Python: 3.8 License: MIT SERVIR: Global
Python implementation of the Forecasting Inundation Extents using REOF method
Based off of the methods from Chang et al., 2020
$ conda create -n fier -c conda-forge python=3.8 netcdf4 qt pyqt rioxarray numpy scipy xarray pandas scikit-learn eofs geoglows $ conda activate fier $ pip install git+https://github.com/servir/fierpy.git
To Install in OpenSARlab:
$ conda create --prefix /home/jovyan/.local/envs/fier python=3.8 netcdf4 qt pyqt rioxarray numpy scipy xarray pandas scikit-learn eofs geoglows jupyter kernda $ conda activate fier $ pip install git+https://github.com/servir/fierpy.git $ /home/jovyan/.local/envs/fier/bin/python -m ipykernel install --user --name fier $ conda run -n fier kernda /home/jovyan/.local/share/jupyter/kernels/fier/kernel.json --env-dir /home/jovyan/.local/envs/fier -o
- numpy
- xarray
- pandas
- eofs
- geoglows
- scikit-learn
- rasterio
import xarray as xr import fierpy # read sentinel1 time series imagery ds = xr.open_dataset("sentine1.nc") # apply rotated eof process reof_ds = fierpy.reof(ds.VV,n_modes=4) # get streamflow data from GeoGLOWS # select the days we have observations lat,lon = 11.7122,104.9653 q = fierpy.get_streamflow(lat,lon) q_sel = fierpy.match_dates(q,ds.time) # apply polynomial to different modes to find best stats fit_test = fierpy.find_fits(reof_ds,q_sel,ds)
fierpy is distributed by SERVIR under the terms of the MIT License. See LICENSE in this directory for more information.
fierpy abides to all of SERVIR's privacy and terms of use as described at https://servirglobal.net/Privacy-Terms-of-Use.