"""Extract satellite embeddings from Google Earth Engine and map them to a grid."""

from pathlib import Path
from typing import Literal

import cyclopts
import ee
import geemap
import geopandas as gpd
from rich import pretty, traceback

pretty.install()
traceback.install()
ee.Initialize(project="ee-tobias-hoelzer")

DATA_DIR = Path("data")


def cli(grid: Literal["hex", "healpix"], level: int, year: int):
    """Extract satellite embeddings from Google Earth Engine and map them to a grid.

    Args:
        grid (Literal["hex", "healpix"]): The grid type to use.
        level (int): The grid level to use.
        year (int): The year to extract embeddings for. Must be between 2017 and 2024.

    """
    grid = gpd.read_parquet(DATA_DIR / f"grids/permafrost_{grid}{level}_grid.parquet")
    eegrid = ee.FeatureCollection(grid.to_crs("epsg:4326").__geo_interface__)
    embedding_collection = ee.ImageCollection("GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL").filterDate(
        f"{year}-01-01", f"{year}-12-31"
    )

    def extract_embedding(feature):
        # Filter collection by geometry
        geom = feature.geometry()
        embedding = embedding_collection.filterBounds(geom).mosaic().clip(geom)
        # Get mean embedding value for the geometry
        mean_dict = embedding.reduceRegion(
            reducer=ee.Reducer.median(),
            geometry=geom,
        )
        # Add mean embedding values as properties to the feature
        return feature.set(mean_dict)

    eeegrid = eegrid.map(extract_embedding)
    df = geemap.ee_to_df(eeegrid)
    bands = [f"A{str(i).zfill(2)}" for i in range(64)]
    embeddings_on_grid = grid.merge(df[[*bands, "cell_id"]], on="cell_id", how="left")
    embeddings_on_grid.to_parquet(DATA_DIR / f"embeddings/permafrost_{grid}{level}_embeddings-{year}.parquet")


if __name__ == "__main__":
    cyclopts.run(cli)