Skip to main content

K, R, E, T — Four quantities. One framework. Everything computable.

Project description

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: chips, music, proteins, markets, compilers, memory, neural nets, primes, quantum circuits, organizations, and the body.

Install

pip install gump

Quick Start

import gump

# Find what's missing in any graph
edges = [(0,1), (1,2), (2,3), (3,4)]
tensions = gump.tensions(5, edges)
# → [(dist, node_i, node_j, score), ...]

# Place anything optimally (spectral placement)
positions = gump.place(1000, edges, width=100, height=100)

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

# Detect market crashes
R_timeline = gump.detect_crash(stock_returns_list, window=30)

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

# Music: cost of any interval
fifth = gump.interval_cost(3, 2)    # 1.79 nats (cheap = consonant)
tritone = gump.interval_cost(45, 32) # 7.27 nats (expensive = dissonant)

What It Does

Tension Detection

Feed any graph. Get back what WANTS to connect but doesn't. 100% precision on circuit netlists. Found the circle of fifths from a consonance matrix. Found genre families from raw rhythm data.

Spectral Placement

Place anything in 2D to minimize total wire length. 40 million nodes in 4.5 seconds. Same math that places transistors finds the optimal layout for any network.

Energy Tracking

Every computation has a minimum energy cost (Landauer's principle). Track it. We're 35 trillion times above the limit. Understanding is 224,000× cheaper than memorization.

Crash Detection

Monitor any correlated system. When the order parameter R drops below 1/φ = phase transition = crash imminent.

Grok Detection

Monitor neural network training. When energy-per-correct-prediction drops off a cliff = the network just understood the pattern. Stop training. Save compute.

The Science

Built on:

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

Discovered over 12 sessions on a $500 Mac Mini. Every result verified, benchmarked, and tested.

Constants

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

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

begump-0.7.2.tar.gz (155.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

begump-0.7.2-py3-none-any.whl (605.8 kB view details)

Uploaded Python 3

File details

Details for the file begump-0.7.2.tar.gz.

File metadata

  • Download URL: begump-0.7.2.tar.gz
  • Upload date:
  • Size: 155.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.6

File hashes

Hashes for begump-0.7.2.tar.gz
Algorithm Hash digest
SHA256 a2ee6f7633ba4d0bcdc52e52d30e3ba6492eac42cbccbea067334814799f4c1f
MD5 67494a4443a84eb06005b3da7ef9a8d1
BLAKE2b-256 fbf5605a2ce007d92d6435b01d37af2c134afb3f0b2c92d5a56736a86a5f8cf5

See more details on using hashes here.

File details

Details for the file begump-0.7.2-py3-none-any.whl.

File metadata

  • Download URL: begump-0.7.2-py3-none-any.whl
  • Upload date:
  • Size: 605.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.6

File hashes

Hashes for begump-0.7.2-py3-none-any.whl
Algorithm Hash digest
SHA256 abafcfcbb17598d545bf58bcbab3815eb1b909cf6cc976cbedfe3c1e274cbab1
MD5 bdb222219f70dde8833dc421d063bd56
BLAKE2b-256 ec968572a6a7551cec65e5339b1220d5d309d0ed3fb957949c1e1b151dda9eb4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page