Saravanan666/pypi-org huggingface dataset:
Package: meteofrance-api
Client Python pour l’API Météo-France. | Python client for Météo-France API.You will find English README content in the sectionFor English speaking users.Vous trouverez le contenu francophone du README dans la sectionPour les francophones.Pour les francophonesDescriptionCe package Python permet de gérer la communication avec l’API non publique de
Météo-France utilisée par les applications mobiles officielles.Le client permet:Rechercher des lieux de prévisions.Accéder aux prévisions météorologiques horaires ou quotidiennes.Accéder aux prévisions de pluie dans l’heure quand disponibles.Accéder aux alertes météo pour chaque département français et d’Andorre. Deux
bulletins sont disponibles : un synthétique et un second avec l’évolution des alertes
pour les prochaines 24 heures (exempleici).Ce package a été développé avec l’intention d’être utilisé parHome-Assistantmais il peut être utilisé dans d’autres contextes.InstallationPour utiliser le module Pythonmeteofrancevous devez en premier installer
le package en utilisantpipdepuisPyPI:$pipinstallmeteofrance-apiVous pouvez trouver un exemple d’usage dans un module Python en regardantle test d’intégration.ContribuerLes contributions sont les bienvenues. Veuillez consulter les bonnes pratiques
détaillées dansCONTRIBUTING.rst.For English speaking usersDescriptionThis Python package manages the communication with the private Météo-France API
used by the official mobile applications.The client allows:Search a forecast location.Fetch daily or hourly weather forecast.Fetch rain forecast within the next hour if available.Fetch the weather alerts or phenomenoms for each French department or Andorre.
Two bulletin are availabe: one basic and an other advanced with the timelaps evolution
for the next 24 hours (examplehere).This package have been developed to be used withHome-Assistantbut it can be used in other contexts.InstallationTo use themeteofrancePython module, you have to install this package first viapipfromPyPI:$pipinstallmeteofrance-apiYou will find an example ot usage in a Python program in theintegration test.ContributingContributions are welcomed. Please check the guidelines inCONTRIBUTING.rst.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: meteomatics
Python connector to theMeteomatics Weather APIMeteomatics provides a REST-style API to retrieve historic, current, and forecast data globally. This includes model data and observational data in time series and areal formats. Areal formats are also offered through a WMS/WFS-compatible interface. Geographic and time series data can be combined in certain file formats, such as NetCDF.Install by typingpip install meteomaticsin your favourite shell.For a start we recommend to run the example script.
Package: meteoredpy
meteoredpyLibreria para consumir y parsear a JSON API de meteored.clUsoAntes de comenzar, es necesario registrarse en la página demeteored.clpara así obtener unaaffiliate_id_(API KEY).frommeteoredpyimportMeteoredpyAPI_KEY=''clima=Meteoredpy(API_KEY).get('conce')print(clima)'''puede entregar 4 resultados:1. {'ciudad': 'Concepción [Biobío;Chile]', 'maxima': '18', 'error': 0}2. {'error': 1, 'msg': NameError("name 'xmltodict' is not defined")}3. {'error': 2, 'msg': 'La ciudad no existe'}4. {'error': 3, 'msg': 'Hubo un problema al intentar conectarse a la API de búsqueda de ciudades'}'''Tipo de resultadosSi el resultado que entrega es el (1), quiere decir que la consulta fue realizada con éxito.Si el resultado que entrega es el (2), quiere decir que hubo una excepción, tal como muestra el ejemplo, la cual se solucionaría instalando la librería faltante. También, puede mostrar otro tipo de excepciones, tales como que hubo problemas para conectarse a la API del meteored.clSi el resultado que entrega es el (3), quiere decir que la ciudad no existe.Si el resultado que entrega es el (4), quiere decir que hubo problemas para conectarse a la API de búsqueda de ciudades.
Package: meteorite
☄️ MeteoriteA fast and simple web server to host your Machine Learning model.☄️ MeteoriteInstall the pip packageWrite your serverProject statusContribute to ☄️ MeteoriteInstall the pip packagepipinstallmeteoriteWrite your serverimportjsonimportmeteoriteapp=meteorite.Meteorite()app.set_webhook_url("https://testapp.via.routehead.com")@app.predictdefpredict(data):body=data.decode("utf-8")"""Run your model on the input"""returnbodyapp.start(port=4000)# port is 4000 by defaultBy default, the server starts at port4000. Thepredictfunction will run with GET/POST requests on/predict.Get your result: a webhookTheset_webhook_urlfunction has been added to the Meteorite API to get responses from prediction requests. This makes Meteorite suitable for use in long-running ML tasks.The webhook URLYour webhook URL must accept a POST request at the specific route. The result JSON will be sent as body of the request.Project statusThis project is under active development. We will not recommend you to use this package for critical applications.
We will welcome all contributions! Please refer to the contributions section for more details.Some of the features we're still working on:Pass POST request String and JSON into the Python function.Return String and JSON with the correct content type headers.Graceful error handling (⚠️ Priority).Customise the port for the serverAllow more datatypes for POST request to the model.Create more examples.Contribute to ☄️ MeteoritePlease refer to theCONTRIBUTING.mddocs for details.Join our Discord channelif you have more questions.
Package: meteorologicalData
meteorologicalData==============================Atraves desse pacote, o usuario podera visualizar os dados meteorologicos e a localizacao exata de uma determinada cidade.InstalacaoPara instalar o pacote, basta executar o comando abaixo:pip install meteorologicalDataUsofrom meteorologicalData import main
md = main.MD(cidade, tipo) """ tipo == 'visualizar' or 'retornar' """
md.retorna_dados()

Se tipo for igual a 'retornar', use uma variavel para receber a lista com os dados meteorologicos.

lista = md.retorna_dados()
Package: meteorology
This package provides routines for meteorological calculations.PrinciplesNumbers are treated in SI units (except for some documented exceptions).Routines are tested to work withnumpy.ndarrayand floats as input but may
work with lists or other data types as well.Everything is tested and supposed to return correct values. Warnings are given
if invalid values occur. Unphysical values are masked withNaN.CapabilitiesCurrently,meteorologycan only do very basic things:converting between degreesCelsiusandKelvinfrommeteorology.temperatureimportcel2kel,kel2celcel2kel(np.array([0,-20.4,30.1]))# array([ 273.15,  252.75,  303.25])kel2cel(np.array([0,273.15,345.54]))# array([-273.15,    0.  ,   72.39])converting betweenradianstodegreesfrommeteorology.windimportrad2deg,deg2radrad2deg(np.array([np.pi,2*np.pi,np.pi/4]))# array([ 180.,  360.,   45. ])deg2rad(np.array([360,45,135]))# array([ 6.28318531,  0.78539816,  2.35619449 ])calculating the wind directionfrommeteorology.windimportwind_vector_angle# meteorological wind direction angle (0° = North, 90° = East, ...)rad2deg(wind_vector_angle(u=-1,v=0))# easterly wind# 90.0rad2deg(wind_vector_angle(u=1,v=1))# south-westerly wind# 225.0calculating thesaturation water vapour pressureat a given temperaturefrommeteorology.humidityimportsaturation_water_vapour_pressurease_se_s(np.array([273.15,250.1,320]))# array([   611.2       ,     76.7876872 ,  10532.91207709])calculating thetotal blackbody radiationat a given temperaturefrommeteorology.radiationimportblackbody_radiationblackbody_radiation(np.array([0,273.15,250.1,1000]))# array([     0.  ,    315.6574093 ,    221.85332157,  56703.67 ])adjusting aradiation temperaturetoanother emissivity:frommeteorology.radiationimport\adjust_radiation_temperature_to_other_emissivityasadjtempadjtemp(T=300,emissivity_old=0.9,emissivity_new=0.8,T_ambient=285)# 293.92070228214675But stay tuned! More is about to come!InstallThis package is onPyPi. To
installmeteorology, runpipinstall--usermeteorologyNoteYou might need to usepip3or skip the--userfor your setup.DocumentationYou can find detailed documentation of this packagehere on on Gitlab.DevelopmentThe following might only be interesting for developersLocal installationInstall this module from the repository root viapip:# local user library under ~/.localpip3install--user.# in "editable" modepip3install--user-e.Testing# Run all tests./setup.pytest# install coveragepip3install--usercoveralls# Run all tests and determine a test coveragemakecoverageVersioningmakeincrease-patchto increase the patch version numbermakeincrease-minorto increase the minor version numbermakeincrease-majorto increase the major version number
Package: meteosatpy
MeteoSatPyWhat is it?MeteoSatPyis a Python library designed for downloading and managing hydro-meteorological data sourced from satellites and global models. It offers users efficient access to near-real-time and historical weather conditions globally. With features for data acquisition, processing, and analysis.MeteoSatPyis a versatile tool for meteorological research, forecasting, and decision-making across various sectors.Where to get it?The source code is currently hosted on GitHub at:https://github.com/jusethCS/meteosatpyBinary installers for the latest released version are available at thePython Package Index (PyPI)and onConda# PyPIpipinstallmeteosatpy# Condacondainstalljuseth.chancay::meteosatpyDependenciesrasterio: Reads and writes raster formats based on Numpy N-dimensional arrays.xarray: Works with labelled multi-dimensional arrays simple and efficient.geopandas: Analysis and manipulation of geographical data.request: HTTP library for making requests and working with web APIs.Prior to installingMeteoSatPyusing PyPi, we recommend creating a new conda environment with dependencies:# Condacondacreate-n[env_name]geopandasrasterioxarrayrequestIf you need to downloadMSWEPdata, you'll need to installRclone.# Condacondainstallconda-forge::rcloneTo set up Rclone with a Google Drive account, we recommend watching thistutorial. Note the MSWEP data are store on this Google Driverepository.Examplesimportdatetimeasdtimportmeteosatpy# Target datedate=dt.datetime(2010,1,1)# year, month, day# Download CHIRPS datach=meteosatpy.CHIRPS()ch.download(date=date,timestep="daily",outpath=date.strftime("chirps_%Y-%m-%d.tif"))# Download CMORPH datacm=meteosatpy.CMORPH()cm.download(date=date,timestep="daily",outpath=date.strftime("cmorph_%Y-%m-%d.tif"))# Download MSWEP datamw=meteosatpy.MSWEP()mw.download(date=date,timestep="daily",dataset="Past",outpath=date.strftime("mswep_%Y-%m-%d.tif"))# Download IMERG v07 final runim=meteosatpy.IMERG(user="username",pw="pass")im.download(date=date,version="v07",run="final",timestep="daily",outpath=date.strftime("imerg_%Y-%m-%d.tif"))
Package: meteostat
Meteostat Python PackageThe Meteostat Python library provides a simple API for accessing open weather and climate data. The historical observations and statistics are collected byMeteostatfrom different public interfaces, most of which are governmental.Among the data sources are national weather services like the National Oceanic and Atmospheric Administration (NOAA) and Germany's national meteorological service (DWD).Are you looking for ahosted solution? Try ourJSON API.InstallationThe Meteostat Python package is available throughPyPI:pipinstallmeteostatMeteostatrequires Python 3.6or higher. If you want to visualize data, please install Matplotlib, too.DocumentationThe Meteostat Python library is divided into multiple classes which provide access to the actual data. Thedocumentationcovers all aspects of the library:Selecting LocationsGeographical PointWeather StationsTime SeriesHourly DataDaily DataMonthly DataMiscellaneous DataClimate NormalsLibraryContributingFormats & UnitsData SourcesTerms & LicenseExampleLet's plot 2018 temperature data for Vancouver, BC:# Import Meteostat library and dependenciesfromdatetimeimportdatetimeimportmatplotlib.pyplotaspltfrommeteostatimportPoint,Daily# Set time periodstart=datetime(2018,1,1)end=datetime(2018,12,31)# Create Point for Vancouver, BClocation=Point(49.2497,-123.1193,70)# Get daily data for 2018data=Daily(location,start,end)data=data.fetch()# Plot line chart including average, minimum and maximum temperaturedata.plot(y=['tavg','tmin','tmax'])plt.show()Take a look at the expected output:ContributingInstructions on building and testing the Meteostat Python package can be found in thedocumentation. More information about the Meteostat bulk data interface is availablehere.DonatingIf you want to support the project financially, you can make a donation using one of the following services:GitHubPatreonPayPalData LicenseMeteorological data is provided under the terms of theCreative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). You may build upon the material
for any purpose, even commercially. However, you are not allowed to redistribute Meteostat data "as-is" for commercial purposes.By using the Meteostat Python library you agree to ourterms of service. All meteorological data sources used by the Meteostat project are listedhere.Code LicenseThe code of this library is available under theMIT license.
Package: meteoswiss
Library to get data from meteo swiss
NOT ready for production ! for the moment it’s just a draft to do some testsUsageInstallationRequirementsrequests>=2.22.0
pandas>=0.25.3
beautifulsoup4>=4.8.2May probably work with older versions but not testedCompatibilityLicenceAuthorsmeteoswisswas written bywebsylv.
Package: meter-digits-recognizer
AboutThis libary is developed for recognizing utility meter digits by using neural network classifier (see the image). The neural network is implemented in Pytorch and the details are available in thecode.The datset (fromhere+ self-made images) has 959 images that are classified into eleven categories: digits "0"-"9" and "10" for cases where digit is not recognizable.InstallationInstall from PyPI:pip install meter-digits-recognizerUsageMinimal example:importcv2frommeter_digits_recognizerimportMeterDigitsRecognizer# Read image, must be in BGR format (standard in OpenCV)image=cv2.imread("images/0/1_hot_water_meter_20210212_065239.jpg")# Run recognizerdr=MeterDigitsRecognizer()predictions,confidences=dr.run([image])# Accepts list of images# Printprint("Prediction%d(confidence%.1f%%)"%(predictions[0],confidences[0]))Expected output:Prediction 0 (confidence 100.0 %)For additional examples seeexample.ipynbTraingTo retrain the neural network follow the steps in thetrain_neural_net.ipynbnotebook.Creditshttps://github.com/jomjol/neural-network-digital-counter-readout(dataset & inspiration)
Package: meter-gear
Meter-gearProxyMeter's RESTful API to Eth JSON-RPC, to support Remix, Truffle and more (You should give priority to using Meter's RESTful API).Quick StartInstallationOn OS XPython 3.8+ supportInstall the system-dependeciesbrew install openssl
export CFLAGS="-I$(brew --prefix openssl)/include $CFLAGS"
export LDFLAGS="-L$(brew --prefix openssl)/lib $LDFLAGS"Installation of Meter-Gear and it's dependent Python packages via PyPIpip3 install meter-gearOn UbuntuPython 3.8+ supportInstall the system-dependeciessudo apt-get install build-essential libssl-dev python-devUse a virtual environment to isolate your meter-gear projectpython3 -m venv meter-venv
source meter-venv/bin/activateInstallation of Meter-Gear and it's dependent Python packages via PyPIpip3 install meter-gearNote that you can activate and deactivate your virtual environment nowsource meter-venv/bin/activate
deactivateSeehttps://docs.python.org/3/library/venv.htmlfor more informationOn WindowsPython 3.8+ supportInstall Visual C++ Build Tools.Installscrypt-pyuse the precompiled wheels.Installation of Meter-Gear and it's dependent Python packages via PyPIpip3 install meter-gearRunInstalling through pip will make themeter-gearcommand available on your machine (must run meter client first.)meter-gearThis will run meter-gear on127.0.0.1:8545.You can change its default behavior with the following parameters:host: rpc service host, eg:--host 127.0.0.1port: rpc service port, eg:--port 8545endpoint: meter restful service endpoint, eg:--endpoint http://127.0.0.1:8669keystore: keystore file path, eg:--keystore /Users/(username)/keystore), default=meter stand-alone(solo) built-in accountspasscode: passcode of keystore, eg:--passcode xxxxxxxxdebug: bool default=false, whether to display debug logs, eg:--debug truelog: bool default=false, whether to display rpc logs, eg:--log falseUse RemixChange the Remix environment to Web3 provide.Use TruffleTruffle 4.0.6+ supportModify the configuration of truffle first(truffle.js):module.exports={networks:{development:{host:"localhost",port:8545,network_id:"*"// Match any network id}}};Then you can use truffle's command line tool.
Package: meteron
Meteron AIInstallation:pipinstallmeteronUsage:importjsonfrommeteronimportCluster# Create your cluster that has one or more servers.# In this example we will use https://lightning.ai/musecluster=Cluster(cluster='lightning-muse',servers=[{'url':'https://ulhcn-01gd3c9epmk5xj2y9a9jrrvgt8.litng-ai-03.litng.ai/api/predict'}])cluster.initialize()# Send the request to the clusterresult=cluster.image_gen(data=json.dumps({'prompt':'spaceships above alien planet'}))# Do whatever you want with the result
Package: meterplugin
TRUESIGHT PULSE METER PLUGIN SDK FOR PYTHONThis provides a framework for the development of the TrueSight Pulse
Meter Plugins[Build Status]Measurement TransportsThe SDK supports sending measurements to Pulse via 3 different methods:Standard OutMeter RPCMeasurement APIStandard OutPlugin writes to standard out which is read by the Plugin Manager which
then forwards to the meter via Meter RPC API, which then sends to Pulse.Meter RPCPlugin writes to Meter RPC channel which then sends to Pulse.Measurement APIPlugin writes to Pulse Measurement REST API directly sending to PulseDataSourcesThe framework supports the following data sources, custom data sources
can be created by extending the default data sourceDatabaseExecHTTP/HTTPSLogfileTCP/UDP Socket
Package: metest
metestTest, check and compare logs and data from meteorological computations.UsageSubmodules can be imported, but this software is designed to be used as a CLI (Command-Line-Interface), by calling the__main__module.python -m metest: Prints a help messagepython -m metest logmetric: Calls the logmetric submodule and prints a help message (as flags are needed for this command to work)python -m metest logmetric -m harmonie --individual -f /path/to/log: Computes standard metrics on a single logfile from HarmonieDocumentationDocumentation is auto-build from in-line code using sphinx: dmidk.github.io/metest/Prerequisitesmetestis a script tool based on python. Dependencies is defined inmetest.ymland a python environment can be made using (mini)conda:conda env create -f metest.ymlFor developersBuild documentationcddoc
sphinx-apidoc-f--private--ext-autodoc--separate-o.../metest/
makehtmlThe html source files goes intodoc/_build/html
Package: metevents
meteventsMeteorlogical Eventsmetevents is a python library created with the goal of consistent, simple identification of
meteorology timeseries data. metevents is developed byM3 Worksas a tool
for performing analysis for hydrology models and station data. Contributions welcome!Warning - This software is provided as is (see the license), so use at your own risk.
This is an opensource package with the goal of making data wrangling easier. We make
no guarantees about the quality or accuracy of the data and any interpretation of the meaning
of the data is up to you.Free software: BSD licenseFeaturesRequirementspython >= 3.7Installpython3-mpipinstallmeteventsLocal install for devThe recommendation is to use virtualenv, but other local python
environment isolation tools will work (pipenv, conda)python3-mpipinstall--upgradepippython3-mpipinstall-rrequirements_devpython3-mpipinstall.TestingpytestIf contributing to the codebase, code coverage should not decrease
from the contributions. Make sure to check code coverage before
opening a pull request.pytest--cov=meteventsDocumentationreadthedocs coming soonhttps://metevents.readthedocs.io.UsageSee usage documentationhttps://metevents.readthedocs.io/en/latest/usage.htmlUsage ExamplesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (TBD)Creation of the project
Package: metex
Metex - Maximum Entropy TEXturesUtilities for generatingmaximumentropytextures,
according toVictor and Conte
2012.Metex can be used as a standalone software from the command line, or
as a Python package to generate and manipulate textures.RequirementsPython 3numpy ≥ 1.7matplotlibInstallationTo install the latest release, run:pipinstallmetex(depending on your system, you may need to usepip3instead ofpipin the command above).This should install themetexpackage on yourPYTHONPATH, as well
as an executable script calledmetexto your regularPATH.Testing(requiressetuptools). Ifmetexis already installed on your
system, look for the copy of thetest_library.pyandtest_cli_interface.shscripts installed alongside the rest of themetexfiles and execute it. For example:python/usr/lib/python3.X/site-packages/metex/test/test_library.py
/bin/bash/usr/lib/python3.X/site-packages/metex/test/test_cli_interface.shUsage (command line)Generate one sample of a fully random, square 10x10 texture, which
will be saved to the current folder as '0.png':metex10Save 10 samples of a 100x150 texture in folder named 'fig', with
parameter 'alpha' set to -0.6. Name each sample 'fig_[n].png', where
[n] takes on values 0,...,9:metex--folder=fig--prefix=fig_--n_samples=10--alpha=-0.6100150For more information on the command line parameters,metex--help(also seemetex/core.py).Usage (library)The library implements aTextureclass that represents the maximum
entropy distribution of textures with a given level of a statistic and
a given size. Objects of classTexturecan be sampled, generating
objects of classTextureSample. This is a subclass ofnumpy.ndarraywith support for array-like manipulation, image
generation for use in experiments (via matplotlib) and terminal-based
pretty-printing (for messing around).>>> import metex
>>> texture = metex.Texture(height=15, width=25, beta1=0.7)
>>> sample = texture.sample()
>>> print(sample)

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>>> print(sample[:5,:])

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>>> print(sample[:,:5])

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⬜⬜⬜⬜⬛The exact rendering of the texture will depend on your terminal.ChangelogSee theCHANGELOG.mdfile for a list of changes from
older versions.Authorsmetexis maintained by Eugenio Piasini.
Package: meth
meth: A mathematical expression parser.A python package to parse and evaluate mathematical expressions.Installationpipinstallmethor install it from source:gitclonehttps://github.com/sertdfyguhi/meth/cdmeth
python3-mbuild
pipinstalldist/*.whlExamplesMore examples in theexamples/directory.importmeth# tokenizing equationsmeth.tokenize("5 + 2")# INT(5), PLUS, INT(2)# parsing equationsmeth.parse("2 * 10")# BinaryOpNode(INT(2), MUL, INT(10))# evaluating equationsmeth.evaluate("2 + 2")# 4meth.evaluate("sqrt(9)")# 3# evaluation with variablesevaluator=meth.Evaluator()evaluator.evaluate("x = 5")evaluator.evaluate("x")# 5TodoLexerParserBracketingMultiplication using bracketsNegative NumbersVariablesFunctionsInterpreterBinary OperationsUnary OperationsVariablesFunctionsAdd mathematical functionsAccurate float calculationsSimplify an expressionExpand an expressionAST to Equation StringDocumentationPublish to PyPI