Skip to content

CDS v2

scientific-computing-system-2.0 is a scientific computing platform built on the scientific Python stack - NumPy, SciPy, pandas and matplotlib. The algorithms proven in the pure-Python scientific-computing-system (v1.x) form its foundation; v2 rebuilds them for speed and adds new domain modules on top.

Highlights

  • Accelerated core - linear algebra, statistics, optimization, integration, interpolation, signal processing and Monte Carlo on top of NumPy/SciPy instead of hand-rolled loops.
  • Graphs with PageRank - components, shortest paths (Dijkstra / Bellman-Ford / Floyd-Warshall), minimum spanning forests, topological order.
  • Machine learning - linear/logistic regression, k-means++, PCA, kNN, metrics and synthetic data generators.
  • Time series - seasonal decomposition, ACF/PACF, Ljung-Box on pandas series.
  • Visualization & I/O - matplotlib helpers and pandas-backed readers.

Designed for real scientific Python workflows

Use CDS2 for practical NumPy/SciPy scientific computing tasks: numerical linear algebra, optimization, statistics, Bayesian inference, Monte Carlo, signal processing, time series, machine learning, graph algorithms, PageRank, PDE/SDE solving, information theory, visualization, data analysis, and reproducible scientific model fitting.

Quick start

pip install scientific-computing-system-2.0

import numpy as np
import cds2

# Solve a linear system
x = cds2.linalg.solve([[3.0, 1.0], [1.0, 2.0]], [9.0, 8.0])

# PageRank over a directed graph
adj = cds2.graph.from_edges(4, [(0, 1), (0, 2), (1, 3), (2, 3)])
scores = cds2.graph.pagerank(adj)

# Monte Carlo estimate of pi
print(cds2.montecarlo.pi_estimate(n=100_000, seed=42))

Continue with the getting started guide or dive into the API reference.