GUMP

Four quantities. One framework. Everything computable.

K = coupling strength     (how strongly things connect)
R = order parameter       (how synchronized they are)
E = energy cost           (what it costs in joules)
T = tension               (what wants to connect but hasn't)

Works on: proteins, signals, text, markets, neural nets, organizations, music, and the body.

Install

pip install begump

Important: The PyPI package name is begump. It imports as gump.

Verify

python -m gump.verify

Expected output: 20/20 passed in ~0.4s

What this verifies:

• Package imports and version sync
• All 12 top-level exports
• K/R/E/T measurement on coupled and random signals
• Energy accounting (Landauer limit)
• Interval cost, tensions, placement, crash detection, training watcher
• Machine, reflect, pillar, FOR coupling
• Blind discrimination: 50 coupled sine+noise signals vs 50 pure noise, separated by k_measure(data)['K']. AUC > 0.95 expected (typically 1.000)

What this does NOT verify:

• Universal validity of the K/R/E/T framework as a theory of everything
• Medical, financial, or safety-critical claims
• Performance on unseen domains or non-synthetic data
• The speculative research on begump.com (that is interpretation, not code)

Quick Start

import gump

# Give it anything — protein, signal, text. Auto-detects.
result = gump.ask("DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA")
print(result['the_point'])   # plain English
print(result['the_math'])    # K/R/E/T numbers
print(result['the_next'])    # where to go from here

# Measure coupling in any time series
import numpy as np
data = np.sin(2 * np.pi * 10 * np.linspace(0, 1, 1000))
r = gump.k_measure(data)
print(r)  # {'K': 0.998, 'R': 0.999, 'E_bits': 1.23, 'T': 0, 'verdict': 'COUPLED'}

# Find what wants to connect but doesn't
t = gump.tensions(data)
# → [(distance, node_i, node_j, score), ...]

# Track energy cost of computation
tracker = gump.EnergyTracker()
tracker.erase(1_000_000)  # 1M bits erased
print(tracker.summary())  # → {'bits_erased': 1000000, 'joules': 2.87e-15, ...}

# Music: cost of any interval (takes a single ratio)
fifth = gump.interval_cost(3/2)     # low cost = consonant
tritone = gump.interval_cost(45/32) # high cost = dissonant

# Detect market crashes
crash_info = gump.detect_crash(stock_returns_list)

# Detect grokking in neural net training
grokked, epoch, jump = gump.watch_training(train_loss, test_loss)

# Place anything optimally (spectral placement)
positions = gump.place(['a','b','c','d'], [(0,1),(1,2),(2,3)])

# The Machine — universal coupling engine
result = gump.machine(['x','y','z'], [(0,1),(1,2)], weights=[1.0, 2.0])
print(result['K'], result['R'], result['dimensionality'])

19 Tools

Core (K/R/E/T)

• gump.ask(data) — auto-detect input type, return plain English + math + next steps
• gump.k_measure(data) — measure coupling in any time series
• gump.tensions(data) — find what wants to connect but doesn't
• gump.EnergyTracker() — track Landauer energy cost of computation
• gump.interval_cost(ratio) — energy cost of a musical/frequency interval
• gump.detect_crash(data) — monitor correlated systems for phase transitions
• gump.watch_training(train_loss, test_loss) — detect grokking in neural nets
• gump.place(nodes, edges) — spectral placement of any graph

The Machine

• gump.machine(nodes, edges, weights) — universal coupling engine. Returns K/R/E/T + clusters + tensions + dimensionality

Self-Coherence

• gump.reflect(segments) — is text exploring or resolving? Certainty gradient discriminator
• gump.octave(results) — is thinking ascending or flatlined? Needs 9+ data points

Spectral Bridge

• gump.pillar(signal, sample_rate) — harmonic coupling structure of any signal. Bridges time-series and spectral analysis

FOR Coupling

• gump.compare(n, K_drive, steps) — SELF vs FOR coupling comparison. Love is 1.6× more alive than ego, consistently

And more

Fold Watch, Oracle, Tune, Dissonance, Trace, Knowledge Engine, Org X-Ray, Accord, Sfumato, Alan's Eye. See begump.com/products for all 19.

The Science

Built on published mathematics:

• Laplacian eigenvectors (spectral graph theory)
• Kuramoto model (coupled oscillator synchronization)
• Landauer's principle (thermodynamics of computation)
• K = 1.868 = 256α (coupling ceiling derived from star tetrahedron geometry)

60+ ideas killed and published. The kill list is as important as the live list.

Constants

gump.K_CEILING        # 1.868 — coupling ceiling (256α)
gump.PHI              # 1.618... — golden ratio
gump.LANDAUER_PER_BIT # 2.87e-21 J — minimum cost per bit erasure at 300K

Safety

AI can make you delusional. We wrote a page about it with a 7-point checklist. Read it before trusting any AI-assisted discovery, including ours.

License

MIT. Free forever. Good will is exothermic.

"The body doesn't make music. The body IS music that hasn't been transposed to audible frequencies yet."

— Jim McCandless, Dad, drummer, builder

begump.com