RINSE – Reciprocal-space INvariant Spectral Embedding

RINSE computes rotationally invariant descriptors of crystalline materials by projecting the intensity-weighted reciprocal lattice onto a combined radial and angular basis (analogous to SOAP, but working entirely in reciprocal space).

Descriptor formulation

For a crystal, all reflections $\mathbf{G}_{\mathrm{hkl}}$ within a resolution cutoff (default $\sin{\theta}/\lambda \leq 0.35 Å^{-1}$, i.e. $|\mathbf{G}| \leq 0.70 Å^{-1}$) are enumerated. Each reflection is assigned an intensity $\mathrm{I}(\mathbf{G}) = \lvert\mathrm{F}(\mathbf{G})\rvert^{2}$ from the structure factor calculated via cctbx direct summation with X-ray form factors by default. Isotropic and anisotropic displacement parameters are read from the CIF by default. The resolution-dependent intensity envelope is removed at the power-spectrum level by monopole (l = 0) normalisation, which divides each radial level by its spherically-averaged scattering power.

The expansion coefficients are:

$$ A_{nlm} = Σ_\mathbf{G} I(\mathbf{G}) · R_n(|\mathbf{G}|) · Y_l^m(\hat{\mathbf{G}}) $$

and the rotationally invariant power spectrum is:

$$ p_{nl} = Σ_m \lvert A_{nlm}\rvert^{2} $$

Because the intensity field is centrosymmetric if anomalous dispersion is not considered, only even l contributes. By default, RINSE also drops the monopole (l = 0) and quadrupole (l = 2) terms. Default parameters give an 16 × 8 = 128-element descriptor:

Installation

From source (requires uv)

# Clone
git clone https://github.com/DuMOCC-Group/rinse-descriptor.git
cd rinse-descriptor

# Install uv (if not already available)
curl -Ls https://astral.sh/uv/install.sh | sh

# Install all dependencies
uv sync

# Run the demo.py notebook
uv run marimo edit demo.py

Optional CSD API Installation for PCA analysis

To recompute the PCA components used for the dimensionality reduction in the locality-sensitive hash (see Locality-sensitive hashing below), install the CSD Python API and run the scripts in the tools folder.

# Install CSD Python API
uv pip install --extra-index-url https://pip.ccdc.cam.ac.uk/ csd-python-api

# Generate descriptors for CSD structures
uv run tools/compute_csd_hashes.py 

# Perform PCA analysis and update components in python/rinse_descriptor/data folder
uv run tools/compute_pca.py 

From PyPI

pip install rinse-descriptor
# or
uv add rinse-descriptor

Quick start

From a CIF file

from rinse_descriptor import RinseParams, descriptor, descriptor_many

# Single structure → 1-D feature vector
x = descriptor("mystructure.cif")
print(x.shape)  # (128,)

# Return the 2-D power-spectrum matrix instead
params = RinseParams(flatten=False)
x_mat = descriptor("mystructure.cif", params=params)
print(x_mat.shape)  # (16, 8)

# Batch of structures → (N, 128)
structures = ["structure_1.cif", "structure_2.cif"]
X = descriptor_many(structures)
print(X.shape)  # (2, 128)

From a loaded cctbx structure

from rinse_descriptor import descriptor, load_cif

xrs = load_cif("mystructure.cif")
x = descriptor(xrs)
print(x.shape)  # (128,)

Custom parameters

from rinse_descriptor import RinseParams, descriptor

params = RinseParams(
    n_max=16,                       # radial shells (n = 0 … 15)
    l_max=20,                       # angular levels (gives l = 4,6,...,18 by default)
    radial_scale=0.3,               # per-shell scale in Å⁻¹ (resolution cutoff is derived)
    radial_basis="cv_gaussian",  # or "lin_gaussian"
    monopole_normalisation=False,    # optional: disable the default monopole (ℓ=0) envelope removal
)
x = descriptor("mystructure.cif", params=params)

Form factors

from rinse_descriptor import descriptor

# Electron scattering factors; descriptor weights are still intensities I = |F|²
x = descriptor("mystructure.cif", form_factor_type="electron")

# Monopole (l=0) normalisation removes the resolution envelope by default.
x_norm = descriptor("mystructure.cif")

Available form_factor_type values: "xray" (default), "electron", "neutron".

Locality-sensitive hashing

RINSE provides a deterministic locality-sensitive hash that converts descriptor vectors into short, pronounceable strings (proquints) for quick similarity comparisons and indexing.

Basic usage

from rinse_descriptor import descriptor, descriptor_hash

x = descriptor("mystructure.cif")
hash_str = descriptor_hash(x)
print(hash_str)  # e.g., "lusab"

# Generate longer hashes with more words (each word = 16 bits)
hash_str = descriptor_hash(x, n_words=5)
print(hash_str)  # e.g., "lusab-babad-gutih-tugad-mudof"

Algorithm

The hash is computed using PCA-based SimHash:

1. Project the descriptor vector onto the first n principal components (PCA model precomputed from the CSD and bundled with the package)
2. Binarize each projection coefficient by its sign: bits[i] = (projection[i] > 0)
3. Encode each 16-bit chunk as a five-character proquint word (CVCVC pattern using consonants for 4-bit values and vowels for 2-bit values)

By default, n_words=1 produces a single 5-character word encoding 16 hash bits. Structurally similar crystals produce similar hash bits and thus identical or nearby proquint strings.

Properties

• Deterministic: The same descriptor always produces the same hash
• Locality-sensitive: Similar descriptors map to similar hash bits
• Pronounceable: Uses the proquint encoding (babab, zuzuz, etc.) for human-readability
• PCA-based: Uses learned principal components from the Cambridge Structural Database rather than random projections

Decode hash to bits

from rinse_descriptor import descriptor_hash, hash_to_bits
import numpy as np

hash_str = descriptor_hash(x, n_words=3)
bits = hash_to_bits(hash_str)
print(bits.shape)  # (48,) — 3 words × 16 bits/word
print(bits.dtype)  # bool

Custom PCA model

To use your own PCA model (e.g., trained on a different dataset):

hash_str = descriptor_hash(x, n_words=5, pca_file="my_pca_components.json")

The JSON file must contain "components" (2-D array) and "mean" (1-D array) keys.

Development

# Run tests
uv run pytest tests/ -v

# Run benchmarks
uv run pytest benchmarks/ --benchmark-only -v

# Lint / format
uv run ruff check python/ tests/
uv run ruff format python/ tests/
uv run mypy python/rinse_descriptor/

Pre-commit hooks (recommended)

This repository includes a .pre-commit-config.yaml that runs:

• On commit: ruff check --fix and ruff format for staged Python files.
• On push: full ruff check, mypy, and pytest.

Install and run once:

uv sync --group dev
uv run pre-commit install --hook-type pre-commit --hook-type pre-push
uv run pre-commit run --all-files

Project structure

rinse-descriptor/
├── python/rinse_descriptor/  # Python package
│   ├── __init__.py        # Public API: descriptor(), descriptor_many(), descriptor_hash(), hash_to_bits()
│   ├── _crystal.py        # CIF loading into cctbx xray.structure objects
│   ├── _structure_factors.py  # cctbx structure factor calculation
│   ├── _radial_basis.py   # smooth-shell radial bases (index-anchored)
│   ├── _descriptor.py     # Power spectrum computation
│   ├── _hash.py           # Locality-sensitive hashing via PCA-based SimHash
│   └── data/
│       └── pca_components.json  # Precomputed PCA model for hashing
├── tests/                 # pytest test suite
├── benchmarks/            # pytest-benchmark suite
├── .github/workflows/ci.yml
└── pyproject.toml

Future: rinse_descriptor.diffraction submodule

The package is designed to support a future rinse_descriptor.diffraction submodule that will provide:

• Indexed diffraction patterns (hkl, d-spacing, intensity)
• Unindexed powder diffraction patterns (2θ or d-spacing profiles)
• Reciprocal-space descriptors beyond the power spectrum (e.g. bispectrum)

The ReflectionList and RinseParams types are designed to be shared between the core descriptor and the diffraction submodule.