Saravanan666/pypi-org huggingface dataset:
Package: darwin-fiftyone
darwin_fiftyoneProvides integration between Voxel51 and V7 Darwin. This enables Voxel51 users to send subsets of their datasets to Darwin for annotation and review. The annotated data can then be imported back into Voxel51.This integration is currently in beta.Example UsageTo illustrate, let's upload all files from the zoo dataset "quickstart" into a Darwin dataset named "quickstart-example". If the dataset doesn't already exist in Darwin, it will be created.importfiftyone.zooasfozdataset=foz.load_zoo_dataset("quickstart",dataset_name="quickstart-example")#If video annotationdataset.ensure_frames()dataset.annotate("annotation_job_key",label_field="ground_truth",atts=["iscrowd"],launch_editor=True,backend="darwin",dataset_slug="quickstart-example",external_storage="example-darwin-storage-slug",base_url="https://darwin.v7labs.com/api/v2/teams",)Note: You will have to use theensure_frames()method on the dataset/view if you are annotating videos. You must also ensure that the label_field begins withframes.e.g.frames.detectionsAfter the annotations and reviews are completed in Darwin, you can fetch the updated data as follows:dataset.load_annotations("annotation_job_key")APIIn addition to the standard arguments provided by dataset.annotate(), we also support:backend=darwin, Indicates that the Darwin backend is being used.atts, Specifies attribute subannotations to be added in the labelling jobdataset_slug, Specifies the name of the dataset to use or create on Darwin.external_storage, Specifies the sluggified name of the Darwin external storage and indicates that all files should be treated as external storageChecking StatusYou can check the status of your V7 Darwin dataset by calling thecheck_status()methodresults=dataset.load_annotation_results(anno_key)results.check_status()ConfigurationTo integrate with the Darwin backend:Install the backend:pipinstall.Configure voxel51 to use it.cat~/.fiftyone/annotation_config.json{"backends":{"darwin":{"config_cls":"darwin_fiftyone.DarwinBackendConfig","api_key":"d8mLUXQ.**********************"}}}Note: Replace the api_key placeholder with a valid API key generated from Darwin.Supported Annotation TypesThe integration currently supports bounding boxes, polygons (closed polylines), keypoints, and tags (classification). It also supports attributes, text, instance ids, and properties subtypes.Future development work will focus on the addition of annotation and subannotation types. Do reach out if you have suggestions.TODOSupport for read only external data storageSupport for mask and keypoint skeleton types
Package: darwinio
Darwinio______   _______  _______          _________ _       _________ _______
(  __  \ (  ___  )(  ____ )|\     /|\__   __/( (    /|\__   __/(  ___  )
| (  \  )| (   ) || (    )|| )   ( |   ) (   |  \  ( |   ) (   | (   ) |
| |   ) || (___) || (____)|| | _ | |   | |   |   \ | |   | |   | |   | |
| |   | ||  ___  ||     __)| |( )| |   | |   | (\ \) |   | |   | |   | |
| |   ) || (   ) || (\ (   | || || |   | |   | | \   |   | |   | |   | |
| (__/  )| )   ( || ) \ \__| () () |___) (___| )  \  |___) (___| (___) |
(______/ |/     \||/   \__/(_______)\_______/|/    )_)\_______/(_______)This is a Python program that simulates the process of evolution, complete with
organisms, genetic codes, and neural networks.FeaturesYou get to randomly generate organisms, and see how they evolve.Change the conditions while the simulation is running.You can control - Food and TemperatureScreenshotSetup*Nix(Gnu-Linux, *BSD, MacOS)Make sure your python is up-to dateInstall pipxFor Debian and it's derivativesapt install pipxFor ArchLinux and it's derivativespacman -S python-pipxFor Gentoosudo emerge --sync
sudo emerge --ask --update --deep --newuse @world
sudo emerge --ask dev-python/pip
sudo pip install pipxOpen your terminal, and install darwinio by runningpipxinstalldarwinioTo run the program,pipxrundarwinioMicrosoft WindowsMake sure you python is up-to date, if you don't have python installed
install a recent version frompython's official website..Or, if you havewingetavailable, you can use,wingetinstallpythonOpen your terminal/Powershell, and install darwinio by runningpy-mpipinstalldarwinioTo run the program,py-mdarwinioBuilding and testing*Nix(Gnu-Linux, *BSD, MacOS)Clone the repository to your local machine using the following command in your terminalgit clone https://github.com/tusharhero/darwinio.gitCreate a virtual environment and source itpython -m venv .venv
source ./.venv/bin/activateGet the requirementspip install buildBuild and install itpython -m build
pip install .Test it by running it.python -m darwinioLicenseThis program is copyright © 2023 Tushar Maharanatusharhero@sdf.org,
and Mihir Nallagondaadhikshithamihir@gmail.com.This program is licensed under the terms of theGNU GENERAL PUBLIC LICENSE VERSION 3which means this is Free Software.
Package: darwinpyspark
V7 DarwinPysparkDarwinPysparkis the official library for managing data in Databricks and seamlessly sending that data to be managed and used inV7. It provides an easy-to-use interface for interacting with V7's API, allowing users to upload and download datasets, as well as perform various operations on the data.FeaturesUpload data from a PySpark DataFrame to V7Download data from V7 and load it into a PySpark DataFrameHandle data registration, uploading, and confirmation with V7Efficiently manage large datasets and data exportsInstallationpip install darwinpysparkUsageThis framework is designed to be used alongside our python SDK, see examples of darwin-py in our docshere.To get started with DarwinPyspark, you'll first need to create a DarwinPyspark instance with your V7 API key, team slug, and dataset slug:fromdarwinpysparkimportDarwinPysparkAPI_KEY="your_api_key"team_slug="your_team_slug"dataset_slug="your_dataset_slug"dp=DarwinPyspark(API_KEY,team_slug,dataset_slug)Uploading DataTo upload a PySpark DataFrame to V7, use the upload_items method:# Assume `df` is your PySpark DataFrame with columns 'object_url' and 'file_name'dp.upload_items(df)The upload_items method takes a PySpark DataFrame with columns 'object_url' (accessible open or presigned URL for the image) and 'file_name' (the name you want the file to be listed as in V7).Now, users would interact with this data in the V7 platform - e.g. create ML workflows, annotate files such as images, videos, dicoms or test model performance etc.Downloading DataTo download data from V7 as a PySpark DataFrame, use the download_export method:export_name="your_export_name"export_df=dp.download_export(export_name)LicenseDarwinPyspark is released under theMIT License.
Package: darwin-ssl-sniffer
DescriptionUtility for sniffing SSL/TLS encrypted traffic on a darwin-based platforms.CFNetwork.frameworkcontains a debug/verbosity global, enforcing a dump of every packet transferred through it, to be
logged into device syslog in plaintext form. In order to have a nicer view with clean control flow of this traffic, we
attach each such packet appropriate TCP flags and write it back into a PCAP file.This allows us to later dissect this traffic using popular and convenient tools (e.g. Wireshark 🦈).On iOS, this will require a jailbroken iOS device.Installationpython3-mpipinstall-Udarwin-ssl-snifferUsageLocal macOS machineSimply execute:# output file can be given using the -o option (traffic.pcapng by default)python3-mdarwin_ssl_sniffersniffJailbroken iOS deviceDownload and installrpcserveron
a jailbroken device.Execute:python3-mdarwin_ssl_sniffermobilesetup-p5910This step should be performed only once on the device. The first time will require a device reboot (you will be
prompted to if this is indeed the first time).Execute:# output file can be given using the -o option (traffic.pcapng by default)python3-mdarwin_ssl_sniffermobilesniff
Package: das
Deep Audio SegmenterDASis a method for automatically annotating song from raw audio recordings based on a deep neural network.DAScan be used with a graphical user interface, from the terminal, or from within python scripts.If you have questions, feedback, or find bugs please raise anissue.Please citeDASas:Elsa Steinfath, Adrian Palacios, Julian Rottschäfer, Deniz Yuezak, Jan Clemens (2021).Fast and accurate annotation of acoustic signals with deep neural networks.eLifeInstallationPre-requisitesAnaconda:DASis installed using an anaconda environment. For that, first install theanaconda python distribution(orminiconda).If you have conda already installed, make sure you have conda v4.8.4+. If not, update from an older version withconda update conda.Libsoundfile on linux: The graphical user interface (GUI) reads audio data usingsoundfile, which relies onlibsndfile.libsndfilewill be automatically installed on Windows and macOS. On Linux, the library needs to be installed manually with:sudo apt-get install libsndfile1. Note thatDASwill work w/olibsndfilebut will not be able to load exotic audio formats.InstallDASCreate an anaconda environment calleddasthat contains all the required packages:condainstallmamba-cconda-forge-nbase-y
mambacreatepython=3.9das=0.32.0-cconda-forge-cncb-canaconda-cnvidia-capple-ndas-yFor linux, the last line needs to be:CONDA_OVERRIDE_CUDA=11.2mambacreatepython=3.9das=0.32.0-cconda-forge-cncb-canaconda-cnvidia-ndas-yUsageTo start the graphical user interface:condaactivatedas
dasguiThe documentation athttps://janclemenslab.org/das/provides information on the usage ofDAS:Aquick start tutorialwalks through all steps from manually annotating song, over training a network, to generating new annotations.How to use thegraphical user interface.How to useDASfrom the terminal or from python scripts.AcknowledgementsThe following packages were modified and integrated into das:Keras implementation of TCN models modified fromkeras-tcn(indas.tcn)Trainable STFT layer implementation modified fromkapre(indas.kapre)See the sub-module directories for the original READMEs.
Package: das7pad-dns-lexicon
Manipulate DNS records on various DNS providers in a standardized/agnostic way.Table of ContentsWhy using Lexicon?Supported providersDocumentationContributingLicensingWhy using Lexicon?Lexicon provides a way to manipulate DNS records on multiple DNS providers in a standardized way.Lexicon can be used as:a CLI tool:# Create a TXT entry in domain.net zone hosted by CloudFlarelexiconcloudflarecreatedomain.netTXT--namefoo--contentbaror a Python library:# Create a TXT entry in domain.net zone hosted by CloudFlarefromlexicon.clientimportClientfromlexicon.configimportConfigResolveraction={"provider_name":"cloudflare","action":"create","domain":"domain.net","type":"TXT","name":"foo","content":"bar",}config=ConfigResolver().with_env().with_dict(action)Client(config).execute()Lexicon was designed to be used in automation, specifically letsencrypt.Generating Intranet & Private Network SSL Certificates using Lets Encrypt & LexiconSupported providersOnly DNS providers who have an API can be supported bylexicon.The current supported providers are:Aliyun.comAuroraDNSAWS Route53Azure DNSCloudflareClouDNSCloudXNSConoHaConstellixDigitalOceanDinahostingDirectAdminDNSimplev1,v2DnsMadeEasyDNSParkDNSPodDreamhostDynuEasyDNSEasynameEUservExoScaleGandiRPC (old)/LiveAPIGehirnGlesysGoDaddyGoogle Cloud DNSGransy (sites subreg.cz, regtons.com and regnames.eu)HoverHurricane Electric DNSHetznerInfobloxInternet.bsINWXJoker.comLinodeLinode v4LuaDNSMemsetNjallaNamecheapNamesiloNetcupNFSN (NearlyFreeSpeech)NS1OnAppOnlineOVHPleskPointHQPowerDNSRackspaceRage4RcodeZeroSakura Cloud by SAKURA Internet Inc.SafeDNS by UKFastSoftLayerSubreg (deprecated, use Gransy)TransipUltraDNSVultrYandexZeitZiloreZonomiDocumentationOnline documentation (user guide, configuration reference) is available in theLexicon documentation.For a quick start, please have a look in particular at theUser guide.ContributingIf you want to help in the Lexicon development, you are welcome!Please have a look at theDeveloper guidepage to know how to start.LicensingMITLogo: transform by Mike Rowe from the Noun Project
Package: dasb-translator
Dasb_TranslatorA CLI translation tool by reading the clipboard using deepl translator.大帅比翻译助手一个通过读取剪切板，并将翻译内容呈现于命令行的翻译助手，翻译引擎为deepl，在读论文文献时很有帮助。项目契机自己是科研狗又习惯使用 Linux 作为个人机，但 Linux 系统上的论文翻译助手使用起来都不太方便，先后用了好几个，没有用到满意的。偶然间看到了这个项目：CBDict，这个项目在我的 i3 桌面下非常好用，但是因为只能翻译单词、翻译引擎经常出现翻译错误、翻译引擎的 API 时常出现不能响应的 bug 等诸多问题，我决定在这个项目的基础上着手自己编写一个以 Deepl 作为翻译引擎的翻译助手。本项目的代码非常短，和 CBDict 一样，都是写出来方便自己读论文的小玩具。欢迎各位提 issue 或者 pull requests，但我会不会维护就不一定了。安装Step 1 安装依赖sudoaptinstallxclipStep 2 安装大帅比翻译助手sudopipinstalldasb_translatorStep 3 启动大帅比翻译助手dasb_translator
Package: daschat-base
daschat-handsoff-baseBase package for the development of integration modules with chat applications for the handsoff of conversations.FeaturesFree software: MITDocumentation:https://daschat-io.github.io/daschat_base/Enabling Python versionsInstall Python versions usingpyenvand enable all versions to be used withtox:$pyenvinstall3.8.103.9.5$pyenvshell3.8.103.9.5$pyenvlocal3.8.10AcknowledgementsCookiecutterzillionare/cookiecutter-pypackageNaereen/badges
Package: dash-access-manager
Dash-Access-ManagerDash-Access-Manager provides user access management forDash.It is based onFlask-Loginto manage user session and
onMongoEngineto use MongoDB databases.Table of contentsDescriptionInstallationUsageContributingChangelogLicenseDescriptionThis is an implementation of an access management to be used in a dash app.
It provides login, logout and sign up buttons that can be integrated in a layout of a dash app.InstallationInstall the extension with pip:pip install dash-access-managerUsageOnce installed, import the package in yourapp.py.import dash-access-manager as dam

app = dash.Dash(__name__)

server = app.serverThen, you have to set up the access manager. Youneed to changeyour_secret_key_hereas it not secret.
To do so, you can generate a secret key with theosmodule by usingos.urandom(12)#Define a secret key that is required for Flask-Login to manage user session
server.secret_key = 'your_secret_key_here' 

# Suppress errors for callbacks acting on layouts that are not displayed yet
app.config.suppress_callback_exceptions = True

# Initialize the acess manager
dam.init_access_manager(app)After this you will define the layouts and callbacks of your dash app. Here is an simple example that can be used as it is.app.layout = html.Div(children=[dcc.Location(id='url', refresh=False),
                            html.Div(id='root'),
                            html.Div(id='container')
                            ])


def render_default_page(navbar_button=[], page_content=[html.H3("Login successfull")]):
    return [
               dbc.Navbar([
                              dbc.NavbarBrand("Navbar"),
                          ] + navbar_button,
                          color="primary")
           ] + page_content


@app.callback(Output('root', 'children'),
              [Input('url', 'pathname')])
def display_page(pathname):
    if dam.current_user.is_authenticated:
        return render_default_page(dam.render_logout_button(), dam.render_logged_page())
    else:
        return render_default_page(dam.render_navbar_login() + dam.render_navbar_sign_up(), dam.render_login_page())Finally, you need to connect the app with your MongoDB database before running your app.
You have to changeDatabaseNameandDatabaseURLwith the ones you want to use.
If you want to use MongoDB you need to this change thehostparameter atmongodb+srv://username.password@cluster.urlwhere you have to replaceusername,passwordandcluster.urlby your information.if __name__ == "__main__":
    dam.connect(
        db='DatabaseName',
        host='DatabaseURL'
    )
    app.run_server()You can use this fileapp.pyto try it out.ContributingThis project is under development so contributions are welcome to improve the code.
Have a look atCONTRIBUTING.ChangelogTake a look atCHANGELOGfor more details.LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: dash-ag-grid
Dash AG GridDash AG Grid is a Dash component wrapper for the AG Grid JavaScript package, enabling you to display AG Grid components natively in your Dash app.The underlying AG Grid JavaScript package is a third-party software component developed byAG Grid Ltd. Many features are available for free in the AG GridCommunity version. Some features require a paid subscription to the AG Grid Enterprise version (more info available here). Both the community and enterprise versions are included in this component, but the enterprise features require you to provide a valid AG Grid license key. The demos which use Enterprise features are clearly marked.DocumentationPlease see thePlotly Dash AG Grid Documentation.Quick Startpip install dash-ag-gridThis basic grid has the following features enabled by default:Alpine themeSortResize, reorder and pin columnsBoolean values rendered as check boxesRow animation on sortimportdash_ag_gridasdagfromdashimportDashimportpandasaspdapp=Dash()df=pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/ag-grid/space-mission-data.csv")app.layout=dag.AgGrid(rowData=df.to_dict("records"),columnDefs=[{"field":i}foriindf.columns],)app.run(debug=True)AG Grid Community FeaturesHere are a few of the AG Grid Community features available:Column Interactions (resize, reorder, and pin columns)Column SpanningColumn GroupingPaginationSortingRow SelectionRow ReorderingRow SpanningPinned RowsFull Width RowsCell data types with automatic type inferenceCustom FilteringCell EditingProvided components including number editors, date picker, select editor, text editor, large text editorCustom Components in cells - add your own components such as buttons, graphs, indicators, markdown and moreTooltips in cells and headersProvided themes with light/dark versionsCustomizable themesFigma Design System to design and customize themesFormat Cell DataConditional formattingData Export to CSVKeyboard NavigationAccessibility supportLocalizationAG Grid Enterprise featuresHere are a few of the features available in AG Grid Enterprise.  See the AG Grid docs for more information.Grouping / AggregationAdvanced FilteringRecords Lazy LoadingServer-Side Records OperationsHierarchical Data Support & Tree ViewData Export to ExcelExcel-like PivotingCopy / PasteSparklinesSee alive demoof AG Grid Community and Enterprise FeaturesContributingWe welcome contributions todash-ag-grid.  Please see ourcontributing guidefor more information.
Package: dash-aladin-lite
dash_aladin_liteDash Aladin Lite is a Dash component library forAladin Sky Atlas.Free software: BSD licenseDocumentation:https://toltec-astro.github.io/dash_aladin_liteInstallationPyPIpipinstalldash-aladin-liteAnacondacondainstall-cconda-forgedash-aladin-liteDash for Rinstall.packages("devtools")library(devtools)install_github('toltec-astro/dash_aladin_lite@r-release')Dash.jlpkg>addDashAladinLiteGet startedInstall dependencies for theusage.pydemo:pipinstall-rrequirements-demo.txtRunpython usage.pyVisithttp://localhost:8050in your web browserContributingSeeCONTRIBUTING.mdAuthorsZhiyuan Ma
Package: dash-alert
Dash AlertDash Alert is a Dash component library.
Alert component for Dash.Installation$ pip install dash_alertPropertiesid (string)message (dash component; require)Message to be displayed in the alert.title: (dash component)Alert Title.buttons: (list[dict])Button list for alerts.title (string; require)Button Title.value (string | int | float; require)Value returned when a button is pressed.color (string)Button theme colorvalue (string | int | float; readonly)Value of button pressed.is_open (bool)Alerts are open or not.duration (int)Drawing time for opening and closing animation (milliseconds).output_targetFor targets in Output. Does not affect anything internally.
Package: dashamail
DashaMailAPI client for PythonUsageSign up for a DashaMail accountGet your API access token: My account -> IntegrationsLook upDashaMail APImethods you needUsedashamailclient to call the method:fromdashamailimportDashaMailClientclient=DashaMailClient(api_key="YOUR-API-KEY")client.lists_get()InstallationJust run:pipinstall-UdashamailContributingSeeCONTRIBUTING.rst.Changelog0.1.0 (2022-04-20)Support forlists.*API methods
Package: dash-antd
Dash Ant Design ComponentsAnt Design components for Plotly Dash.Installto install in your python environment.poetryadddash-antdor via pip.pipinstalldash-antdDevelopmentWe usejustas command runner, if you prefer not to install
just, have a look at thejustfilefor detailed commands.To manage python dependencies, we utilizepoetry.Getting StartedInstall python and node dependencies.justinstallBuild the Dash packages.justbuildSee all commands withjust -lExample appAn example app is contained in theexamplefolder. To run it execute:justrun
Package: dash-arcgis
dash-arcgisdash-arcgis is a part ofDash Enterprise,
Plotly's end-to-end Dash application platform. This public copy of the package
is a stub published on pypi.org designed to shield Dash Enterprise users fromdependency confusion,
a remote code execution attack publicly disclosed in 2021 by Alex Birsan.If you are a Dash Enterprise user, you will only see this public package if your
app is not properly configured to find the local package repository built into
Dash Enterprise. Visit the docs for your Dash Enterprise installation for details.If you never see this public package, it's still doing its job of protecting you
from the dependency confusion attack!
Package: dash-auth0
Dash Auth0This is a simple library using Auth0 OAuth2.0 to authenticate and view a Dash app
written based ondash-authanddash-google-oauth.
Upon authentication, a cookie is created and kept for 2 weeks.SetupSetup an Auth0 tenant and application.Add <your_app_base_url>/login/callback as allowed callback url and <your_app_base_url> as allowed logout url in the Auth0 Application settings.
For example:allowed callback url: https://www.myapp.com/login/callback
allowed logout url:   https://www.myapp.comInstall the package:poetry add git+https://github.com/flojoy-ai/dash-auth0.git#masterDefine following environment variables:FLASK_SECRET_KEY

AUTH0_AUTH_URL
AUTH0_AUTH_SCOPE
AUTH0_AUTH_TOKEN_URI
AUTH0_AUTH_USER_INFO_URL
AUTH0_AUTH_CLIENT_ID
AUTH0_AUTH_CLIENT_SECRET
AUTH0_LOGOUT_URL
AUTH0_API_AUDIENCE
AUTH_FLASK_ROUTESfor exampleFLASK_SECRET_KEY="<some secret key>"

AUTH0_AUTH_URL="https://<your tenant url>/authorize"
AUTH0_AUTH_SCOPE="openid profile email"
AUTH0_AUTH_TOKEN_URI="https://<your tenant url>/oauth/token"
AUTH0_AUTH_USER_INFO_URL="https://<your tenant url>/userinfo"
AUTH0_AUTH_CLIENT_ID="<your app client id>"
AUTH0_AUTH_CLIENT_SECRET="<your app client secret>"
AUTH0_LOGOUT_URL="https://<your tenant url>/v2/logout"
AUTH0_API_AUDIENCE="<your_api_audience_identifier>"
AUTH_FLASK_ROUTES="true"Add it to the application:app = Dash(__name__)

from dash_auth0_oauth.auth0_auth import Auth0Auth
auth = Auth0Auth(app)You have access to/logoutroute in order to logout user by making aGETrequest.User's name stored in cookie:flask.request.cookies.get('AUTH-USER')