SciRS2 - Python Bindings

SciRS2: High-performance scientific computing in Rust with Python bindings. A comprehensive, type-safe alternative to SciPy with exceptional performance for statistical analysis, linear algebra, FFT, signal processing, clustering, and more.

Testing status: the Rust-side test suite is green (39/39 cargo nextest tests passing as of v0.6.3). A broader Python-facing pytest sweep across the full test suite currently shows approximately 1,146 passed / 404 failed, concentrated in the vision, neural, sparse, pandas-compat, async, io, and text binding modules — see TODO.md for details. This is a pre-existing binding-surface gap, not a regression; treat module-level completeness claims below as "bindings exist and are registered," not as "fully verified end-to-end."

Overview

scirs2-python provides Python bindings for the entire SciRS2 scientific computing ecosystem using PyO3 and Maturin. It leverages scirs2-numpy — a SciRS2-maintained fork of rust-numpy with native ndarray 0.17 support — for zero-copy NumPy array interoperability.

Key Characteristics

Exceptional Statistics Performance: Up to 410x faster than SciPy for higher-order moments (skewness, kurtosis) on small/medium datasets
Pure Rust Stack: OxiBLAS for linear algebra, OxiFFT for transforms — no system OpenBLAS or FFTW required
Zero-Copy NumPy Interop: Direct memory sharing between Rust and NumPy arrays via scirs2-numpy
SciPy-Compatible API: Familiar naming conventions for Python scientists migrating from SciPy
Maturin Build System: Single pip install scirs2 — no C/Fortran compiler needed
Type Stubs: .pyi files for full IDE autocompletion and type checking
Feature-Gated Modules: Enable only the SciRS2 crates you need

Architecture

scirs2-python/
├── src/
│   ├── lib.rs          - PyO3 module registration
│   ├── linalg.rs       - Linear algebra (OxiBLAS-powered)
│   ├── stats.rs        - Statistics and distributions
│   ├── fft.rs          - FFT (OxiFFT-powered)
│   ├── cluster.rs      - Clustering algorithms
│   ├── series.rs       - Time series analysis
│   ├── signal.rs       - Signal processing
│   ├── optimize.rs     - Optimization algorithms
│   ├── spatial.rs      - Spatial algorithms
│   ├── sparse.rs       - Sparse matrix operations
│   ├── ndimage.rs      - N-dimensional image processing
│   ├── graph.rs        - Graph algorithms
│   ├── metrics.rs      - ML evaluation metrics
│   ├── io.rs           - File I/O (CSV, HDF5, Parquet, etc.)
│   ├── datasets.rs     - Dataset loading and generation
│   ├── transform.rs    - Dimensionality reduction
│   ├── text.rs         - NLP and text processing
│   ├── vision.rs       - Computer vision
│   ├── linalg_ext.rs   - Extended linear algebra (v0.3.4+)
│   ├── signal_ext.rs   - Extended signal processing (v0.3.4+)
│   ├── optimize_ext.rs - Extended optimization (v0.3.4+)
│   └── stats/
│       └── mcmc_gp.rs  - MCMC and Gaussian process bindings (v0.3.4+)
├── tests/
│   └── test_module_structure.py
├── scirs2.pyi          - Type stubs
└── pyproject.toml

Installation

pip install scirs2

For development (builds Rust from source):

pip install maturin
git clone https://github.com/cool-japan/scirs
cd scirs/scirs2-python
maturin develop --release

No system BLAS, OpenBLAS, or FFTW installation required — SciRS2 uses pure Rust OxiBLAS and OxiFFT.

Quick Start

Statistics (Fastest Module)

SciRS2 delivers exceptional speed for statistical analysis:

import numpy as np
import scirs2

data = np.random.randn(1000)

# Basic statistics (5-25x faster than NumPy on small-medium data)
mean     = scirs2.mean_py(data)           # 8x faster
std      = scirs2.std_py(data, 0)         # 14x faster
var      = scirs2.var_py(data, 1)
median   = scirs2.median_py(data)
iqr      = scirs2.iqr_py(data)

# Higher-order moments (50-410x faster than SciPy!)
skewness = scirs2.skew_py(data)           # 52x faster
kurtosis = scirs2.kurtosis_py(data)       # 52x faster

# Correlation (17-127x faster)
x = np.random.randn(100)
y = np.random.randn(100)
corr = scirs2.correlation_py(x, y)
cov  = scirs2.covariance_py(x, y, 1)

# Full descriptive summary
summary = scirs2.describe_py(data)
# Returns: {'mean', 'std', 'min', 'max', 'skewness', 'kurtosis', ...}

Linear Algebra

import numpy as np
import scirs2

A = np.array([[4.0, 2.0], [2.0, 3.0]])
b = np.array([1.0, 2.0])

# Basic operations
det   = scirs2.det_py(A)
inv   = scirs2.inv_py(A)
trace = scirs2.trace_py(A)

# Decompositions
lu   = scirs2.lu_py(A)       # {'L', 'U', 'P'}
qr   = scirs2.qr_py(A)       # {'Q', 'R'}
svd  = scirs2.svd_py(A)      # {'U', 'S', 'Vt'}
chol = scirs2.cholesky_py(A)

# Eigenvalues/vectors
eig  = scirs2.eig_py(A)      # {'eigenvalues_real', 'eigenvalues_imag', 'eigenvectors'}
eigh = scirs2.eigh_py(A)     # For symmetric matrices

# Solvers
x     = scirs2.solve_py(A, b)
lstsq = scirs2.lstsq_py(A, b)

# Norms and properties
norm_fro = scirs2.matrix_norm_py(A, "fro")
norm_vec = scirs2.vector_norm_py(b, 2)
cond     = scirs2.cond_py(A)
rank     = scirs2.matrix_rank_py(A)

FFT (OxiFFT Backend)

import numpy as np
import scirs2

data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])

# FFT (2-5x faster than NumPy on small data < 2K samples)
fft_result = scirs2.fft_py(data)           # {'real', 'imag'}
real, imag = fft_result['real'], fft_result['imag']

ifft_result = scirs2.ifft_py(
    np.array(real), np.array(imag)
)

rfft  = scirs2.rfft_py(data)
irfft = scirs2.irfft_py(
    np.array(rfft['real']), np.array(rfft['imag']), len(data)
)

# DCT
dct  = scirs2.dct_py(data, 2)             # Type-II DCT
idct = scirs2.idct_py(np.array(dct), 2)

# Helpers
freqs    = scirs2.fftfreq_py(len(data), 1.0)
rfreqs   = scirs2.rfftfreq_py(len(data), 1.0)
shifted  = scirs2.fftshift_py(data)
fast_len = scirs2.next_fast_len_py(100, False)

Clustering

import numpy as np
import scirs2

X = np.vstack([
    np.random.randn(100, 2) + [0, 0],
    np.random.randn(100, 2) + [5, 5],
])

# K-Means
kmeans = scirs2.KMeans(n_clusters=2)
kmeans.fit(X)
labels   = kmeans.labels
inertia  = kmeans.inertia_

# Cluster quality metrics
silhouette = scirs2.silhouette_score_py(X, labels)
db_score   = scirs2.davies_bouldin_score_py(X, labels)
ch_score   = scirs2.calinski_harabasz_score_py(X, labels)

# Preprocessing
X_std  = scirs2.standardize_py(X, True)
X_norm = scirs2.normalize_py(X, "l2")

Time Series

import numpy as np
import scirs2

data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
ts   = scirs2.PyTimeSeries(data, None)

# Transformations
diff1    = scirs2.apply_differencing(ts, 1)
seasonal = scirs2.apply_seasonal_differencing(ts, 4)

# ARIMA modelling
arima = scirs2.PyARIMA(1, 1, 0)
arima.fit(ts)
forecast = arima.forecast(5)
print(arima.summary())

# Box-Cox transform
result      = scirs2.boxcox_transform(ts, None)   # auto lambda
transformed = result['transformed']
lambda_val  = result['lambda']
recovered   = scirs2.boxcox_inverse(np.array(transformed), lambda_val)

# Stationarity test
adf = scirs2.adf_test(ts, None)
print(f"ADF statistic: {adf['statistic']}, p-value: {adf['p_value']}")

# STL decomposition
decomp   = scirs2.stl_decomposition(ts, 4)
trend    = decomp['trend']
seasonal = decomp['seasonal']
residual = decomp['residual']

MCMC and Gaussian Processes (v0.3.4+)

import numpy as np
import scirs2

# Gaussian Process regression
X_train = np.linspace(0, 2 * np.pi, 20).reshape(-1, 1)
y_train = np.sin(X_train.ravel()) + 0.1 * np.random.randn(20)

gp = scirs2.GaussianProcessRegressor(kernel='rbf', length_scale=1.0, noise=0.01)
gp.fit(X_train, y_train)

X_test = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)
mean, std = gp.predict(X_test, return_std=True)

# MCMC sampling
sampler = scirs2.MetropolisHastings(log_prob_fn=my_log_prob, step_size=0.1)
samples = sampler.sample(initial=np.zeros(3), n_samples=10000, burnin=1000)

Extended Optimization (v0.3.4+)

import numpy as np
import scirs2

# Gradient-free optimization
result = scirs2.minimize_nelder_mead(
    lambda x: (x[0] - 1)**2 + (x[1] + 2)**2,
    x0=np.array([0.0, 0.0]),
    tol=1e-8
)

# Constrained optimization
result = scirs2.minimize_slsqp(
    fun=objective,
    x0=x0,
    constraints=[{'type': 'eq', 'fun': equality_constraint}],
    bounds=[(0, None), (0, None)]
)

Performance Guide

Where SciRS2 Excels

Operation	Data Size	Speedup vs SciPy	Notes
Skewness	100	410x	Higher-order moments
Kurtosis	100	408x	Higher-order moments
Pearson correlation	100	127x	Small dataset
IQR	100	95x	Quartile calculations
Percentile	100	49x	Distribution analysis
Skewness	1,000	52x	Medium dataset
Std	100	25x	Small data variability
Mean	100	11x	Small data average
FFT (rfft)	128	5.2x	Small signal
Linear solve	10x10	9.4x	Small systems

Where NumPy/SciPy May Win

Operation	Size	Notes
Linear algebra (SVD, QR)	200x200+	SciPy LAPACK highly optimized
FFT	32K+ samples	NumPy FFT optimized for large N
Basic stats	100K+ elements	NumPy C SIMD optimizations

Recommended Hybrid Approach

import numpy as np
import scirs2

data = np.random.randn(1000)

# Use scirs2 for statistics on small-medium data
skewness = scirs2.skew_py(data)      # 52x faster than SciPy
kurtosis = scirs2.kurtosis_py(data)  # 52x faster than SciPy
mean     = scirs2.mean_py(data)      # 8x faster
std      = scirs2.std_py(data, 0)    # 14x faster

# Use scirs2 for FFT on small signals
signal = np.random.randn(512)
rfft   = scirs2.rfft_py(signal)      # 3x faster than NumPy

# Fall back to NumPy/SciPy for large transforms
large_signal = np.random.randn(65536)
spectrum     = np.fft.rfft(large_signal)  # NumPy wins here

Feature Flags

Enable specific SciRS2 crates in pyproject.toml:

[features]
default = ["linalg", "stats", "fft", "cluster", "series"]
linalg      = ["scirs2-linalg"]
stats       = ["scirs2-stats"]
fft         = ["scirs2-fft"]
cluster     = ["scirs2-cluster"]
series      = ["scirs2-series"]
signal      = ["scirs2-signal"]
optimize    = ["scirs2-optimize"]
spatial     = ["scirs2-spatial"]
sparse      = ["scirs2-sparse"]
ndimage     = ["scirs2-ndimage"]
graph       = ["scirs2-graph"]
metrics     = ["scirs2-metrics"]
io          = ["scirs2-io"]
datasets    = ["scirs2-datasets"]
transform   = ["scirs2-transform"]
text        = ["scirs2-text"]
vision      = ["scirs2-vision"]

Type Hints

import scirs2

# IDE provides full autocompletion from .pyi stubs
result: float    = scirs2.det_py(matrix)
svd_res: dict    = scirs2.svd_py(matrix)
fft_res: dict    = scirs2.fft_py(data)
labels: np.ndarray = scirs2.KMeans(n_clusters=3).fit_predict(X)

Building from Source

# 1. Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# 2. Install maturin
pip install maturin

# 3. Build and install in development mode
cd scirs2-python
maturin develop --release

# 4. Run tests
pip install pytest numpy
pytest tests/

Dependencies

PyO3 - Rust/Python FFI
scirs2-numpy - SciRS2 fork of rust-numpy with ndarray 0.17 support
Maturin - Build system for Python/Rust extensions
OxiBLAS - Pure Rust BLAS/LAPACK (no system dependencies)
OxiFFT - Pure Rust FFT

Related Projects

SciRS2 Core - Rust scientific computing library
scirs2-numpy - NumPy/ndarray 0.17 bridge
NumPy - Array operations
SciPy - Python scientific computing (API inspiration)

License

Licensed under the Apache License 2.0. See LICENSE for details.

Authors

COOLJAPAN OU (Team KitaSan)

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SHA256	`9ce272e12621f9f38c5b612e6068162fd3512b17c970d3e11e2f16b57e12ef5a`
MD5	`6475f1e9e8cfa6b8b99b8b0ecbf92469`
BLAKE2b-256	`6ecf6de74008091876be2c7ddc4c1466d3a3befa035caa5a5b0d533a6c3d0c54`

Algorithm	Hash digest
SHA256	`4771b81c23913b49595577b8fad2ca7b2aae7df40532b5c3f78689f3b8d07e00`
MD5	`446fe0b5a9d7d9c714aa39cd71958313`
BLAKE2b-256	`9f294c64a15d98508cb37e38d8b2a7054053cd14a604b3ea90626a46b965c8e2`

Algorithm	Hash digest
SHA256	`a97a77e95a75558663633e1a23f6def9a34328034570a837b78c884bb8af15ca`
MD5	`b4c62b6811888c2adbbf877fa9164272`
BLAKE2b-256	`049b0ae8694af979955a70f04a1a523dd934c771eb58e3805af64c04bcfef1fa`

Algorithm	Hash digest
SHA256	`0d0f3cad856fee11fd3f3f38e3b25c1d832c59ea3f4525914ce045303616eae5`
MD5	`16c37091d8a63f29f0f68b8562b2fb6f`
BLAKE2b-256	`1408c039236f3d54d3be85cfbda4fed6032810999c84bfbe991850979edb9d3d`

Algorithm	Hash digest
SHA256	`0c1d8fd3967cc6679e15e62a3b5630054adceb2c2e25f0937ea5019727e8f023`
MD5	`74fb53e5e718ee0f227b759801af528d`
BLAKE2b-256	`7ebd6429b0715e0469a3fb6bcd962c8738d74a1c6250dd84ee5567a732a0c8b9`

Algorithm	Hash digest
SHA256	`2e5a3aa274c0d1501e5d5196d127cdb2059ee76b45782c62178433060f18439b`
MD5	`46302d5e4f38b03c4873fed3181eb8d5`
BLAKE2b-256	`4456002cef80803422a3f5e0378d38efe448a775acb4f6a9cb39067d98ab1382`

Algorithm	Hash digest
SHA256	`f1e6f94d76abc2e1925830710b46603087c0a4a14f6d66917c110cf60736af85`
MD5	`5a34e26444a8ca2d1c69cbbcaae063f9`
BLAKE2b-256	`91379811cafcd28d93be5be82599958dfc8c8051064c4f453647472c86dd7b91`

Algorithm	Hash digest
SHA256	`44dc2bfd27c0fee49939167a48625d7ae02a96599fd782c8bc6a5593e8871082`
MD5	`68f6b892bbff88a1d9d2beed744229fc`
BLAKE2b-256	`c1a539ccfe7c428b072ae0e8476ce80d096871a9b7af123734b362de575f38ec`

Algorithm	Hash digest
SHA256	`496e2a54f2ad9b1612b55a7fa1936ff3c7ba9567597084514d55b8031ebc8f41`
MD5	`691dccff33242b5b55e258b7cd9b3778`
BLAKE2b-256	`1b4aa4346b7ce64fe8c0d6c1ac91a11e5d6143ffbf80662cbfe0dc96bb6b52a5`

Algorithm	Hash digest
SHA256	`9488d8b53cb488a65a2a9c23fd7b1080e336fd4bedd96892b8258cb7f4427f65`
MD5	`c7034d1fdd5ee349cb03cd908c4d9828`
BLAKE2b-256	`d38e0e6bca8e25c72b0f7b2b68a4a8745db5bc416431ffe6dee8c4d429630001`

Algorithm	Hash digest
SHA256	`9fb2ab86d666a7d0d78102957f50c1e62ea249e09db2b33fc49ea9c2bd700b29`
MD5	`3ef72711b19ceeb3226aad17e4347ff9`
BLAKE2b-256	`4b99f99278f3c49420a470f33d4e615a3225998f4f681ed4710bf6cb81f7f8f1`

Algorithm	Hash digest
SHA256	`2b32b3ef697b2129928aac2e788a93928e4dc4e4aacb8b67070ea236c7e42446`
MD5	`f154d900aa008989d134d6600487583e`
BLAKE2b-256	`c482dc2f65a3ec49dc2ce6274eb3daca4c6f300775c700b3f100d27be6dd5c35`

scirs2 0.6.4

SciRS2 - Python Bindings

Overview

Key Characteristics

Architecture

Installation

Quick Start

Statistics (Fastest Module)

Linear Algebra

FFT (OxiFFT Backend)

Clustering

Time Series

MCMC and Gaussian Processes (v0.3.4+)

Extended Optimization (v0.3.4+)

Performance Guide

Where SciRS2 Excels

Where NumPy/SciPy May Win

Recommended Hybrid Approach

Feature Flags

Type Hints

Building from Source

Dependencies

Related Projects

License

Authors

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Provides Extra

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Algorithm	Hash digest
SHA256	`0cffa1a67262f7bb69b2ff8ba81efc81e34fa03c99e359797ca95456b515e6ce`
MD5	`43301167ce1abc1f22bb63eef9dfed66`
BLAKE2b-256	`30dc1d6c5e300712c5b835dc43dedb816c2606042729b83d4347d91dc1b4b22e`

Algorithm	Hash digest
SHA256	`32aef0f4f4799aee766adb06f72064f3402f12df0cb1d2b118d812c6f94af591`
MD5	`6f00880682fe4e99a902a03b81681f08`
BLAKE2b-256	`daeb1d8c736eccf1802d21e1db1738debbe6ca8f29c82afce60d66e8a25bd6e3`

Algorithm	Hash digest
SHA256	`cc872b94e89f820478931e62e592db3ca1e5b88fcb5f70d259f03be9b91f943e`
MD5	`0823439acc6941a61cce354321af597d`
BLAKE2b-256	`d290d6d43bc187ca189067d9cd62fda7043102e50a803ec489d0258c462ee159`

Algorithm	Hash digest
SHA256	`4fa00875b8edb96145ab4a49a666aec3f96ea6d3e32aa04bd51c48ff20a8f90d`
MD5	`c38425fa0085bdf621139a2618230b7d`
BLAKE2b-256	`147841f605c62764d86198b59c41ca444021ac0bbce0fc6d5a8954b6daa6fedf`

Algorithm	Hash digest
SHA256	`59efaab7343d4ad937f0c9ac84210a6c5b1437db90655fd5e357e070ced65041`
MD5	`8367ff0652f541fd0f0b7fe142c09f00`
BLAKE2b-256	`21fbe49fc725cc21397651f96caec6d880a2448d0be0784fef992f26c2bbebf1`

Algorithm	Hash digest
SHA256	`7c1139adfb4e9b95b992effb12af38ca239ccf3fbb8f4ba6496803ccf70224cb`
MD5	`a9fae8ad5c5fa2198df769eea87b137a`
BLAKE2b-256	`84811434055adca5dcb775be7c218ecafdb27671ad5d4c0e26eb4918ab13b4bb`

Algorithm	Hash digest
SHA256	`54e93c9d869b3a98f95bbea62e0b1b7b7ee99cebfe81dadd9771fa8a33cf76d8`
MD5	`c22c668341fbe5a92cc9cbcd66943538`
BLAKE2b-256	`5ac36f5afe68426f42e67449852f30e2634194875addca3ff33c0a197cbe9b0e`

Algorithm	Hash digest
SHA256	`b56bd0d5e126533c87f7b270e094125b714b758363d98662151a9b105dc10d35`
MD5	`bec7abda9704d2c8a6544f2fbeea9919`
BLAKE2b-256	`a92e1f69314959e1239f8bd50e6fd288ec5596107aa373ac38ae28cf136b8de3`

Algorithm	Hash digest
SHA256	`5d196fcbea443c2013cb311b882d4d93838b6069081bea564d1ea1ab2ea8ff2d`
MD5	`f8adf0df6f9849f061da6eb6698f0216`
BLAKE2b-256	`9f140cd1717ecfe039fb9f97b5bf6bb4649aa2cb7eac1ff76b21b48a2f756e4f`

Algorithm	Hash digest
SHA256	`3d46eec35686b816f9808202feef24be69abb67f70f30de16fe05c3786d79f01`
MD5	`2ab19b2c37ea9bc8e165f834290c4953`
BLAKE2b-256	`bfd5a289453116b9e96bb6bd22a6f6df1b35640d9e3633e4dc4d39f88bbe9da4`