scitex-linalg

Small linear-algebra helpers — distances, NaN-aware norms, geometric median, vector projections.

Full Documentation · uv pip install scitex-linalg[all]

Quick Start

import scitex_linalg as sxl

sxl.cdist(u, v)                # pairwise distances
sxl.cosine(v1, v2)             # cosine similarity (NaN-safe)
sxl.nannorm(v, axis=-1)        # norm; propagates NaN
sxl.rebase_a_vec(v, v_base)    # project v onto v_base basis

Demo

flowchart LR
    A["u, v (np.ndarray)"] --> B["scitex_linalg.cdist"]
    B --> C["pairwise distance matrix"]
    A2["v with NaNs"] --> D["scitex_linalg.nannorm"]
    D --> E["vector norm; NaN propagates"]
    A3["v, v_base"] --> F["scitex_linalg.rebase_a_vec"]
    F --> G["projected coords"]
    A4["xx (torch.Tensor)"] --> H["scitex_linalg.geometric_median"]
    H --> I["robust median point"]

_{Figure 1. Demo. Distances, NaN-propagating norms, projections, and the torch geometric median.}

>>> import numpy as np, scitex_linalg as sxl
>>> sxl.cosine(np.array([1, 0]), np.array([1, 1]))
0.7071...
>>> sxl.nannorm(np.array([3.0, np.nan, 4.0]))
nan
>>> sxl.nannorm(np.array([3.0, 4.0]))
5.0

Installation

uv pip install "scitex-linalg[all]"

Architecture

flowchart LR
    UV["u, v"] --> DIST[euclidean_distance / cdist / edist]
    V["vectors"] --> MISC[cosine / nannorm / rebase_a_vec]
    XX["xx (torch.Tensor)"] --> GM[geometric_median]
    DEC[vendor decorators] --> NP[numpy_fn]
    DEC --> TF[torch_fn]

_{Figure 2. Architecture. Pure numpy/scipy core plus the torch geometric-median path and vendored decorators.}

Tiny single-purpose helpers. Core dependencies cover numpy/scipy/sympy/pandas. Install scitex-linalg[torch] for the geometric median and Torch numerical helpers; the package imports without that optional extra.

1 Interfaces

import scitex_linalg as sxl

sxl.euclidean_distance(u, v, axis=0)      # element-wise Euclidean distance
sxl.cdist(u, v)                           # pairwise distances
sxl.edist(u, v)                           # alias for euclidean_distance
sxl.cosine(v1, v2)                        # cosine similarity (NaN-safe)
sxl.nannorm(v, axis=-1)                   # NaN-aware vector norm
sxl.rebase_a_vec(v, v_base)               # project v onto v_base basis
sxl.three_line_lengths_to_coords(a, b, c) # triangle side lengths -> 2-D coords
sxl.geometric_median(xx, dim=-1)          # torch geometric median (requires [torch] extra)

Status

Standalone fork of scitex.linalg — intended to remain importable as scitex.linalg via the SciTeX umbrella package's bridge module. Decorators (numpy_fn, torch_fn, wrap) are vendored under _vendor_decorators/ to keep the package free of scitex.* runtime deps; when scitex-decorators is split out, those will be replaced with a direct dependency.

Part of SciTeX

scitex-linalg is part of SciTeX. Install via the umbrella with pip install scitex[linalg] to use as scitex.linalg (Python).

Four Freedoms for Research

0. The freedom to run your research anywhere — your machine, your terms.
1. The freedom to study how every step works — from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

License

AGPL-3.0-only (see LICENSE).