The modules available in this package are designed to capture and proccess satellite data from Copernicus
Project description
Satellite Weather Downloader
| Xarray | Copernicus |
|---|---|
 |
 |
SWD is a system for downloading, transforming and analysing Copernicus weather data using Xarray. It consists in two major apps, satellite_downloader and satellite_weather. downloader is responsible for extracting NetCDF4 files from Copernicus API, and the weather implements Xarray extensions for transforming and visualizing the files.
Installation
The app is available on PYPI, you can use the package without deploying the containers with the command in your shell:
$ pip install satellite-weather-downloader
Requirements
For downloading data from Copernicus API, it is required an account. The credentials for your account can be found in Copernicus' User Page, in the API key section. User UID and API Key will be needed in order to request data. Paste them when asked in satellite_downloader connection methods.
Notes
Python Versions = [3.10, 3.11]
Version 1.X includes only methods for Brazil's data format and cities.
Creating requests via Interactive shell
Since SWT version 1.5, it is possible to create dynamic requests using the interactive python shell or via method call:
from satellite_downloader import request
file = request.ERA5_reanalysis(
filename = 'my_dataset_file'
# Any ERA5 Reanalysis option can be passed in the method
)
NOTE: This feature is still in experimental versions, please submit an issue if you find any bug.
Usage of copebr extension
import satellite_downloader
import satellite_weather
file = satellite_downloader.download_br_netcdf('2023-01-01', '2023-01-07')
br_dataset = satellite_weather.load_dataset(file)
rio_dataset = br_dataset.copebr.ds_from_geocode(3304557) # Rio de Janeiro's geocode
rio_dataframe = rio_dataset.to_dataframe()
It is also possible to create a dataframe directly from the National-wide dataset:
br_dataset.copebr.to_dataframe(3304557)
All Xarray methods are extended when using the copebr extension:
rio_dataset.precip_med.to_array()
rio_dataset.temp_med.plot()
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