Saravanan666/pypi-org huggingface dataset:
Package: lvsfunc
lvsfuncA collection of VapourSynth functions and wrappers
written and/or modified by LightArrowsEXE.
Full information on how every function/wrapper works,
as well as a list of dependencies and links,
can be found in thedocumentation.
For further support,
drop by#lvsfuncin theIEW Discord server.How to installIf you have the oldlvsfunc.pymodule,
remove that from your system first.Installlvsfuncwith the following command:$pip3installlvsfunc--no-cache-dir-UOr if you want the latest git version, install it with this command:$pip3installgit+https://github.com/Irrational-Encoding-Wizardry/lvsfunc.git--no-cache-dir-UUsageAfter installation, functions can be loaded and used as follows:importlvsfuncaslvfaa=lvf.aa.clamp_aa(...)comp=lvf.comparison.compare(...)...DisclaimerAnythingMAYchange at any time.
The public APISHOULD NOTbe considered stable.
If you use lvsfunc in any of your projects,
consider hardcoding a version requirement.
Package: lv-ui-testing
LabVIEW User Interface testingThe aim of that package is to create LabVIEW User Interface testing through scripts through pyTest.RequirementsThis package allows python to communicate with the LabVIEW package "ui-testing". You need to have that package installed within your LabVIEW IDE and have the main.vi put in your top VI.DocumentationExamples can be found on thewikiof the project.The following example will set a value to control labelled "myNumber", the boolean indicator "greater" should be TRUE when myNumber is > 5.NOTE:Even tough it's recommended to start the VI first, it should also work as the python script is waiting to connect to it.# Start the VI "example 2 - value" first then run that scriptfromlv_ui_testingimportui_testingimportpytestdeftest_front_most_vi():# Ask the tester daemon what is the front most VIfront_most_vi=ui_testing.FMV_get_vi_name()assertfront_most_vi=="example 2 - value.vi"@pytest.mark.parametrize("number, expected_bool",[(x,x>5)forxinrange(1,11)])deftest_set_value(number,expected_bool):# Set value to 0ui_testing.FMV_set_value_DBL("myNumber",number)data_number=ui_testing.FMV_get_value_DBL("myNumber")data_bool=ui_testing.FMV_get_value_bool("greater")assertdata_bool==expected_boolassertdata_number==numberRun the following command in your command line:python -m pytest example2.pyYou should see the following test happening:LicenseDistributed under the MIT License. Copyrights Thomas Zilliox and others.
Package: lwa-fastapi-middleware-bedrock-agent
FastAPI Middleware for "Agents for Amazon Bedrock"This is FastAPI Middleware for "Agents for Amazon Bedrock". Use withAWS Lambda Web Adapter.Agents for Amazon Bedrock requires:Lambda function that defines the business logic for the action that your agent will carry out.OpenAPI schema with the API description, structure, and parameters for the action group.By using this middleware, you can create Lambda as a FastAPI. OpenAPI schemas can also be easily generated.How to installpipinstalllwa-fastapi-middleware-bedrock-agentHow to usefrombedrock_agent.middlewareimportBedrockAgentMiddleware
fromfastapiimportFastAPIapp=FastAPI(description="This agent allows you to query the information.",)app.add_middleware(BedrockAgentMiddleware)If you specified AWS_LWA_PASS_THROUGH_PATH, add thepass_through_pathparameter. (Default is "/events")app.add_middleware(BedrockAgentMiddleware, pass_through_path="/pass_through_path")ExampleExamples are located in theexampledirectoryLicenseThis project is licensed under the MIT License.
Package: lwbna-unet
Light Weight Bottle Neck Attention UnetTF implementation of the architecture described inA lightweight deep learning model for automatic segmentation and analysis of ophthalmic imagesby Sharma et al.This is an independent implementation unrelated to the autors of the paper. I have used it for segmenting fibers in my ownproject. Please leave aStarif this code is useful to you :smile:.Usage# install your favorite version of tensorflow2pipinstalltensorflow# install this packagepipinstalllwbna-unetimportlwbna_unetasunetimportnumpyasnp# input has shape `(Batch size, Height, Width, Channels)`# input has dtype float and is expected to be normalized to the range [0,1].# output has shape `(Batch size, Height, Width, n_classes)`my_unet=unet.LWBNAUnet(n_classes=1,filters=128,depth=4,midblock_steps=4,dropout_rate=0.3,name="my_unet")# the network is untrained. Dummy input.my_unet.build(input_shape=(8,320,320,3))my_unet.predict(np.random.rand(8,256,256,3))my_unet.summary()# you can now train `my_unet` as a regular `keras.Model`<script async defer src="https://buttons.github.io/buttons.js"></script>
Package: lwc
A sample Python projectA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.Most of the configuration for a Python project is done in thesetup.pyfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.
Package: lwdp
Lightweight Data Pipeline (LWDP)LWDP attempts to fill the niche for structuring pure-Python data transformations, with robust data- and
code-based-cacheing across a few locales.Because sometimes Spark or Dask or AWS Glue or anything other than a 5kb library and some dumbly hashed files is just
too much.LWDP is meant for the case where you're doing a few data transformations, possibly across multiple input file types (
csvs, Excel, parquet, etc.). Each of these files can generally (although not strictly) be held in memory. 25 csvs with
structured transformations that you'd like to keep organized and possibly streamline with cacheing?LWDPcouldbe the answer.If the data changes or your code changes, you want to be able to refresh the data pipelineonce- and, ideally, only
those parts of the data pipeline who need to be refreshed.InstallationYou should be able to install from PyPi withpip install lwdpUsageDecorate functions to represent a stage; and chain together functions to make a pipeline.# read from a raw file and cachefromlwdpimportstageimportpandasaspd@stage(some_raw_file="raw/input.csv",cache=True)defstg_read_format_raw(**kwargs)->pd.DataFrame:pdf=pd.read_csv(kwargs.get('some_raw'))# some stuff to clean itreturnpdf# read from a previous stage and cache@stage(basic_raw=stg_read_format_raw,cache=True,cache_format='parquet')defstg_format_more(**kwargs)->pd.DataFrame:raw=kwargs.get('basic_raw')raw['new_analysis_column']=3returnraw# read from a previous stage without cacheing@stage(formatted_src=stg_format_more)defstg_final_process(**kwargs)->pd.DataFrame:result=kwargs.get("formatted_src")result['wizard']=5returnresultstg_final_process()Just call the last stage in the pipeline (as you would any other function) to run all ancestors, reading/writing from
cached stages as needed.How it worksEach stage has a hash computed based on its code (excluding white space and docstrings), its "raw"
ancestors, and its stage ancestors. Hash computation for a stage is recursive; and, if any stages change their code, all
child stages will have new hashes.Stages can optionally be cached, and, if so, a format supported by pandas (to_<format>andread_<format>can be
specified). If a stage is cached and there exists a file with the specified hash, we read the file instead of
recomputing the stage.Ideally we could do this using distributed persistent storage
(e.g. on S3), which is what I'd like to work on next. Then teams who are working on a data pipeline can read from a
common source of "raw" files (and cached computations!).TODODeleting cached files after some TTLUsing S3Hashing the actual data in raw files and using that as part of "raw" data hash (instead of just the filename)
Package: lweb
Lweb is a light-weight python wsgi framework, shameless copy from bottle.
No builtin template engine.
No builtin server, need thirdpart wsgi driver.InstallationThe last stable release is available on PyPI and can be installed withpip:$ pip install lwebExampleimportlwebdefr_index():return"hello!"urls=[('/',r_index),]app=lweb.Application()app.add_urls(urls)if__name__=='__main__':importbjoernprint'Wsgi server start!'bjoern.run(app,'0.0.0.0',7000)For more examples, seeexample.pyandexample_projectFeaturesOnly pure WSGI app is providedDjango style url routerConcurrent safeGevent friendly
Package: lwh
Lacework Helios cliCreate a Lacework build artifact scan report.Usage:Options:--input TEXT: path to Lacework scan results file  [required]--output TEXT: [optional] path to store html report.  [default: .]--template TEXT: [optional] path to custom html template--help: Show this message and exit.Usage:lwh [OPTIONS] COMMAND [ARGS]...Options:--help  Show this message and exit.Commands:opa     Validate Scan Results against OPA policyreport  Create a Lacework build artifact scan reportInstallationpip install lwhCustom ReportsIf you're interested in customizing the report check out the sample template in the examples folder.
lwh cli simply renders the scan results json object to any provided template as {{ scan_result_json }}.
You also have access to a python dictionary {{ data }} as a convenience object if using jinja. If using opa integration
policy decision is also passed to report as {{result}}. You don't need to use React or Jinja. Feel free to use your
existing tool chain.From here thelacework-razrReact components take over. But they don't have to.
Feel free to customize or get in touch if you need any changes.
Package: lwjs
lwjsLight Weight JSON Shellis a package to allow simple inline"like-in-bash-shell"expressions in JSON documents. Technically, no limits exist to apply on Python objects as well. It recursively scans any given object and performs evaluation offunandrefandsubandescexpressions.Consider the example:importlwjsdata="$(calc 5 + 5)"data=lwjs.cook(data)print(data)data={"tasks":["1+1","2+2"],"solve":"$(map $(@calc) ${tasks})"}data=lwjs.cook(data)print(data)data={"in":{"v1":2,"v2":5},"r":"$(calc ${in.v1}+ ${in.v2})"}data=lwjs.cook(data)print(data)data="Must escape '$$' character"data=lwjs.cook(data)print(data)Legend:funexpression example is"$(calc)"or"$(map)"refexpression example is"$(@calc)"subexpression example is"${tasks}"or"${in.v1}"or"${in.v2}"escexpression example is$$: whenever you need a$you have to pay$$Output:10
{'tasks': ['1+1', '2+2'], 'solve': [2, 4]}
{'in': {'v1': 2, 'v2': 5}, 'r': 7}
Must escape '$' characterNB:calcandmaparelwjs-shipped funs:calc.py,map.pyinstallationpipinstalllwjsmoarVisit projecthomepagefor more documentation and examples
Package: lwksprefs
Lightworks NLE preferences readerlwksprefsis a little Python module developed
for parsing Lightworks preferences.Usageimportlwksprefsp1=lwksprefs.parse_user_settings()p2=lwksprefs.parse('path-to-prefs-file.prefs')Currently only key mapping is read. Commands mapped to keyboard
shortcuts are availabe ascommandsproperty:print(p1.commands)NoteLightworks is a professional NLE (non linear editor) developed by
EditShare and available natively on Linux platform.For more information please visit:https://www.lwks.com/
Package: lwm2pdf
Lightweight Markup to PDF Builderlwm2pdfis a Python script for building lightweight markup content into styled PDFs viaWeasyPrint.To install:pip install lwm2pdfBasic usage:lwm2pdf -i myfile.adoc --open yRequirementsSeepyproject.tomlfor Python requirements. For optimal asciidoc conversion, I strongly recommend that you install some version ofAsciidoctorfor asciidoc conversion, but a port of the originalasciidocconverter is used as a backup.Supported FiletypesCurrently,lwm2pdfsupports the following filetypes:Asciidoc (.adocor.asciidoc)Markdown (.md)OptionsOptionDescriptionRequired?-i,--inputThe file to convert (full or relative path)True-o,--outputOutput filename and destination (optional)False-od,--output-dirOutput directory and destination (optional); not recommended for use with the-ooptionFalse-s,--stylesheetSelect user stylesheet (css) (optional)False-p,--preserve-buildfilesPreserve buildfiles in output/src in current working directory or buildfile directoryFalse-b',--buildfile-dirDestination for buildfile(s) directoryFalse-rsp',--run-smartypantsRunsmartpantson resultant HTMLFalse--open"y" or "n" to automatically open or not open the pdf (doesn't ask)'ask'Stylesheets and ThemesA "manuscript" stylesheet is provided and selected as default.Known IssuesSome known issues include:Table handling is not the bestAll footnotes are rendered as end notes (this is a constraint from WeasyPrint)Markdown support is spottyCode highlighting is not working as expectedImage URIs have to be absolute paths for Weasyprint to process them successfully. The script will take care of that...eventually.Release Notesv.0.1.4Add option to runsmartypantsfor nicer quotes and thingsv.0.1.3Add description for pypy pagev.0.1.2First release to pypiIncludes many more tests (~82% coverage)Removes handling of smart quotes due to bugs (noted for future development)Move to a Poetry-based workflow
Package: lwn2md
lwn2mdCLI tool converting Lwn.net articles to markdown.Installationpipinstalllwn2md

or

pipxinstalllwn2mdUsagelwn2mdhttps://lwn.net/Articles/953738/

or

lwn2mdhttps://lwn.net/Articles/953738/MyArticle.mdOutput name is optional, default is{title}.md
Package: lwsspy.meshslice
MeshSliceSimple/small tool to plot and slice 3D meshes and create reproducible 2D slices.Documentationhttps://lwsspy.github.io/lwsspy.meshslice/DeploymentBuild StatusLicenseQuick-Installpipinstalllwsspy.meshsliceQuick-Usagefromlwsspy.meshliceimportMeshPlotSph,read_meshM=read_mesh(<yourmeshfile>)Meshplot(M)
Package: lw-trv-proxy
Lightwave TRV Integration for HomeAssistantThis adds support for Thermostatic Radiator Valves (TRVs) to the LightwaveRF integration into HomeAssistant.
This is for Series 1 Lightwave.Getting StartedLightwave does not provide an interface, via the API, to query the Lightwave Hub for the current TRV status.  Instead the status is broadcast to the LAN.
This proxy captures the TRV broadcast messages, and makes them available to a HomeAssistant polling loop.Installinggithubgit clone https://github.com/ColinRobbins/Homeassistant-Lightwave-TRV.gitpippip3 install lw-trv-proxyRequirementHomeAssistant 0.109 or greaterConfigurationSeeHomeAssistant/LightwaveRun the proxyThe proxy is (by default) configured to run on the same server as HomeAssistant.
No configuration should be needed.   Simply run it...lwproxy &Command line options:-vverbose-i ip_addressIP addres to run the proxy on.  Default 127.0.0.1-p portPort to run the proxy on. Default 7878RebootYou will need to run this as a service to survive reboots etc.  I've created asystemctltemplate service description inlwproxy.service.TestRestart hass.  (A restart is required, simply re-reading config is not sufficient)AuthorsColin Robbins-Initial work-ColinRobbinsLicenseThis project is licensed under the same term as HomeAssistant - see theLICENSE.mdfile for detailsAcknowledgementsGeoffAtHomefor the initial work that started me investigating how to do this!
Package: lxb-pylib
LXB PylibIntroductionLXB Pylib -Lixibox Python Libraryis a package of common tools.
The package contains various functions and classes for quick analysis, processing and visualization.InstallationYou can install this package usingpippipinstalllxb_pylibOr, re-compile and setup from source:gitclonehttps://github.com/lixibox/lxb_pylib.gitcdlxb_pylib# For productionpython-msetup.pyinstall# For developmentpipinstall-e.# Testing# Here we use docker and docker-compose to do unittestcdtests/
docker-composeup-d
pytestContributionIf you want to contribute to this package, you can push your code to our repository.Contributors[1] CongVM -congvm.it@gmail.com
Package: lxc4u
Warning! This is still a work in progress.Provides tools for managing LXC in Ubuntu 12.04 and Ubuntu 12.04 Containers.
Other containers are not supported (yet?)Creates containers using overlayfsCan handle multiple overlayfs layersWorking examplesCreate a container namedtest1:import lxc4u

test1_lxc = lxc4u.create('test1')
# Wait a long time...

# Start the container
test1_lxc.start()Create a container that overlaystest1:import lxc4u

test1_overlay_lxc = lxc4u.create('test1_overlay', base='test1',
                                overlays=['overlay_path'])

# Start the container
test1_overlay_lxc.start()

test1_overlay_lxc.destroy()Create a container with multiple overlays oftest1. The right most overlay
is the upper most overlay directory:import lxc4u

test1_overlay_lxc = lxc4u.create('test1_overlay', base='test1',
                            overlays=['overlay1_path', 'overlay2_path'])

# Start the container
test1_overlay_lxc.start()These examples have not yet been implemented. Soon!Starting a container namedtest1. This assumes lxc is properly configured
in your system:import lxc4u

# Start container
test1_lxc = lxc4u.start('test1')

# Stop container
test1_lxc.stop()
# or lxc4u.stop('test1')Possible interface examplesStarting an ephemeral container using test1 as a base:import lxc4u

# Start container
test1_ephemeral_lxc = lxc4u.start('test1', ephemeral=True)

# Stop container
test1_ephemeral_lxc.stop()Starting an ephemeral container using test1 with static network settings:import lxc4u

network_settings = dict(
    ip="10.0.3.5",
    gateway="10.0.3.1",
    netmask="255.255.255.0",
    network="10.0.3.0",
)

# Start container
test1_ephemeral_lxc = lxc4u.start('test1', ephemeral=True,
                                static_network=network_settings)

# Stop container
test1_ephemeral_lxc.stop()Starting an ephemeral container using with a startup command (must be available
on the container’s path):import lxc4u

# Start container
test1_ephemeral_lxc = lxc4u.start('test1', ephemeral=True,
                    startup_command="/usr/local/bin/somecommand")

# Stop container
test1_ephemeral_lxc.stop()
Package: lxcommondata
Common Data gRPC API client灵犀公共数据微服务接口配置settings配置指定serverCOMMON_DATA_SERVER = 'host:port'使用# 导入查询接口
from commondata.apis import do_query

# 定义查询对象
query_obj = {
    'model': 'CompanyModify',		# model名（必须）
    'operate': {		# 操作（必须）
        'filter': "entid_id=363248",		# 方法、参数
        'order_by': "'-ALTDATE'",
    },
    'paging': '[0:5]'		# 分页（可选）
}

# 查询并接收结果

status, data = do_query(query_obj)
Package: lxcraft
LXCraftA Declarative Configuration Build Tool for Linux SystemsA simple declarative configuration build tool for Linux systems.🐍 Pure Python declarative codeNo need to learn a new DSL/programming languageLeverage IDE features like inline documentation, code completion, linting, etc.📏 Ephemeral target systemNo need to worry about maintaing the system stateSimpler than Chef, Puppet, Ansible, Terraform, SaltStack, etc. due to the aboveHow to usefromlxcraftimportPlanfromlxcraft.debianimportAptPackagesPlan([AptPackages(["nginx"])]).execute()How to developRequirementsPython 3.10+ is required.Linux or WSL.In order to develop you must use Linux or WSL with docker.pythondevelop/docker-bash.py
invokesetup
invoketest
Package: lxd-client
lxd-clientA client library for accessing LXDUsageFirst, create a client:fromlxd_clientimportClientclient=Client(base_url="https://api.example.com")If the endpoints you're going to hit require authentication, useAuthenticatedClientinstead:fromlxd_clientimportAuthenticatedClientclient=AuthenticatedClient(base_url="https://api.example.com",token="SuperSecretToken")Now call your endpoint and use your models:fromlxd_client.modelsimportMyDataModelfromlxd_client.api.my_tagimportget_my_data_modelfromlxd_client.typesimportResponsemy_data:MyDataModel=get_my_data_model.sync(client=client)# or if you need more info (e.g. status_code)response:Response[MyDataModel]=get_my_data_model.sync_detailed(client=client)Or do the same thing with an async version:fromlxd_client.modelsimportMyDataModelfromlxd_client.api.my_tagimportget_my_data_modelfromlxd_client.typesimportResponsemy_data:MyDataModel=awaitget_my_data_model.asyncio(client=client)response:Response[MyDataModel]=awaitget_my_data_model.asyncio_detailed(client=client)By default, when you're calling an HTTPS API it will attempt to verify that SSL is working correctly. Using certificate verification is highly recommended most of the time, but sometimes you may need to authenticate to a server (especially an internal server) using a custom certificate bundle.client=AuthenticatedClient(base_url="https://internal_api.example.com",token="SuperSecretToken",verify_ssl="/path/to/certificate_bundle.pem",)You can also disable certificate validation altogether, but beware thatthis is a security risk.client=AuthenticatedClient(base_url="https://internal_api.example.com",token="SuperSecretToken",verify_ssl=False)Things to know:Every path/method combo becomes a Python module with four functions:sync: Blocking request that returns parsed data (if successful) orNonesync_detailed: Blocking request that always returns aRequest, optionally withparsedset if the request was successful.asyncio: Likesyncbut async instead of blockingasyncio_detailed: Likesync_detailedbut async instead of blockingAll path/query params, and bodies become method arguments.If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)Any endpoint which did not have a tag will be inlxd_client.api.defaultBuilding / publishing this ClientThis project usesPoetryto manage dependencies  and packaging.  Here are the basics:Update the metadata in pyproject.toml (e.g. authors, version)If you're using a private repository, configure it with Poetrypoetry config repositories.<your-repository-name> <url-to-your-repository>poetry config http-basic.<your-repository-name> <username> <password>Publish the client withpoetry publish --build -r <your-repository-name>or, if for public PyPI, justpoetry publish --buildIf you want to install this client into another project without publishing it (e.g. for development) then:If that projectis using Poetry, you can simply dopoetry add <path-to-this-client>from that projectIf that project is not using Poetry:Build a wheel withpoetry build -f wheelInstall that wheel from the other projectpip install <path-to-wheel>
Package: lxd-dyndns
Program to monitor the LXD API endpoint for container and VM changes and performs DDNS updates to keep DNS records up to date.Installationpipinstalllxd-dyndnsUsage$lxd-dyndns-f/etc/lxd-dyndns.conf-dinfoAn example config file is shown below:cache_dir="/var/lib/lxd-dyndns"[projects.k8s]dns_server="192.168.2.1"dns_port=8053dns_transport="TCP"dns_key_name="knot_lxd"dns_key_secret="Zj7NdR9/6DJonRuTt/++QgMyvSlD4Ndv+i5SvGtGY3Q="dns_zone="lxd.domain.tld."lxd_server="https://lxd-leader.domain.tld:8443"lxd_verify=falselxd_client_cert="/etc/ssl/lxd-dyndns/client.crt"lxd_client_key="/etc/ssl/lxd-dyndns/client.key"ipv6_prefixes=["dead:beef::0/96"]ipv4_prefixes=["255.255.255.255/32"]# to disable IPv4 recordsrefresh_interval=600# 10 minutes