Add hexagon backend

pipeline/processors/h3_aggregator.py

@@ -0,0 +1,42 @@
+from pathlib import Path
+import polars as pl
+
+from pipeline.config import AGGREGATES_DIR, H3_RESOLUTIONS
+
+
+def aggregate(df: pl.LazyFrame, resolution: int) -> pl.LazyFrame:
+    """Aggregate property data by H3 cell and year."""
+    h3_col = f"h3_res{resolution}"
+
+    return (
+        df.group_by(h3_col, "year")
+        .agg(
+            pl.len().alias("count"),
+            pl.col("price").mean().alias("avg_price"),
+            pl.col("price").median().alias("median_price"),
+            pl.col("price").min().alias("min_price"),
+            pl.col("price").max().alias("max_price"),
+        )
+        .rename({h3_col: "h3"})
+    )
+
+
+def aggregate_all(df: pl.LazyFrame) -> dict[int, pl.LazyFrame]:
+    """Aggregate at all H3 resolutions."""
+    return {res: aggregate(df, res) for res in H3_RESOLUTIONS}
+
+
+def save_aggregates(df: pl.LazyFrame, output_dir: Path | None = None) -> list[Path]:
+    """Aggregate and save at all H3 resolutions."""
+    output_dir = output_dir or AGGREGATES_DIR
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    saved_paths = []
+    aggregates = aggregate_all(df)
+
+    for res, agg_df in aggregates.items():
+        output_path = output_dir / f"res{res}.parquet"
+        agg_df.collect().write_parquet(output_path)
+        saved_paths.append(output_path)
+
+    return saved_paths