Saravanan666/pypi-org huggingface dataset:
Package: li-common
# LI-Common## Versão:[![PyPi version](https://pypip.in/version/li-common/badge.svg?text=versão)](https://pypi.python.org/pypi/li-common)
[![PyPi downloads](https://pypip.in/download/li-common/badge.svg)](https://pypi.python.org/pypi/li-common)## Build Status### Master[![Build Status](https://travis-ci.org/lojaintegrada/LI-Common.svg?branch=master)](https://travis-ci.org/lojaintegrada/LI-Common)
[![Coverage Status](https://coveralls.io/repos/lojaintegrada/LI-Common/badge.svg?branch=master)](https://coveralls.io/r/lojaintegrada/LI-Common?branch=master)### Desenvolvimento[![Build Status](https://travis-ci.org/lojaintegrada/LI-Common.svg?branch=desenvolvimento)](https://travis-ci.org/lojaintegrada/LI-Common)
[![Coverage Status](https://coveralls.io/repos/lojaintegrada/LI-Common/badge.svg?branch=desenvolvimento)](https://coveralls.io/r/lojaintegrada/LI-Common?branch=desenvolvimento)## DescriçãoEstrutura básica para os aplicativos dos subsistemas da LI. As bases para entidades, repositórios, serviços e etc estarão disponíveis nessa lib, que fara o trabalho comum de serializar entidades, formatar saídas de API, validar dados de entrada e etc.
Package: licor
licor (pronounciated liquor) is a little script that allows
programmers to add copyright/license/warranty disclaimers to
all their files easily.ContentsUsageLicenseContributingUsageUsage:licor list-db [<path>] [options]
licor list-all [<path>] [options]
licor list-path [<path>] [options]
licor list-templates [options]
licor print-templ <format> [options]
licor insert-header <format> [<path>] [options]Options:--comment-start=<comment-start>                 Comment start token to use [default: //]
--comment-stop=<comment-stop>                   Comment stop token to use [default: */]
--border=<border>                               Border character for some fancy stuff [default: *]
-f --fancy                                      Use more fancy comments
--after-comment=<after-comment>                 A string to seperate border and content (defaults to one blank)
-c --confirm                                    Wait for user confirmation before modifying files
--format=<format>                               Use a special comment format [default: block]
--license=<license>                             Use this license template [default: GPLv3]
--single-file                                   Use single-file templates
--copyright                                     Use templates containing copyright information
-a <author> --author=<author>                   Set the author (required for --copyright)
-p <project> --project=<project>                Set the project (required unless --single-file is specified)
-e <ending> --file-ending=<ending>              Search for files ending with this ending [default: c]
-i --ignore-db                                  Ignore the database of processed files
--ignore-paths=<paths>                          Ignore all paths with one of `<paths>` in it (comma-seperated) [default: .git]
--pad-to=<pad-to>                               Pad comment blocks to this width [default: 0]Licenselicor is licensed under the GNU AGPLv3.ContributingJust send a pull request. Or an email.
Package: licorice
LiCoRICE is an open-source computational platform implementing model-based design forrealtimeprocessing of streaming data.It facilitates the execution of numerical operations inpythonwith empirical realtime guarantees, supports storage of streaming data to anSQLite Database, and supports USB peripherals and output to a display viapygame, all while maintaining at least 1ms ticks.LiCoRICE was developed in theStanford Brain Interfacing Laboratory, where it is currently used to control and conduct closed-loop neuroscience experiments.MotivationWhy Realtime?While offline data analysis collects data and processes it in batches, realtime processing is the analysis of data as it is collected, allowing for predictable response time (latency) and minimal timing variation (jitter). Systems in which delays are consequential, such as conducting closed-loop experiments or high-frequency trading, can benefit from implementing realtime practices.Why Licorice?Although realtime computational platforms do exist, many of them are inaccessible due to programming language, hardware, cost, or being proprietary. The high-level, POSIX-compliant, and open-source nature of LiCoRICE forgoes these problems and allows users to easily install LiCoRICE and adapt it to their use case.System Requirementsx86_64 systemModern UNIX environment (Linux, MacOS)Python 3.6+GCC toolchain, libevent, SQLite3, and MessagePackNOTE: Soft realtime timing guarantees are only currently tested for Ubuntu 20.04 LTS (Focal Fossa) and Ubuntu 18.04 LTS (Bionic Beaver).We recommend starting with a stock install of Ubuntu Server 20.04 LTS.
Use of other UNIX platforms besides MacOS requires manual installation of packages.InstallationLiCoRICE can be installed with pip, but requires some dependencies and permissions detailed inthe documentation.To achieve minimal jitter, it is recommended to install the stocklinux-lowlatencykernel or a custom kernel. We provide a one-click setup script to install a suitable realtime kernel.User GuideLiCoRICE hasa growing user guidewhich walks through creating some basic models from the ground up.ExamplesLiCoRICE also ships witha number of examplesthat highlight its capabilities.Some examples require external hardware to run.UsageIn general, only one command needs to be issued to parse, compile, and run a model:licoricego<model_name>For a more detailed description, take a look at theAPI reference.
Package: licorice-font
A python utility library for creating and printing more elegant strings.Example use-casesFont-formatting usinglicorice.font_format()importlicorice_fontmessage=licorice_font.font_format("This is licorice",['BOLD','CYAN'])print(message)Font-underlining usinglicorice.underline()licorice_font.underline("This is licorice:",['BOLD','PURPLE'])Installationpipinstalllicorice_font
Package: licplot
Line Integral ConvolutionThe Line Integral Convolution (LIC) is an algorithm used to image a vector
field. Its main advantage is to show in intricate detail the fine
structure of the vector field. It does not display the direction or
magnitude of the vectors, although this information can be color coded
in a postprocessing step.The result of course depends on the shape of the kernel and the lengthof the streamline. If it is too small, the texture is not sufficiently
filtered and the motion is not clear. If it is too large, the image is
smoothed and details of the motion are lost. For an image of size
(256, 256), a value of 20 provides acceptable results.InstallIf you want to install LIC you can clone the repo and run.pipinstall-e.or install from pypipipinstalllicplotUsageThe basic usage is shown in and a runnable example can be found underexamples/lic_demo.pyfromlicimportlic_internalimportnumpyasnpimportmatplotlib.pyplotasplt# create vector field and kernelsize=500u=np.zeros((size,size),dtype=np.float32)v=np.zeros((size,size),dtype=np.float32)texture=np.random.rand(size,size).astype(np.float32)# create a kernelkernel_length=31kernel=np.sin(np.arange(kernel_length)*np.pi/kernel_length).astype(np.float32)# compute the licimage=lic_internal.line_integral_convolution(u,v,texture,kernel)plt.imshow(image,cmap="hot")plt.show()Forked fromhttps://github.com/aarchiba/scikits-vectorplotby Anne Archibald
Package: licsber
Licsber 工具箱代码说明个人娱乐, 供参考.Shell命令介绍licsber: Hello world.memobird: 发送咕咕机消息.count-dir: 统计目录下的文件与目录数.flatten-dir: 递归展开当前目录下所有子文件夹到当前目录.empty-dir: 递归删除当前目录下所有空文件夹.rename: 将单个目录内所有文件重命名为SHA1值.save-115-dir: 递归将整个目录目录保存为115Link形式.主要包介绍cv: 数据集拍照助手|notebook环境imshowdatasets: 个人制作发布的数据集(仅在群里公开过)dl: 深度学习相关工具包.mail: 含一个美化过的提醒模板用于bot任务mongo: PyMongo的封装|与s3配合可以实现一个元数据与内容分离的FSs3: MinIO库的封装|包含离线制作OSS签名|随机取OSS内容shell: licsber库提供的shell命令|查看代码后谨慎使用spider: 爬虫封装|获取自定义session|多线程下载utils: 标准库封装 不会出现任何第三方库wisedu: 金智教务验证码识别|模拟登录|腾讯云函数hmac认证版本说明6.15.0 thumb命令正式支持webp.6.12.7 umeta支持多线程 增加预取功能 瓶颈在sha256了.6.11.1 适配夸克云盘.6.10.1 增加thumb功能、增加小米监控批量合并.6.9.0 增加友好的文件大小显示.6.8.0 修改Meta的默认行为.6.7.0 修改简易的带进度条多进程/线程处理函数.6.6.0 更改MongoClient的UUID默认行为.6.5.2 兼容阿里云盘最新的proof_code.6.4.1 修复115保存json的unicode问题.6.3.0 save-115-dir目前也支持单文件校验了.6.2.1 修复115链接 需要用到115功能请至少更新到此版本.6.0.0 支持pymongo4.0版本!5.8.0 修改s3_signer的默认参数获取.5.7.0 增加sct_send函数 支持Server酱.5.6.1 save-115-dir支援ipfs的sha2-256模式.5.6.0 get_wisedu_session支持传入已有session.5.5.0 utime库默认时区为Asia/Shanghai.5.4.0 更改save-115-dir的行为 不会创建更多文件.5.3.2 修改文档 修改parse_arg的默认参数.5.2.9 增加UrlCache 方便爬虫快速过滤无效host.5.1.9 增加基础cifar10分类示例.5.0.0 依赖torch和torchvision.4.5.0 增加ThreadPoolExecutor与tqdm的封装.4.4.4 增加batch_update函数.4.4.1 增加爬虫使用的check_force函数.4.4.0 增加get_s3函数获取S3对象存储.4.3.0 rename命令增加文件size防sha-1碰撞.4.2.2 增加save-115-dir命令的缓存功能.4.1.3 修复wisedu模块找不到模型问题.4.1.2 重构umeta 现在额外兼容了百度网盘梦姬格式.4.0.1 重构 改进部分依赖为可选依赖 兼容嵌入式设备.3.4.2 save-115-dir时忽略QNAP缓存文件.3.3.1 兼容gawwo/fake115-go的文件夹格式.3.2.2 优化utils.umeta的内存占用.3.1.0 增加115链接格式转换阿里云盘.3.0.1 增加tqdm依赖.2.8.0 增加递归获取115链接.2.7.4 增加rename命令.2.6.0 增加archive命令.2.4.0 部分重构项目目录 修复S3Saver创建bucket.2.3.2 mongo+minio分别存储元数据与文件本身.2.2.1 mongo获取最新数据(sort+limit)函数.2.1.2 优化验证码模型为3通道直接输入 完全端到端.2.1.0 新增PaddlePaddle验证码识别模型.2.0.0 增加cv.imshow 自动转化bgr为rgb.1.8.0 更改get_mongo的连接行为.1.7.3 更改empty-dir也会删除.DS_Store.1.7.1 增加删除空文件夹命令empty-dir.1.6.0 增加腾讯云API网关hmac签名算法.1.5.0 增加离线S3存储签名功能.1.4.3 增加random_get方法project参数.1.4.2 mongo改为默认连接时connect=False.1.4.0 增加mongo的random_get方法.1.3.0 迁移spider函数, 增加mul_get方法.1.2.0 增加ml处理xyxy和xywh相互转换.1.1.0 增加log_message函数.1.0.1 增加注释.1.0.0 移除pycrypto 替换为pycryptodome.0.2.0 增加咕咕机提醒功能.0.1.0 增加wisedu的登录api.0.0.12 增加shell命令 flatten-dir.0.0.7 增加shell命令 count-dir.0.0.6 增加了邮件提醒功能.0.0.4 增加获取mongo数据库功能.更新说明x.y.zx: 不兼容的大更新 如依赖库发生的改变y: 兼容的功能更新z: bug fix中间被略过的版本一般是bug fix, 切记不要使用.环境变量exportSCT_KEY='{Server酱SendKey}'exportL_MONGO_USERNAME='{mongo用户名}'exportL_MONGO_PWD_B64='{mongo服务器密码}'exportL_MONGO_HOST='{mongo服务器地址}'exportL_MONGO_PORT='{mongo端口}'exportL_MONGO_DEFAULT_DB='{默认数据库名称}'exportL_S3_ENDPOINT='{s3服务器地址 不带schema}'exportL_S3_ACCESS='{s3服务器ak}'exportL_S3_SECRET='{s3服务器sk}'exportL_SMTP_SERVER='{邮箱smtp服务器}'exportL_SMTP_ADDRESS='{发件邮箱}'exportL_SMTP_PWD='{邮箱密码}'exportL_MEMO_AK='{咕咕机ak}'exportL_MEMO_DEVICE='{咕咕机device id}'exportDATASETS_ROOT='{数据集文件夹根目录}'exportCHECKPOINT_ROOT='{训练中间文件目录}'
Package: liczonko
liczonkodiscrete mathematicsInstallationRun the following to install:pipinstallliczonkoUsagefromabstract_algebraimportsay_hello# Generate "Hello, World!"say_hello()# Generate "Hello, Everybody!"say_hello("Everybody")Developing LiczonkoTo install liczonko, along with the tools you need to develop and run tests, run the following in your virtualenv:$pipinstall-e.[dev]
Package: lidario
LidarioHigh-level python library to manipulate LIDAR raster and point cloud.InstallingInstall and update using pip:pipinstalllidarioLidario depends on Rasterio, which depend on many other Python and C libraries. In case of problem, please refer to theRasterio installation instructions.Quick startlidario.Translatortransform a given data structure (ie:a raster), to a point cloud (ie:a numpy array).importlidarioaslio# Translate a raster to a numpy point cloud.translator=lio.Translator("geotiff","np")point_cloud=translator.translate("/path/to/file.tif")# point_cloud: np.array([...])In this example, we initialize aTranslatorobject to convert ageotifffile into anumpy arraycloud point.
Then, we use this object to effectively convert atiffile.Going furtherTransform Rasterio mask and GeoTiff files into numpy array, pandas dataframe, CSV, PLY, and many other format:Read thedocumentation on ReadTheDocs.io.About the authorJoffrey Bienvenu, Machine Learning student @Becode.Website:https://joffreybvn.beTwitter:@joffreybvnGithub:https://github.com/Joffreybvn
Package: lidar-processing
DescriptionThis module collects basic processing routines for aerosol lidar systems.The module should include only the pre-processing and optical processing functions. Reading data, visualization, etc.
should be handled by different modules.InstallationThe module is tested for Python 2.7.* and slightly for Python 3.6The suggested method to install is to clone the repository and install it using the -e command.pip install -e ./lidar_processingassuming that the module is cloned in the lidar_processing directory.The installation procedure is not yet fully automatic. You may need to install numpy, scipy manually. Probably
the best way to install numpy and scipy is through a distribution likeanaconda.DocumentationEach function should be documented following the Numpy doc style.For details see thenumpy documentation.All docstrings are collected to a single documentation file using theSphinxmodule.
The documentation is located in the docs/ folder. The documentation is written inrestructured textformat.You can rebuild the docs by running the following command from the docs folder.make htmlThe documentation is also built automatically every time you push your changes to the repository. You can find it online
inRead the docs.TestingSome tests, based on unittest2 library, are located in the lidar_processing/tests/ folder.You can run all the test using the commands from the project directory.python -m unittest discoverNotebooks and dataThe project includes some test data in the /data/ folder. It also includes some ipython notebooks with some
example processing of the data. You can run the notebook with the command:jupyter notebookSponsorsThe development of this module is supported byRaymetrics S.A..
Package: lidarToGeo
LidarToGeoIn order to use this package you should have aws credentials setup in a .aws folder in your home directory, you can a guide to help you set this upheres
Contains code that goes through theusgs-lidar-publicdataset, fetches the las and tif files from a region specified by an input, processes these files to give a dictionary that contains a key of year or region of the data and a value of geodataframe that has the elevation, geometry point and the topographic wetness index of the geometry pointThis package will write the following to the directory you are running the script in: a laz file, tif file, shp file and a png file depending on the saved_png flag below.DocumentationYou can view the package's documentationhereinstallationpip install lidarToGeoUsageimportlidar_to_geo# bounds = "([xMin, xMax], [yMin, yMax])"raster=lidar_to_geo.RasterGetter(bounds,crs)gpd_dict=raster.region_gdf_dict(saved_png=False,resolution=5)
Package: lidar-visualizer
LiDAR Visualizer 🚀A flexible, easy-to-use, LiDAR (or any point cloud) visualizer for Linux, Windows, and macOS.If you also need to obtain poses from your dataset, consider checking outKISS-ICP.Install (*)pipinstalllidar-visualizer(*) This package relies on the power ofOpen3Dbut does not list it as a dependency. If you haven't installedopen3dthenpip install open3dor checkthe official instructionsOptional dependenciesDepending on thedataloadersyou plan to use you might need to install optional dependencies. The tool will prompt which tools is the one you are requesting and is not accessible, but if you want to go for brute force and install all of it just run:pipinstalllidar-visualizer[all]Usagelidar_visualizer--helpCitationIf you use this visualizer for any academic work, please cite our originalpaper.@article{vizzo2023ral,author={Vizzo, Ignacio and Guadagnino, Tiziano and Mersch, Benedikt and Wiesmann, Louis and Behley, Jens and Stachniss, Cyrill},title={{KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way}},journal={IEEE Robotics and Automation Letters (RA-L)},pages={1029--1036},doi={10.1109/LRA.2023.3236571},volume={8},number={2},year={2023},codeurl={https://github.com/PRBonn/kiss-icp},}
Package: lidia
LidiaLightweight Instrument Display Interface for Aircraftlidia is a Python package for serving an aircraft instruments panel as a web page.InstallationUse the package managerpipto installlidia.pipinstalllidiaUsagelidiademo# if your Scripts folder isn't in Path:python3-mlidiademo# use other sourcelidiarpctask# show general helplidia--help# show help for a specific sourcelidiademo--help# pass the main server arguments before the source namelidia-P5556demoThen open the served page in a browser, by defaultlocalhost:5555.
The controls for showing and hiding elements of the GUI are shown when hovering the mouse in the bottom left region of the page.SupportReport problems inGitLab IssuesRoadmapAdditional PFD indicators: ILS, VOR on HSIShip approach screen with views from the side and behindCAS (Crew Alerting System) screenSimple second order aircraft modelsUSB HID joystick sourceContributingContributions should be made to theGitLab repositoryPython code should be formatted with autopep8Other source files should be formatted with PrettierInstall packages for development withpip install -r requirements.txtTo properly run as a module without building and installing,cd intosrc/and runpython3 -m lidiaAPIs deprecated in previous versions, waiting to be changed in next major release are marked with@deprecatedcommentAcknowledgementsThis software was developed inDepartment of Aerospace Science and Technologyof Politecnico di Milano.Instrument graphics designed by Davide Marchesoli and Qiuyang Xia.LicenseMIT
Package: lido-cli
Node Operator Key Checker CLIInstallationYou can get this tool usingpip:pip install lido-cliOr if you cloned this repository, install Python dependencies via:./init.shDepending on how it's installed use:lido-cli ...opts command ...optsorpython lido_validate_keys.py ...opts command ...optsRunningRPC ProviderThis is the only thing required, the rest will be handled automatically unless overridden.lido-cli --rpc https://mainnet.provider.io/v3/XXX validate_network_keysOptional ParametersBy default, CLI will use embedded strings and ABIs, but you can specify your own arguments if needed. Make sure to use them on CLI itself and not on the command eg:lido-cli --rpc https://mainnet.provider.io/v3/XXX --multicall_max_bunch 100 --multicall_max_workers 3 --multicall_max_retries 5 validate_network_keys--rpc                               RPC provider for network calls.
--multicall_max_bunch               Max bunch amount of Calls in one Multicall (max recommended 300).
--multicall_max_workers             Max parallel calls for multicalls.
--multicall_max_retries             Max call retries.
-d --details                        More logs about node operators and keys. (only for validate_network_keys)Checking Network KeysCommand:validate_network_keysExample:lido-cli --rpc https://mainnet.provider.io/v3/XXX validate_network_keysChecking Keys from FileCommand:validate_file_keysSpecify the input file via--fileExample:lido-cli --rpc https://mainnet.provider.io/v3/XXX validate_file_keys --file input.json---You can also get all commands and options viapython lido_validate_keys.py --help
Package: lidtk
lidtklidtk - the language identification toolkit - was written in order to
investigate the current state of language performance.InstallationThe recommended way to install clana is:$ pip install lidtk --userIf you want the latest version:$ git clone https://github.com/MartinThoma/lidtk.git; cd lidtk
$ pip install -e . --userI recommend getting theWiLI-2018 dataset.Usage$ lidtk --help

Usage: lidtk [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  analyze-data           Utility function for the languages...
  analyze-unicode-block  Analyze how important a Unicode block is for...
  char-distrib           Use the character distribution language...
  cld2                   Use the CLD-2 language classifier.
  create-dataset         Create sharable dataset from downloaded...
  download               Download 1000 documents of each language.
  google-cloud           Use the CLD-2 language classifier.
  langdetect             Use the langdetect language classifier.
  langid                 Use the langid language classifier.
  map                    Map predictions to something known by WiLI
  nn                     Use a neural network classifier.
  textcat                Use the CLD-2 language classifier.
  tfidf_nn               Use the TfidfNNClassifier classifier.For example:$ lidtk cld2 predict --text 'This is a test.'
engThe usual order is:lidtk download: Please useWiLI-2018instead of downloading the dataset on your own.lidtk create-dataset: This step can be skipped if you use WiLI-2018lidtk analyze-unicode-block --start 0 --end 128lidtk tfidf_nn train vectorizer --config lidtk/classifiers/config/tfidf_nn.yamllidtk tfidf_nn train vectorizer --config lidtk/classifiers/config/tfidf_nn.yamllidtk tfidf_nn wili --config lidtk/classifiers/config/tfidf_nn.yamlOr to use one directly:$ lidtk cld2 predict --text 'This text is written in some language.'

engDevelopmentCheck tests withtox.
Package: liebres
liebresDescriptionFlexible and powerful data analysis / manipulation library for Python on top of SQL, providing labeled data structures.IMPORTANT: This project is still under its early stage of development. So it's not recommended yet to use on real world projects.How to installpipinstallliebresGetting Startedimportliebresaslbdf=lb.DataFrame({'a':[1,2,3],'b':[4,5,6]})...DocumentationYou can find the documentation at:https://liebres.readthedocs.ioDevelopmentYou can install it simply typing:pipenvinstall--devTo run the tests perform:pipenvpython-munittestdiscovertestsRepository Contents[TODO]LICENSEThis project is licensed underMITlicense.
Package: liecasadi
liecasadiliecasadiimplements Lie groups operation written in CasADi, mainly directed to optimization problem formulation.Inspired byA micro Lie theory for state estimation in roboticsand the libraryManif.🐍 InstallCreate avirtual environment, if you prefer. For example:pipinstallvirtualenv
python3-mvenvyour_virtual_envsourceyour_virtual_env/bin/activateInside the virtual environment, install the library from pip:pipinstallliecasadiIf you want the last version:pipinstall"liecasadi @ git+https://github.com/ami-iit/lie-casadi.git"Implemented GroupsGroupDescriptionSO33D RotationsSE33D Rigid Transform🚀 OperationsBeing:$X, Y \in SO3, \ SE3$$w \in \text{SO3Tangent}, \ \text{SE3Tangent}$$v \in \mathbb{R}^3$OperationCodeInverse$X^{-1}$X.inverse()Composition$X \circ Y$X*YExponential$\text{exp}(w)$phi.exp()Act on vector$X \circ v$X.act(v)Logarithm$\text{log}(X)$X.log()Manifold right plus$X \oplus  w = X \circ \text{exp}(w)$X + phiManifold left plus$w \oplus X = \text{exp}(w) \circ X$phi + XManifold minus$X-Y = \text{log}(Y^{-1} \circ X)$X-Y🦸‍♂️ ExamplefromliecasadiimportSE3,SO3,SE3Tangent,SO3Tangent# Random quaternion + normalizationquat=(np.random.rand(4)-0.5)*5quat=quat/np.linalg.norm(quat)# Random vectorvector3d=(np.random.rand(3)-0.5)*2*np.pi# Create SO3 objectrotation=SO3(quat)# Create Identityidentity=SO3.Identity()# Create SO3Tangent objecttangent=SO3Tangent(vector3d)# Random translation vectorpos=(np.random.rand(3)-0.5)*5# Create SE3 objecttransform=SE3(pos=pos,xyzw=quat)# Random vectorvector6d=(np.random.rand(3)-0.5)*5# Create SE3Tangent objecttangent=SO3Tangent(vector6d)Dual Quaternion examplefromliecasadiimportSE3,DualQuaternionfromnumpyimportnp# orientation quaternion generationquat1=(np.random.rand(4)-0.5)*5quat1=quat1/np.linalg.norm(quat1)quat2=(np.random.rand(4)-0.5)*5quat2=quat2/np.linalg.norm(quat2)# translation vector generationpos1=(np.random.rand(3)-0.5)*5pos2=(np.random.rand(3)-0.5)*5dual_quaternion1=DualQuaternion(quat1,pos1)dual_quaternion2=DualQuaternion(quat2,pos2)# from a homogenous matrix# (using liecasadi.SE3 to generate the corresponding homogenous matrix)H=SE3.from_position_quaternion(pos,quat).as_matrix()dual_quaternion1=DualQuaternion.from_matrix(H)# Concatenation of rigid transformsq1xq2=dual_quaternion1*dual_quaternion2# to homogeneous matrixprint(q1xq2.as_matrix())# obtain translationprint(q1xq2.translation())# obtain rotationprint(q1xq2.rotation().as_matrix())# transform a pointpoint=np.random.randn(3,1)transformed_point=dual_quaternion1.transform_point(point)# create an identity dual quaternionI=DualQuaternion.Identity()🦸‍♂️ Contributingliecasadiis an open-source project. Contributions are very welcome!Open an issue with your feature request or if you spot a bug. Then, you can also proceed with a Pull-requests! :rocket: