Speedups

C and Cython extensions for fast STL I/O and PostgreSQL-to-NumPy conversion.

Performance

Cython extensions run ~3x faster than pure Python, consistent across data sizes.

Install

pip install speedups

With PostgreSQL support:

pip install speedups[postgres]

PostgreSQL Array → NumPy

Convert PostgreSQL arrays directly to NumPy ndarrays using psycopg's binary COPY protocol. Bypasses Python object creation for a significant speedup over the default loader.

Supports float4, float8, smallint, integer, and bigint arrays, from 1D to N-D.

import psycopg
from speedups.psycopg_loaders import NumpyLoader

with psycopg.connect("dbname=mydb") as conn:
    cursor = conn.cursor(binary=True)
    NumpyLoader.install(cursor)

    query = """
    COPY (
        SELECT array_agg(x)
        FROM generate_series(1, 100000) x
    ) TO STDOUT WITH BINARY
    """

    with cursor.copy(query) as copy:
        copy.set_types(["integer[]"])

        for row in copy.rows():
            print(row)  # numpy.ndarray

ASCII STL I/O

Read and write ASCII STL files at C speed. This module is used internally by numpy-stl — if you want to read or write STL files, use numpy-stl for a full-featured API. The Cython implementation uses direct sscanf/fprintf calls, avoiding Python string overhead entirely.

from speedups.stl import ascii_read, ascii_write

# Read
with open("model.stl", "rb") as f:
    buf = f.read(8192)
    name, mesh = ascii_read(f, buf)

# Write
with open("output.stl", "wb") as f:
    ascii_write(f, b"my_model", mesh)

Supported Types

Compatibility

• Python 3.10, 3.11, 3.12, 3.13, 3.14
• NumPy 1.x and 2.x

License

BSD-3-Clause