semvec/pypi-packages huggingface dataset:
xcube-4d-viewer 1.0.16
API extension to the xcube server allowing cubes to be viewed in the Earthwave 4D viewer.
License: MIT
Requires Python: >=3.9

![example workflow](https://github.com/earthwave/xcube_4d_viewer/actions/workflows/xcube_4d_viewer_test.yml/badge.svg)
![example workflow](https://github.com/earthwave/xcube_4d_viewer/actions/workflows/xcube_4d_viewer_deploy.yml/badge.svg)
# xcube_4d_viewer
This repository is a plugin for the xcube server.

xcube (https://xcube.readthedocs.io/en/latest/overview.html) is a Python package for generating and exploiting data
cubes powered by xarray, dask, and zarr. It also provides a web API and server which can be used to access and
visualise these data cubes.

This repository serves as an API extension to the xcube server, allowing xcube data cubes to be analysed and
visualised within Earthwave's 4D viewer. It computes configuration details and
heatmap/3D heatmap/terrain tiles from the server's data cubes and provides them in a format expected by the 4D viewer.

In order to connect to Earthwave's 4D viewer, the xcube server needs to be registered within a Middle Tier service.
Please contact support@earthwave.co.uk for information on how to proceed.

This work is done as part of the DeepESDL project (https://deepesdl.readthedocs.io/en/latest/).
frame-cpt 0.8.0
Módulo para distribuição de Modelos Numéricos.
License: MIT
Requires Python: >=3.10
Keywords: recuperação de dados climáticos

Framework

Descrição
É um pacote in Python para a distribuição de dados brutos dos Modelos Numéricos de forma segmentada/particionada. Com esse pacote o usuário não necessita fazer o Download de todo o volume bruto o pacote auxilia a manipular somente a sua necessidade.

support Python >= 3.10.

Instalação
conda create -n  python=3.10

conda activate 

conda install -c conda-forge matplotlib pycurl cfgrib netCDF4 pynio xarray dask esmpy scipy mpi4py xesmf

conda install -c anaconda ipykernel

pip install -i https://test.pypi.org/simple/ 

Como Usar
import cptecmodel.CPTEC_BAM as BAM

bam = BAM.model()

date = '2023010800'

vars = ['t', 'u10m']

levels = [1000, 850]

steps = 1

f = bam.load(date=date, var=vars,level=levels, steps=steps)