Saravanan666/pypi-org huggingface dataset:
Package: xfox
Asynchronous Low-code compiler for pythongithubHow to installpipinstallxfoxClear Exampleimportasyncioimportxfox,time#Custom Function@xfox.addfunc(xfox.funcs)asyncdeftest_func(item:str,*args,**kwargs):print("-----",kwargs)@xfox.addfunc(xfox.funcs)asyncdefsomef(item:str,*args,**kwargs):returnkwargs["ctx"]@xfox.addfunc(xfox.funcs)asyncdeffunction4(*args,**kwargs):return"FOX"@xfox.addfunc(xfox.funcs)asyncdeffunction1(item:int,type:bool,*args,**kwargs):returntype,args@xfox.addfunc(xfox.funcs)asyncdeffunction2(item:str,*args,**kwargs):awaitxfox.isempty(item)returneval(item)@xfox.addfunc(xfox.funcs)asyncdefinside(item:str,type:bool=False,*args,**kwargs):returntype@xfox.addfunc(xfox.funcs,'usefunc')asyncdeffunctest(func:xfox.AnonFunction,*args,**kwargs):return(awaitfunc.compile(),func.name)# Sync Parsingprint(asyncio.run(xfox.parse("""// TRASH (xd) //$test_func[$test_func[sfsaf]]$function1[1;$inside[true];$sadsadsdsd[]] $onlyif[1<2;ONLYIF]$function2[1+2;dsadasd] sometext$function4[]$function1[1;$inside[fsfdfd;true];$sadsadsdsd[]]$sdasdasd[sadsadsad]sdasdsa $exec[true;print(1+231321, end='')] $exec[true;print(1+231321, end='')] $exec[true;print(1+231321, end='')]// Let/Get //$let[test;good]$get[test]// Internal Functions Test //$usefunc[$def[$print[hello!]]]$Test[]$def[$print[hello!];Test]$Test[]// While Test //$let[a;0]$while[$eval[$get[a]<=5];$let[a;$eval[$get[a]+1]]]$get[a]123 $while[True;$if[$get[a]<15;$let[a;$eval[$get[a]+1]];$break[]] $get[a]]123 $while[True;$break[]]$break[]// For Test //$for[1..5;$get[i]]$for[5;$get[i] $break[]]// Try Test //$try[ERROR $get[_];$function1[]]$try[$print[$get[_]];$function2[]]$try[$get[_];$raise[name;text]]// Import usage$import[ttest]$functiomm[] <- (custom function from ttest)//// Exit - $exit[]//// a Comment // | &s Not a Comment &s""",ctx="asdsdsdasds")))Output:----- {'ctx': 'asdsdsdasds'}
----- {'ctx': 'asdsdsdasds'}
[LOG] hello!
[LOG] hello!
[LOG] Mising var item in function2
None
(False, ('$sadsadsdsd[]',)) 
3 sometext
FOX
(True, ('$sadsadsdsd[]',))
$sdasdasd[sadsadsad]
sdasdsa 231322 231322 231322



good


('', 'd489c9')
$Test[]






6
123 789101112131415
123



12345



ERROR Mising var type in function1

[ERROR] name: text



Hello, world!



 | // Not a Comment //
PS C:\Users\user\Documents\xfox> & C:/Users/user/AppData/Local/Programs/Python/Python310/python.exe c:/Users/user/Documents/xfox/test.py
----- {'ctx': 'asdsdsdasds'}
----- {'ctx': 'asdsdsdasds'}
[LOG] hello!
[LOG] hello!
[LOG] Mising var item in function2
None
(False, ('$sadsadsdsd[]',))
3 sometext
FOX
(True, ('$sadsadsdsd[]',))
$sdasdasd[sadsadsad]
sdasdsa 231322 231322 231322



good


('', 'dd6fe4')
$Test[]






6
123 789101112131415
123



12345



ERROR Mising var type in function1

[ERROR] name: text

// Import usage

Hello, world! <- (custom function from ttest)
//



 | // Not a Comment //Interactive Xfox Interpreterpython-mxfoxOrxfoxStart xfox filespython-mxfox(filename).xfoxOrxfox(filename).xfoxExamplespackage for creation Discord bots (outaded)
Package: xframe
IntroductionThe main objective ofxFrameis to provide an easy and flexible framwork that handles details such asData storage / File accessSettings managementMultiprocessingGPU accessAllowing users to focus on the "scientific" part of their algorithm.xFramehas been created during our quest of writing a toolkit for fluctuation X-ray scattering.
To installxFramesimply callpip install xframe
# For the fluctuation scattering toolkit use the following command instead (see next section)
pip install 'xframe[fxs]'For more information and tutorials visit the documentaiton atxframe-fxs.readthedocs.ioFluctuation X-Ray Scattering Toolkit [fxs]The projectfxscomes bundled withxFrameand provides a workflow for 3D structure determination from fluctuation X-ray scattering data.Note: No data is required to perform test reconstructions, further information as well as tutorials can be found atxframe-fxs.readthedocs.io/en/latest/fxsTo installxFrametogether with the dependencies needed byfxssimply callpip install 'xframe[fxs]'please make sure that the following libraries are installed on your system before callingpip installGNU Scientific LibraryOpenCLCitationIf you find this piece of software usefull for your scientific project consider citing the paperStill in review ... :)
Package: xfs
This will be a set of modules for working with XFS filesystems.  It will bind xfslibs APIs in a pythonic manner.Currently, the only API implemented is bulkstat.bulkstat(mountpoint)Returns an iterator of stat-like objects for inodes in the filesystem.  The stat-like objects have the same properties as stat results:st_size,st_ino,st_mode,st_dev,st_nlink,st_uid,st_gid,st_atime,st_ctime, andst_mtimeThey also contain:st_fatime,st_fctime,st_fmtime: same asst_atime,st_ctimeandst_mtime, except they return a float including subseconds, similar totime.time()st_xflags: xfs extended flagshas_xattrs(): returns true if the file contains extended attributes.isreg(),isdir(),isfifo(),islink(): convenience methods for identifying file type by mode.open(): contextmanager that returns a read-only file descriptor for the fileDefined constants:XFS_XFLAG_HASATTR,XFS_XFLAG_REALTIME,XFS_XFLAG_PREALLOC,XFS_XFLAG_IMMUTABLE,XFS_XFLAG_APPEND,XFS_XFLAG_SYNC,XFS_XFLAG_NOATIME,XFS_XFLAG_NODUMP,XFS_XFLAG_RTINHERIT,XFS_XFLAG_PROJINHERIT,XFS_XFLAG_NOSYMLINKS,XFS_XFLAG_EXTSIZE,XFS_XFLAG_EXTSZINHERIT,XFS_XFLAG_NODEFRAG,XFS_XFLAG_FILESTREAM,XFS_XFLAG_HASATTR
Package: xftpd
XFTPDAs a network automation engineer, do you get frustrated at not having instant access to an SFTP/FTP server when you need it most?!Well look no further!Simple, Secure, and Randomized...Right at your fingertips, and ready for API integrations, you have come to the right Python Module!XFTPD ServerSFTP and FTP server Class that can be started and stopped programmaticallyWith randomized username, password, RSA keysIdeal for on-demand SFTP and FTP and file transfersSFTP Example>>>fromxftpdimportsftp_server>>>SFTP=sftp_server(Port=2222)>>>SFTP.level='DEBUG'>>>SFTP.start()>>>print(SFTP.Addr)10.8.2.5>>>print(SFTP.User)w7Kg0Fo4Xp6Xo9C>>>print(SFTP.Pass)k2Ea0Ko4Rz6Gz3Z>>>>>>SFTP.stop()>>>Example connecting to SFTP server[root@CentOS]# sftp -P 2222 w7Kg0Fo4Xp6Xo9C@10.8.2.5Theauthenticityofhost'[10.8.2.5]:2222 ([10.8.2.5]:2222)'can't be established.RSA key fingerprint is SHA256:khgoV/GQIShUf4KWr2ZqvpI68KMUFRsedwx4E0hWgi0.RSA key fingerprint is MD5:d2:45:1b:d8:44:66:5f:66:b9:5e:8d:33:fb:01:0b:b1.Are you sure you want to continue connecting (yes/no)? yesWarning: Permanently added '[10.8.2.5]:2222' (RSA) to the list of known hosts.w7Kg0Fo4Xp6Xo9C@10.8.2.5'spassword:
Connectedto10.8.2.5.
sftp>dir
new.pytest.py
sftp>bye[root@CentOS]#FTP ExamplePython3.7.4(default,Sep72019,18:27:02)[Clang10.0.1(clang-1001.0.46.4)]ondarwinType"help","copyright","credits"or"license"formoreinformation.>>>fromxftpdimportftp_server>>>FTP=ftp_server()>>>FTP.start()[I2020-07-1521:14:48]concurrencymodel:multi-thread[I2020-07-1521:14:48]masquerade(NAT)address:None[I2020-07-1521:14:48]passiveports:None>>>print(SFTP.Addr)10.8.2.5>>>print(FTP.User)h0Dy0Yq9Ks0Dm3T>>>print(FTP.Pass)m5Sg2Yk5Mk8Fa4K>>>>>>FTP.stop()>>>
Package: xfunctions
xfunctionsUsageimportxfunctinosasxfmethods:xf.sleep(t:int)xf.get_dict_by_keys(source,keys:listordict,default_none:bool=True)xf.list_strip(the_list)xf.list_remove(the_list:list,ele=None)xf.list_strip_and_remove(the_list:list,ele=None)xf.x_mix(list_in_list,i=1,target=None)xf.path_join(base_dir:str,paths)
Package: xfyun-tts
not work yet, the audio buffer returned save to a mp3 or pcm file
and the audio player not workxfyun_ttsthis is a tts driver usingxfyunfor unittsDependentwebsocket-clientapppublicInstallationpip install xfyun_ttsUsagein the beginning,from xfyun_tts import set_app_info
import unitts

set_app_info(appid, apikey, apisecret)
tts = unitts.init('xfyun_tts')

tts.startLoop()
while True:
	x = input()
	tts.say(x)
tts.endLoop()
Package: xgbexcel
xgbexcelPython package that converts an XGBRegressor model to an Excel formula expression.How to startFirst, you have to install the package.pipinstallxgbexcelHow to convert XGBRegressor model to an Excel formulaLoad packagesfrom xgbexcel import XGBtoExcel
import numpy as np
from xgboost import XGBRegressorCreate dummy dataset and fit XGBRegressor modelX_train, y_train = np.random.randint(0, 1000, (100, 2)), np.random.randint(0, 10, 100)
model = XGBRegressor(n_estimators=2, max_depth=1)
model.fit(X_train, y_train)Convert XGBRegressor model to an Excel formulaxgb_excel_expr = XGBtoExcel(model)
xgb_excel_expr.expressionThe features in the Excel formula are represented using thex1,x2,x3, etc. notation, where the numbers correspond to the enumeration of the features in theXGBRegressormodel andX_train. You can manually rename the features in the Excel formula to match the desired column names in the Excel sheet. Once you have renamed the features, you can copy the expression in the Excel sheet as a formula.feature_map = {'x1': 'feature1', 'x2': 'feature2'}
xgb_excel_expr.rename_features(feature_map)
xgb_excel_expr.expressionSave Excel expression to a filexgb_excel_expr.save_expr('dummy.txt')EnjoyTry it yourself in the example notebook:howXGBtoExcel.ipynb
Package: xgboost-distribution
xgboost-distributionXGBoost for probabilistic prediction. LikeNGBoost, butfaster, and in theXGBoost scikit-learn API.Installation$pipinstallxgboost-distributionDependencies:python_requires = >=3.8

install_requires =
    scikit-learn
    xgboost>=2.0.0UsageXGBDistributionfollows theXGBoost scikit-learn API, with an additional keyword
argument specifying the distribution, which is fit viaMaximum Likelihood Estimation:fromsklearn.datasetsimportfetch_california_housingfromsklearn.model_selectionimporttrain_test_splitfromxgboost_distributionimportXGBDistributiondata=fetch_california_housing()X,y=data.data,data.targetX_train,X_test,y_train,y_test=train_test_split(X,y)model=XGBDistribution(distribution="normal",n_estimators=500,early_stopping_rounds=10)model.fit(X_train,y_train,eval_set=[(X_test,y_test)])See thedocumentationfor all available distributions.After fitting, we can predict the parameters of the distribution:preds=model.predict(X_test)mean,std=preds.loc,preds.scaleNote that this returned anamedtupleofnumpy arraysfor each parameter of the
distribution (we use thescipy statsnaming conventions for the parameters, see e.g.scipy.stats.normfor the normal distribution).NGBoost performance comparisonXGBDistributionfollows the method shown in theNGBoostlibrary, using natural
gradients to estimate the parameters of the distribution.Below, we show a performance comparison ofXGBDistributionand theNGBoostNGBRegressor, using the California Housing dataset, estimating normal distributions.
While the performance of the two models is fairly similar (measured on negative
log-likelihood of a normal distribution and the RMSE),XGBDistributionis around15x faster(timed on both fit and predict steps):Please see theexperiments pagefor results across various datasets.Full XGBoost featuresXGBDistributionoffers the full set of XGBoost features available in theXGBoost scikit-learn API, allowing, for example, probabilistic regression
withmonotonic constraints:AcknowledgementsThis package would not exist without the excellent work from:NGBoost- Which demonstrated how gradient boosting with natural gradients
can be used to estimate parameters of distributions. Much of the gradient
calculations code were adapted from there.XGBoost- Which provides the gradient boosting algorithms used here, in
particular thesklearnAPIs were taken as a blue-print.NoteThis project has been set up using PyScaffold 4.0.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: xgboost-label-encoding
Xgboost Label Encoding (WIP)TODOs: Configuring this templateCreate a Netlify site for your repository, then turn off automatic builds in Netlify settings.Add these CI secrets:PYPI_API_TOKEN,NETLIFY_AUTH_TOKEN(Netlify user settings - personal access tokens),DEV_NETLIFY_SITE_ID,PROD_NETLIFY_SITE_ID(API ID from Netlify site settings)Set up Codecov at TODOOverviewInstallationpipinstallxgboost_label_encodingUsageDevelopmentSubmit PRs againstdevelopbranch, then make a release pull request tomaster.# Optional: set up a pyenv virtualenvpyenvvirtualenv3.9xgboost_label_encoding-3.9echo"xgboost_label_encoding-3.9">.python-version
pyenvversion# Install requirementspipinstall--upgradepipwheel
pipinstall-rrequirements_dev.txt# Install local packagepipinstall-e.# Install pre-commitpre-commitinstall# Run testsmaketest# Run lintmakelint# bump version before submitting a PR against master (all master commits are deployed)bump2versionpatch# possible: major / minor / patch# also ensure CHANGELOG.md updatedChangelog0.0.1First release on PyPI.
Package: xgboostlss
XGBoostLSS - An extension of XGBoost to probabilistic modellingWe introduce a comprehensive framework that models and predicts the full conditional distribution of univariate and multivariate targets as a function of covariates. Choosing from a wide range of continuous, discrete, and mixed discrete-continuous distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of XGBoost, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived.FeaturesEstimation of all distributional parameters.Normalizing Flows allow modelling of complex and multi-modal distributions.Mixture-Densities can model a diverse range of data characteristics.Multi-target regression allows modelling of multivariate responses and their dependencies.Zero-Adjusted and Zero-Inflated Distributions for modelling excess of zeros in the data.Automatic derivation of Gradients and Hessian of all distributional parameters usingPyTorch.Automated hyper-parameter search, including pruning, is done viaOptuna.The output of XGBoostLSS is explained usingSHapley Additive exPlanations.XGBoostLSS provides full compatibility with all the features and functionality of XGBoost.XGBoostLSS is available in Python.News2023-08-25: Release of v0.4.0 introduces Mixture-Densities. See therelease notesfor an overview.2023-07-19: Release of v0.3.0 introduces Normalizing Flows. See therelease notesfor an overview.2023-06-22: Release of v0.2.2. See therelease notesfor an overview.2023-06-21: XGBoostLSS now supports multi-target regression.2023-06-07: XGBoostLSS now supports Zero-Inflated and Zero-Adjusted Distributions.2023-05-26: Release of v0.2.1. See therelease notesfor an overview.2023-05-18: Release of v0.2.0. See therelease notesfor an overview.2021-12-22: XGBoostLSS now supports estimating the full predictive distribution viaExpectile Regression.2021-12-20: XGBoostLSS is initialized with suitable starting values to improve convergence of estimation.2021-12-04: XGBoostLSS now supports automatic derivation of Gradients and Hessians.2021-12-02: XGBoostLSS now supports pruning during hyperparameter optimization.2021-11-14: XGBoostLSS v0.1.0 is released!InstallationTo install XGBoostLSS, please runpipinstallxgboostlssAvailable DistributionsOur framework is built upon PyTorch and Pyro, enabling users to harness a diverse set of distributional families. XGBoostLSS currently supports thefollowing distributions.How to UsePlease visit theexample sectionfor guidance on how to use the framework.DocumentationFor more information and context, please visit thedocumentation.FeedbackWe encourage you to provide feedback on how to enhance XGBoostLSS or request the implementation of additional distributions by opening anew discussion.How to CiteIf you use XGBoostLSS in your research, please cite it as:@misc{Maerz2023,author={Alexander M\"arz},title={{XGBoostLSS: An Extension of XGBoost to Probabilistic Modelling}},year={2023},howpublished={\url{https://github.com/StatMixedML/XGBoostLSS}}}Reference Paper
Package: xgb-rhomut
XGBoost - $\rho-\mu-T$Next-generation non-linear and collapse prediction models for short to long period systems via machine learning methodsThe machine learning approach: Exterme Gradient Boosting (XGBoost)Makes predictions for a strength ratio - ductility - period relationshipsKey arguments:$R$ - strength ratio based on spectral acceleration$\rho$ - strength ratio based on average spectral acceleration$\mu$ - ductility$T$ - period$$
R=\frac{Sa(T)}{Sa_y}
$$$$
\rho_2=\frac{Sa_{avg,2}(T)}{Sa_y}
$$$$
\rho_3=\frac{Sa_{avg,3}(T)}{Sa_y}
$$where$Sa(T)$ stands for spectral acceleration at fundamental period$Sa_y$ stands for spectral acceleration at yield$Sa_{avg,2}(T)$ stands for average spectral acceleration computed at periods
$∈ [0.2T:2T]$$Sa_{avg,3}(T)$ stands for average spectral acceleration computed at periods
$∈ [0.2T:3T]$Installationpip install xgb-rhomutExample predictionExample 1: Dynamic strength ratio prediction of non-collapse scenarios at a dynamic ductility level of 3.0:import xgbrhomut
model = xgbrhomut.XGBPredict(im_type="sa_avg", collapse=False)
prediction = model.make_prediction(
  period=1, 
  damping=0.05, 
  hardening_ratio=0.02, 
  ductility=4, 
  dynamic_ductility=3.0
)Example 2: Dynamic ductility prediction given a strength ratio of 3.0 (since im_type is "sa_avg", and collapse is False, \rho_2 is being estimated):import xgbrhomut
model = xgbrhomut.XGBPredict(im_type="sa_avg", collapse=False)
prediction = model.make_prediction(
  period=1, 
  damping=0.05, 
  hardening_ratio=0.02, 
  ductility=4, 
  strength_ratio=3.0
)prediction:{
  "median": float,
  "dispersion": float
}Other methodsxgbrhomut.r_mu_t.ec8.strength_ratio(mu=3, T=1, Tc=0.5)LimitationsLimitations in terms of input parameters are:$T$ ∈ [0.01, 3.0] seconds$\mu$ ∈ [2.0, 8.0]$\xi$ ∈ [2.0, 20.0] %$a_h$ ∈ [2.0, 7.0] %$a_c$ ∈ [-30.0, -100.0] %$R$ ∈ [0.5, 10.0]where$T$ stands for period$\mu$ stands for ductility$\xi$ stands for damping$a_h$ stands for hardening ratio$a_c$ stands for softening ratio (necessary to compute fracturing ductility, where collapse is assumed)Predictions made using the non-linear analysis resutls of 7292 unique SDOF systems amounting in total to 26,000,000 observations (collapse + non-collapse).ReferencesShahnazaryan D., O'Reilly J.G., 2023, Next-generation non-linear and collapse prediction models for short to long period systems via machine learning methods,Under Review
Package: xgbse
hide:navigationxgbse: XGBoost Survival Embeddings"There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown."- Leo Breiman,Statistical Modeling: The Two CulturesSurvival Analysis is a powerful statistical technique with a wide range of applications such as predictive maintenance, customer churn, credit risk, asset liquidity risk, and others.However, it has not yet seen widespread adoption in industry, with most implementations embracing one of two cultures:models with sound statistical properties, but lacking in expressivess and computational efficiencyhighly efficient and expressive models, but lacking in statistical rigorxgbseaims to unite the two cultures in a single package, adding a layer of statistical rigor to the highly expressive and computationally effcientxgboostsurvival analysis implementation.The package offers:calibrated and unbiased survival curves with confidence intervals (instead of point predictions)great predictive power, competitive to vanillaxgboostefficient, easy to use implementationexplainability through prototypesThis is a research project byLoft Data Science Team, however we invite the community to contribute. Please help by trying it out, reporting bugs, and letting us know what you think!
Package: xgbtune
XGBTune is a library for automated XGBoost model tuning. Tuning an XGBoost
model is as simple as a single function call.Get Startedfromxgbtuneimporttune_xgb_modelparams,round_count=tune_xgb_model(params,x_train,y_train)InstallXGBTune is available on PyPi and can be installed with pip:pip install xgbtuneTuning stepsThe tuning is done in the following steps:compute best roundtune max_depth and min_child_weighttune gammare-compute best roundtune subsample and colsample_bytreefine tune subsample and colsample_bytreetune alpha and lambdatune seedThis steps can be repeated several times. By default, two passes are done.
Package: xgcm
Binder ExamplesLinkProviderDescriptionmybinder.orgBasic self-contained examplePangeo BinderMore complex examples integrated with other Pangeo tools (dask, zarr, etc.)Descriptionxgcmis a python package for working with the datasets produced by numericalGeneral Circulation Models(GCMs) and similar gridded datasets that are amenable tofinite volumeanalysis.
In these datasets, different variables are located at different positions with
respect to a volume or area element (e.g. cell center, cell face, etc.)
xgcm solves the problem of how to interpolate and difference these variables
from one position to another.xgcm consumes and producesxarraydata structures, which are coordinate and
metadata-rich representations of multidimensional array data. xarray is ideal
for analyzing GCM data in many ways, providing convenient indexing and grouping,
coordinate-aware data transformations, and (viadask) parallel,
out-of-core array computation. On top of this, xgcm adds an understanding of
the finite volumeArakawa Gridscommonly used in ocean and atmospheric
models and differential and integral operators suited to these grids.xgcm was motivated by the rapid growth in the numerical resolution of
ocean, atmosphere, and climate models. While highly parallel supercomputers can
now easily generate tera- and petascale datasets, common post-processing
workflows struggle with these volumes. Furthermore, we believe that a flexible,
evolving, open-source, python-based framework for GCM analysis will enhance
the productivity of the field as a whole, accelerating the rate of discovery in
climate science. xgcm is part of thePangeoinitiative.Getting StartedTo learn how to install and use xgcm for your dataset, visit thexgcm documentation.
Package: xgi
XGI is a Python package for higher-order networks.CompleXGroupInteractions (XGI) provides an ecosystem for the
representation, manipulation, and study of the
structure, dynamics, and functions of
complex systems with group (higher-order) interactions.InstallationXGI runs on Python 3.8 or higher.To install the latest version of XGI, run the following command:$ pip install xgiTo install this package locally:Clone this repositoryNavigate to the folder on your local machineRun the following command:$ pip install -e .["all"]Getting StartedTo get started, take a look at thetutorialsillustrating the library’s basic functionality.Corresponding DataA number of higher-order datasets are available in theXGI-DATA repositoryand can be easily accessed with theload_xgi_data()function.ContributingIf you want to contribute to this project, please make sure to read thecontributing guidelines.
We expect respectful and kind interactions by all contributors and users
as laid out in ourcode of conduct.The XGI community always welcomes contributions, no matter how small.
We’re happy to help troubleshoot XGI issues you run into,
assist you if you would like to add functionality or fixes to the codebase,
or answer any questions you may have.Some concrete ways that you can get involved:Get XGI updatesby following the XGITwitteraccount, signing up for ourmailing list, or starring this repository.Spread the wordwhen you use XGI by sharing with your colleagues and friends.Request a new feature or report a bugby raising anew issue.Create a Pull Request (PR)to address anopen issueor add a feature.Join our Zulip channelto be a part of thedaily goings-on of XGI.How to CiteWe acknowledge the importance of good software to support research, and we note
that research becomes more valuable when it is communicated effectively. To
demonstrate the value of XGI, we ask that you cite XGI in your work.
Currently, the best way to cite XGI is to go to ourrepository pageand
click the “cite this repository” button on the right sidebar. This will generate
a citation in your preferred format, and will also integrate well with citation managers.LicenseReleased under the 3-Clause BSD license (see thelicense file)Copyright (C) 2021-2023 XGI Developers