Metadata-Version: 2.1
Name: fierpy
Version: 0.0.2
Summary: Python implementation of the Forecasting Inundation Extents using REOF method
Home-page: http://github.com/servir/fierpy
Author: Kel Markert
Author-email: kel.markert@gmail.com
License: MIT
Description: # fierpy
        Python implementation of the Forecasting Inundation Extents using REOF method
        
        Based off of the methods from [Chang et al., 2020](https://doi.org/10.1016/j.rse.2020.111732)
        
        ## Installation
        
        ```bash
        $ 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:
        
        ```bash
        $ 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
        
        ```python
        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)
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: opensarlab
