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Speedups

CI Build Wheels PyPI Python License

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.

STL I/O Performance

Operation Facets Pure Python speedups Speedup
Write 10,000 28.5 ms 9.9 ms 2.9x
Read 10,000 21.6 ms 7.0 ms 3.1x
Write 100,000 283.4 ms 89.3 ms 3.2x
Read 100,000 218.7 ms 71.0 ms 3.1x
Write 1,000,000 2.81 s 897.5 ms 3.1x
Read 1,000,000 2.19 s 711.5 ms 3.1x
Write 10,000,000 28.62 s 9.22 s 3.1x
Read 10,000,000 22.03 s 7.32 s 3.0x

PostgreSQL COPY to NumPy Performance

Type Elements Pure Python speedups Speedup
int32 100K 7.3 ms 2.6 ms 2.8x
int32 1M 73.8 ms 24.8 ms 3.0x
int32 10M 747.5 ms 216.1 ms 3.5x
int32 50M 3.75 s 1.05 s 3.6x
float64 100K 8.0 ms 3.3 ms 2.4x
float64 1M 78.3 ms 32.1 ms 2.4x
float64 10M 786.5 ms 268.5 ms 2.9x
float64 50M 4.01 s 1.31 s 3.1x

Benchmarked on Apple M2 Pro, Python 3.14, macOS 15.4. PG data pre-populated in tables to isolate COPY+conversion time. Array dimensionality (1D/2D/3D) has no significant effect on performance.

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

PostgreSQL NumPy Dimensions
float4 float32 1D – ND
float8 float64 1D – ND
smallint int16 1D – ND
integer int32 1D – ND
bigint int64 1D – ND

Compatibility

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

License

BSD-3-Clause

Metadata

Release files for speedups 2.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for speedups 2.2.0
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speedups-2.2.0.tar.gz 359.9 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for speedups 2.2.0
File
speedups-2.2.0-cp314-cp314t-win_amd64.whl CPython 3.14 CPython 3.14 free-threading Windows x86-64 Details
speedups-2.2.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.14 CPython 3.14 free-threading Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
speedups-2.2.0-cp314-cp314t-macosx_11_0_arm64.whl CPython 3.14 CPython 3.14 free-threading macOS 11.0+ ARM64 Details
speedups-2.2.0-cp314-cp314-win_amd64.whl CPython 3.14 CPython 3.14 Windows x86-64 Details
speedups-2.2.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.14 CPython 3.14 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
speedups-2.2.0-cp314-cp314-macosx_11_0_arm64.whl CPython 3.14 CPython 3.14 macOS 11.0+ ARM64 Details
speedups-2.2.0-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
speedups-2.2.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
speedups-2.2.0-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
speedups-2.2.0-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
speedups-2.2.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
speedups-2.2.0-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
speedups-2.2.0-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
speedups-2.2.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
speedups-2.2.0-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
speedups-2.2.0-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
speedups-2.2.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ x86-64, Linux glibc 2.17+ x86-64 Details
speedups-2.2.0-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details

Total release size: 9.6 MB

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Tags CPython 3.10 Windows x86-64
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Release files / speedups-2.2.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl

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Release files / speedups-2.2.0-cp310-cp310-macosx_11_0_arm64.whl

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Tags CPython 3.10 macOS 11.0+ ARM64
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Release history Release notifications | RSS feed

2.3.3

50 release files

2.3.2

50 release files

2.3.1

50 release files

2.3.0

50 release files

This release

2.2.0 This release

19 release files

2.1.0

19 release files

1.4.0

17 release files

1.3.2

16 release files

1.3.0

2 release files

1.2.0

17 release files

1.1.0

18 release files

1.0.0

17 release files

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