Resampling with pyresample (s3 storage, NetCDF File, H5NetCDF driver, earthaccess auth)

import argparse

import earthaccess
import xarray as xr
from pyresample.area_config import create_area_def
from pyresample.gradient import block_nn_interpolator, gradient_resampler_indices_block
from pyresample.resampler import resample_blocks

def warp_resample(dataset, zoom=0):
    from common import earthaccess_args, target_extent

    te = target_extent[zoom]

    # Define filepath, driver, and variable information
    args = earthaccess_args[dataset]
    input_uri = f'{args["folder"]}/{args["filename"]}'
    src = f's3://{args["bucket"]}/{input_uri}'
    # Define source and target projection
    dstSRS = "EPSG:3857"
    srcSRS = "EPSG:4326"
    width = height = 256
    # Authentical via earthaccess
    earthaccess.login()
    fs = earthaccess.get_s3_filesystem(daac=args["daac"])
    # Specify fsspec caching since default options don't work well for raster data
    fsspec_caching = {
        "cache_type": "none",
    }
    with fs.open(src, **fsspec_caching) as f:
        # Open dataset
        da = xr.open_dataset(f, engine="h5netcdf", chunks={})[args["variable"]]
        # Rechunk MURSST to operate on fewer chunks
        if dataset == "mursst":
            da = da.chunk({"time": -1, "lat": 4000, "lon": 4000})
        elif dataset == "gpm_imerg":
            # Transpose dims to align with pyresample expectations
            da = da.transpose("time", "lat", "lon").squeeze()
        # Create area definition for the target dataset
        target_area_def = create_area_def(
            area_id=1,
            projection=dstSRS,
            shape=(height, width),
            area_extent=te,
        )
        # Create area definition for the source dataset
        source_area_def = create_area_def(
            area_id=2,
            projection=srcSRS,
            shape=(da.sizes["lat"], da.sizes["lon"]),
            area_extent=[-179.995, 89.995, 180.005, -89.995],
        )
        # Compute indices for resampling
        indices_xy = resample_blocks(
            gradient_resampler_indices_block,
            source_area_def,
            [],
            target_area_def,
            chunk_size=(1, height, width),
            dtype=float,
        )
        # Apply resampler
        resampled = resample_blocks(
            block_nn_interpolator,
            source_area_def,
            [da.data],
            target_area_def,
            dst_arrays=[indices_xy],
            chunk_size=(1, height, width),
            dtype=da.dtype,
        )
        # Reproject dataset
        return resampled.compute()

if __name__ == "__main__":
    if "get_ipython" in dir():
        # Just call warp_resample if running as a Jupyter Notebook
        da = warp_resample("gpm_imerg")
    else:
        # Configure dataset via argpase if running via CLI
        parser = argparse.ArgumentParser(description="Set environment for the script.")
        parser.add_argument(
            "--dataset",
            default="mursst",
            help="Dataset to resample.",
            choices=["gpm_imerg", "mursst"],
        )
        parser.add_argument(
            "--zoom",
            default=0,
            help="Zoom level for tile extent.",
        )
        user_args = parser.parse_args()
        da = warp_resample(user_args.dataset, int(user_args.zoom))