GETTER One

Geometric Event-driven Tensor-based Time-series Extraction & Recognition

Omnidimensional Network Engine

GETTER One is a discrete geometric framework for structural event detection and causal network extraction in N-dimensional time series.

It is not a forecasting model. It detects what is changing, when, and which dimensions are driving the change — using closed-form geometric computations with no MCMC, no gradient descent, and no threshold tuning.

Key Features

• Lambda³ (Λ³) structural analysis — Computes displacement vectors (ΛF), structural tension (ρT), cross-dimension synchrony (σₛ), and cooperative events (ΔΛC) in closed form
• Causal network extraction — Identifies directed, time-lagged causal relationships between dimensions
• Statistical confidence — Permutation tests, bootstrap confidence intervals, and effect sizes (no Bayesian inference required)
• Domain-agnostic — Works on any N-dimensional time series: weather, finance, sensors, biology
• Minimal dependencies — Core requires only numpy, scipy, pandas
• GPU-ready — Optional CUDA acceleration for large-scale datasets via CuPy

Installation

pip install getter-one

Optional extras:

pip install getter-one[gpu]       # CUDA/CuPy acceleration
pip install getter-one[viz]       # matplotlib + plotly
pip install getter-one[full]      # everything

Quick Start

from getter_one.pipeline import run

# One line — full pipeline
result = run("weather.csv", target="precipitation")
print(result.report)

Step by Step

from getter_one.data import load
from getter_one.structures import LambdaStructuresCore
from getter_one.analysis import NetworkAnalyzerCore, assess_confidence

# 1. Load data
dataset = load("weather.csv", target="precipitation", normalize="range")

# 2. Compute Λ³ structures
core = LambdaStructuresCore()
structures = core.compute_lambda_structures(
    dataset.state_vectors, window_steps=24,
    dimension_names=dataset.dimension_names,
)

# 3. Extract causal network
analyzer = NetworkAnalyzerCore(sync_threshold=0.3, causal_threshold=0.25, max_lag=12)
network = analyzer.analyze(dataset.state_vectors, dimension_names=dataset.dimension_names)

# 4. Assess confidence
confidence = assess_confidence(
    state_vectors=dataset.state_vectors,
    lambda_structures=structures,
    network_result=network,
    dimension_names=dataset.dimension_names,
)

CLI

# Run full pipeline
getter-one run weather.csv --target precipitation --report report.md

# Data preparation
getter-one-loader load weather.csv --target precipitation -o prepared.csv
getter-one-loader merge weather.csv air_quality.json --time date -o merged.csv
getter-one-loader info data.csv

# System info
getter-one info
getter-one check-gpu

Pipeline Architecture

Input Data (csv/json/parquet/xlsx/npy)
  │
  ▼
┌─────────────────────────────────┐
│  Lambda³ Structures (Λ³)        │  Closed-form geometric computation
│  ΛF, ΛFF, ρT, σₛ, Q_Λ, ΔΛC   │  No parameters to tune
└──────────────┬──────────────────┘
               │
  ┌────────────┼────────────┐
  ▼            ▼            ▼
┌──────┐  ┌──────┐  ┌──────────┐
│Bound │  │Topo  │  │Anomaly   │  Detection modules
│ary   │  │Break │  │Detection │  (GPU-accelerated)
└──┬───┘  └──┬───┘  └────┬─────┘
   └─────────┼───────────┘
             ▼
┌─────────────────────────────────┐
│  Causal Network Analysis        │  Sync + directed causal links
│  Sync matrix, lag estimation    │  Hub/driver/follower detection
└──────────────┬──────────────────┘
               ▼
┌─────────────────────────────────┐
│  Confidence Assessment          │  Permutation test → p-values
│  No MCMC, no Bayesian inference │  Bootstrap → confidence intervals
└──────────────┬──────────────────┘
               ▼
┌─────────────────────────────────┐
│  Report Generation              │  Markdown report with all results
└─────────────────────────────────┘

What GETTER One Is (and Isn't)

GETTER One detects what changed and why, not what will happen next. It can be used as a feature extractor for prediction models — experiments show that combining raw data with GETTER One features improves Transformer event detection F1 by +5.7% over raw data alone, and outperforms adding 7 additional weather variables (+4.3% vs +6.1%).

Benchmark Results

Standard Benchmark

Causal inference benchmark against 5 established methods on synthetic data with ground truth (6 scenarios, 20 repeats each):

Per-scenario F1 (directed):

GETTER One achieves F1 = 1.000 with σ = 0.000 (perfectly deterministic) on all event-driven scenarios, with zero lag error and perfect sign accuracy across all 20 trials. Its closed-form computation produces identical results regardless of noise realization — a direct consequence of the displacement-based (diff) approach inherited from molecular dynamics trajectory analysis.

Hell Mode Robustness Benchmark

Causal detection under non-stationary, non-linear, non-Gaussian conditions (8 scenarios × 5 repeats, ground truth: X0→X1 lag=2, X0→X2 lag=3):

Per-scenario F1 (Hell Mode):

GETTER One achieves the highest F1 and precision under non-ideal conditions, with the fewest detected links (1.4 avg vs ground truth of 2) — demonstrating conservative, high-confidence detection rather than over-reporting. Each method has characteristic strengths: GETTER One excels at phase jumps and resonance, PCMCI+ at cascades, and VAR Granger at structural decay.

Real-World Application: Weather Network

Analysis of 7 cities around Tokyo (hourly data, October 2024) reveals physically correct causal structures:

Precipitation causal network:

• Nagano → Gunma (lag=1h) → Saitama (lag=1h) — inland-to-plains propagation
• Yamanashi → Gunma (lag=2h) — basin-to-plains flow
• Drivers: Nagano, Yamanashi (mountain sources)
• Followers: Saitama, Tochigi (downstream receivers)

Cross-variable causality (42D full analysis):

• Temperature changes → Surface pressure changes (lag=2-3h) — thermodynamic law rediscovered from data alone

Supported Data Formats

Project Structure

getter_one/
├── data/           Data loading & merging (multi-format)
├── structures/     Lambda³ structural computation (CPU)
├── core/           GPU infrastructure (CuPy/CUDA)
├── detection/      Boundary, topology, anomaly, phase-space detection
├── analysis/       Network analysis, confidence assessment, report generation
├── pipeline.py     Full pipeline orchestration
└── cli.py          Command-line interface

Related Projects

• BANKAI-MD — GPU-accelerated molecular dynamics analysis built on the same Lambda³ theory. GETTER One is the domain-agnostic generalization; BANKAI-MD is the MD-specialized implementation.

Citation

Paper in preparation. If you use GETTER One in your research, please cite this repository.

License

MIT License — see LICENSE for details.

Built with 💕 by Masamichi & Tamaki

CHANGE EAGLE // Aerial Type // Structure Detection