The most important part of the article seems to be that this Python code is taking "an avg of 293.41ms per iteration":
def find_close_polygons(
polygon_subset: List[Polygon], point: np.array, max_dist: float
) -> List[Polygon]:
close_polygons = []
for poly in polygon_subset:
if np.linalg.norm(poly.center - point) < max_dist:
close_polygons.append(poly)
return close_polygons
And after replacing it with this Rust code, it is taking "an avg of 23.44ms per iteration":
use pyo3::prelude::*;
use ndarray_linalg::Norm;
use numpy::PyReadonlyArray1;
#[pyfunction]
fn find_close_polygons(
py: Python<'_>,
polygons: Vec<PyObject>,
point: PyReadonlyArray1<f64>,
max_dist: f64,
) -> PyResult<Vec<PyObject>> {
let mut close_polygons = vec![];
let point = point.as_array();
for poly in polygons {
let center = poly
.getattr(py, "center")?
.extract::<PyReadonlyArray1<f64>>(py)?
.as_array()
.to_owned();
if (center - point).norm() < max_dist {
close_polygons.push(poly)
}
}
Ok(close_polygons)
}
Why is the Rust version 13x faster than the Python version?
