The persiann_api python package was built in order to make data more readily available for data scientists, analysts, etc, that wish to work with weather data through the PERSIANN database. It is not an official API for the PERSIANN project.
The PERSIANN database is made from the "system developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) uses neural network function classification/approximation procedures to compute an estimate of rainfall rate at each 0.25° x 0.25° pixel of the infrared brightness temperature image provided by geostationary satellites."
As our API currently download the data in the GeoTIFF format, every "pixel" in the .tiff file would be the value of the rainfall rate at the 0.25° x 0.25° space.
The screenshot above was taken from https://chrsdata.eng.uci.edu/, which is the official site for the PERSIANN project. This was taken from Brazil in the 28th of December!
Install it throught pip or Poetry, if you already have the gdal package.
pip install persiann_apipoetry add persiann_apiThe gdal package is not so easy to straightforward install. Click in this link for the official guide in PyPi, click here.
There is also this guide for Ubuntu users, click here. This is the summary for Ubuntu users:
sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update
sudo apt-get update
sudo apt-get install gdal-bin
ogrinfo --versionThen, install the specific version of GDAL from the previous output.
sudo apt-get install libgdal-dev
export CPLUS_INCLUDE_PATH=/usr/include/gdal
export C_INCLUDE_PATH=/usr/include/gdal
pip install GDAL==[SPECIFIC VERSION FROM THE 'ogrinfo' OUTPUT]The API has one main function that downloads the data inside a folder given some parameters.
from persiann_api.main import download_data
from PIL import Image
# downloads daily data and store each data from date inside the folder.
download_data(
from_date=datetime.date(2021, 12, 28),
to_date=datetime.date(2021, 12, 28),
folder='data/',
# bounding box for Brazil
lat_bb=(-35, 6),
lon_bb=(-69, -36)
)
# read the GeoTIFF with your favorite package
geotiff_path = 'data/2021_12_28.tiff'
arr = np.asarray(Image.open(geotiff_path, mode='r'))[::-1] # we must mirror the array for the GeoTIFF.
plt.imshow(arr)
plt.clim(0, 78)
See? This is the same data as the first original data!
This is a longer example to put here, but in the notebooks folder you can find the code to generate this picture:
Contributions are very much welcome! Please, open issues and Pull Requests! There are lots to do. :)