fierpy

Python implementation of the Forecasting Inundation Extents using REOF method

Based off of the methods from Chang et al., 2020

Installation

$ 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

Requirements

• numpy
• xarray
• pandas
• eofs
• geoglows
• scikit-learn
• rasterio

Example use

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)