Saravanan666/pypi-org huggingface dataset:
Package: lbstanza-wrappers
This project contains some python tools for making wrappers around C
libraries in stanza. The idea is to convert the C syntax into something
that stanza can read and process.These tools are based onpycparser.Setup::pip install lbstanza-wrappersExamplesExtract an Enumerated Type in C:Example Header:libtidy/** A Tidy configuration option can have one of these data types. */typedefenum{TidyString,/**< String */TidyInteger,/**< Integer or enumeration */TidyBoolean/**< Boolean */}TidyOptionType;First pass this though the C PREPROCESSOR so that we get rid of symbols
and things that thepycparsercan’t handle:gcc -E -std=c99 ./tidy-html5/include/tidyenum.h > header.hThen we can run the tool:convert2stanza.py --input header.h enums --out-dir ./temp --pkg-prefix "tidy/Enums"This will create a file./temp/TidyOptionType.stanza(among others)
containing:defpackage tidy/Enums/TidyOptionType :
  import core

public deftype TidyOptionType
public deftype TidyString <: TidyOptionType
public deftype TidyInteger <: TidyOptionType
public deftype TidyBoolean <: TidyOptionType

public defn to-int (v:TidyOptionType) -> Int:
  match(v) :
    (x:TidyString) : 0
    (x:TidyInteger) : 1
    (x:TidyBoolean) : 2

public defn TidyOptionType (v:Int) -> TidyOptionType :
  switch {v == _}:
    0 : new TidyString
    1 : new TidyInteger
    2 : new TidyBoolean
    else: throw(Exception("Invalid Exception Value"))

public lostanza defn TidyOptionType (v:int) -> ref<TidyOptionType> :
  return TidyOptionType(new Int{v})

defmethod print (o:OutputStream, v:TidyOptionType) :
  match(v) :
    (x:TidyString) : print(o, "TidyString")
    (x:TidyInteger) : print(o, "TidyInteger")
    (x:TidyBoolean) : print(o, "TidyBoolean")Seelbstanza-tidy
Package: lbt-dragonfly
lbt-dragonflyCollection of all Dragonfly core Python libraries.Note that this repository and corresponding Python package does not contain any source
code and it simply exists to provide a shortcut for installing all of the dragonfly
core libraries and extensions together.Included Dragonfly ExtensionsRunningpip install lbt-dragonflywill result in the installation of the following
dragonfly Python packages:dragonfly-energydragonfly-radiancedragonfly-uwgdragonfly-doe2Included Dragonfly Core LibrariesSince both dragonfly extensions use the dragonfly core libraries, the following
dependencies are also included:dragonfly-coredragonfly-schemaAll Ladybug and Honeybee Packages Are Also IncludedSince dragonfly uses ladybug and honeybee, the following dependencies are also included:lbt-ladybuglbt-honeybee
Package: lb-telemetry
lb-telemetryA utility for logging telemetry data about LHCb packages toMONIT.
Usage data graphs specific to each package can be viewed on theMONIT Grafana.The package is not user-callable. It is intended to be imported and called by other LHCb packages such asPIDCalib2.SetupInstalling from PyPIThe package is available onPyPI.
It can be installed on any computer viapipby running (preferably in avirtual environment):pipinstalllb-telemetryUsageAdding to a packagefromlb_telemetryimportLogger# Time execution (optional)start_time=time.perf_counter()do_some_task()exec_time=time.perf_counter()-start_timetelemetry={"exec_time":exec_time,"version":VERSION,"some_field":"field_value","some_tag":"tag_value",}Logger().log_to_monit("NameOfThisPackage",# Or other readable identifiertelemetry,tags=["version","some_tag"],# `exec_time` and `some_field` interpreted as fields)Running the CLIThe default value for--tableis 'CLI'.lb-telemetry send '{"test_field": "test_value"}' --include-host-infoTags can be specified by:lb-telemetry send '{"field1": 0, "tag1": 5, "tag2": 2}' --table some_table --tags tag1 tag2Viewing the telemetryLogged telemetry is usually visible in under a minute and can be accessed via thisGrafana dashboard. Request permission to edit the dashboard from an LHCb Grafana org admin if necessary. Then create a new row on the dashboard for your package and add the desired graphs.
Package: lbt-grasshopper
lbt-grasshopperCollection of all Ladybug Tools plugins for Grasshopper.Note that this repository and corresponding Python package does not contain any
code and it simply exists to provide a shortcut for installing all of the grasshopper
plugin packages together. The repository CI also manages the assignment of version
numbers to the totality of the grasshopper plugins together.Included Grasshopper PluginsRunningpip install lbt-grasshopperwill result in the installation of the
following Grasshopper plugin packages:ladybug-grasshopperhoneybee-grasshopper-corehoneybee-grasshopper-radiancehoneybee-grasshopper-energydragonfly-grasshopperAll of the repositories above contain only Grasshopper components and their
source code.InstallationSeethe wiki of this repositoryfor a list of instructions to install the Grasshopper plugin for free.Alternatively, you can use thePollination Grasshopper single-click installerto install the Ladybug Tools plugin for free.
Package: lbt-honeybee
lbt-honeybeeA collection of Honeybee core Python libraries and extensions.Note that this Python package and corresponding repository does not contain any source
code and it simply exists to provide a shortcut for installing all of the honeybee
extension packages together.Installationpip install lbt-honeybee -UIncluded Honeybee ExtensionsRunningpip install lbt-honeybee -Uwill result in the installation of the following
honeybee Python packages:honeybee-displayhoneybee-radiance-postprocesshoneybee-radiancehoneybee-radiance-folderhoneybee-radiance-commandhoneybee-energyhoneybee-energy-standardsIncluded Honeybee Core LibrariesSince the honeybee extensions use the honeybee-core libraries, the following
dependencies are also included:honeybee-corehoneybee-schemaAll Ladybug Packages Are Also IncludedSince honeybee uses ladybug, the following are also included (installed throughlbt-ladybug):ladybug-geometryladybug-coreladybug-comfortladybug-display
Package: lbt-ladybug
lbt-ladybugCollection of all Ladybug core Python libraries.Note that this repository and corresponding Python package does not contain any
code and it simply exists to provide a shortcut for installing all of the ladybug
core libraries together.Included Ladybug PackagesRunningpip install lbt-ladybug -Uwill result in the installation of the
following Python packages:ladybug-geometryladybug-coreladybug-comfortladybug-radianceladybug-display
Package: lb-toolkits
lb_toolkits安装命令：
python -m pip install lb_toolkits
python -m pip install --upgrade lb_toolkitsAPI 参考官方使用说明获取最新更新信息，请关注作者公众号依赖库库名版本库名版本库名版本numpy1.2.0pyhdf0.10.0h5py1.0.0netcdf41.0.0tqdm4.0.0gdal2.0.0pillow7.0.0paramiko2.10.0cdsapi0.5.0downloadcentre可以对各类卫星数据下载：卫星数据：MODIS、OCO-2/3、GOSAT；FTP下载：风云、葵花8；spider下载: LandSat、sentinel；再分析资料：EAR5、GFS等；tools对hdf4、hdf5、netcdf4、geotiff等格式文件进行读写操作；calc提供了气象、遥感方向的各类计算公式；fypy提供了针对FY3、FY4卫星数据的预处理；预处理包含对极轨卫星FY3的5分钟块、轨道数据的投影、拼接裁剪；对FY3的10度块数据进行投影、拼接、裁剪功能对FY4卫星数据的经纬度转行列号，行列号转经纬度，
以及对数据进行投影转换、裁剪等功能；
Package: lbt-recipes
lbt-recipesA collection of recipes that ship with the Ladybug Tools plugins.This includes Radiance recipes for daylight and radiation studies
usingRadiance.It also includes microclimate mapping recipes, which spatially map thermal
comfort usingEnergyPlus/OpenStudioandRadiance.Installationpip install -U lbt-recipesTo check if command line interface is installed correctly uselbt-recipes vizand you
should get aviiiiiiiiiiiiizzzzzzzzz!back in response!Local DevelopmentClone this repo locallygit clone git@github.com:ladybug-tools/lbt-recipes#orgit clone https://github.com/ladybug-tools/lbt-recipesInstall dependencies:cd lbt-recipespip install -r dev-requirements.txtpip install -r requirements.txtRun Tests:python -m pytest ./tests
Package: lbvh
LBVHAn implementation of the LBVH algorithm for performing fast bounding-box intersections.
NVIDIA has a great series of blog posts that describes, in detail, the algorithm for LBVH construction/traversal:https://developer.nvidia.com/blog/thinking-parallel-part-i-collision-detection-gpu/https://developer.nvidia.com/blog/thinking-parallel-part-ii-tree-traversal-gpu/https://developer.nvidia.com/blog/thinking-parallel-part-iii-tree-construction-gpu/This repo's implementation is CPU-only for now, but it does include python and Mathematica bindings.PythonPrecompiled binaries are available for many versions of python on mac, linux and windows.
To install python bindings for use withjaxornumpy, dopip3installlbvhThe python bindings expect arrays of bounding boxes to be specified in one of the following formats:# list of 2D bounding boxes, as arrays with shape (*, 4)[[min_x,min_y,max_x,max_y],...]# list of 2D bounding boxes, as arrays with shape (*, 2, 2)[[[min_x,min_y],[max_x,max_y]],...]# list of 3D bounding boxes, as arrays with shape (*, 6)[[min_x,min_y,min_z,max_x,max_y,max_z],...]# list of 3D bounding boxes, as arrays with shape (*, 2, 3)[[[min_x,min_y,min_z],[max_x,max_y,max_z]],...]Seehttps://github.com/samuelpmish/LBVH/tree/main/pythonfor some basic examples of bounding box queries.C++To build from source:1. clone the repogitclonegit@github.com:samuelpmish/LBVH.git2. configure CMakecdLBVH
cmake.-Bbuild-DCMAKE_BUILD_TYPE=ReleaseThis project has a few (off by default) configure arguments that control which targets get built, includingLBVH_ENABLE_PYTHON_BINDINGSLBVH_ENABLE_MATHEMATICA_BINDINGSLBVH_ENABLE_TESTS3. buildcmake--buildbuildTo use LBVH in another CMake project, you can useFetchContentto automatically build it along with the parent CMake project...# fetch and build LBVHinclude(FetchContent)FetchContent_Declare(LBVHGIT_REPOSITORYhttps://github.com/samuelpmish/LBVH.gitGIT_TAGmain)set(LBVH_ENABLE_PYTHON_BINDINGSOFFCACHEINTERNAL"")set(LBVH_ENABLE_MATHEMATICA_BINDINGSOFFCACHEINTERNAL"")set(LBVH_ENABLE_TESTINGOFFCACHEINTERNAL"")FetchContent_MakeAvailable(LBVH)...# link it against any targets in the parent projecttarget_link_libraries(my_projectPUBLICLBVH)
Package: lbwmath
libowei是本人开发的python第三方库.主要服务于民科(不黑)数学家,追求不严格但实用的工科数学(区别于逻辑严谨的理科数学).
首先必须承认,数学是逻辑严谨的科学,但是数学也有自身的历史进程,数学成果最终要落地应用.
数学追求严谨,应用追求效率.
在今天这个信息泛滥的时代,身为开发者,眼光比技术重要.
我见过太多的数学老顽固(爱称,无恶意)，固执地着眼于小，为细枝末节的所谓“严谨”不惜吹毛求疵，纠缠不休.而当面临真正变革时，又坐井观天，守旧拒新，或畏畏缩缩，裹足不前。
所以在pypi发布了这个项目,坚持Pythonic精神,实用优先,坚持不严格但实用的设计理念,提供简单高效/可视化的数学工具.
在2019年,我会继续抽空完善这个pip项目.
Package: lc
This tool helps you β-reduce terms in the λ-calculus by providing you with a
REPL:λ> (λc.c(λx.λy.x))((λx.λy.λf.fxy)(λf.λx.f(fx))(λf.λx.f(f(fx))))
INPUT (λc.c(λx.λy.x))((λx.λy.λf.fxy)(λf.λx.f(fx))(λf.λx.f(f(fx))))
β ==> (λx.λy.λf.fxy)(λf.λx.f(fx))(λf.λx.f(f(fx)))(λx.λy.x)
β ==> (λy.λf.f(λf.λx.f(fx))y)(λf.λx.f(f(fx)))(λx.λy.x)
β ==> (λf.f(λf.λx.f(fx))(λf.λx.f(f(fx))))(λx.λy.x)
β ==> (λx.λy.x)(λf.λx.f(fx))(λf.λx.f(f(fx)))
β ==> (λy.λf.λx.f(fx))(λf.λx.f(f(fx)))
β ==> λf.λx.f(fx)

Potential shorthand representations:
-> As Church numeral 2InputNotice the pretty unicodeλin the input: this REPL uses Readline to input
aλwhen you type backslash (\).ShorthandsYou can start any ‘Monty Python’ routine and people finish it for you.
Everyone knows it like shorthand.—Robin WilliamsNotice above the input was converted to its corresponding shorthand Church
numeral at the end. This interpreter has the notion of shorthand notations:Shorthands are written in braces ({}) and arecase insensitive.Church numerals, as well as many other common shorthands, are predefined for
you.Braces can be omitted on church numerals, as digits are not allowed to be
used as variables.Use=operator to define a shorthand.λ> {ABC}=λf.λx.x
λ> {ABC}
INPUT λf.λx.x

Potential shorthand representations:
-> As Church numeral 0
-> As {FALSE}
-> As {ABC}
λ> {IF}{FALSE}1{ABC}
INPUT (λp.λa.λb.pab)(λx.λy.y)(λf.λx.fx)(λf.λx.x)
β ==> (λa.λb.(λx.λy.y)ab)(λf.λx.fx)(λf.λx.x)
β ==> (λb.(λx.λy.y)(λf.λx.fx)b)(λf.λx.x)
β ==> (λx.λy.y)(λf.λx.fx)(λf.λx.x)
β ==> (λy.y)(λf.λx.x)
β ==> λf.λx.x

Potential shorthand representations:
-> As Church numeral 0
-> As {FALSE}
-> As {ABC}Builtin ShorthandsBesides a countably infinite number of Church numerals, the following
shorthands come builtin to the interpreter:{succ}=λn.λf.λx.f(nfx)
{add}=λm.λn.(m{succ}n)
{mult}=λm.λn.(m({add}n)0)
{true}=λx.λy.x
{false}=λx.λy.y
{and}=λp.λq.pqp
{or}=λp.λq.ppq
{not}=λp.p{false}{true}
{if}=λp.λa.λb.pab
{cons}=λx.λy.λf.fxy
{car}=λc.c{true}
{cdr}=λc.c{false}
{nil}=λx.{true}
{pred}=λn.λf.λx.n(λg.λh.h(gf))(λu.x)(λu.u)
{sub}=λm.λn.n{pred}m
{zero?}=λn.n(λx.{false}){true}
{nil?}=λp.p(λx.λy.{false})
{lte?}=λm.λn.{zero?}({sub}mn)BugsNone.  Mutts have fleas, not bugs.—Mutt Manual Page{pred}(and, consequently,{sub}) appear to have some sort of issue.When variables conflict as a result of application, they will display as the
same variable, but the interpreter will still treat them as separate
variables. What needs to be done is some sort of detection mechanism when
this happens, and an automatic α-rename of the variable.If you solve either of these, a PR is much appreciated!
Package: lc4
LC4A Python library that implements ElsieFour (LC4) encryption, based on the algorithm in
(Alan Kaminsky 2017).FeaturesSupport for custom alphabetsAninteractive command-line utilityOptional verbose output that visually shows the steps of the algorithmRequirementsLC4 supports Python 3.x.Linux, Mac, and Windows are supported.Other operating systems may be compatible if NumPy can be properly installed.InstallationLC4is available on PyPI, the Python Package Index.$pip3installlc4DocumentationSeedocumentation.md.Example UsageSeeexample.py.TestsTests are intests/.# Run tests$python3-munittestdiscovertests-vLicenseThe code in this repository has anMIT License.SeeLICENSE.ReferencesKaminsky, Alan. “ElsieFour: A Low-Tech Authenticated Encryption Algorithm
For Human-to-Human Communication,” 2017.https://eprint.iacr.org/2017/339.
Package: lc7001
lc7001Legrand LC7001 Python interface.Seecli.pyfor a demonstration of thelc7001.aiomodule usage.See alsoLegrand/On-Q LC7001 Whole House Lighting ControllerLC7001 on AmazonLegrand Radio Frequency Lighting Control (RFLC)Legrand Developer Documentation: RFLC API for LC7001
Package: lc8_download
lc8_downloadUtility to download Landsat imagery from AWS or Google Earth Engine servers.It’s quite similar tolandsat-util,
but if you just need to download imagery and need the BQA band and metadata information,
it can fit better for you.Installationpip install lc8_downloadUsageYou can use lc8_download command line interface or use it as a Python
library.Command line interfaceUsage: lc8_download [OPTIONS] <scene>Options:-bBands to be downloaded. Use commas as delimiter. Example: ‘-b 2,3,4,BQA’--allDownload all bands and metadata--pathDirectory where the files will be saved. Default: ~/landsat/--metadataDownload scene metadata file.--helpShow this message and exit.For example:lc8_download LC80030172015001LGN00 -b 2,3,4,BQA –metadata –path lc8/Download the bands 2, 3, 4 and BQA of the sceneLC80030172015001LGN00.
Also download the metadata file and save all the files in the folderlc8.Python Library>>> from lc8_download import lc8
>>> scene = lc8.Downloader('LC80030172015001LGN00')
>>> scene.download([2, 3, 4, 'BQA'], 'lc8/', True)It will download the bands 2, 3, 4 and BQA and the metadata of the sceneLC80030172015001LGN00and save all the files in the folderlc8.LicenseGPLv3
Package: lca-algebraic
IntroductionThis library is a small layer abovebrightway2, designed for the definition ofparametric inventorieswith fast computation of LCA impacts, suitable formonte-carloanalyis.lca-algebraicprovides a set ofhelper functionsfor :compact&human readabledefinition of activites :search background (tech and biosphere) activitiescreate new foreground activites with parametrized amountsparametrize / update existing background activities (extending the classActivity)Definition of parametersFast computation of LCAsComputation of monte carlo method and global sensivity analysis (Sobol indices)InstallationIf you already have Anaconda & Jupyter installed, you can install the library with eitherpiporconda:Condaconda install -c oie-minesparistech lca_algebraicPIPpip install lca_algebraicPre-packaged installer for WindowsAlternatively, you can download and executethis installer. It will setup a full anaconda environment withJupyter,Brightway2andLCA Algebraic.Usage & documentationPlease refer to thesample notebook (Markdown)(or here as ipynb).The full API isdocumented here.Licence & CopyrightThis library has been developed byOIE - MinesParistech, for the projectINCER-ACV,
lead byADEME.It is distributed under theBSD licence.PrinciplesThe main idea of this libray is to move fromprocedural definitionof models (slow and prone to errors) to adeclarative / purely functionnaldefinition of parametric models (models aspure functions).This enablesfast computation of LCA impacts.
We leverage thepower of symbolic calculusprovided by the great libarySymPy.We define our model in aseparate DB, as a nested combination of :other foreground activitiesbackground activities :Technical, referingecoinvent DBBiopshere, referingbrightway2biosphere activitiesTheamountsin exchanges are expressed either asstatic amounts, orsymbolic expressionsof pre-definedparameters.Each activity of ourroot modelis defined as aparametrized combinationof theforeground activities, which can themselves be expressed by thebackground activities.When computing LCA for foreground models, the library develops the model as a combination ofonly background activities. It computesonce for allthe impact ofbackground activitiesand compiles afast numpy(vectorial) function for each impact, replacing each background activity by thestatic value of the corresponding impact.By providinglarge vectorsofparameter valuesto those numpy functions, we can compute LCA forthousands of valuesat a time.Compatibility with brightway2Under the hood, the activities we define withlca-algebraicare standardbrightway2activities.
The amounts of exchanges are stored asfloat valuesorserialized as stringin the propertyformula.Parameters are also stored in thebrightay2projets, making it fully compatible withbrightway.Thus, a model defined withlca-algebraicis stored as a regularbw2projet. We can usebw2native support forparametrized datasetfor computing LCAs, even if much more slower than the method explain here.
Package: lca-algebraic-dev
IntroductionThis library is a small layer abovebrightway2, designed for the definition ofparametric inventorieswith fast computation of LCA impacts, suitable formonte-carloanalyis.lca-algebraicprovides a set ofhelper functionsfor :compact&human readabledefinition of activites :search background (tech and biosphere) activitiescreate new foreground activites with parametrized amountsparametrize / update existing background activities (extending the classActivity)Definition of parametersFast computation of LCAsComputation of monte carlo method and global sensivity analysis (Sobol indices)InstallationIf you already have Anaconda & Jupyter installed, you can install the library with eitherpiporconda:Condaconda install -c oie-minesparistech lca_algebraicPIPpip install lca_algebraicPre-packaged installer for WindowsAlternatively, you can download and executethis installer. It will setup a full anaconda environment withJupyter,Brightway2andLCA Algebraic.Usage & documentationPlease refer to thesample notebook (Markdown)(or here as ipynb).The full API isdocumented here.Licence & CopyrightThis library has been developed byOIE - MinesParistech, for the projectINCER-ACV,
lead byADEME.It is distributed under theBSD licence.PrinciplesThe main idea of this libray is to move fromprocedural definitionof models (slow and prone to errors) to adeclarative / purely functionnaldefinition of parametric models (models aspure functions).This enablesfast computation of LCA impacts.
We leverage thepower of symbolic calculusprovided by the great libarySymPy.We define our model in aseparate DB, as a nested combination of :other foreground activitiesbackground activities :Technical, referingecoinvent DBBiopshere, referingbrightway2biosphere activitiesTheamountsin exchanges are expressed either asstatic amounts, orsymbolic expressionsof pre-definedparameters.Each activity of ourroot modelis defined as aparametrized combinationof theforeground activities, which can themselves be expressed by thebackground activities.When computing LCA for foreground models, the library develops the model as a combination ofonly background activities. It computesonce for allthe impact ofbackground activitiesand compiles afast numpy(vectorial) function for each impact, replacing each background activity by thestatic value of the corresponding impact.By providinglarge vectorsofparameter valuesto those numpy functions, we can compute LCA forthousands of valuesat a time.Compatibility with brightway2Under the hood, the activities we define withlca-algebraicare standardbrightway2activities.
The amounts of exchanges are stored asfloat valuesorserialized as stringin the propertyformula.Parameters are also stored in thebrightay2projets, making it fully compatible withbrightway.Thus, a model defined withlca-algebraicis stored as a regularbw2projet. We can usebw2native support forparametrized datasetfor computing LCAs, even if much more slower than the method explain here.
Package: lcacollect-config
LCAcollect Shared Config PackageThis package contains shared config and utils to be used across LCAcollect backends. It is a Python package, distributed to
PyPI to be consumed from the relevant backends.Getting StartedInstall the following in your virtual environment:Python 3.10Poetrypre-commitSetup local Python environmentpoetryinstall
pre-commitinstallRun testspytesttests/PublishingAzure Artifacts are being used to
manageversioning of this package. When a new version is ready to be published, remember to update the version by running the following command:poetryversionminorotherwise the pipeline will fail to publish the package.
Publishing happens automatically on merges tomainin an Azure Pipeline.LicenseUnless otherwise described, the code in this repository is licensed under the Apache-2.0 License. Please note that some
modules, extensions or code herein might be otherwise licensed. This is indicated either in the root of the containing
folder under a different license file, or in the respective file's header. If you have any questions, don't hesitate to
get in touch with us viaemail.