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starmap w/ generator function (memory feature enhancement) #116
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from multiprocessing import Pool
from typing import Generator, Tuple
import geopandas as gpd
import pandas as pd
def new_function(
block_id: str,
blocks: gpd.GeoSeries,
buildings: gpd.GeoSeries,
streets: gpd.GeoSeries,
) -> dict:
block_area = blocks.to_crs(3395).area * 1e-6
building_count = buildings.explode(ignore_index=True).shape[0]
street_length = streets.to_crs(3395).length * 0.001
return {
"block_id": block_id,
"block_area": block_area.iloc[0],
"building_count": building_count,
"street_length": street_length.iloc[0],
}
def make_inputs(
blocks: gpd.GeoDataFrame,
buildings: gpd.GeoDataFrame,
streets: gpd.GeoDataFrame,
) -> Generator[
Tuple[str, gpd.GeoSeries, gpd.GeoSeries, gpd.GeoSeries], None, None
]:
for block_id in buildings["block_id"].unique():
yield (
block_id,
blocks[blocks["block_id"] == block_id]["geometry"],
buildings[buildings["block_id"] == block_id]["geometry"],
streets[streets["block_id"] == block_id]["geometry"],
)
if __name__ == "__main__":
syc_streets = gpd.read_parquet("input_data/syc_streets.parquet")
syc_blocks = gpd.read_parquet("input_data/syc_blocks.parquet")
syc_buildings = gpd.read_parquet("input_data/syc_buildings.parquet")
inputs = make_inputs(syc_blocks, syc_buildings, syc_streets)
with Pool() as pool:
results = pool.starmap(new_function, inputs)
df = pd.DataFrame.from_records(
results,
columns=["block_id", "block_area", "building_count", "street_length"],
)
print(df.head()) |
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