fierpy 0.0.4

Python implementation of the Forecasting Inundation Extents using REOF method

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)