1. Modify the config["ntems"] in main.py to include the ntems you want to clip. Possible ntems values are proxies, elev_p95, elev_cv, gross_stem_volume, total_biomass, loreys_height, and age.

2. Ensure your data directory structure is as follows:

rasin_dir/
    proxies/ # directory name is fixed
        SRef_2019_proxy_v2.dat
    structure/
        elev_p95/
            elev_p95_2019.dat # can be any name as long as it ends with .dat
        elev_cv/
            elev_p95_2019.dat

3. Invoke the python script by running python main.py --out_dir={your_path} --rasin_dir={your_path} --aoi_path={your_path}. Optionally, vri_path of the inventory shapefile can be passed in to clip the inventory as well.

4. if pass in the --merge_structures flag, the normalized structure layers will be merged into a single file under merged/ directory. It will only merge structural layers specified in ntems from config.

5. For the usage of defining an extra bounding box to clip, refer to the top comments in main.py.

It is assumed that your raster files are in the same CRS as the your AOI shapefile.

This is outside the scope of the main.py as it assumes different input data structure.

crop_species_prob.py: Crops and processes the species probability raster files. See the top comments in the script for usage.

mosaic_rasters.py: After getting different raster layers (by running Bud's R script) under UTM zone directory such as 11S, use this script to mosaic each type (proxies, elev_cv, etc) of rasters into one raster. The default crs to reproject is EPSG:3978. Ensure to update the input_base (where your UTM directory lies) and output_base (where you want to put the mosaicked files). The script should be run before running main.py to clip the raster layers.