Saravanan666/pypi-org huggingface dataset:
Package: lsmtest10
This is not an officially supported Google product.In MMD-MA (Liu et al. 2019, Jointly Embedding Multiple
Single-Cell Omics Measurements), we project two sets of points, from two different spaces endowed
with a positive definite kernel, to a shared Euclidean space of dimensionlow_dim. The mappings from high to low dimensional space are
obtained using functions belonging to the respective RKHS. To obtain the
mappings, we minimise a loss function that is composed of three terms:an MMD term between the low dimensional representations of the two views,
which encourages them to have the same distribution.two non-collapsing penalty terms (corresponding to thepen_dualorpen_primalfunctions), one for each view. These terms ensure that
the low dimensional representations are mutually orthogonal, preventing
collapsing.two distortion penalties (corresponding to thedis_dualordis_primalfunctions), one for each view. These terms encourage the
low dimensional representation to obtain the same pairwise structure as
the original views.MMD-MA can be formulated using either the primal (when we use the linear
kernel in the input spaces) or the dual problem. Each has
advantages or disadvantages depending on the input data. In each view, when the
number of features is larger than the number of samples
p_featureX >> n_sampleX, then the dual formulation is beneficial in terms
of runtime and memory, while if n_sampleX >> p_sampleX, the primal
formulation is favorable.ExampleWork in progress. See main.py for usage.
Package: lsmtool
LSMTool: the LOFAR Sky Model ToolLSMTool allows the manipulation of LOFAR sky models (in the makesourcedb format).Author:David RaffertyBased on contributed scripts by:Bjoern AdebahrFrancesco de GasperinReinout van WeerenContents:docs/: documentationtests/: contains test sky models and scripts useful for validationbin/: contains lsmtool executablelsmtool/: contains the main LSMTool scriptslsmtool/operations/: contains the modules for operationsparsets/: some example parsetsThe following operations are available:SELECT: Select sources by source or patch propertiesREMOVE: Remove sources by source or patch propertiesTRANSFER: Transfer a patch scheme from one sky model to anotherGROUP: Group sources into patchesUNGROUP: Remove patchesMOVE: Move a source or patch positionMERGE: Merge two or more patches into oneCONCATENATE: Concatenate two sky modelsADD: Add a sourceSETPATCHPOSITIONS: Calculate and set patch positionsPLOT: Plot the sky modelCOMPARE: Compare source fluxes and positions of two sky modelsFor details, please see thefull documentation.InstallationLSMTool is already installed on the LOFAR CEP3 cluster. Users on CEP3
should run the following commands before using LSMTool:source ~rafferty/init_lsmtoolIf you want to install LSMTool yourself, follow the instructions below.DependenciesNumpyScipyMatplotlibAstropyEveryBeamPyVO(optional, for VO access)Downloading and InstallingInstall the latest release from PyPI:pip install lsmtoolOr install the latest developer version from the git repository:pip install git+https://git.astron.nl/RD/LSMTool.gitIf you have a C++11-compliant compiler, you can build a faster
version of the mean shift grouping algorithm by compiling it
yourself:git clone https://git.astron.nl/RD/LSMTool.git
cd LSMTool
python setup.py install --build_c_extentionsNote that the C++ version will give slightly different results compared to the
Python version, but such differences are not expected to be important
in practice.TestingYou can test that the installation worked with:python setup.py testIf no errors occur, LSMTool is installed correctly.UsageThe LSMTool executable can be used from the command line with a parset that defines the steps
to be done. E.g.:$ lsmtool model.sky lsmtool.parsetThe parset follows the usual DP3 format. E.g.:# Select individual sources with Stokes I fluxes above 1 Jy
LSMTool.Steps.select.Operation = SELECT
LSMTool.Steps.select.FilterExpression = I > 1.0 Jy
LSMTool.Steps.select.OutFile = out_model.skyLSMTool can also be used in Python scripts by importing the lsmtool module. E.g.:>>> import lsmtool
>>> skymod = lsmtool.load('model.sky')
>>> skymod.select('I > 1.0 Jy')
>>> skymod.write('out_model.sky')For further details, please see thefull documentation.
Package: lsoc
### lsoc / LS OCTAL ###
A lite Pyhton package that lets you view file and directory access rights with octal.Equivilant to typing: stat -c “%a %n” *Like this:
 alper >~> lsoc
755 Desktop
755 Documents
755 Downloads
755 Music
600 myfile.tar.gz
755 Pictures
755 Public
400 Python.pem
775 Steamalper >~> lsoc /usr/bin/sudo
4755 /usr/bin/sudo
4755 /usr/bin/sudo
Package: lsolib
alsolib v3.4alsolib开发者：苦逼者钟宇轩说明:本库为原创库，火焰工作室精心打造版权所有，侵权必究alsolib 一个最重要的功能想知道是谁在看你的学而思作品吗?
他来了~fromalsolib.xesimport*a=getnowuser()print(a)#正常:{'state':True,'user_id':"7821237", "user_name":"苦逼者钟宇轩"}#未登录:{"state":False,'user_id':"未登录","user_name":"未登录"}###alsolib(alsolib)主要库介绍说明
###注:alsolib介绍成功全为0，失败全为-1
内置函数 导入方式：fromalsolibimport*百度百科a=baidubaike("***")print(a)翻译a=translate("Hello")print(a)天气a=getweather("guangdong","guangzhou")#省，市的英文print(a)内置网页浏览器 title为自定义标题a=getweather("http://www.asunc.cn/","title")#一定要加http://或https://print(a)学而思库说明(alsolib.xes)###注:alsolib.xes介绍成功全为0，失败全为-1
导入方式fromalsolib.xesimport*a=LoadWork("一个作品的pid")学而思获取作品赞b=a.get_likes()print(b)学而思获取作品作者b=a.get_user()print(b)学而思获取作品踩的数量b=a.get_unlikes()print(b)学而思获取作品介绍b=a.get_description()print(b)学而思获取作者信息b=a.get_name_as_pid()print(b)学而思获取你对这个作品点赞没有~while1:b=a.is_like()ifb==0:print("你点赞了")ifb==-1:print("你踩了")ifb==1:print("你没点赞也没踩")hyChat库(alsolib.hychat)importalsolib.hychat###api库(alsolib.api)导库fromalsolib.apiimport*a=Alsoai()1.获取IP地理位置:print(a.get_ipaddress(ip,get_point))#第一个参数必填,第二个参数选填，填True后会返回城市名和这个城市的中心经纬度城市名#不填或False则只返回城市名#错误返回值为-1,成功为02.获取道路交通路况:print(a.get_traffic(road,city))#全部参数都要填，会返回这个道路的路况，像堵车，畅通......#错误返回值为-1,成功为03.制作AI音频:print(a.makevoice(text,filename,mode=1,speed=40))#第一二个参数必填,三四个选填,第一个参数填入发音的文字，第二个参数填路径#第三个参数填发音人(1为男人，5，6，7为三种不同声音的女人),第四个参数填声音范围(0-100)#返回值成功为0，错误返回-14.AI语音:print(a.speak(text,mode=1,speed=40))#第一个参数必填,二三个选填,第一个参数填入发音的文字,第二个参数填发音人#(1为男人，5，6，7为三种不同声音的女人),第三个参数填声音范围(0-100)#返回值成功为0，错误返回-15.AI智能聊天机器人:print(a.AI_chat(text,is_speak=False,mode=5))#第一个参数必填,二三个选填,第一个参数传入你想对机器人说的话，第二个传入#是否以AI说语音,第三个填发音人(1为男人，5，6，7为三种不同声音的女人)#成功返回0,AI智能聊天机器人的回答，失败返回-1,404为操作太快###结束:本介绍解释终归火焰工作室，感谢您的收看~~~
Package: lsp-devtools
lsp-devtools: Developer tooling for language serversThis package provides a collection of cli utilities to support the development of language servers.
While this is a Python package, it can be used with language servers written in any langauge.Available commands:agent: Wrap a language server allowing other commands to access the messages sent to and from the clientclient:Experimentala language client with an embedded inspector. Powered bytextualrecord: Connects to an agent and record traffic to file, sqlite db or console. Supports filtering and formatting the outputinspect: A text user interface to visualise and inspect LSP traffic. Powered bytextualSee thedocumentationfor more informationInstallationInstall usingpipxpipx install lsp-devtools
Package: lspi-python
This is a Python implementation of the Least Squares Policy Iteration (LSPI) reinforcement learning algorithm.
For more information on the algorithm please refer to the paper“Least-Squares Policy Iteration.”Lagoudakis, Michail G., and Ronald Parr.Journal of Machine Learning Research 4, 2003.https://www.cs.duke.edu/research/AI/LSPI/jmlr03.pdfYou can also visit their website where more information and a Matlab version is provided.http://www.cs.duke.edu/research/AI/LSPI/
Package: lspm
LSPM - Laptop Smart Power ManagerTheLaptop Smart Power Manageris a lightweight tool
designed to autonomously manage laptop battery
charge cycles with the help of aSmart Plug.DescriptionWhen we are at home or at work, we may tend to leave our
laptop plugged in and constantly charging.
The problem of this practice is that it will damage the
laptop battery prematurely.To extend the battery life, it is recommended tounplug
the charger before the battery reaches 100% and plug it
in long before the battery is completely discharged.
However, constantly keeping an eye on your computer's
battery level turns out to be difficult in practice.This is what theLaptop Smart Power Managerhas to
offer: it takes care of managing, for you, the charging
cycles of your laptop in order to optimize its battery
life.To achieve this,LSPMneeds an additional accessory:
aSmart Plug.The laptop must be plugged into theSmart Plugand both
must be connected to the same local network:The operating principle is quite simple:if the battery level drops below 20%,LSPMtells theSmart Plugto turn on. The laptop then starts charging.if the battery level reaches 100%,LSPMtells theSmart Plugto turn off. The laptop then stops charging.Table of ContentsInstallationConfigurationUsageSupported devicesContributingLicenseInstallationlspmcan be installed using the package managerpip.pipinstalllspmConfigurationPrerequisitesBefore usingLSPM, yourSmart Plugmust be plugged in,
configured and connected to the same local network as
your laptop.To work properly,LSPMneeds some information
about theSmart Plug:IP AddressUsernamePasswordCommand Line InterfaceThe easiest way is to use the command line toollspm.For the first use, enter the configuration parameters
of yourSmart Plugusing the commandlspm config:$>lspmconfig
EntertheSmartPlugIPAddress:
Enteranewusername:Enteranewpassword:To get the available options for this command,
runlspm config --help.UsageCommand Line InterfaceThe easiest way is to use the command line toollspm.To startLSPM, run the commandlspm start:$>lspmstart
LaptopSmartPowerManagerstartedcorrectly
Tostopit,pressCTRL+C(onmacOS,Command+.)To stop it, pressCTRL + C(orCommand + .onmacOS). Then, the following message
should show up:LaptopSmartPowerManagerstoppedsuccessfullySupported devicesTP-LinkTapoP100P105 (not tested)P110 (not tested)ContributingContributions, issues and feature requests are
always welcome!LicenseCopyright © 2023Imanol Setoain.This project isMITlicensed.
Package: lsports
lsportsA simple Python 3.7+ module to list serial ports on Windows, Linux, and macOS.This is a modified version ofserial.tools.list_portsfrompySerialby Chris Liechti.InstallationpipinstalllsportsUsageThe module provides a single functioncomportsthat returns a list ofPortInfoobjects.
EachPortInfoobject contains information about a connected serial port.fromlsportsimportcomportsforportincomports():print(port.device,port.product,port.hwid)For a full list of available attributes, see thePortInfoclass. OnlycomportsandPortInfoare considered public API.
Package: lspr
What is lspr?lspris a Python 3 scientific package for modelling UV-Vis spectral behaviour of metallic nanoparticle-dieletric composites.DependenciesTo runlsprsmoothly you will need:Python 3.4+Pandas 1.0.3+NumPy 1.18.1+SciPy 1.4.1+Statsmodels 0.11.0+Matplotlib 3.1.3+What does lspr enable?lsprenables one to approximate some spectroscopic features of metallic nanoparticle-dielectric composites, e.g. transmittance, absorbance, scattering cross-section. The model utilizes dipole approximation of Mie solution to Maxwell equations, described in more detail in article by Olson et al.1To perform a simulation, one needs to construct a composite, so matrix and metallic nanoparticles, so for each a set of refractive index and extinction coefficient data for simulated spectral range are necessary. The modeldoes not take into account possible interactions at the nanoparticle/matrix interface, i.e. the composite absorbance is a simple sum of matrix and nanoparticle absorbances calculated for pristine materials.1Optical Characterization of Single Plasmonic Nanoparticles- J. Olson, S. Dominguez-Medina, A. Hoggard, L.-Y. Wang, W.-Sh. Chang, S. Link;Chem Soc Rev. 2015 January 7; 44(1): 40-57. doi: 10.1039/c4cs00131a.How to get it running?PyPIpip install lsprAnacondaconda install -c angantyr lsprUsageMore examples on how to use the lspr and specific methods can be found in the documentation.
The transmittance of 31nm silver nanoparticles homogeneously distributed in vacuum with concentration 5.8 · 1013cm-3import lspr

# Get the csv file with n,k values for silver in range 200-1000nm
# spaced evenly every 1nm
Ag = lspr.OpticalMaterial(name = "Silver",source = "n-k_Silver_200-1000nm_(1nm).csv")

# Get n,k values for vacuum
Vacuum = lspr.OpticalMaterial(name = "Vacuum",
                         source = "n-k_data_for_vacuum_200-1000nm_(1nm).csv")

# Create Ag nanoparticles with mean size of 31nm, standard deviation of 1nm
# and concentration of 5.8e13 cm^-3
nAg = lspr.Nanoparticle(name = "Silver nanoparticles", material=Ag,
                   size=31, concentration=5.8e13)

# Set the transmittance measurement of a 1 micron thick sample of the composite
(silver nanoparticles dispersed in vacuum); use "T_corr" for reflectance corrected result
Transmittance = lspr.SpectralMeasurement("Measure the transmittance",
                                    matrix=Vacuum, nanoparticle=nAg,
                                    thickness=3).plot_results("T_corr")Licenselspr is distributed underMITlicense.SupportIf issue is spotted please open an issue on theGitHub repo of the project. For changes, upgrades and simillar refer to theproject's wiki site.
Package: lsprlib
What is lsprlib?lsprlibis a Python 3 scientific package for modelling UV-Vis spectral behaviour of metallic nanoparticle-dieletric composites.DependenciesTo runlsprlibsmoothly you will need:Python 3.4+Pandas 1.0.3+NumPy 1.18.1+SciPy 1.4.1+Statsmodels 0.11.0+Matplotlib 3.1.3+What does lsprlib enable?lsprlibenables one to approximate some spectroscopic features of metallic nanoparticle-dielectric composites, e.g. transmittance, absorbance, scattering cross-section. The model utilizes dipole approximation of Mie solution to Maxwell equations, described in more detail in article by Olson et al.1To perform a simulation, one needs to construct a composite, so matrix and metallic nanoparticles, so for each a set of refractive index and extinction coefficient data for simulated spectral range are necessary. The modeldoes not take into account possible interactions at the nanoparticle/matrix interface, i.e. the composite absorbance is a simple sum of matrix and nanoparticle absorbances calculated for pristine materials.1Optical Characterization of Single Plasmonic Nanoparticles- J. Olson, S. Dominguez-Medina, A. Hoggard, L.-Y. Wang, W.-Sh. Chang, S. Link;Chem Soc Rev. 2015 January 7; 44(1): 40-57. doi: 10.1039/c4cs00131a.How to get it running?To install lsprlib package usepippackage manager:pip install lsprlibUsageMore examples on how to use the lspr_library and specific methods can be found in the documentation.
The transmittance of 31nm silver nanoparticles homogeneously distributed in vacuum with concentration 5.8 · 1013cm-3import lsprlib as lpl

# Get the csv file with n,k values for silver in range 200-1000nm
# spaced evenly every 1nm
Ag = lpl.OpticalMaterial(name = "Silver",source = "n-k_Silver_200-1000nm_(1nm).csv")

# Get n,k values for vacuum
Vacuum = lpl.OpticalMaterial(name = "Vacuum",
                         source = "n-k_data_for_vacuum_200-1000nm_(1nm).csv")

# Create Ag nanoparticles with mean size of 31nm, standard deviation of 1nm
# and concentration of 5.8e13 cm^-3
nAg = lpl.Nanoparticle(name = "Silver nanoparticles", material=Ag,
                   size=31, concentration=5.8e13)

# Set the transmittance measurement of a 1 micron thick sample of the composite
(silver nanoparticles dispersed in vacuum); use "T_corr" for reflectance corrected result
Transmittance = lpl.SpectralMeasurement("Measure the transmittance",
                                    matrix=Vacuum, nanoparticle=nAg,
                                    thickness=3).plot_results("T_corr")Licenselsprlib is distributed underMITlicense.SupportIf issue is spotted please open an issue on theGitHub repo of the project. For changes, upgrades and simillar refer to theproject's wiki site.
Package: lspr-library-Piast-Kolodziej
What is LSPR-library?LSPR-libraryis a Python 3 scientific package for modelling UV-Vis spectral behaviour of metallic nanoparticle-dieletric composites.DependenciesTo runLSPR-librarysmoothly you will need:Python 3.4+Pandas 1.0.3+NumPy 1.18.1+SciPy 1.4.1+Statsmodels 0.11.0+Matplotlib 3.1.3+What does LSPR-library enable?LSPR-libraryenables one to approximate some spectroscopic features of metallic nanoparticle-dielectric composites, e.g. transmittance, absorbance, scattering cross-section. The model utilizes dipole approximation of Mie solution to Maxwell equations, described in more detail in article by Olson et al.1To perform a simulation, one needs to construct a composite, so matrix and metallic nanoparticles, so for each a set of refractive index and extinction coefficient data for simulated spectral range are necessary. The modeldoes not take into account possible interactions at the nanoparticle/matrix interface, i.e. the composite absorbance is a simple sum of matrix and nanoparticle absorbances calculated for pristine materials.1Optical Characterization of Single Plasmonic Nanoparticles- J. Olson, S. Dominguez-Medina, A. Hoggard, L.-Y. Wang, W.-Sh. Chang, S. Link;Chem Soc Rev. 2015 January 7; 44(1): 40-57. doi: 10.1039/c4cs00131a.How to get it running?To install lspr_library package usepippackage manager:pip install lspr_libraryUsageMore examples on how to use the lspr_library and specific methods can be found in the documentation.
The transmittance of 31nm silver nanoparticles homogeneously distributed in vacuum with concentration 5.8 · 1013cm-3import lspr_library as lpl

# Get the csv file with n,k values for silver in range 200-1000nm
# spaced evenly every 1nm
Ag = lpl.OpticalMaterial(name = "Silver",source = "n-k_Silver_200-1000nm_(1nm).csv")

# Get n,k values for vacuum
Vacuum = lpl.OpticalMaterial(name = "Vacuum",
                         source = "n-k_data_for_vacuum_200-1000nm_(1nm).csv")

# Create Ag nanoparticles with mean size of 31nm, standard deviation of 1nm
# and concentration of 5.8e13 cm^-3
nAg = lpl.Nanoparticle(name = "Silver nanoparticles", material=Ag,
                   size=31, concentration=5.8e13)

# Set the transmittance measurement of a 1 micron thick sample of the composite
(silver nanoparticles dispersed in vacuum); use "T_corr" for reflectance corrected result
Transmittance = lpl.SpectralMeasurement("Measure the transmittance",
                                    matrix=Vacuum, nanoparticle=nAg,
                                    thickness=3).plot_results("T_corr")Licenselspr_library is distributed underMITlicense.SupportIf issue is spotted please open an issue on theGitHub repo of the project. For changes, upgrades and simillar refer to theproject's wiki site.
Package: lsprotocol
Language Server Protocol Types implementation for Pythonlsprotocolis a python implementation of object types used in the Language Server Protocol (LSP). This repository contains the code generator and the generated types for LSP.OverviewLSP is used by editors to communicate with various tools to enables services like code completion, documentation on hover, formatting, code analysis, etc. The intent of this library is to allow you to build on top of the types used by LSP. This repository will be kept up to date with the latest version of LSP as it is updated.Installationpython -m pip install lsprotocolUsageUsing LSP typesfromlsprotocolimporttypesposition=types.Position(line=10,character=3)Using built-in type converters# test.pyimportjsonfromlsprotocolimportconverters,typesposition=types.Position(line=10,character=3)converter=converters.get_converter()print(json.dumps(converter.unstructure(position,unstructure_as=types.Position)))Output:> python test.py{"line": 10, "character": 3}
Package: lsq-ellipse
Least Squares fitting of ellipses, python routinebased on the  publicationHalir, R., Flusser, J.: 'Numerically Stable Direct Least Squares
Fitting of Ellipses'Installpipinstalllsq-ellipsehttps://pypi.org/project/lsq-ellipse/Example executionimportnumpyasnpfromellipseimportLsqEllipseimportmatplotlib.pyplotaspltfrommatplotlib.patchesimportEllipseif__name__=='__main__':# avalible in the `example.py` script in this repoX1,X2=example.make_test_ellipse()X=np.array(list(zip(X1,X2)))reg=LsqEllipse().fit(X)center,width,height,phi=reg.as_parameters()print(f'center:{center[0]:.3f},{center[1]:.3f}')print(f'width:{width:.3f}')print(f'height:{height:.3f}')print(f'phi:{phi:.3f}')fig=plt.figure(figsize=(6,6))ax=plt.subplot()ax.axis('equal')ax.plot(X1,X2,'ro',zorder=1)ellipse=Ellipse(xy=center,width=2*width,height=2*height,angle=np.rad2deg(phi),edgecolor='b',fc='None',lw=2,label='Fit',zorder=2)ax.add_patch(ellipse)plt.xlabel('$X_1$')plt.ylabel('$X_2$')plt.legend()plt.show()Cite this work@software{ben_hammel_2020_3723294,
  author       = {Ben Hammel and Nick Sullivan-Molina},
  title        = {bdhammel/least-squares-ellipse-fitting: v2.0.0},
  month        = mar,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v2.0.0},
  doi          = {10.5281/zenodo.3723294},
  url          = {https://doi.org/10.5281/zenodo.3723294}
}Ben Hammel, & Nick Sullivan-Molina. (2020, March 21). bdhammel/least-squares-ellipse-fitting: v2.0.0 (Version v2.0.0). Zenodo.http://doi.org/10.5281/zenodo.3723294
Package: lsqfit
This package facilitates least-squares fitting of noisy data by
multi-dimensional, nonlinear functions of arbitrarily many parameters.lsqfitsupports Bayesian priors for the fit parameters, with arbitrarily
complicated multidimensional Gaussian distributions. A tutorial on fitting is
included in the documentation; documentation is in thedoc/subdirectory: seedoc/html/index.htmlor <https://lsqfit.readthedocs.io>.The fitter uses automatic differentiation to compute gradients of the fit
function. This greatly simplifies coding of the fit function since only the
function itself need be coded. Coding is also simplified by using dictionaries
(instead of arrays) for representing fit data and fit priors.lsqfitmakes heavy use of Python packagegvar, which
simplifies the analysis of error propagation and the creation of
multi-dimensional Gaussian distributions (for fit priors).This code has been used on a laptop to fit functions of tens-to-thousands of
parameters to tens-to-thousands of pieces of data.lsqfituses the GNU
Scientific Library (GSL) and/orscipyto do the fitting,numpyfor
efficient array arithmetic, andcythonto compile efficient code that
interfaces between Python and the C-based GSL.Information on how to install the components is in theINSTALLATIONfile.To test the libraries trymake tests. Some examples are give in theexamples/subdirectory.Version numbers: Incompatible changes are signaled by incrementing
themajorversion number, where version numbers have the formmajor.minor.patch. Theminornumber signals new features, and thepatchnumber bug fixes.Created by G. Peter Lepage (Cornell University) 2008Copyright (c) 2008-2021 G. Peter Lepage
Package: lsqfitgp
lsqfitgpPython module to do inference with Gaussian processes. Features:Based onJAX.Interoperates withgvarandlsqfitto facilitate inexpert users.Recursively structured covariates.Apply arbitrary linear transformations to the processes, finite and
infinite.SmallPPL based on Gaussian
copulasto specify the hyperparameters prior.Rich
collectionof covariance functions.Good GP versions ofBART(Bayes Additive Regression Trees) andBCF(Bayesian Causal Forests).Seethis reportfor the
theory behind lsqfitgp.InstallationPython >= 3.8 required. Then:$ pip install lsqfitgpDocumentationThe complete manual is available online atgattocrucco.github.io/lsqfitgp/docs.
All the code is documented with docstrings, so you can also use the Python help
system directly from the shell:>>>importlsqfitgpaslgp>>>help(lgp)>>>help(lgp.something)or, in an IPython shell/Jupyter notebook/Spyder IDE, use the question mark
shortcut:In [1]: lgp?

In [2]: lgp.something?Similar librariessthenotinygpGPJaxSee alsoComparison of Gaussian process Softwareon Wikipedia.LicenseThis software is released under theGPL.
Amongst other things, it implies that, if you release an adaptation of this
software,or even a program just importing it as external
library, you
have to release its code as open source with a license at least as strong as
the GPL.