Saravanan666/pypi-org huggingface dataset:
Package: lakeml
AboutDioptra is a ML intelligence platform. It provides real time observability of models in production as well as error analysis frameworks to understand model performance and inform retraining strategies.Quickstart1. Create your accountSign up for a free account by reaching out toinfo@dioptra.ai.2. Get your service API keyWhen you create an account, we generate a service API key.3. Instrument your codeInstall LibraryInstall the Dioptra library in an environment using Python > 3.5.3.$pip3installdioptraOr clone the repo:$gitclonehttps://github.com/dioptra-ai/collector-py.git
$python3setup.pyinstallRunnin the exampleYou can run the provided example to get a sense of how Dioptra works$exportDIOPTRA_API_KEY=<your_api_key>
$python3examples/image_classification.pyAnd log in to the dioptra app to see what happens :)
Package: lakers-python
Lakers PythonPython wrapper for thelakerscrate.Installation and usagepip install lakers-pythonimportlakers# generate a keypairlakers.p256_generate_key_pair()# instantiate a initiator and prepare EDHOC's message 1initiator=lakers.EdhocInitiator()message_1=initiator.prepare_message_1(c_i=None,ead_1=None)# for more examples, see the tests in the repositoryDevelopmentTo build and test:maturindevelop
pytestRequirementsThe maturin executable must be available. The recommended way is to install and use it in a virtual environment:python3 -m venv .venv
source .venv/bin/activate
pip install -U pip maturin pytest
pip freeze
Package: lakeshack
LakeshackA simplified data lakehouse, more of a data lakeshack, optimized for retrieving
filtered records from Parquet files. Similar to the various lakehouse solutions
(Iceberg, Hudi, Delta Lake), Lakeshack gathers up the min/max values for specified
columns from each Parquet file and stores them into a database (Metastore). When you
want to query for a set of records, it first checks the Metastore to get the list of
Parquet files thatmighthave the desired records, and then only queries those
Parquet files. The files may be stored locally or in S3. You may query using either
native pyarrow or leverage S3 Select.To acheive optimal performance, a partitioning & clustering strategy (which specifies
how the records are written to the Parquet files) should align with the main query
pattern expected to be used on the data.  See the documentation for more information on
this.InstallationLakeshack may be install using pip:pip install lakeshackDocumentationDocumentation can be found athttps://mhendrey.github.io/lakeshack
Package: lakeshore
Lake Shore Python DriverTheLake Shorepython driver allows users to quickly and easily communicate with Lake Shore instruments. It automatically establishes a connection and provides a variety of functions specific to the product that configure settings and acquire measurements.DocumentationClick here to read the documentation and some example scriptsSupported ProductsAdvanced supportF41 and F71 TeslametersModel 121 Programmable DC Current Source155 Precision I/V SourceM91 FastHall ControllerM81 Synchronous Source Measure SystemModel 224 Temperature MonitorModel 240 Input ModulesModel 335 Cryogenic Temperature ControllerModel 336 Cryogenic Temperature ControllerModel 372 AC Resistance BridgeModel 643 and 648 Electromagnet Power SuppliesBasic supportModel 350 Cryogenic Temperature ControllerModel 425 GaussmeterGetting StartedInstall the driver usingpip:pip install lakeshoreIt should be noted that the lakeshore driver requires a minimum python version of 3.7.A Simple ExampleThe following code will connect to a 155 Precision Source over USB and print what is returned by an identification query.from lakeshore import PrecisionSource

my_instrument = PrecisionSource()
print(my_instrument.query('*IDN?'))ContributeWe want your feedback!Please request changes, features, and additional instruments through the GitHub issues page.Don't hesitate to create pull requests. They make the driver better for everyone!ResourcesLake Shore websiteGitHub repoChange logDocumentation
Package: lakeshore336
READMELakeshore336 Temperature Controller MonitorThelakeshore336is a monitor and control software for the Lakeshore 336 Temperature Controller. The
package includes a device driver which manages the connection and communication through Ethernet. The
application is run as a command-line.AuthorsCarlos Vigo-Initial work-GitLabContributingPlease read ourcontributing policyfor details on our code of
conduct, and the process for submitting pull requests to us.VersioningWe useGitfor versioning. For the versions available, see thetags on this repository.LicenseThis project is licensed under theGNU GPLv3 LicenseBuilt WithPyCharm Community Edition- The IDE usedSphinx- DocumentationAcknowledgmentsNobody so far
Package: lakestream
Lakestream is a tool for interacting with object stores such as S3. It is built from the ground up in Rust, with APIs available for both Python and the web via JS/WASM.The idea behind Lakestream is to create a high-performance and future-proof data tool that can scale with new (AI-driven) networking and usage patterns. This includes the ability to work in both client and service mode, and a modular design to allow compute functions on the network.In the short term, the focus is on implementing basic features such as List, Copy, and Delete. The current version (0.0.2) enables listing and searching items on an S3 bucket or Local Filesystem.PrerequisitesPython or RustOptional: S3 account with valid access key and secret keyInstallationLakestream can be used via Python (API) or directly via Rust (CLI).
A (local-first) browser-based version is on the short-term roadmap.Python (API)Only Linux and MacOS wheels are pre-compiled. A Windows version should follow soon.pip install lakestreamRust (CLI)Clone the repository and compile the project using Cargo:git clone https://github.com/serverlessnext/lakestream.git
cd lakestream
cargo build --releaseNext, copy the binary from./target/release/lakestreamto your local path.UsageQuickstart#fors3://buckets:AWS_ACCESS_KEY_IDandAWS_SECRET_ACCESS_KEYmustbesetexport AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
export AWS_REGION=us-east-1  # optional#Findallfilesinthe"reports"directory,withnamescontaining"2023"and#modifiedwithinthelast30days,inagivenS3bucket.lakestream ls s3://bucket-name/reports/ --name "*2023*" --mtime "-30D#Findallfilesinthecurrentdirectory,largerthan100MBandmodified#withinthelast2days.lakestream ls . --size "+100M" --mtime "-2D"

Find all files larger than 1 megabyte (MB) in a given S3 Bucket
lakestream ls s3://bucket-name/ --size "+1M" --recursive#Findallfilesmodifiedmorethan1hourago,recursivelylakestream ls . --mtime "+1h" --recursiveMoreCLIexampleshere.Python can also be used as a CLI. Arguments are mapped 1:1 to the Rust library.#Pythonpython -m lakestream ls ./#Rustlakestream ls ./Python APIimportlakestreamclient=lakestream.Client()# Define a filter dictionaryfilter_dict={"name":"example.txt","size":"5","mtime":"1D",}# List the contents of a storage location with the filterresult=client.list("s3://your-bucket",recursive=True,filter_dict=filter_dict)print(result)Python API Documentationhere.ContributingContributions to the Lakestream project are welcome. Please open an issue or submit a pull request on the GitHub repository.LicenseLakestream is released under the MIT license. See LICENSE for more details.LinksDocumentation:https://lakestream.dev
Package: lakesuperior
Lakesuperior is a Linked Data repository software. It is capable of storing and
managing  large volumes of files and their metadata regardless of their
format, size, ethnicity, gender identity or expression.Lakesuperior is an alternativeFedora Repositoryimplementation. Fedora is a mature repository
software system historically adopted by major cultural heritage institutions
which extends theLinked Data Platformprotocol.Guiding PrinciplesLakesuperior aims at being a reliable and efficient Fedora 4 implementation.Its main goals are:Reliability:Based on solid technologies with stability in mind.Efficiency:Small memory and CPU footprint, high scalability.Ease of management:Tools to perform migration, monitoring and
maintenance included.Simplicity of design:Straight-forward architecture, robustness
over features.Key featuresStores binary files and RDF metadata in one repository.Multi-modal access: REST/LDP, command line and native Python API.(almost) Drop-in replacement for Fedora4Very stable persistence layer based onLMDBand filesystem. Fully
ACID-compliant writes guarantee consistency of data.Term-based search and SPARQL Query API + UINo performance penalty for storing many resources under the same
container, or having one resource link to many URIsExtensible provenance metadata trackingFits in a pocket: you can carry 50M triples in an 8Gb memory stick.Installation & DocumentationWith Docker:git clone --recurse-submodules https://github.com/scossu/lakesuperior.git
cd lakesuperior
docker-compose upWith pip (requires a C compiler to be installed):pip install lakesuperiorThe full, current documentation is maintained inRead The Docs. Please refer to that for more info,
including installation instructions.
Package: lakeview
LakeviewLakeview is a Python 3 library for creating publication-qualityIGV-style genomic visualizations. Lakeview is based onMatplotlib.A quick example:# Import Lakeviewimportlakeviewaslv# Load aligned segments in a selected region from a BAM filepainter=lv.SequenceAlignment.from_file("PacBio_HiFi.bam",region="chr14:105,660,000-105,780,000")# Create an empty GenomeViewer with two tracksgv=lv.GenomeViewer(tracks=2,figsize=(8,5),height_ratios=(1,4))# Plot alignment pileuppainter.draw_pileup(gv.axes[0],# Plot on the first track of the GenomeViewershow_mismatches=False,# Do not highlight mismatched bases)# Plot aligned segmentspainter.draw_alignment(gv.axes[1],# Plot on the second track of the GenomeViewershow_mismatches=False,# Do not highlight mismatched basessort_by="length",# Plot longer reads firstlink_by="name",# Link primary and supplementary alignments of the same readmax_rows=30,# Only show the first 30 alignment rows)# Adjust x axis limitsgv.set_xlim(105_670_000,105_777_000)# Save the plotgv.savefig("example.png")Lakeview currently supports visualzing sequence alignment, pileup, and gene annotation tracks. Additional functionality will be added in the future.Try Lakeview interactively with Binder:FeaturesImproved clarity. Lakeview inherits the familiar and intuitive visual style ofIGV, with a clear layout designed for publication and presentation.Programmable plotting. Multiple files and genomic regions can be visualized automatically through a Pythonic interface inspired bySeabornandPandas.Support for remote data. Genomic data are often stored in remote servers without display devices. With Lakeview, you can plot remotely and view the output figures locally. Lakeview works well withJupyterLabto streamline this workflow.Transparency and reproducibility. Figures are plotted transparently and annotated explicitly. The input data and the plotting code contain all the information needed to reproduce the figure.Customizable layouts. Lakeview supports many layouts implemented inIGV, while allowing the user to define custom rules for ordering, grouping, and coloring each segment. Advanced customization is possible via theMatplotlibAPI.Interactive plotting. Lakeview ships with an interactiveJupyter Widgetto help users locate their regions of interest.InstallationpipinstalllakeviewDocumentationLakeview documentation is available athttps://jzhang-dev.github.io/lakeview/.ContributeIssues and pull requests are welcome viaGitHub.A ready-to-use development environment is available via Gitpod:LicenseLakeview is licensed under the GPL-3.0 license.
Package: lakh-crore-numbers
Lakh Crore NumbersA Python library for formatting numbers into the "Lakh Crore" (...हजार, लाख, करोड, अर्ब...) system.This numbering system (also known as Indian numbering system) is widely used in the subcontinent, including Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lankato express large numbers. The termslakhandcroreare commonly used in everyday scenarios. The library currently supports formatting all the way up tosankha.Installationlakh-crore-numbers is available on PyPI:python-mpipinstalllakh-crore-numbersSupported Language(s)As of now, the library is written for the following languages. Support for more subcontinental languages across other programming languages will be added over time. Please feel free to open a PR for a language you want this library to support.Programming LanguagesPython (3.6 and up)Spoken LanguagesNepali / नेपालीDocumentationThe current goal of the library is to be format numbers into two types of representation.Number expressed as numeral in the target languageNumber expressed as words in the target languageA handful of functions are available on the top level import to facilitate this.Formatting to Numerals (Nepali / नेपाली)importlakh_crore_numbersaslcnassertlcn.format_to_nepali_numeral(783456)=='७८३४५६'Formatting to Words (Nepali / नेपाली)importlakh_crore_numbersaslcnassertlcn.format_to_nepali_words(901489783456)=='नौ खर्ब एक अर्ब अठचालीस करोड सन्तानब्बे लाख त्रियासी हजार चार सय छपन्न'For more formatting examples, pleasetake a look here.ChangelogPlease check theCHANGELOG.mdfor details.LicenseCopyright © 2021, Suvash ThapaliyaDistributed under theISC License.
Package: lakkavokka
Lakkavokkais a JOSM plugin for digitizing indexed color
images in a controlled manner. This tool is useful for semi-automated imports in
ML/AI mapping pipelines.UsageThe following steps requires you have JSOM ext_tools plugin installed.Make sure you have Python >3.8 and Pip installed:$ python3 --version
Python 3.8.10

$ python3 -m pip --version
pip 20.0.2 from /usr/lib/python3/dist-packages/pip (python 3.8)Install Lakkavokka:python3 -m pip install lakkavokkaSetup external script in JOSMUseLakkavokkaas a name andlakkavokka --lat {lat} --lon {lon} --zoom 16 --source /path/to/tiles/{zoom}/{x}/{y}.pngas a command line.Now, you can open your area in JOSM, enable Lakkavokka tool and click on the map.
Lakkavokka will load the area around the clicked point and build a way nearest
to the point. It doesn't matter if you click inside or outside the colored area.
If the area is small enough, the way will be automatically closed. Otherwise,
you can combine those line to the multipolygon later.Command line options--sourceTMS tiles source, can be either a file path template
or URL template. Variables {zoom}, {x}, and {y} will be automatically
substituted to tile coordinates--buffernumber of tiles around the clicked point to be loaded. Increasing
this value will significantly increase required RAM and calculation time.
You can adjust this setting according to your data. Most useful values are
from 1 (default) to 3.--simplify-factorsimplification factor for the resulting geometry.
You should adjust this setting according to your data if you're getting
too many points or too coarse geometry.--tagscomma-separated list of tags to be added to generated ways. It's a
good idea to addsourcetag to indicate other mappers that is an automated or
semi-automated load of data.--latand--lonlatitude and longitude of a clicked point--zoomzoom of tiles will be processed. It may be different from
the zoom level in JOSM but it should always match the best zoom of tiles.
Package: laktory
LaktoryA DataOps framework for building Databricks lakehouse.Laktory makes it possible to express and bring to life your data vision, from raw to enriched analytics-ready datasets and finely tuned AI models, while adhering to basic DevOps best practices such as source control, code reviews and CI/CD.Using a declarative approach, you define your datasets and transformations, validate them and deploy them into Databricks workspaces.
Once deployed, you can once again leverage Laktory for debugging and monitoring.HelpSeedocumentationfor more details.InstallationInstall usingpip install laktory[{cloud_provider}]where{cloud_provider}isazure,awsorgcp.For more installation options,
see theInstallsection in the documentation.A Basic Examplefromlaktoryimportmodelstable=models.Table(name="brz_stock_prices",catalog_name="prod",schema_name="finance",timestamp_key="data.created_at",builder={"layer":"SILVER","table_source":{"name":"brz_stock_prices",}},columns=[{"name":"symbol","type":"string","sql_expression":"data.symbol"}])print(table)#> catalog_name='prod' columns=[Column(catalog_name='prod', comment=None, name='symbol', pii=None, schema_name='finance', spark_func_args=[], spark_func_kwargs={}, spark_func_name=None, sql_expression='data.symbol', table_name='brz_stock_prices', type='string', unit=None)] comment=None data=None grants=None name='brz_stock_prices' primary_key=None schema_name='finance' timestamp_key='data.created_at' builder=TableBuilder(drop_source_columns=True, drop_duplicates=None, event_source=None, joins=[], pipeline_name=None, table_source=TableDataSource(read_as_stream=True, catalog_name='prod', cdc=None, selects=None, filter=None, from_pipeline=True, name='brz_stock_prices', schema_name='finance', watermark=None), layer='SILVER')To get started with a more useful example, jump into theQuickstart.A full Data Ops templateA comprehensive template on how to deploy a lakehouse as code using Laktory is maintained here:https://github.com/okube-ai/lakehouse-as-code.In this template, 4 pulumi projects are used to:{cloud_provider}_infra: Deploy the required resources on your cloud providerunity-catalog: Setup users, groups, catalogs, schemas and manage grantsworkspace-conf: Setup secrets, clusters and warehousesworkspace: The data workflows to build your lakehouse.Okube CompanyOkubeis dedicated to building open source frameworks, known as thekubes, empowering businesses to build, deploy and operate highly scalable data platforms and AI models.
Package: lala
DependenciestwistedSetupCopy /usr/share/lala/config.example to either /etc/lala.config or
$XDG_CONFIG_HOME/lala/config or (if and only if the latter is not set)
$HOME/.lala/configEdit the file you just copied to your likingUse twistd to start the bot:twistd lalaIf you want to get more verbose output in the log file, add the--verboseoption.For more information about the setup, the available plugins and the API, read
thedocumentation.
Package: lalakaka
📦 setup.py (for humans)This repo exists to providean example setup.pyfile, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices forsetup.py, as well as some
commented–out nice–to–haves.For example, thissetup.pyprovides a$ python setup.py uploadcommand, which creates auniversal wheel(andsdist) and uploads
your package toPyPiusingTwine, without the need for an annoyingsetup.cfgfile. It also creates/uploads a new git tag, automatically.In short,setup.pyfiles can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)Check out the example!Installationcdyour_project# Download the setup.py file:#  download with wgetwgethttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py-Osetup.py#  download with curlcurl-Ohttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.pyTo DoTests via$ setup.py test(if it's concise).Pull requests are encouraged!More ResourcesWhat is setup.py?on Stack OverflowOfficial Python Packaging User GuideThe Hitchhiker's Guide to PackagingCookiecutter template for a Python packageLicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.
Package: lalalang
A programming language with a silly nameI saw recently theamazingmovieLa La Landand inspired by only the movie's name and my learning obsession I decided to learn how to make a programming language and give it a funny name 🙂.Warning: This shouldn't be used in any production enviroment, it is very slow and poorly designed, it was made with educational purposes only.InstallationThe only requirement is to have Python 3.9+ (you can install itdirectlyor withpyenv).You can install with pip:$pipinstalllalalangOr by source$gitclonehttps://github.com/manoloesparta/lalalang&&cdalalang
$makesetupenv=python# Here goes the path of your python 3.9 executable$makeinstallInfo: If the terminal returnscommand not found: 3langyou should see where pip is installing your package and add that directory to PATH, sometimes usingsudowith pip solves it, or adding the--userflag, if none of these options works can still use the interpreter with the commandpython -m lalalang.UsageYou can see the main options with this command$3lang--help

Usage:3lang[OPTIONS]Options:--modeTEXTREPLmode(lex|parse|eval)--srcTEXTInputfilewith3langcode--helpShowthismessageandexit.To enter the interpreter and run code on the fly, type$3lang

Welcometothecityofstars!🌟
ThisistheLaLaLangProgrammingLanguagv1.2!
♪♪>To read a file and execute it, type$3lang--srcrandom_program.3laYou can enter other in the stages of lexing and parsing of the interpreter with the--modeflag.SyntaxYou can see the syntax of lalalanghere.See theexamplesdirectory to get a wider idea.LicenseThis project is under the MIT license.
Package: lalang
LALANGA language transpiler for lazy polyglot programmers.Current status is still buggy (or rather, incomplete)Some updates have not been applied to current version due to not enough free time.Usagelanguage_specifier/lalang_codeFor example:py/the-code-goes-hereExamplesVariable declarationCreate a variablenamewith valueSarahgo/$name='Sarah'Output:var name = "Sarah"Same as above, with type annotation:
Output:var name string = "Sarah"Similarly for Typescript:ts/$name='Sarah'the output will be:let name: string = "Sarah";Constant declarationControl flowFunctionClass...(more to come)
Package: lale
LaleREADME in other languages:中文,deutsch,français,
orcontributeyour own.Lale is a Python library for semi-automated data science.
Lale makes it easy to automatically select algorithms and tune
hyperparameters of pipelines that are compatible withscikit-learn, in a type-safe fashion.  If
you are a data scientist who wants to experiment with automated
machine learning, this library is for you!
Lale adds value beyond scikit-learn along three dimensions:
automation, correctness checks, and interoperability.
Forautomation, Lale provides a consistent high-level interface to
existing pipeline search tools including Hyperopt, GridSearchCV, and SMAC.
Forcorrectness checks, Lale uses JSON Schema to catch mistakes when
there is a mismatch between hyperparameters and their type, or between
data and operators.
And forinteroperability, Lale has a growing library of transformers
and estimators from popular libraries such as scikit-learn, XGBoost,
PyTorch etc.
Lale can be installed just like any other Python package and can be
edited with off-the-shelf Python tools such as Jupyter notebooks.Introductory guidefor scikit-learn usersInstallation instructionsTechnical overviewslides,notebook, andvideoIBM'sAutoAI SDKuses Lale, see demonotebookGuide for wrappingnew operatorsGuide forcontributingto LaleFAQPapersPythonAPI documentationThe name Lale, pronouncedlaleh, comes from the Persian word for
tulip. Similarly to popular machine-learning libraries such as
scikit-learn, Lale is also just a Python library, not a new stand-alone
programming language. It does not require users to install new tools
nor learn new syntax.Lale is distributed under the terms of the Apache 2.0 License, seeLICENSE.txt.
It is currently in anAlpha release, without warranties of any
kind.