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Scientific Computing System (CDS)

Computational science, from scratch.

CDS is an open-source platform for research, simulation and discovery: 19 modules spanning quantum simulation, FFT, linear algebra, statistics, ODEs, symbolic math, machine learning and NLP, implemented in readable pure Python with zero runtime dependencies.

Get started Take the tour API reference

pip install scientific-computing-system
cds modules          # see what's inside

Start here

Getting Started

Install, run your first simulation, and learn the CLI in about five minutes.

Quick Start Tutorial

A guided first session: constants, statistics, a quantum circuit, an ODE.

Tour of Numerical Methods

How the solvers actually work: quadrature, RK45, LU, FFT, with worked output.

Cookbook

Problem-oriented recipes: pick the task, copy the snippet.

API Reference

Every public function and class, generated from the source docstrings.

Architecture

Module dependency graph and data flow, for contributors and auditors.

New to scientific Python?

CDS is designed as a first stop: every algorithm is readable pure Python, so you learn how things work, not just how to call them. Follow the tutorials in order: Quick Start, Statistics, Machine Learning, then branch out.

Key Features

  • Pure Python: Every module is implemented from scratch using the Python standard library. No heavy dependencies like NumPy or SciPy required.
  • Quantum Simulation: Full state-vector simulation for single and multi-qubit circuits with entanglement and O(1) sampling.
  • Advanced Mathematics: O(N³) Partial Pivoting LU decomposition, vectorized optimizers, and adaptive ODE solvers (RK45).
  • Hypothesis Engine: Built-in tools for generating and statistically validating scientific hypotheses, complemented by effect-size measures (Cohen's d, Cramér's V) that quantify the magnitude of an effect alongside its significance.
  • High Reliability: Comprehensive test suite with 100% code coverage (statement + branch) on the reference CI cell. See the CI and codecov badges in the README for the live test count and coverage.
  • Interactive Tools: Beautiful CLI and a Streamlit-based web dashboard.

Overview of Modules

Module Description
cds.core Shared data models (Domain, Hypothesis, HypothesisStatus)
cds.quantum Single & multi-qubit quantum circuit simulation
cds.optimization Gradient-based and numerical optimizers
cds.ml Pure Python Neural Networks (MLP, Adam-based training)
cds.signals Fast signal processing (DFT, FFT/IFFT, convolution) + Butterworth IIR filter design & moving-median denoiser
cds.probability Probability distributions & sampling
cds.stats Descriptive stats, regression, hypothesis testing, effect-size measures (Cohen's d, Cramér's V) & time-series analysis (ACF/PACF, KPSS, Ljung-Box, decomposition)
cds.math_utils Numerical calculus, linear algebra, eigenvalues
cds.data_analysis Structured data management, visualization & optional pandas interop (cds[pandas])
cds.scientific Physical constants & scientific formulas
cds.graph Graph algorithms (BFS, DFS, Dijkstra, Kruskal MST)
cds.modeling Symbolic algebra: expressions, differentiation, simplification, LaTeX export, MathModel equation systems, root-finding & parameter fitting
cds.knowledge Knowledge organization: concept graph with typed relations, research notes notebook, ranked structured retrieval (JSON persistence)
cds.montecarlo Monte Carlo integration, π estimation, random walks
cds.diffeq ODE solvers (Euler, RK4, midpoint)
cds.numerical_integration Deterministic quadrature (trapezoid, Simpson, Romberg) + 2-D tensor-product rules (Simpson, Gauss-Legendre)
cds.nlp Educational NLP from scratch (BPE, embeddings, attention, autograd, MiniGPT)
cds.hypothesis Cognitive discovery and structured hypothesis generation
cds.plot Optional matplotlib charts (series, scatter, regression, spectra, ACF, seasonal, heatmaps, …) via cds[plot]

Quick Navigation

CDS v1.6.0 is stable and actively developed. Contributions are welcome!