Saravanan666/pypi-org huggingface dataset:
Package: cnn-blocks
# How to use this library?
This library containts all famous CNN blocks like VGG Block , residual block , inception modules etc implemented in keras.
Blocks are constructed in a function so that one can simply write a function call and construct a CNN block.
Various CNN blocks can be sequentially joined with one another to construct an entire architecture.
Implementation of popular architectures like VGG16 , ResNet-50 , Inception-v4 are already present in the repository.
One can also create there own architecture by stacking varoius CNN blocks and have fun while learning ðŸ˜‰.# Why this library?
It is sometimes difficult to understand how such deeper networks like inception-v4 or resnets are implemented.
Most of the times we simple load a pre-trained model available in keras which is good for practical implementation
but hinders the educational process. Using this repository one can understand in depth how each and every block of
CNN architecture is implemented and how these blocks are stacked together to form a large network.
One can also use this repository to create new architectures using various CNN blocks present.
If these blocks are not enough, one can create new cnn blocks in a similar fashion as given in this repository.Change Log0.0.1 (5/20/2021)First Release
Package: cnncam
cnncamIntroductionAn open python library for researchers and developers to generate GradCAM explanations for the tensorflow CNN models.Other popular python ML frameworks will soon be supported by the cnn-cam libraryInstallThe below instructions assume you already havepipinstalled and exposed to the python environment where you want to runcnncam.
Official instructions for installingpipcan be foundhereRun the below pip command in a shell of your choice.pip install cnncamDemoWe currently support two ways of obtaining GradCAM heatmaps:Display heatmap.
If you're only interested in seeing the heatmap images for your model's prediction, you can run the below script:fromcnncamimportdisplay_heatmapdisplay_heatmap(model=model,# your keras modelimg=img,# your imagepredicted_class=pred,# your models prediction for the imagelayer_name='block5_conv3',# the layer you would like to see GradCAM foralpha=0.6# opacity of heatmap overlayed on image)See/examplesfor executable examples, including the above application of our implementation of GradCAM on VGG-16 with the ower of this repo's very cute cat, meso.
Package: cnncc
CNNCC: Cell-Cycle phase Classification using CNNsThis is a student project. Please use cautiously.Project EPFL 2023 @ ML4Science Machine Learning ClassThis package provide multiple ML models that can be used to make real-time inference on images.How to use it ?$cnncc--model<model>--image<path/to/img.[tif,png]>modelcan be one of:NagaoResNetLRSee our report for more details.
Package: cnnclustering
Common-nearest-neighbours clusteringNOTEThis project is currently under development.The implementation may change in the future. Check the examples and the documentation for up-to-date information.cnnclusteringThecnnclusteringPython package provides a flexible interface to use thecommon-nearest-neighbours cluster algorithm. While the method can be applied to arbitrary data, this implementation was made before the background of processing trajectories from Molecular Dynamics simulations. In this context the cluster result can serve as a suitable basis for the construction of a core-set Markov-state (cs-MSM) model to capture the essential dynamics of the underlying molecular processes. For a tool for cs-MSM estimation, refer to this separateproject.The package provides a main module:cluster: User interface to (hierarchical) common-nearest-neighbours clusteringFurther, it contains the modules:plot: Convenience functions to evaluate cluster results_types: Direct access to generic types representing needed cluster components_fit: Direct access to generic clustering proceduresFeatures:Flexible: Clustering can be done for data sets in different input formats. Easy interfacing with external methods.Convenient: Integration of functionality, handy in the context of Molecular Dynamics.Fast: Core functionalities implemented in Cython.Please refer to the following papers for the scientific background (and consider citing if you find the method useful):B. Keller, X. Daura, W. F. van GunsterenJ. Chem. Phys.,2010,132, 074110.O. Lemke, B.G. KellerJ. Chem. Phys.,2016,145, 164104.O. Lemke, B.G. KellerAlgorithms,2018,11, 19.DocumentationThe package documentation (under developement) is availablehereonline or underdocs/index.html.
The sources for the documentation can be found underdocsrc/.InstallRefer to thedocumentationfor more details. Install from PyPi$pipinstallcnnclusteringor clone the development version and install from a local branch$gitclonehttps://github.com/janjoswig/CommonNNClustering.git
$cdCommonNNClustering
$pipinstall.Quickstart>>>fromcnnclusteringimportcluster>>># 2D data points (list of lists, 12 points in 2 dimensions)>>>data_points=[# point index...[0,0],# 0...[1,1],# 1...[1,0],# 2...[0,-1],# 3...[0.5,-0.5],# 4...[2,1.5],# 5...[2.5,-0.5],# 6...[4,2],# 7...[4.5,2.5],# 8...[5,-1],# 9...[5.5,-0.5],# 10...[5.5,-1.5],# 11...]>>>clustering=cluster.Clustering(data_points)>>>clustering.fit(radius_cutoff=1.5,cnn_cutoff=1,v=False)>>>clustering.labelsarray([1,1,1,1,1,0,0,0,0,2,2,2])Alternative scikit-learn implementationWe provide an alternative approach to common-nearest-neighbours clustering in the spirit of the scikit-learn project withinscikit-learn-extra.
Package: cnn-framework
CNN frameworkRun CNN models for classification, regression, segmentation, VAE, contrastive learning with any data set.InstallationFirst, create a dedicated conda environment using Python 3.9condacreate-ncnn_frameworkpython=3.9
condaactivatecnn_frameworkTo install the latest github version of this library run the following using pippipinstallgit+https://github.com/15bonte/cnn_frameworkor alternatively you can clone the github repositorygitclonehttps://github.com/15bonte/cnn_framework.gitcdcnn_framework
pipinstall-e.If you want to run jupyter tutorials, you also need to install ipykernelpipinstallipykernelIf you want to work with VAE, you must also installPythaeandWandB, which is not the case by default.pipinstallpythaepipinstallwandb
Package: cnnlite
cnnliteDescriptioncnnliteis a web scraper for CNN Lite. This package collects CNN Lite articles athttps://lite.cnn.com.Installation>>>pipinstallcnnliteUsage# instantiate scraperimportcnnlitefrompprintimportpprint# start up scraperscraper=cnnlite.CNNLite()# export all articles to a json file# default name: 'cnn_lite_<timestamp>.json'scraper.to_json_file()# show sample of headlines and URL listprint(scraper.headlines[:5])print(scraper.urls[:5])# large collection / nest dict containing each article for the daydocs=scraper.all_articles# articles can be access one at a time tooarticle_name=scraper.headlines[0]pprint(docs[article_name])Sample CNN Lite Home Page and Output JSONBot EtiqueteeBe a good bot and comply with the publisher'srobots.txtguidelines.
Package: cno
cellnopt/cnoNote:Cellnopt is tested under Python2.7 and portage to Python3 is on the wayContributions:Please joincellnopt github projectand share yournotebooksIssues:Please usecellnopt issues and bug reportDocumentation:Some documentation (mostly API) are also available onpypiThis is a stable version that contains datasets and tools to manipulate
models and data sets used in CellNOpt project. The sub-package cno.io is ready and
is a replacement forcellnopt.corepackage.
cno.datasets sub-package is a replacement forcellnopt.datapackage.As for the formalisms, pipelines are provided for each formalism and are available in the
boolean, ode and fuzzy packages.See also:http://www.cellnopt.orgfor the context.InstallationFrom Pypi / pip:sudo pip install cnoFrom github:# Get the source either from http
git clone http://github.com/cellnopt/cellnopt.git
# or SSH
# git clone git@github.com:cellnopt/cellnopt.git
cd cellnopt
sudo python setup.py install
Package: cnode
CNode(Complex node)The actual object can be abstracted as a node, and the relationship between the objects is expressed by the connections between the nodes.Each node has a specific meaning and can correspond to a data object or process object. The connection between nodes represents the relationship between them. If the status of a node is active, the status of the related node may be active too. In general, there are many factors that cause the state of a node to change. Each factor is associated with multiple other nodes(objects). The structure of the node needs to reflect this case, so a node is not a simple abstract point, but should have relatively complex structure.Cnode consists of multiple input points and an output point. Each input point is connected to multiple other nodes, which can be regarded as a factor affecting the state of the node. The input point and other nodes are connected through a network. The output point is connected to all input points. The number of input points and connected cnodes can be changed dynamically.Given status of some nodes, taken these nodes as the input points of other nodes, and get the status of output point of these nodes. A node can correspond to an actual object. When the node is activated, the corresponding object can do some processing. The connections between nodes can be judged based the actual object activation is correct or not.It's features :Each node can correspond to an actual objectEach node is true (activated) can be determined by multiple factors (points), each factor is related to multiple other nodes.Each node forms a network through connections and their parameters, supporting inference based on the relationship between nodes.Supports dynamic execution(reasoning) to obtain new active nodesThe execution or inference logic is placed in NodePoint's OnExecute method, which can be flexibly defined, including the use of neural networks algorithm.Supports dynamic establishment and optimization of connections between nodes.More detail
Package: cnos-connector
CnosDB Python ConnectorCnosDB Python Connector repository contains the Python client library for theCnosDB. cnosdb_connector adapted for PEP249.Installationuse pip install it from pypi,Python 3.6or later is required.pip install cnos-connectorThen import the package:import cnosdb_connectorGetting StartedQuery use SQLfromcnosdb_connectorimportconnectconn=connect(url="http://127.0.0.1:31007/",user="root",password="")resp=conn.execute("SHOW DATABASES")print(resp)Query use interfacefromcnosdb_connectorimportconnectconn=connect(url="http://127.0.0.1:31007/",user="root",password="")conn.create_database("air")resp=conn.list_database()print(resp)Query use PEP-249fromcnosdb_connectorimportconnectconn=connect(url="http://127.0.0.1:31007/",user="root",password="")cursor=conn.cursor()cursor.execute("SHOW DATABASES")resp=cursor.fetchall()print(resp)Query use pandasimportpandasaspdfromcnosdb_connectorimportconnectconn=connect(url="http://127.0.0.1:31007/",user="root",password="")resp=pd.read_sql("SHOW DATABASES",conn)print(resp)Write use LineProtocolfromcnosdb_connectorimportconnectline0="test_insert,ta=a1,tb=b1 fa=1,fb=2 1"line1="test_insert,ta=a1,tb=b1 fa=3,fb=4 2"line2="test_insert,ta=a1,tb=b1 fa=5,fb=6 3"conn=connect(url="http://127.0.0.1:31007/",user="root",password="")conn.create_database_with_ttl("test_database","100000d")conn.switch_database("test_database")conn.write_lines([line0,line1,line2])resp=conn.execute("SELECT * FROM test_insert;")print(resp)Write use SQLfromcnosdb_connectorimportconnectconn=connect(url="http://127.0.0.1:31007/",user="root",password="")query="insert test_insert(TIME, column1, column2, column3, column4, column5, column6, column7) values (100, -1234, 'hello', 1234, false, 1.2, 'beijing', 'shanghai'); "conn.execute(query)resp=conn.execute("SELECT * FROM test_insert;")print(resp)Write use CSVfromcnosdb_connectorimportconnectimportosquery="CREATE TABLE test_insert(column1 BIGINT CODEC(DELTA),\column2 BOOLEAN,\column3 DOUBLE CODEC(GORILLA),\TAGS(column4));"conn=connect(url="http://127.0.0.1:31007/",user="root",password="")# table schema must same with csv fileconn.execute(query)path=os.path.abspath("test.csv")conn.write_csv("test_insert",path)resp=conn.execute("SELECT * FROM test_insert;")print(resp)LicenseCnosDB Python Connector use MIT License
Package: cnparser
cnparsercnparseris a python library for loading and enrichmentCorporate Number Publication Sitedata that is provided from National Tax Agency Japan. cnparser only support to parse latest data now.Installationcnparser is available on pip installation.$ python -m pip install cnparserGitHub InstallInstalling the latest version from GitHub:$ git clone https://github.com/new-village/cnparser
$ cd cnparser
$ python setup.py installUsageMany properties are available once the cnparser object is created.Collect basic information (基本3情報)>>>importcnparser>>>cndata=cnparser.bulk_load("Shimane")>>>cndata.show[{'sequence_number':'1','corporate_number':'1000013050246',...,'hihyoji':'0'},{...}]Enrich information>>>importcnparser>>>cndata=cnparser.bulk_enrich(cndata)>>>cndata.show[{'sequence_number':'1',...,'lat':34.978982,'lng':132.525163,'level':3},{...}]
Package: cnp-generator
CNP generator and decoder📖 AboutCnp-generator package contains both a Python lib and CLI tool for generate and decode CNPs.With CLI tool you can:generate a valid CNP based on gender, region, and birth date.check if a CNP is validdecode CNP informationgenerate random CNPs for testing purposesPython lib containsCnpclass that will provide all functionalities available in tool to be used in code.💻 Tool UsageRun CNP tool with:python -m cnpgenAvailable options:-h --help   Show help.
--version   Show program version.
-c          Start new CNP wizard. Guided wizard for genereating a CNP based on input info.
-g N        Generate N random CNPs.
-i CNP      Show relevant information about CNP.🧪 API UsageQuick startfromdatetimeimportdatefromcnpgenimportCnp,Gender,Regionprint(Cnp(Gender.F,date(1993,3,4),Region.Bucuresti))>>>2930304400014Full documentationSoon available on Readthedocs.
Package: cnpjinfo
cnpjinfocnpjinfois a library for obtaining cnpj information via scraping from the receitaws website.FeaturesCNPJ information via receitaws website.InstallationRunpip install cnpjinfoExxemplefromcnpjinfoimportcnpjinfo# For one CNPJinfo=cnpjinfo('12345678901234')nome=info.get('nome')tel=info.get('telefone')print(f'Nome:{nome}, Tel:{tel}'))# For CNPJ listcnpj_list=["05720854000166","00623904000173","15436940000103"]result=cnpjinfo_list(cnpj_list)forcnpjinresult:info=cnpjinfo('12345678901234')nome=info.get('nome')tel=info.get('telefone')print(f'Nome:{nome}, Tel:{tel}'))console outputfoo@bar:~$./myscript.py
Nome:COMPANYNAME.,Tel:(11)1111-1111
foo@bar:~$Upgrade-Run`pipinstallcnpjinfo--upgrade`
Package: cnprep
Chinese text preprocessYou can extract numbers, email, website, emoji, tex, and delete spaces, punctuations.Install>> pip install cnprepUsagefrom cnprep import Extractor
ext = Extractor(args=['email', 'number'], limit=5)
ext.extract(message)args: option
    e.g. ['email', 'telephone'] or 'email, telephone'
    email
    telephone
    web
    QQ
    tex
    wechat
    message (without punctuation)
    blur (Ⅰ①壹...)
limit: parameter for get_number (blur)Also, you can use ‘’ext.reset_param()’’ to reset the parameters.AttentionThe URL extractor only support ASCII
Package: cnr
# CNR Command Line Tool## Install the Helm Registry PluginFirst, Install the latest [Helm release](https://github.com/kubernetes/helm#install).If you are an OSX user, quickstart with brew:brew install kubernetes-helmNext download and install the registry plugin for Helm.### OSX` wgethttps://github.com/cn-app-registry/cnr-cli/releases/download/v0.3.7-dev/registry-cnr-v0.3.7-dev-osx-x64-helm-plugin.tar.gzmkdir-p~/.helm/plugins/tar xzvfregistry-cnr-v0.3.7-dev-osx-x64-helm-plugin.tar.gz-C~/.helm/plugins/`### Linux` wgethttps://github.com/cn-app-registry/cnr-cli/releases/download/v0.3.7-dev/registry-cnr-v0.3.7-dev-linux-x64-helm-plugin.tar.gzmkdir-p~/.helm/plugins/tar xzvfregistry-cnr-v0.3.7-dev-linux-x64-helm-plugin.tar.gz-C~/.helm/plugins/`### Windows` wgethttps://github.com/cn-app-registry/cnr-cli/releases/download/v0.3.7-dev/registry-cnr-v0.3.7-dev-win-x64-helm-plugin.tar.gzmkdir-p~/.helm/plugins/tar xzvfregistry-cnr-v0.3.7-dev-linux-x64-helm-plugin.tar.gz-C~/.helm/plugins/`Note: You must have bash in your path and change theregistry/plugin.yamlexecution to callbash -c $HELM_PLUGIN_DIR/cnr.sh## Deploy Jenkins Using Helm from the Quay Registry` helm registry version app.quay.io `Output should be:`Api-version:{u'cnr-api':u'0.X.Y'}Client-version:0.X.Y `### Install Jenkins` helm init helm registry list app.quay.io helm registry install app.quay.io/helm/jenkins `## Create and Push Your Own ChartFirst, create an account onhttps://app.quay.io(staging server) and login to the CLI using the username and passwordSet an environment for the username created at Quay to use through the rest of these instructions.` export USERNAME=philips `Login to Quay with the Helm registry plugin:` helm registry login-u$USERNAME app.quay.io `Create a new Helm chart, the default will create a sample nginx application:` helm create nginx `Push this new chart to Quay and then deploy it from Quay.` cd nginx helm registry push--namespace$USERNAME app.quay.io helm registry installapp.quay.io/$USERNAME/nginx`
Package: cnregion
[![Build Status](https://travis-ci.org/xiachufang/cnregion.svg)](https://travis-ci.org/xiachufang/cnregion) [![Coverage Status](https://coveralls.io/repos/github/xiachufang/cnregion/badge.svg?branch=coveralls)](https://coveralls.io/github/xiachufang/cnregion?branch=coveralls)# cnregionUtils for Administrative divisions of China## Api```pythonIn [1]: import cnregionIn [2]: print cnregion.get_region('156110101001000')<Region 110101001000/东华门街道>```
Package: cns
cnscns常用的工具包, 内置大量常用工具, 正在不断增加中......安装方法pip install cns使用方法import cns

if __name__ == "__main__":

    dfx  = cns.DateTimeX()  # 可初始化 "2023-05-28 23:23:51"

    print("初始时间:", dfx)

    print("昨天的本月区间:", dfx.get("{[年-月-初||复位-1日]}~{[年-月-日]}"))

    print("昨天的去年本月区间:", dfx.get("{[年-月-初||复位-1日-1年]}~{[年-月-末]}"))

    print("昨天的上月区间:", dfx.get("{[年-月-初||复位-1日-1月]}~{[年-月-末]}"))

    print("昨天的近35天区间:", dfx.get("{[年-月-日||复位-35日]}~{[年-月-日||复位-1日]}"))

    print("前35天的日期:", dfx.get("{[年-月-日||复位-35日]}"))

    print("昨天的月1号:", dfx.get("{[年-月-初||复位-1日]}"))

    print("昨天的本月:", dfx.get("{[年-月||复位-1日]}"))


'''
# 说明：
1、分隔符“||” 左侧是获取的时间格式，右侧是时间计算的指令；
2、左左侧的字符串中通包含年、月、日、时、分、秒、初、末，用来获取时间和格式，其中“初”是获数字1，“末”最后一天的日期；
3、右侧是时间计算的指令支持年、月、日、时、分、秒、复位，用来对当前的时间进行加和减的计算，其中“复位”代表恢复到初始时间；

目前支持WINDOWS下 Python 3.6 和 3.9
联系作者：6566666@qq.com
'''
Package: cn-sentiment-measures
Chinese Sentiment MeasuresA toolkit for estimating Chinese sentiment scores with multiple measures.pip install cn-sentiment-measuresMeasuresAbsolute Proportional Difference - APD. Bounds: [-1, 1]Relative Proportional Difference - RPD. Bounds: [-1, 1]Logit scale - LC.  Bounds: [-infinity, +infinity]Improved versions of the above measures by integrating degree words are marked with_with_degreein the toolkit.Examplesfromcn_sentiment_measures.sentiment_measuresimportSentimentMeasuressm=SentimentMeasures()word_list=["我","今天","很","高兴"]print("Here are measure values with normal measures!")print('APD = ',sm.APD(word_list))print('RPD = ',sm.RPD(word_list))print('LS = ',sm.LS(word_list))print()print("Here are measure values optimized by using adverb words!")print('APD* = ',sm.APD_with_degree(word_list))print('RPD* = ',sm.RPD_with_degree(word_list))print('LS* = ',sm.LS_with_degree(word_list))LicenseThe project is provided byDonghua Chen.
Package: cn-smtp-sink-server
Not On PyPiThis is a reserved package name to preventdependency confusion / substitution attack. If installed, will immediately raise an error indicating that the wrong PyPi repository was used.If someone would like to use this package name, I would be happy to discuss handoff. Please contact me through a Github issue onKyleKing/not-on-pypior if I don't respond, you can try mygmailaccountdev.act.kyle
Package: cnsp
cnsp🎌🇺🇸English|🇨🇳大陆简体|🇭🇰香港繁體|🇹🇼臺灣正體Search predictions in mainland ChinaInstallationpipinstallcnspUsage>>>fromcnspimportgoogle,baidu>>>google('Google')['google','google play','google翻译','google translate','google scholar','google map','google drive','google earth','google voice','google play下载']>>>baidu('baidu')['百度翻译','百度','百度识图','百度地图','百度网盘','baidu.com','百度翻译在线翻译','百度贴吧','百度网盘破解版开发者','百度推荐为什么关不了']>>>For more search engines, see§Supported search engines.Supported search enginesFunctionSearch engineURLAPI domainbaidu百度一下，你就知道https://www.baidu.comwww.baidu.comsm神马搜索https://m.sm.cnsugs.m.sm.cnsogou搜狗搜索引擎 - 上网从搜狗开始https://www.sogou.comwww.sogou.comso360搜索，SO靠谱https://www.so.comsug.so.360.cngoogleGooglehttps://www.google.com.hkwww.google.cnyamyam蕃薯藤-搜尋https://search.yam.comsearch.yam.com
Package: cnspy-csv2dataframe
cnspy_csv2dataframeThe classCSV2DataFrameis intended to loadCSV-filesinto apandas.DataFrame. The CSV files need to match known formats (defined by their header in the first line) according to those defined in the packagespatial_csv_formats.In case no format is specified, it tries to match the first line of the CSV-File with known headers fromcnspy_spatial_csv_formatsand loads the data in that format.There a some specialization ofCSV2DataFramethat support different operations on the data:PoseCovCSV2DataFramePoseWithCov2DataFrameTimestampCSV2DataFrameTUMCSV2DataFrameInstallationInstall the current code base from GitHub and pip install a link to that cloned copygit clone https://github.com/aau-cns/csv2dataframe.git
cd csv2dataframe
pip install -e .DependenciesIt is part of thecnspy eco-systemof thecns-githubgroup.numpypandascnspy_spatial_csv_formatscnspy_numpy_utilsLicenseSoftware License Agreement (GNU GPLv3  License), refer to the LICENSE file.Sharing is caring!-Roland Jung
Package: cnspy-numpy-utils
cnspy_numpy_utils PackageA package holding useful scripts based on the numpy package, that cannot be associated to any other (dangling code, that is used in different places).Most important functions:tri_vec_to_mat(tri_vec, n=None)mat_to_tri_vec(P)accumulated_distance(p_vec)total_distance(p_vec)numpy_statistics(vNumpyprint_statistics(metricsInstallationInstall the current code base from GitHub and pip install a link to that cloned copygit clone https://github.com/aau-cns/cnspy_numpy_utils.git
cd cnspy_numpy_utils
pip install -e .DependenciesIt is part of thecnspy eco-systemof thecns-githubgroup.numpyLicenseSoftware License Agreement (GNU GPLv3  License), refer to the LICENSE file.Sharing is caring!-Roland Jung
Package: cnspy-script-utils
cnspy_script_utilsA package holding useful scripts, that cannot be associated to any other (dangling code, that is used in different places).Most important functions:get_list_of_dir(directory)get_list_of_files(directory)string_to_list(string, pattern)string_to_dict(string, pattern)InstallationInstall the current code base from GitHub and pip install a link to that cloned copygit clone https://github.com/aau-cns/cnspy_script_utils.git
cd cnspy_script_utils
pip install -e .DependenciesIt is part of thecnspy eco-systemof thecns-githubgroup.Main dependencies are:resysosLicenseSoftware License Agreement (GNU GPLv3  License), refer to the LICENSE file.Sharing is caring!-Roland Jung
Package: cnspy-space
Web3.pyA Python library for interacting with Conflux EVM network (Also called Conflux Esapce), inspired byweb3.py.Python 3.7.2+ supportTest for the CNSexport WEB3_PROVIDER_URI=https://evmtestnet.confluxrpc.com
cat test.py
python3 test.pyTest mainnet CNSecho "RPC_URL = https://evm.confluxrpc.com" > .env
cat mainnet_test.py
python3 mainnet_test.pyQuickstartGet started in 5 minutesortake a tourof the library.DocumentationFor additional guides, examples, and APIs, see thedocumentation.Want to help?Want to file a bug, contribute some code, or improve documentation? Excellent! Read up on our
guidelines forcontributing,
then check out issues that are labeledGood First Issue.Questions on implementation or usage? Join the conversation ondiscord.