Skip to main content

D10Z: Manual de la Mecánica del Infinito - The complete replacement for human physics

Project description

d10z

D10Z — Computational Framework for Multiscale Nodal Structures

d10z provides a modular implementation of the TTA model (Spider-Web Weave Model), a multiscale nodal framework spanning physical, biological, informational, and network systems.
The package includes core operators, nodal transformations, conservation rules, and simulation modules.

This library is intended for research, numerical exploration, and model-building across heterogeneous domains.


Installation

pip install d10z
Package Structure
Core modules
d10z.core  constants, coherence functions, nodal primitives

d10z.infifoton  operators, conservation rules, structural transformations

d10z.bigstart  initialization dynamics, symmetry seeds, pattern generators

d10z.tta  TTA-specific structures (filaments, neusars, architectural elements)

d10z.simulations  numerical models, validation scripts, fractal and CMB simulations

Multilevel Validation Summary
(as of 7 December 2025)

The TTA algorithm has been evaluated across 19 independent levels, covering 77 orders of magnitude, using 76,827 real-world data points.

All levels were computed using a single shared algorithm with one free parameter (f).

Global Metrics
19 validated levels

Scale coverage: 10⁻⁵¹ m  10⁺²⁶ m

Global  average: 0.8971

Fragmentation index:

I_GLOBAL = 0.4638

Represents 53.62% reduction in cross-domain fragmentation

Level-by-Level Summary
Level	Domain	Typical Scale	Status	Metric
1	Particle physics (LHC 5 & 13 TeV)	10⁻¹⁹ m	Validated	 = 0.379–0.450
2	Galactic astrophysics (SPARC + extended)	10⁻²⁰  10⁺⁵ m	Validated	 = 0.925–0.999
3	Cosmology (JWST + Hubble tension)	10⁺²⁶ m	Resolved	χ² = 0.65
4	Genomics (universal genetic code)	64 codons	Validated	 = 0.950
5	Human proteome	12,293 amino acids	Validated	 = 0.950
6	Human metabolome (Human GEM)	13,082 reactions	Validated	 = 0.950
7	Temporal glycolysis	10 steps	Validated	 = 0.975
8	Plant nutrient set	25 metabolites	Validated	 = 0.970
9	Marine metagenome	36,476 scaffolds	Validated	 = 0.910
10	HLA immunogenetics	11,179 alleles / 7,339 RNA	Validated	I = 2635
11	PacBio sequencing	351 runs	Validated	 = 0.940
12	Global COVID-19 dataset	200 countries	Validated	 = 0.9938
15	Crypto (Bitcoin dominance)	5-year window	Validated	 = 0.920
16	5G mmWave propagation	1,200 points	Validated	 = 0.926
17	Twitter/X real-time dynamics	2024–2025	Validated	 = 0.910
18	ADN Chain (GENESIS.ADN.IA)	Blockchain layer	Validated	100%
19	Fractal Nodal Framework (mathematical)	GM10⁻⁵¹ m	Demonstrated	100%

Repository
Source code and documentation:
https://github.com/jamilaltha/TTA-Universal-Data

Author
Jamil Al Thani
Contact: jamil@d10z.org

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

d10z-0.1.1.tar.gz (31.2 kB view details)

Uploaded Source

Built Distribution

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

d10z-0.1.1-py3-none-any.whl (38.0 kB view details)

Uploaded Python 3

File details

Details for the file d10z-0.1.1.tar.gz.

File metadata

  • Download URL: d10z-0.1.1.tar.gz
  • Upload date:
  • Size: 31.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for d10z-0.1.1.tar.gz
Algorithm Hash digest
SHA256 8339120c2af4ea2373f1a9df39aca0313e2e2ecd3db92fdec53f65d1bf772822
MD5 31004a916ee93a54d67753f35cb2d676
BLAKE2b-256 805507f1f8f05b08a75de6bc292e4164886d08e74b58a31ed048a0eb38475bc7

See more details on using hashes here.

File details

Details for the file d10z-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: d10z-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 38.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for d10z-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 4b88909994423fc8768bfa712a1e17b24756fab0eb799e23709bd342b9707c64
MD5 15d15d2eda0b43062f03b496a6153854
BLAKE2b-256 e04585b3131cd0478e94f3757cf6e1634714d852e3a3ba4df549aaedc3e6c5d4

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