudlepython-mlan2best practice schemaenvGenerated bymake envThis is first step you need to do in any python projectit will create a virtual envauto source itinstall requirements for the start uptestrunmake testrunmake runwill run in module modemake mainwill run in normal modeYou can see thatmain.pyandlan/__main__.pyare the same  content file.why do we needmain.py?
It is eay to make an  entry in IDE , like intellij.Could I just use__main__.pyto run  like this?pythonlan/__main__.pyYou cound not.That is because the top-level package problem.Ex:python main.py 
top-level package is the same as main.py

python src/func/main.py  
top-level package is the same as main.py, aka func 
top-level package is the folder where you run this command fromSo , whylan/__main__.pydoes not work?because top-level package is lan now.loggingAlways use logging for log print,dont useprintlogging config is controlled bylogging.yml, which can control every module level callsetup_logging` in main file (ra)make moduleupdate readmeput moudle in the folder parallel with logx folder.logxyour_modulepacking module or cmdlocalfor quick test purposeinstall:pip install .uninstall:pip uninstall <module_name>make install
make uninstallupload to test or prod PYPI serverupload to test server:make upload-to-testupload to prod server:make upload-to-prodtest and coveragepure testmake testtest with coveragemake coverage
Package: lana
Lana 🧶LinearAlgebra fornocturnalandadventurousdata scientists.Installpip install lanaExamplefromlanaimportMatrix,injectzeros=Matrix.zeros((3,3))print(zeros)print(f"shape:{zeros.shape}, type:{type(zeros)}")print(zeros.to_list()[0],zeros.to_list()[0][0])mat=Matrix.matrix([[1,2,3],[4,5,6]])print(mat)print(f"shape:{mat.shape}, type:{type(mat)}")forrowsinmat.to_list():print(rows,type(rows))submat=Matrix.matrix(inject(mat.to_list()[0]))print(submat)print(submat.shape,type(submat))
Package: lanablas
LanaBLAS 🐑LinearAlgebra fornocturnalandadventurousdata scientists exploringBLASLanaBLASis a repository primarily used for testing the development of CPython extension modules with external dependencies, such as (OpenBLAS). It's a work-in-progress memetic project aimed at having fun and learning new things.Installbrew updatebrew install openblasexport BLA_VENDOR=OpenBLASpip install lanablas==0.1.7ExamplefromlanablasimportMatrix,injecta=Matrix.ones(3,3)print(a)print(type(a),a.shape)b=Matrix.eye(3)print(b)print(type(b),b.shape)c=a+bprint(c)print(type(c),b.shape)forrowinc.tolist():print(row,type(row))# sub-matrixd=Matrix.new(inject(c.tolist()[0]))print(type(d),d.shape)For more comprehensive examples, please visit theexamplesfolder.
Package: lanarky
The web framework for building LLM microservices.Lanarky is apython (3.9+)web framework for developers who want to build microservices using LLMs.
Here are some of its key features:LLM-first: Unlike other web frameworks, lanarky is built specifically for LLM developers.
It's unopinionated in terms of how you build your microservices and guarantees zero vendor lock-in
with any LLM tooling frameworks or cloud providersFast & Modern: Built on top of FastAPI, lanarky offers all the FastAPI features you know and love.
If you are new to FastAPI, visitfastapi.tiangolo.comto learn moreStreaming: Streaming is essential for many real-time LLM applications, like chatbots. Lanarky has
got you covered with built-in streaming support overHTTPandWebSockets.Open-source: Lanarky is open-source and free to use. Forever.To learn more about lanarky and get started, you can find the full documentation onlanarky.ajndkr.comInstallationThe library is available on PyPI and can be installed viapip:pipinstalllanarkyGetting StartedLanarky provides a powerful abstraction layer to allow developers to build
simple LLM microservices in just a few lines of code.Here's an example to build a simple microservice that uses OpenAI'sChatCompletionservice:fromlanarkyimportLanarkyfromlanarky.adapters.openai.resourcesimportChatCompletionResourcefromlanarky.adapters.openai.routingimportOpenAIAPIRouterapp=Lanarky()router=OpenAIAPIRouter()@router.post("/chat")defchat(stream:bool=True)->ChatCompletionResource:system="You are a sassy assistant"returnChatCompletionResource(stream=stream,system=system)app.include_router(router)VisitGetting Startedfor the full tutorial on building
and testing your first LLM microservice with Lanarky.ContributingContributions are more than welcome! If you have an idea for a new feature or want to help improve lanarky,
please create an issue or submit a pull request onGitHub.SeeCONTRIBUTING.mdfor more information.LicenseThe library is released under theMIT License.
Package: lanas
# lanasUna consola interactiva de python simple y bonita.![](https://raw.github.com/hugoruscitti/lanas/master/image/lanas.png)## Instalacióngit submodule init
git submodule update
sudo python setup.py install## Gracias!A los desarrolladores [kanzen] por su magnifica biblioteca.[kanzen]:https://github.com/ninja-ide/kanzen
Package: lancaster
lancaster=========A python extension wrapper for [avro-c][avro-c].Currently only supports reading a stream of avro serialized data.Does not support writing, nor the avro container format.  For moredetails on what lancaster is willing to read, see the[lancaster spec](docs/spec.md).See also [the Avro project page][avro].Usage-----schema = '{ ... }'with open('data.avro', 'rb') as f:data = list(lancaster.read_stream(schema, f))`lancaster.read_stream()` accepts a json string describing the schema,and a stream to read from, and returns a generator which will producepython versions of the avro data (dicts, lists, ints, strings).Installation------------A conda package is provided at [anaconda.org][anaconda].  This dependson conda packages providing the C libraries required, `libsnappy`,`jansson`, and `libavro-c`.conda create -n lancaster -c leif python lancasterYou can also install using just pip or setuptools, assuming you have`avro-c` and `libsnappy` installed on your system, which you canprobably get from your OS package manager.  On debian and ubuntusystems, you can install `libavro-dev` and `libsnappy-dev`.pip install lancasterorgit clone https://github.com/twosigma/lancastercd lancasterpython setup.py installCaveats-------Recursive structures (links) are not supported.  Writing anything, andthe avro container file format, are also not supported.  Happy toaccept pull requests but I don't need those features personally yet.License-------Copyright 2016 Two Sigma Open Source, LLC.  [MIT licensed][license].[anaconda]: https://anaconda.org/leif/lancaster[avro]: https://avro.apache.org/[avro-c]: http://avro.apache.org/docs/1.7.7/api/c/index.html[license]: https://github.com/twosigma/lancaster/blob/master/LICENSE
Package: lancedb
LanceDBA Python library forLanceDB.InstallationpipinstalllancedbUsageBasic Exampleimportlancedbdb=lancedb.connect('<PATH_TO_LANCEDB_DATASET>')table=db.open_table('my_table')results=table.search([0.1,0.3]).limit(20).to_list()print(results)DevelopmentCreate a virtual environment and activate it:python-mvenvvenv
../venv/bin/activateInstall the necessary packages:python-mpipinstall.To run the unit tests:pytestTo run the doc tests:pytest--doctest-moduleslancedbTo run linter and automatically fix all errors:ruffformatpython
ruff--fixpythonIf any packages are missing, install them with:pipinstall<PACKAGE_NAME>ForWindowsusers, there may be errors when installing packages, so these commands may be helpful:Activate the virtual environment:..\venv\Scripts\activateYou may need to run the installs separately:pipinstall-e.[tests]pipinstall-e.[dev]tantivyrequiresrustto be installed, so install it withconda, as it doesn't support windows installation:pipinstallwheel
pipinstallcargo
condainstallrust
pipinstalltantivyTo run the unit tests:pytest
Package: lancelot
lancelot allows class and function behaviour to be specifed and verified
using a DSL-like syntax, for example:@verifiable
def can_peek_and_pop_after_push():
    spec = Spec(Stack, given=new_stack)
    spec.when(spec.push(value='a'))
    spec.then(spec.peek()).should_be('a')
    spec.then(spec.pop()).should_be('a')
    spec.then(spec.peek()).should_raise(IndexError)
    spec.then(spec.pop()).should_raise(IndexError)
Package: lanceotron
LanceOTron CLIA bare-bones interface to the trained LanceOTron (LoT) model from the command line.Installationpip install lanceotronLocal installationClone the repository.Install dependencies with pip.Install the package.Run tests to ensure that everything is working.git clone git@github.com:Chris1221/lanceotron.git; cd lanceotron # Step 1
pip install -r requirements.txt # Step 2
pip install -e . # Step 3
python -m unittestUsageTo see available commands, use the--helpflag.lanceotron --helpCall PeaksTo call peaks from a bigWig track, use thecallPeakscommand.OptionDescriptionDefaultfileBigWig Track to analyse-t, --thresholdThreshold for selecting candidate peaks4-w, --windowWindow size for rolling mean to select candidate peaks400-f, --folderOutput folder"./"--skipheaderSkip writing the headerFalseCall Peaks with InputTo call peaks from a bigWig track with an input file, use thecallPeaks_Inputcommand.OptionDescriptionDefaultfileBigWig track to analyse-i, --inputControl input track to calculate significance of peaks-t, --thresholdThreshold for selecting candidate peaks4-w, --windowWindow size for rolling mean to select candidate peaks400-f, --folderOutput folder"./"--skipheaderSkip writing the headerFalseScore a Bed fileTo score the peaks in an existing Bed file, use thescoreBedcommand.OptionDescriptionDefaultfileBigWig Track to analyse-b, --bedBed file of regions to be scored-f, --folderOutput folder"./"--skipheaderSkip writing the headerFalseExamplesThere is a basic bigWig file included in thetestsubdirectory. To try out the caller, execute it on this file.lanceotroncallPeakstest/chr22.bw-foutput_folderCitation@article {Hentges2021.01.25.428108,
	author = {Hentges, Lance D. and Sergeant, Martin J. and Downes, Damien J. and Hughes, Jim R. and Taylor, Stephen},
	title = {LanceOtron: a deep learning peak caller for ATAC-seq, ChIP-seq, and DNase-seq},
	year = {2021},
	doi = {10.1101/2021.01.25.428108},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2021/01/27/2021.01.25.428108},
	journal = {bioRxiv}
}Building the documentationTo serve the documentation locally, usepython -m mkdocs serveBug Reports and Improvement SuggestionsPleaseraise an issueif there is anything you wish to ask or contribute.
Package: lancet
Fromhttp://en.wikipedia.org/wiki/Scalpel:A scalpel, or lancet, is a small and extremely sharp bladed instrument used
for surgery, anatomical dissection, and various arts and crafts (called a
hobby knife).Lancet is a command line utility to streamline the various activities related
to the development and maintenance of a software package.ContributingThis is a an open-source project. We’ll be delighted to receive your
feedback in the form of issues and pull requests.We’re grateful to all contributors who have helped create and maintain this package.
Contributors are listed at thecontributorssection.DocumentationSeeREQUIREMENTSin thesetup.pyfile for additional dependencies:Please refer to the documentation in the docs/ directory for more information or visit ouronline documentation.Getting startedOnceinstalled,
set up the initial configuration by running:lancet setupFor each not-yet-configured project, you can then run:cd path/to/project
lancet initThis creates a new project-level configuration file that can be shared across
different users (and thus commited to source control).Running TestsYou can run tests by executing:virtualenv env
source env/bin/activate
pip install -r lancet/test/requirements.txt
python setup.py test
Package: lancet-ioam
LancetLaunch jobs, organize the output, and dissect the results.More information about Lancet may be found onLancet’s website. Here you may find a quickstart
example, user documentation, tutorials as well as more information
about publications involving Lancet.To obtain Lancet from PyPI:pip install lancet-ioamIn common with many projects onIOAM,
Lancet usesparam, so make sure to
install it withpip install param.IntroductionLancet is designed to help you organize the output of your research
tools, store it, and dissect the data you have collected. The output
of a single simulation or analysis rarely contains all the data you
need; Lancet helps you generate data from many runs and analyse it
using your own Python code.Parameter spaces often need to be explored for the purpose of
plotting, tuning, or analysis. Lancet helps you extract the
information you care about from potentially enormous volumes of data
generated by such parameter exploration.FeaturesA simple, useful core with advanced functionality strictly
optional. Use what you need without learning all components.All components use a declarative style, helping to ensure
reproduciblity.Succinctly express the high dimensional parameter spaces, without
nested loops.Easily interface with external tools using ShellCommand or quickly
build a flexible, reusable interfaces to advanced simulators or
analysis tools. Currently, the Topographica neural simulator is
actively supported.Seamlessly switch from running jobs locally to launching jobs on a
compute cluster.Keep your output organized together with key metadata for
reproducibility.Quickly load your data and view the parameters from previous runs.Integrates well with other popular tools such as IPython Notebook
and the pandas data analysis library.Actively used in scientific research and publication.ContributorsThe following people have contributed to Lancet’s design and
implementation:Jean-Luc Stevens: Original coding and designMarco Elver: Python 3 fork,
cleaned up many aspects of the design.James A. Bednar: For supporting the development of a solution that
works with any tool and not justTopographica.Philipp Rudiger: Testing, feedback and suggestions.
Package: lanceviewer
LanceViewerView your lance datasets as interactive dataframes.Installationpip install lanceviewerUsageOnce installed, simply launch the app usinglanceviewercommandlanceviewerThere are additional settings you can pass that are applied golabally.Multi-Modal data support [WIP]You can visualize multimodal data stored as bytes in the lance dataset. Currently Image bytes are handled with support for more types coming soonTransforms for MultiModal dataYou need to write configuration maps to handle the columns that contain multi-modal bytes they won't be rendered in the browser
There are 2 configs written to handle img columns:MJ: This configuration assumes that the "img" column contains the image bytes as ["extension", "bytes"]Default: This configuration assumes that the "img" column contains image bytes as the top level, i.e, ["bytes"]NOTE: Currently, MJ is the default golbal settingChanging configuration settingsTo view the global settingslanceviewer settingsChanging Default TransformationTo set the a different column configuration, you can just pass--transforms {NAME}. The settings are persisted in a global settings file so next time you don't to pass it againWorking with Local Lance DatasetsSimply pass the path of the of the lance dataset as the URI.And then pass the SQL query.
Loading dataset and running queries locally are almost real-time.Working with remote bucketsGCSWhen working with remote gcs bucket data, you need to make sure the dataset uri you're passing is accessible to you.
If not already set up, please follow ADC as described in [https://cloud.google.com/docs/authentication/external/set-up-adc]Note: Loading the querying speed depends directly on bandwidth.
Package: lancium-compute-api
lancium-libraryThis is the Python library for Lancium Compute's API.Notes:Must upgrade requests module if you are running Python 3.10 or later.pip3 --install --upgrade requestsIf switching from Python3.9 and under to Python3.10 and over, must uninstall and reinstall the library with the version of pip corresponding to the version of Python you are on.Setting up a Lancium Compute AccountPlease head tohttps://portal.lancium.com/to sign-up for a Lancium Compute account and retrieve an API key.Using the Python LibrarySimplypip install lancium-compute-api.Create an API key for your Lancium Compute account and export the key prior to using any modules like so:export LANCIUM_API_KEY=$KEY_FROM_WEBSITEPython Moduleslancium.api.Joblancium.api.JobInputlancium.api.Datalancium.api.Imagelancium.api.ResourcesLancium Compute Library / API DocumentationDocumentation for the Lancium Library and API can be found here:https://docs.lanciumcompute.com/SupportPlease contactsupport@lancium.comfor support.
Package: landa-chatterbot-corpus
# ChatterBot Language Training Corpus[![Package Version](https://img.shields.io/pypi/v/chatterbot-corpus.svg)](https://pypi.python.org/pypi/chatterbot-corpus/)[![Build Status](https://travis-ci.org/gunthercox/chatterbot-corpus.svg?branch=master)](https://travis-ci.org/gunthercox/chatterbot-corpus)These modules are used to quickly train ChatterBot to respond to various inputs in different languages.Although much of ChatterBot is designed to be language independent, it is still useful to have thesetraining sets available to prime a fresh database and make the variety of responses that a bot can yieldmuch more diverse.For instructions on how to use these data sets, please refer to the [project documentation](http://chatterbot.readthedocs.io/en/latest/training.html#training-with-corpus-data).All training data contained within this corpus is user contributed.If you are interested in contributing support for a new language please create a pull request. Additions are welcomed!# Create your own Corpus Training dataChatterbot is a very flexible and dynamic chatbot that you easily can create your own training data and structure.Create or copy an existing .yml file and put that file in a existing or a new directory you created under `chatterbot_corpus\data\<NEW DIRECTORY>`Edit that file with any text editor that you like to work with.In the beginning of the file you set one or two categories.```categories:- myown- my own categories````Then can you start your actual training conversation data.```conversations:- - Hello- Hello- - Hi- Hello```# Install your training corpus data to DjangoYou need to install chatterbot as the [Quick Start Guide](http://chatterbot.readthedocs.io/en/stable/quickstart.html).When the installation are done, please go to `(Virtual Env)/lib/pythonX.X/site-packages/chatterbot_corpus/data/` directory.Here is the same structure as you can find in this GitHub repo, here is the area where you can create your own directories and conversation files.When you are done with your files, then can you edit the Django `setting.py` file and locate the chatterbot training section.Here do you need to add `chatterbot.corpus.<DIRECTORY>.<FILENAME>````'training_data': ['chatterbot.corpus.english.greeting','chatterbot.corpus.custom.myown','chatterbot.corpus.swedish.food']```When you are done, please proceed with the Django Chatterbot Training session.# Unit Testing“A true professional does not waste the time and money of other people by handing over software that is not reasonably free of obvious bugs; that has not undergone minimal unit testing; that does not meet the specifications and requirements; that is gold-plated with unnecessary features; or that looks like junk.” – Daniel Read```pip install -r requirements.txtnosetests```
Package: landa-django-rest-auth
Welcome to django-rest-auth===========================.. image:: https://travis-ci.org/Tivix/django-rest-auth.svg:target: https://travis-ci.org/Tivix/django-rest-auth.. image:: https://coveralls.io/repos/Tivix/django-rest-auth/badge.svg:target: https://coveralls.io/r/Tivix/django-rest-auth?branch=master.. image:: https://readthedocs.org/projects/django-rest-auth/badge/?version=latest:target: https://readthedocs.org/projects/django-rest-auth/?badge=latestDjango-rest-auth provides a set of REST API endpoints for Authentication and RegistrationDocumentation-------------http://django-rest-auth.readthedocs.org/en/latest/Source code-----------https://github.com/Tivix/django-rest-authStack Overflow-----------http://stackoverflow.com/questions/tagged/django-rest-auth