Skip to main content

npysearch-win

npysearch-win is a Windows-compatible fork of npysearch by Aditya Jeevannavar (Tamminen Lab).

The original package does not compile correctly on Windows with Python 3.10+. This fork patches the build system so that pre-built wheels can be installed directly on Windows 10/11 (64-bit) without requiring a C++ compiler.


npysearch implements an efficient BLAST-like sequence comparison algorithm, written in C++11 and using native Python datatypes and bindings. npysearch is light-weight, fast, and dependency-free. The code base of npysearch is adapted from nsearch. An implementation of nsearch for R is available at blaster.

Installation

from PyPI (this Windows fork)

pip install npysearch-win

from the original project (Linux / macOS)

pip install npysearch

from conda-forge (original)

conda config --add channels conda-forge
conda config --set channel_priority strict
conda install npysearch

from source

# Clone repository from github
git clone https://github.com/tamminenlab/npysearch.git

# Install package using pip
pip install ./npysearch

Examples

# Import npysearch-win package (installed as 'npysearch')
import npysearch as npy

# Read query file into a dictionary
query = npy.read_fasta("npysearch/data/query.fasta")

# Read database file into a dictionary
database = npy.read_fasta("npysearch/data/db.fasta")

# BLAST the query against the database
results_dna = npy.blast(query, database)

# BLAST protein sequence file against itself using filenames as blast function arguments
results_prot = npy.blast(query    = "npysearch/data/prot.fasta",
                         database = "npysearch/data/prot.fasta",
                         alphabet = "protein")

Caveats

  • The blast function automatically detects whether the query and database arguments were passed as string paths to fasta files or as dictionaries of sequences. Both of them need not be input as the same type.
  • Use help(npy) (assuming you've imported npysearch as npy) to get a list of the functions included and their docstrings. For docstrings of specific functions, for example blast, use help(npy.blast)

Supported platforms

Platform Python versions Source
win_64 3.10 – 3.14 this fork (npysearch-win)
linux_64 >= 3.7 original npysearch
osx_64 >= 3.7 original npysearch

Details for the original package: https://anaconda.org/conda-forge/npysearch/files

License

BSD — same as the original npysearch project.

Release files for npysearch-win 1.3.1

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

Source distribution (sdist)

Source distribution for npysearch-win 1.3.1
File Size Uploaded
npysearch_win-1.3.1.tar.gz 39.7 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for npysearch-win 1.3.1
File
npysearch_win-1.3.1-cp314-cp314-win_amd64.whl CPython 3.14 CPython 3.14 Windows x86-64 Details
npysearch_win-1.3.1-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
npysearch_win-1.3.1-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
npysearch_win-1.3.1-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
npysearch_win-1.3.1-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details

Total release size: 745.3 kB

Release files / npysearch_win-1.3.1.tar.gz

Download URL npysearch_win-1.3.1.tar.gz
Size 39.7 kB
Tags Source
SHA-256 checksum
How to use checksums
8eb268daa06ae183b591aff671df6a6995c793ad8804a13fd8c237a0be6b5548
BLAKE2b-256 checksum
How to use checksums
a1de9bfeb095912aa0719e1c942ec77084d3866570a14916328a0ae6cee3d4d6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.0

Release files / npysearch_win-1.3.1-cp314-cp314-win_amd64.whl

Download URL npysearch_win-1.3.1-cp314-cp314-win_amd64.whl
Size 144.3 kB
Tags CPython 3.14 Windows x86-64
SHA-256 checksum
How to use checksums
7e0ed4e48d40a94e527ee977aa208c7ae626021ee5d96881cb0e11d8d26b64dd
BLAKE2b-256 checksum
How to use checksums
1d69cb4e607f6cbf7e976730645467497d630c8f19f22ca91be0517d21003443
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.0

Release files / npysearch_win-1.3.1-cp313-cp313-win_amd64.whl

Download URL npysearch_win-1.3.1-cp313-cp313-win_amd64.whl
Size 140.9 kB
Tags CPython 3.13 Windows x86-64
SHA-256 checksum
How to use checksums
d28b40fd86b2b40012af04d47103c898d18003d248d2234de83fa4bf32992853
BLAKE2b-256 checksum
How to use checksums
71bfdd90dd0cd50e7fa2770ff70e30b32f4c920747ba67bb2576bfc007505cb2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.0

Release files / npysearch_win-1.3.1-cp312-cp312-win_amd64.whl

Download URL npysearch_win-1.3.1-cp312-cp312-win_amd64.whl
Size 141.0 kB
Tags CPython 3.12 Windows x86-64
SHA-256 checksum
How to use checksums
11aec2c31a423c644be270d9a17115267cbf7694e0f171afdaf832e2709595e0
BLAKE2b-256 checksum
How to use checksums
95211e562e16187c52a0537a5a2177e87e50e6b3e531aa811f7c25cabe23feb0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.0

Release files / npysearch_win-1.3.1-cp311-cp311-win_amd64.whl

Download URL npysearch_win-1.3.1-cp311-cp311-win_amd64.whl
Size 140.3 kB
Tags CPython 3.11 Windows x86-64
SHA-256 checksum
How to use checksums
54ab9e9b5419d5bfc58b2af60828d957b3b4e8be9bc11565c593e43de2b70c8b
BLAKE2b-256 checksum
How to use checksums
c659f159f24165150337988cffef26b5b8f19222dd6d30783cf84a335fdc8704
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.0

Release files / npysearch_win-1.3.1-cp310-cp310-win_amd64.whl

Download URL npysearch_win-1.3.1-cp310-cp310-win_amd64.whl
Size 139.1 kB
Tags CPython 3.10 Windows x86-64
SHA-256 checksum
How to use checksums
833f98e71986d0cc229f3d8cef23d91f3dcc678d09e1f75487077015f50571c2
BLAKE2b-256 checksum
How to use checksums
1cd29d68b025b954af80fd0a2b6191baf11867780d7d7114d5498e29dce86d27
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.0

Release history Release notifications | RSS feed

This release

1.3.1 This release

6 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page