Color Wave Analysis and Semantic Mapping

This Jupyter notebook contains two primary analysis modules for color-based data processing and visualization. The project explores the relationship between colors, semantic meaning, and wave-like properties in spatiotemporal sequences.

Table of Contents

Dependencies
Module 1: Color Wave Momentum Analysis
Module 2: Color-Word Semantic Analysis
Usage
Data Requirements

Dependencies

Module 1
import numpy as np
import matplotlib.pyplot as plt

Module 2
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import nltk
from nltk.corpus import wordnet as wn
import spacy
from textblob import TextBlob
from english_words import get_english_words_set


Ensure you have spaCy's English model installed:
!python -m spacy download en_core_web_sm

Module 1: Color Wave Momentum Analysis

Overview
This module treats color sequences as wave functions, calculating and visualizing their momentum properties in 2D space.

Features
Momentum calculation using wave physics principles
Multiple predefined pathways mapped to color sequences
Comprehensive visualization including:
Real and imaginary parts of momentum
Magnitude and phase
Trajectory visualization with time encoding

Module 2: Color-Word Semantic Analysis

Overview
This module analyzes the semantic relationship between colors and words, using NLP techniques to find meaningful associations.

Key Components
RGB normalization Word-color similarity calculation using spaCy Sentiment analysis of word-color relationships
Features
Processes CSV input containing color and word data
Calculates semantic similarity between words and colors
Generates replacement word suggestions based on color properties
Provides detailed scoring including sentiment and color similarity

Usage

Running the Wave Analysis
#Choose a predefined pathway
chosen_pathway = 'knot' # Options: knot, plot, pain, practical, spiritual, etc.

Generate and display analysis

plot_momentum_analysis(x_coords, y_coords, t_coords)
Running the Semantic Analysis
#Load your color-word dataset
results_df, color_similarity = main()

Data Requirements

For Wave Analysis
No external data file required; uses predefined color sequences.
For Semantic Analysis
Requires a CSV file ('la matrice.csv') with columns:
'color': Color name
'r', 'g', 'b': RGB values
'matrice': Original word
'matrice1': Additional word column

Notes

The wave analysis assumes non-commutative properties in color sequences for conformal time values.
Semantic analysis results are influenced by the spaCy model's training data.
Performance may vary based on the size of your word dataset


For questions or discussions about the non-commutative color wave theory, please contact danielle.gauthier6@gmail.com.