Saravanan666/pypi-org huggingface dataset:
Package: description-indexer
description_indexerA tool for working with archival description for public access. description_indexer reads archival description into a minimalist data model for public-facing archival description.description_indexer can index directly from theArchivesSpaceAPIto an Arclight Solr instance usingArchivesSnakeandPySolr.This is still a bit drafty, as its only tested on ASpace v2.8.0 and needs better error handling.Installationpipinstalldescription_indexerFirst, you need to configure ArchivesSnake by creating a~/.archivessnake.ymlfile with your API credentials as detailed by theArchivesSnake configuration docs.Next, you also need a~/.description_indexer.ymlfile that lists your Solr URL and the core you want to index to. These can also be overridden with args.solr_url: http://127.0.0.1:8983/solr
solr_core: blacklight-core
last_query: 0Indexing from ArchivesSpace API to ArclightOnce description_indexer is set up, you can index from the ASpace API to Arclight using theto-arclightcommand.Index by id_0You can provide one or more IDs to index using a resource's id_0` fieldto-arclight --id ua807to-arclight --id mss123 apap106Index by URIYou can also use integers from ASpace URIs for resource, such as 263 forhttps://my.aspace.edu/resources/263to-arclight --uri 435to-arclight --uri 1 755Indexing by modified timeIndex collections modified in the past hour:to-arclight --hourIndex collections modified in the past day:to-arclight --todayIndex collections modified since las run:to-arclight --newDeleting collectionsYou can delete one or more collections using the--deleteargument in addition to--id. This uses the Solr document ID, such asapap106forhttps://my.arclight.edu/catalog/apap106.to-arclight --id apap101 apap301 --delete
Package: descriptive
IntroductionMiniscule is a library for flexible YAML-based configuration files, inspired by
Clojure’sAero.ExampleCreate a fileconfig.yamlwith the following contents:server:host:!or[!envHOST,localhost]port:!or[!envPORT,8000]debug:!envDEBUGdatabase:name:my_databaseuser:my_userpassword:!envDB_PASSWORDsecret:!aws/smsecretThen, in Python:fromminisculeimportread_configconfig=read_config('config.yaml')Now,configholds a dictionary with the structure of theconfig.yamlfile, in which the tagged fields have been replaced.
Package: descriptive-analysis-beginners
example_package_nagore_ainhoaCon este paquete se puede realizar un analisis descriptivo con estad�sticos y distintas visualizaciones.Se ha seguido la siguiente estructura de carpetas y archivos:Carpeta general
En esta carpeta se encuentran las siguientes carpetas y archivos.1.1. Carpeta dist1.2. Carpeta src
En esta carpeta se encuentra la siguiente carpeta:1.2.1. Carpeta descriptive_analysis_beginners
Dentro de esta se encuentran los siguientes 3 archivos .py

1.2.1.1. Archivo __init__.py
Es necesario para poder importar correctamente el directorio como un paquete, asimismo, debe estar vacio.

1.2.1.2. Archivo functions.py
Se encuentra la clase generada para la libreria. Cuenta con diferentes funciones para llevar a cabo el analisis exploratorio de los datos a partir de un archivo csv como input.
- Null_Zeros, devuelve como output una tabla con las siguientes columnas; Nan values, Nan percentage (%), Zero values y Zero percentage(%). 
- Repeated_Rows, devuelve una tabla con las filas duplicadas en caso de haberlas.
- Repeated_Columns, devuelve una tabla con las columnas duplicadas en caso de haberlas.
- Duplicates_UniqueValues, devuelve una tabla con los valores duplicados en caso de haberlos.
- Numeric_Variables, con esta funcion se grafica la distribucion, devuelve descriptivos de las variables numericas.
- Categorical_Variables, devuelve graficas para las variables categoricas.
- Descriptives, devuelve un resumen de todas las funciones anteriores.1.3. Archivo LICENSE
La licencia del paquete. Esta indica a los usuarios que instalen el paquete los terminos bajo los que pueden utilizarlo. En este caso se ha usado la licencia MIT ya que al ser de software libre permisiva pone muy pocas limitaciones en su reutilizacion y posee excelente compatibilidad de licencia. Asimismo, es compatible con muchas licencias copyleft y ni tiene copyright, lo que permite su modificacion.1.4. Archivo pyproject.toml
Se divide en 3 apartados:1.4.1. Build system
- Requires: los paquetes necesarios para instalar la libreria y poder hacer uso de ella
- Build-backend: el nombre del objeto Python que se utilizaran para realizar la construccion.

1.4.2. Project
- Name: nombre del paquete
- Version: version del paquete creado
- Authors: autores del paquete
- Description: breve descripcion de lo que hace este paquete
- Readme: el archivo readme
- Classifiers: el indice y descarga algunos metadatos adicionales sobre el paquete.

1.4.3. Project.urls
Se encuentran los urls tanto de github, como de bug tracker.
This package will consist on some functions to generate a descriptive analysis.Pasos a seguir para usar la librer�a! pip install descriptive-analysis-beginners==0.0.1from descriptive_analysis_beginners import functionseda = functions.EDA_class("ejemplo.csv")eda.Descriptives()
Package: descriptive-analysis-for-beginners
example_package_nagore_ainhoaCon este paquete se puede realizar un an�lisis descriptivo con estad�sticos y distintas visualizaciones.Se ha seguido la siguiente estructura de carpetas y archivos:Carpeta general
En esta carpeta se encuentran las siguientes carpetas y archivos.1.1. Carpeta dist1.2. Carpeta src
En esta carpeta se encuentra la siguiente carpeta:1.2.1. Carpeta example_package_nagore_ainhoa
Dentro de esta se encuentran los siguientes 3 archivos .py

1.2.1.1. Archivo __init__.py
Es necesario para poder importar correctamente el directorio como un paquete, asimismo, debe estar vacio.

1.2.1.2. Archivo functions.py
Se encuentra la clase generada para la libreria. Cuenta con diferentes funciones para llevar a cabo el analisis exploratorio de los datos a partir de un archivo csv como input.
- Null_Zeros, devuelve como output una tabla con las siguientes columnas; Nan values, Nan percentage (%), Zero values y Zero percentage(%). 
- Repeated_Rows, devuelve una tabla con las filas duplicadas en caso de haberlas.
- Repeated_Columns, devuelve una tabla con las columnas duplicadas en caso de haberlas.
- Duplicates_UniqueValues, devuelve una tabla con los valores duplicados en caso de haberlos.
- Numeric_Variables, con esta funcion se grafica la distribucion, devuelve descriptivos de las variables numericas.
- Categorical_Variables, devuelve graficas para las variables categoricas.
- Descriptives, devuelve un resumen de todas las funciones anteriores.1.3. Archivo LICENSE
La licencia del paquete. Esta indica a los usuarios que instalen el paquete los terminos bajo los que pueden utilizarlo. En este caso se ha usado la licencia MIT ya que al ser de software libre permisiva pone muy pocas limitaciones en su reutilizacion y posee excelente compatibilidad de licencia. Asimismo, es compatible con muchas licencias copyleft y ni tiene copyright, lo que permite su modificacion.1.4. Archivo Pruebas.ipynb
Se hace uso de la libreria con el csv que se le indique como input. Para su uso se requiere una instalacion de la libreria. Una vez instalada se llama a las distintas funciones para poner en funcionamiento la libreria.1.5. Archivo pyproject.toml
Se divide en 3 apartados:1.5.1. Build system
- Requires: los paquetes necesarios para instalar la libreria y poder hacer uso de ella
- Build-backend: el nombre del objeto Python que se utilizaran para realizar la construccion.

1.5.2. Project
- Name: nombre del paquete
- Version: version del paquete creado
- Authors: autores del paquete
- Description: breve descripcion de lo que hace este paquete
- Readme: el archivo readme
- Classifiers: el indice y descarga algunos metadatos adicionales sobre el paquete.

1.5.3. Project.urls
Se encuentran los urls tanto de github, como de bug tracker.
This package will consist on some functions to generate a descriptive analysis.Pasos a seguir para usar la librer�a! pip install example-package-nagore-ainhoa2==0.0.1from example_package_nagore_ainhoa import functionseda = functions.EDA_class("ejemplo.csv")eda.Descriptives()
Package: descriptools
descriptoolsA gpu-based toolbox for terrain descriptor calculation/delineation.Â·Report BugÂ·Request FeatureTable of ContentsAbout The ProjectBuilt WithGetting StartedInstallationLicenseContactAbout The ProjectTerrain descriptors present information of the earth surface and its relationship with water movement.
This toolbox was devised to reduce calculation time of these descriptors with GPU parallelization. As well as functions to produce flood map via linear binary classification.This toolbox was made for reviewing process of Computer and Geosciences journal for the manuscript entitled:Parallelized geomorphic descriptors for flood-prone areas delineationBuilt WithPythonNumPyNumbaGetting StartedTo get a local copy up and running follow these simple steps.PIPYou can install descriptools using pip:pipinstalldescriptoolsLicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactAuthors: JosÃ© VinÃ­cius Boinf de Souza and Pedro Luiz Borges ChaffeDeveloper: JosÃ© VinÃ­cius Boing de SouzaContact address:joseboing@gmail.comProject Link:https://github.com/JVBSouza/descriptools
Package: desc-stats
DescriptionDescriptive Statistics is a wide range topic in the world of StatisticsEspecially when we encounter with High-Volume data, It would be better to come up with summarized version of it.Constraints:Features(Attributes/Columns): Needs to be of Integer/Float datatype.FunctionalitiesMeasures of Central TendencyMeanMedianModeMeasures of DispersionRangeQuartilesQ1Q2Q3Adjusted Q1Adjusted Q2OutliersZ-Score methodIQR methodVarianceStandard DeviationCoefficient of VariationMeasures of SymmetrySkewLeft SkewRight SkewSymmetricKurtosisLeptokurticMesokurticPlatykurticMeasures of AssociationCovarianceCorrelationNormal Distribution (Empirical Rule)P68P95P97MulticollinearityVIF (Variance Inflation Factor)MiscMinimumMaximumSumCountMissingOutputHow to importpip install desc-statsHow to Usefrom desc_stats import desc_stats

desc_stats(df, features) # list of features
desc_stats(df, feature1, feature2, ....) # Single featureContributepip install -e .[dev]
Package: desdeo-emo
desdeo-emoThe evolutionary algorithms package within theDESDEO framework.Code for the SoftwareX paper can be found inthis notebook.Currently supported:Multi-objective optimization with visualization and interaction support.Preference is accepted as a reference point.Surrogate modelling (neural networks and genetic trees) evolved via EAs.Surrogate assisted optimizationConstraint handling usingRVEAIOPIS optimization usingRVEAandNSGA-IIICurrentlyNOTsupported:Binary and integer variables.To test the code, open thebinder linkand read example.ipynb.Read the documentationhereRequirementsPython 3.8 or newer.Poetry dependency manager: Only for developersInstallation process for normal usersCreate a new virtual enviroment for the projectRun:pip install desdeo_emoInstallation process for developersDownload and extract the code orgit cloneCreate a new virtual environment for the projectRunpoetry installinside the virtual environment shell.CitationIf you decide to use DESDEO is any of your works or research, we would appreciate you citing the appropiate paper published inIEEE Access(open access).
Package: desec
desec-pythonA desec.io API client for python.Installationpip3 install desecUsageimport desec

desec_token = "xxxx"

client = desec.Client(token=desec_token)# Get info about a domain
result = client.get_domain("example.com")
print(result)# Get all recordsets of a domain
results = client.get_rrsets("example.com")
print(results)
Package: deserve
DeserveDeserve is a nanoframework for serving ML models. Flasker than Flask, faster than FastAPI, Deserve is asynchronous, lightweight and simple.Features🤙 Remote procedure call (RPC) architecture. There are no endpoints, methods, paths, nor resources to make decisions about — just thehost:port.📦 Send JSON, receive JSON, client-side. Accept a Python object, return an object, server-side. Conversions happen under the hood.Installing$pipinstalldeserveAlso install an ASGI server such asUvicornorHypercorn.$pipinstallhypercornQuickstartThis example uses the🤗 Transformerslibrary.# Save this as example.pyimportdeservefromtransformersimportpipeline# Load your modelclassifier=pipeline('sentiment-analysis')@deserveasyncdefpredict(payload:object)->object:returnclassifier(payload)Run the server using the names of your file (example.py) and function (predict).$hypercornexample:predict[INFO]Runningonhttp://127.0.0.1:8000Get some predictions.$curllocalhost:8000--data'["This is the simplest framework.", "You deserve it!"]'[{"label":"POSITIVE","score":0.799},{"label":"POSITIVE","score":0.998}]Serialization and deserializationDeserve takes care of converting the request and response payloads based on thecontent-typeandacceptheaders of the request.List of supportedcontent-typesand the deserialized payload that is passed to the inference handler.content-typePayloadapplication/jsondict/listtext/*rawimage/*binaryaudio/*binaryList of supportedacceptheaders and the serialized payload that is returned.acceptPayloadapplication/jsonJSONtext/*rawimage/*binaryaudio/*binary
Package: de-setup-project
project setup portion of the de namespacethe portions (modules and sub-packages) of the Development Environment for Python are within
thedenamespace and are providing helper methods and classes for your Python projects.installationexecute the following command to use thede.setup_projectmodule in your Python project. it will installde.setup_projectinto your Python (virtual) environment:pipinstallde-setup-projectto contribute to this portion, fork thede-setup-projectrepository at GitLab, pull it to your machine and finally execute the following command in the root
folder of this repository (de_setup_project):pipinstall-e.[dev]the last command will install this module portion into your virtual environment, along with the tools you need to
develop and run tests or to extend the portion documentation. to contribute only to the unit tests, or the documentation
of this portion replace the setup extras keydevin the above command withtestsordocsrespectively.the repository of this portion is using thegit-flow workflowwith the branch namesdevelopandrelease. more
details on this workflow you findhereandhere.namespace portion documentationmore info on the features and usage of this portion is available atReadTheDocs.
Package: desfire
MIFARE DESFire for PythonThis package providesMIFARE DESFirenative communication protocol for NFC cards.Source code:https://github.com/miohtama/desfireDocumentation:https://desfire.readthedocs.orgIn photo: MIFARE DESFire EV1 8kB blank card with Identive CLOUD 4500 F Dual Interface ReaderFeaturesCompatibile with USB-based NFC readers via PCSC interface. PCSC API is available on Linux, OSX and Windows. Linux support includes support for Raspberry Pi.Compatibile with Android mobile phones and their built-in NFC readers. This is done usingKivycross application Python framework and native Android APIs viapyjniusPython to Java bridging.Only some of the commands are implemented in the current alpha quality version, please feel free to add more.Compatible with Python 2 and Python 3BackgroundThe communication protocol specification is not public. The work is based on reverse engineering existing open source DESFire projects, namelyAndroid host card emulation for DESFireandMIFARE SDK.AuthorMikko Ohtamaa.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2 (2016-03-30)Added data file read and write0.1 (2016-03-07)First release on PyPI.
Package: deshima-rawdata
deshima-rawdataDESHIMA raw data and downloader packageUse of the dataPlease contact@astropenguinbefore using the data.Download the data$pipinstalldeshima-rawdata
$deshima-rawdatadownload<observationID>See the command help for more information.$deshima-rawdatadownload--helpList of the data$deshima-rawdatalistObservation IDFile nameSource nameObservation type20171103184436cosmos_20171103184436.tar.gzSaturnzscan20231108052231cosmos_20231108052231.tar.gzJupiterraster20231109015146cosmos_20231109015146.tar.gzJupiterzscan20231109060113cosmos_20231109060113.tar.gzBlank skyskydip
Package: deshima-sensitivity
deshima-sensitivitySensitivity calculator for DESHIMA-type spectrometersOverviewdeshima-sensitivity is a Python package which enables to calculate observation sensitivity of DESHIMA-type spectrometers.
Currently it is mainly used to estimate the observation sensitivity ofDESHIMAand its successors.An online Jupyter notebook is available for DESHIMA collaborators to calculate the sensitivity and the mapping speed of the DESHIMA 2.0 by themselves.
Click the budge below to open it inGoogle colaboratory(a Google account is necessary to re-run it).Stable version (recommended)Latest versionIn the case of running it in a local Python environment, please follow the requirements and the installation guide below.RequirementsPython:3.7, 3.8, or 3.9 (tested by the authors)Dependencies:Seepyproject.tomlInstallation$pipinstalldeshima-sensitivityDevelopment environmentThe following steps can create a standalone development environment (VS Code + Python).InstallVS CodeandDocker Desktop, and launch themInstall theRemote Containersextension to VS CodeClone this repositoryOpen the repository by VS CodeChooseReopen in Containerfrom theCommand Palette
Package: designable-radio-items
Designable Radio ItemsDesignable Radio Items is a Dash component library.
Radio component that assigns a class when selected or read-only.Installation$ pip install designable_radio_itemsPropertiesid (string)options (list[dict]; require)label (string)value (string | number)value (string | number)Value of selected radio.name (string)The name to give to the input tag.className (string)readonly (bool)n_clicks (number)
Package: designate-dashboard
Team and repository tagsdesignate dashboardDesignate Horizon UI bitsFree software: Apache licenseFeaturesTODOHowtoPackage the designatedashboard by running:python setup.py sdistThis will create a python egg in the dist folder, which can be used to install
on the horizon machine or within horizon’s  python virtual environment.– or –Install directly from source by running “python setup.py –install”Note:  On some systems python may throw an error like‘Exception: Versioning for this project requires either an sdist tarball, or accessto an upstream git repository’this seems to be a result of mismatched pbr versioning.  A hacking workaround for development
purposes is replacing the pbr call with a hard-coded version (e.g. ‘1.0.1’) in
designatedashboard/__init__.py.Copy panel plugin files into your Horizon config.  These files can be found in designatedashboard/enabled
and should be copied to /usr/share/openstack-dashboard/openstack_dashboard/local/enabled or the
equivalent directory for your openstack-dashboard install.Copy default config file into your Horizon config. The file can be found in designatedashboard/local_settings.d
and should be copied to /etc/openstack-dashboard/local_settings.d or the equivalent directory
for your openstack-dashboard install.Make sure your keystone catalog contains endpoints for service type ‘dns’.  If no such endpoints are
found, the designatedashboard panels will not render.(Optional) Copy the designate policy file into horizon’s policy files folder.TestHow to run JS tests:Install npm and nodejs=4.8.4$sudoapt-getinstall npm$curl-sLhttps://deb.nodesource.com/setup_4.x| sudo-Ebash -$sudoapt-getinstall nodejsnpm install(to create virtual environment and install all dependencies in package.json)npm run lintfor eslintnpm run testfor JS unit tests
Package: designate-tempest-plugin
Team and repository tagsTempest Integration of DesignateThis directory contains Tempest tests to cover the designate project, as well
as a plugin to automatically load these tests into tempest.See the tempest plugin docs for information on using it:https://docs.openstack.org/tempest/latest/plugin.html#using-pluginsSee the designate docs for information on writing new tests etc:https://docs.openstack.org/designate-tempest-plugin/latest/#writing-new-testsRunning the testsFrom the tempest directory, setup the tempest virtual environment for the
designate tempest plugin:$ tox -e venv-tempest -- pip3 install -e <path to designate-tempest-plugin>For example, when using a typical devstack setup:$ cd /opt/stack/tempest
$ tox -e venv-tempest -- pip3 install -e /opt/stack/designate-tempest-pluginTo run all tests from this plugin, install designate into your environment
and from the tempest repo, run:$ tox -e all -- designateTo run a single test case, run with the test case name, for example:$ tox -e all -- designate_tempest_plugin.tests.api.v2.test_zones.ZonesTest.test_create_zonesTo run all tempest tests including this plugin, run:$ tox -e all
Package: design-config
desing_config.pySmart tiny config library for flask-like configHow to installpip install design-configYou can find source code of library onGitHubHow to useimport os
from design_config import DesignConfig, D, ___

class Config(DesignConfig):

    """ is D("") """
    PROP_ZERO = ___
    
    """ is 'hello' """
    PROP_ONE = 'Hello'

    """ 'World' if in init_data_dict not PROP_TWO
    As usually init_data_dict is os.environ """
    PROP_TWO = D('World')

    """ 'Hello World!' or 'hello {PROP_TWO}!' for any PROP_TWO """
    PROP_THREE = '{PROP_ONE} {PROP_TWO}!'


config = Config()If your global environment variables contain $PROP_TWO , it will be replaced in the config with the appropriate value. Otherwise, it will remain by default (World)Dmeans"default"config.PROP_THREE # "Hello World!"

config['PROP_THREE'] # "Hello World!"
config['{PROP_THREE}!!'] # "Hello World!!!"
config['{PROP_FOUR}'] # "PROP_FOUR"

config('{PROP_ONE} Tom') # "Hello Tom"
config('PROP_FOUR', 'London') # "London"
config('PROP_TWO', 'London') # "World"
config('{PROP_ZERO}', 'London') # ""
config('{PROP_FOUR}', 'London') # "PROP_FOUR"

config.path('{PROP_ONE}', 'some_folder', '{PROP_TWO}2') # os.path.join("Hello","some_folder","World2")Also you can useint,bool, andfloatvalues:class Config(DesignConfig):

    PROP_FIVE = D(55)

    PROP_SIX = D(True)

    PROP_SEVEN = D(7.7)

config = Config({
    'PROP_FIVE': '5', 
    'PROP_SIX': 'FALSE',
    'PROP_SEVEN': '7e7'
})

config.PROP_FIVE # 5
config.PROP_SIX # False
config.PROP_SEVEN # 7e7You can use DesignConfig with Flask like that:class MyFlaskConfig(DesignConfig):

    VERSION = '2'

    FLASK_DEBUG = D(True)
    IS_PRODUCTION = D(False)
    DEBUG = D(True)

    PROJECT_PATH = D('/base')

    REDIS_HOST = D('localhost')
    REDIS_PORT = D('6379')

    """..."""

config = MyFlaskConfig()

my_flask_app = Flask(__name__, static_folder='static', static_url_path='')
my_flask_app.config.from_object(config)

"""..."""
Package: designer
DesignerAn interactive graphics library designed with student data to learn Python.InstallationInstall from the command line:pip install designerOr install fromthis repository.DocumentationFind the documentation for Designerhere.External ResourcesDesigner takes heavily from Spyral, and shares a maintainer in @acbartDesigner's emoji support is viaTwemojiCopyright 2020 Twitter, Inc and other contributorsCode licensed under the MIT License:http://opensource.org/licenses/MITGraphics licensed under CC-BY 4.0:https://creativecommons.org/licenses/by/4.0/Designer is obviously built on PygameFutureMakegroupuse the SpyralViewmechanims. Provide a "frozen group" and a "dynamic group".Support forturtlestyle graphics, using animationsMore basic shapes liketriangle,star, ...?Handlepotentialcollision checks "if the object were placed here", circular, etc.Update documentation to explain thedestroyfunctionImprove the error handling in as many situations as possibleContributorsSpecial thanks to:Robert Deaton (@rdeaton) for the creation of Spyral, which is the basis for Designer@codeBodger for adding in functionality for font files