RGB_Root_Matriz_Color.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1MEaMnbTdOLe71OQAsAxjvtjf8CvBO_hl",
      "authorship_tag": "ABX9TyOD+5vxYzZ6u/45QXPIcvMF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/daniellegauthier/color-data-analysis/blob/main/RGB_Root_Matriz_Color.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To run this code, press the little play button at the top left corner of the code cell.<br><br>\n",
        "The first cell is interactive and requires you to type in the name of the sequence you would like to view from the list. Hit enter.<br><br>\n",
        "The second cell takes a while to load and requires la matrice.csv from https://github.com/daniellegauthier/color-data-analysis<br><br>\n",
        "<br>\n",
        "# Color Sequences as Navigation Tools in Condensed Experience Systems\n",
        "## Theoretical Framework\n",
        "The concept of \"condensed experience systems\" introduced by Stanislav and Christina Grof in their development of holotropic breathwork provides a unique lens through which to view altered states of consciousness. When combined with Roger Walsh's groundbreaking research on synesthesia in meditation practitioners, we can begin to understand the potential of color sequences as navigation tools in consciousness exploration.<br>\n",
        "## Research Background\n",
        "Walsh's findings challenge the traditional view of synesthesia as a rare, fixed perceptual phenomenon:<br>\n",
        "35% of meditation retreatants report synesthetic experiences<br>\n",
        "63% of regular meditators experience synesthesia<br>\n",
        "86% of advanced meditation teachers report synesthetic perceptions<br>\n",
        "These statistics suggest that synesthesia may be a cultivatable skill rather than an innate trait.<br>\n",
        "<br>\n",
        "## Potential Benefits of Color Sequence Navigation\n",
        "\n",
        "1. Enhanced Emotional Processing<br>\n",
        "<br>\n",
        "Color sequences can serve as emotional anchors<br>\n",
        "Each color transition potentially facilitates movement between different emotional states<br>\n",
        "<br>\n",
        "\n",
        "2. Accelerated Integration of Experiences<br>\n",
        "\n",
        "Colors act as condensed symbols for complex experiential states<br>\n",
        "The systematic progression through color sequences may facilitate:<br>\n",
        "Trauma resolution<br>\n",
        "Integration of peak experiences<br>\n",
        "Navigation of challenging psychological material<br>\n",
        "<br>\n",
        "3. Improved Meditation Outcomes<br>\n",
        "\n",
        "Color-based navigation may provide:<br>\n",
        "\n",
        "More reliable access to desired states of consciousness<br>\n",
        "Clearer markers of progression in meditative practice<br>\n",
        "Enhanced ability to map and reproduce beneficial states<br>\n",
        "\n",
        "<br>\n",
        "\n",
        "\n",
        "4. Clinical Applications<br>\n",
        "\n",
        "Potential therapeutic tools for:<br>\n",
        "\n",
        "PTSD treatment<br>\n",
        "Anxiety management<br>\n",
        "Schizophrenic healing<br>\n",
        "\n",
        "\n",
        "Provides a structured approach to exploring non-ordinary states of consciousness<br>\n",
        "\n",
        "\n",
        "\n",
        "## Technical Implementation\n",
        "Our Python-based analysis system builds upon these theoretical foundations by:<br>\n",
        "\n",
        "Mapping color sequences to emotional and experiential states<br>\n",
        "Analyzing the wave-like properties of color transitions<br>\n",
        "Exploring the semantic relationships between colors and psychological states<br>\n",
        "\n",
        "## Research Implications\n",
        "\n",
        "Develops progressively with practice<br>\n",
        "Shows consistent patterns across practitioners<br>\n",
        "Correlates with depth of meditative experience<br>\n",
        "<br>\n",
        "By treating color sequences as non-commutative wave functions, so red is confromal to time, we may better model the actual experiential nature of consciousness exploration, providing a more accurate framework for both research and practical applications.<br>\n",
        "\n",
        "## Key Questions for Exploration:\n",
        "\n",
        "How can we quantify the effectiveness of color sequences in navigating condensed experiences?<br>\n",
        "What role does the order of colors play in the therapeutic process?<br>\n",
        "Can we develop more effective therapeutic protocols by understanding the wave-like properties of color sequences?<br>"
      ],
      "metadata": {
        "id": "A3baGHgLWCok"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def calculate_2d_wave_momentum_interpolation(x_coords, y_coords, t_coords):\n",
        "    \"\"\"\n",
        "    Calculate 2D wave momentum from (x, y) coordinates and corresponding time values using linear interpolation.\n",
        "    \"\"\"\n",
        "    x_coords = np.array(x_coords)\n",
        "    y_coords = np.array(y_coords)\n",
        "    t_coords = np.array(t_coords)\n",
        "\n",
        "    direction = np.array([x_coords[1] - x_coords[0], y_coords[1] - y_coords[0]])\n",
        "    direction_norm = np.linalg.norm(direction)\n",
        "    if direction_norm != 0:\n",
        "        direction = direction / direction_norm\n",
        "\n",
        "    wavelength = np.linalg.norm([x_coords[1] - x_coords[0], y_coords[1] - y_coords[0]])\n",
        "    k_mag = (2 * np.pi / wavelength)\n",
        "    k = k_mag * direction\n",
        "\n",
        "    momentums = []\n",
        "    for x_coord, y_coord, t_coord in zip(x_coords, y_coords, t_coords):\n",
        "        phase = np.dot([x_coord, y_coord], direction) - np.dot([x_coord, y_coord], direction) / wavelength * t_coord\n",
        "        momentum = k * np.exp(1j * phase)\n",
        "        momentums.append(momentum)\n",
        "\n",
        "    return np.array(momentums)\n",
        "\n",
        "# Pathways mapped to color sequences\n",
        "pathways = {\n",
        "    'plot': ['grey', 'pink', 'gold', 'nude', 'orange'],\n",
        "    'knot': ['white', 'blue', 'green', 'red', 'black', 'brown', 'yellow', 'purple'],\n",
        "    'pain': ['gold', 'orange'],\n",
        "    'practical': ['yellow', 'green'],\n",
        "    'spiritual': ['blue', 'brown'],\n",
        "    'prayer': ['nude', 'white'],\n",
        "    'sad': ['purple', 'grey', 'red'],\n",
        "    'precise': ['pink', 'black'],\n",
        "    'fem': ['brown', 'gold', 'orange', 'pink'],\n",
        "    'masc': ['red', 'blue', 'orange'],\n",
        "    'direct': ['red', 'orange']\n",
        "}\n",
        "\n",
        "# Predefined movement sequences for each color\n",
        "color_sequences = {\n",
        "    'gold': {'x': [250], 'y': [200], 't': [0]},\n",
        "    'orange': {'x': [250], 'y': [110], 't': [0]},\n",
        "    'yellow': {'x': [255], 'y': [255], 't': [0]},\n",
        "    'green': {'x': [0], 'y': [255], 't': [0]},\n",
        "    'blue': {'x': [0], 'y': [0], 't': [255]},\n",
        "    'brown': {'x': [180], 'y': [50], 't': [0]},\n",
        "    'nude': {'x': [250], 'y': [180], 't': [120]},\n",
        "    'white': {'x': [255], 'y': [255], 't': [255]},\n",
        "    'purple': {'x': [180], 'y': [50], 't': [255]},\n",
        "    'grey': {'x': [170], 'y': [170], 't': [170]},\n",
        "    'red': {'x': [255], 'y': [0], 't': [0]},\n",
        "    'pink': {'x': [250], 'y': [0], 't': [90]},\n",
        "    'black': {'x': [0], 'y': [0], 't': [0]}\n",
        "}\n",
        "\n",
        "def plot_momentum_analysis(x_coords, y_coords, t_coords):\n",
        "    momentums = calculate_2d_wave_momentum_interpolation(x_coords, y_coords, t_coords)\n",
        "\n",
        "    real_parts = np.real(momentums)\n",
        "    imaginary_parts = np.imag(momentums)\n",
        "    magnitudes = np.abs(momentums)\n",
        "    phases = np.angle(momentums)\n",
        "\n",
        "    fig = plt.figure(figsize=(15, 10))\n",
        "\n",
        "    # Plot momentum components\n",
        "    plt.subplot(2, 3, 1)\n",
        "    plt.plot(real_parts, 'bo-', label='Real Part')\n",
        "    plt.title('Real Part of Momentum')\n",
        "    plt.xlabel('Index')\n",
        "    plt.ylabel('Real Part')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    plt.subplot(2, 3, 2)\n",
        "    plt.plot(imaginary_parts, 'ro-', label='Imaginary Part')\n",
        "    plt.title('Imaginary Part of Momentum')\n",
        "    plt.xlabel('Index')\n",
        "    plt.ylabel('Imaginary Part')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    plt.subplot(2, 3, 3)\n",
        "    plt.plot(magnitudes, 'go-', label='Magnitude')\n",
        "    plt.title('Magnitude of Momentum')\n",
        "    plt.xlabel('Index')\n",
        "    plt.ylabel('Magnitude')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    plt.subplot(2, 3, 4)\n",
        "    plt.plot(phases, 'mo-', label='Phase')\n",
        "    plt.title('Phase of Momentum')\n",
        "    plt.xlabel('Index')\n",
        "    plt.ylabel('Phase (radians)')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    # Plot trajectory\n",
        "    plt.subplot(2, 3, 5)\n",
        "    plt.plot(x_coords, y_coords, 'ko-', label='Trajectory')\n",
        "    plt.scatter(x_coords, y_coords, c=t_coords, cmap='viridis')\n",
        "    plt.colorbar(label='Time')\n",
        "    plt.xlabel('X Coordinate')\n",
        "    plt.ylabel('Y Coordinate')\n",
        "    plt.title('Trajectory (color = time)')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    sequence_name = f\"{chosen_pathway}_{'_'.join(colors)}.png\"\n",
        "    plt.savefig(sequence_name)\n",
        "    plt.show()\n",
        "\n",
        "# User interface\n",
        "print(\"Available pathways:\")\n",
        "for key in pathways:\n",
        "    print(f\"- {key}\")\n",
        "\n",
        "chosen_pathway = input(\"\\nChoose a pathway: \").lower()\n",
        "\n",
        "if chosen_pathway in pathways:\n",
        "    colors = pathways[chosen_pathway]\n",
        "    x_coords, y_coords, t_coords = [], [], []\n",
        "\n",
        "    for color in colors:\n",
        "        if color in color_sequences:\n",
        "            seq = color_sequences[color]\n",
        "            x_coords.extend(seq['x'])\n",
        "            y_coords.extend(seq['y'])\n",
        "            t_coords.extend(seq['t'])\n",
        "\n",
        "    print(f\"\\nAnalyzing pathway '{chosen_pathway}' with colors {colors}...\")\n",
        "    plot_momentum_analysis(x_coords, y_coords, t_coords)\n",
        "else:\n",
        "    print(\"Invalid pathway choice. Please run the program again and select a valid pathway.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "id": "fzpSIk-vxmKt",
        "outputId": "c2c8f6da-d1f2-4fa7-eba6-7e44a947eb7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Available pathways:\n",
            "- plot\n",
            "- knot\n",
            "- pain\n",
            "- practical\n",
            "- spiritual\n",
            "- prayer\n",
            "- sad\n",
            "- precise\n",
            "- fem\n",
            "- masc\n",
            "- direct\n",
            "\n",
            "Choose a pathway: direct\n",
            "\n",
            "Analyzing pathway 'direct' with colors ['red', 'orange']...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import matplotlib.animation as animation\n",
        "\n",
        "# Pathways mapped to color sequences\n",
        "pathways = {\n",
        "    'plot': ['grey', 'pink', 'gold', 'nude', 'orange'],\n",
        "    'knot': ['white', 'blue', 'green', 'red', 'black', 'brown', 'yellow', 'purple'],\n",
        "    'pain': ['gold', 'orange'],\n",
        "    'practical': ['yellow', 'green'],\n",
        "    'spiritual': ['blue', 'brown'],\n",
        "    'prayer': ['nude', 'white'],\n",
        "    'sad': ['purple', 'grey', 'red'],\n",
        "    'precise': ['pink', 'black'],\n",
        "    'fem': ['brown', 'gold', 'orange', 'pink'],\n",
        "    'masc': ['red', 'blue', 'orange'],\n",
        "    'direct': ['red', 'orange']\n",
        "}\n",
        "\n",
        "# Predefined movement sequences for each color\n",
        "color_sequences = {\n",
        "    'gold': {'x': [250], 'y': [200], 't': [0]},\n",
        "    'orange': {'x': [250], 'y': [110], 't': [0]},\n",
        "    'yellow': {'x': [255], 'y': [255], 't': [0]},\n",
        "    'green': {'x': [0], 'y': [255], 't': [0]},\n",
        "    'blue': {'x': [0], 'y': [0], 't': [255]},\n",
        "    'brown': {'x': [180], 'y': [50], 't': [0]},\n",
        "    'nude': {'x': [250], 'y': [180], 't': [120]},\n",
        "    'white': {'x': [255], 'y': [255], 't': [255]},\n",
        "    'purple': {'x': [180], 'y': [50], 't': [255]},\n",
        "    'grey': {'x': [170], 'y': [170], 't': [170]},\n",
        "    'red': {'x': [255], 'y': [0], 't': [0]},\n",
        "    'pink': {'x': [250], 'y': [0], 't': [90]},\n",
        "    'black': {'x': [0], 'y': [0], 't': [0]}\n",
        "}\n",
        "\n",
        "def create_3d_animation(pathway, colors):\n",
        "    # Set up the figure and 3D axis\n",
        "    fig = plt.figure(figsize=(10, 8))\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "    # Initialize empty lists for coordinates\n",
        "    x_coords, y_coords, t_coords = [], [], []\n",
        "\n",
        "    # Populate coordinate lists based on color sequences\n",
        "    for color in colors:\n",
        "        if color in color_sequences:\n",
        "            seq = color_sequences[color]\n",
        "            x_coords.extend(seq['x'])\n",
        "            y_coords.extend(seq['y'])\n",
        "            t_coords.extend(seq['t'])\n",
        "\n",
        "    # Convert to numpy arrays\n",
        "    x = np.array(x_coords)\n",
        "    y = np.array(y_coords)\n",
        "    t = np.array(t_coords)\n",
        "\n",
        "    # Create a line object\n",
        "    line, = ax.plot([], [], [], 'r-', linewidth=2)\n",
        "\n",
        "    # Set axis labels\n",
        "    ax.set_xlabel('X')\n",
        "    ax.set_ylabel('Y')\n",
        "    ax.set_zlabel('Time')\n",
        "\n",
        "    # Set axis limits\n",
        "    ax.set_xlim(0, 255)\n",
        "    ax.set_ylim(0, 255)\n",
        "    ax.set_zlim(0, 255)\n",
        "\n",
        "    # Animation update function\n",
        "    def update(frame):\n",
        "        line.set_data(x[:frame], y[:frame])\n",
        "        line.set_3d_properties(t[:frame])\n",
        "        return line,\n",
        "\n",
        "    # Create the animation\n",
        "    anim = animation.FuncAnimation(fig, update, frames=len(x), interval=50, blit=True)\n",
        "\n",
        "    # Save the animation\n",
        "    anim.save(f'{pathway}_3d_animation.gif', writer='pillow')\n",
        "\n",
        "    plt.close(fig)\n",
        "\n",
        "# Example usage\n",
        "for pathway, colors in pathways.items():\n",
        "    create_3d_animation(pathway, colors)\n",
        "    print(f\"Created 3D animation for pathway: {pathway}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5bv2ieqvDY-",
        "outputId": "e0ba0ce6-d688-48d8-fa93-a685d6fcf70a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created 3D animation for pathway: plot\n",
            "Created 3D animation for pathway: knot\n",
            "Created 3D animation for pathway: pain\n",
            "Created 3D animation for pathway: practical\n",
            "Created 3D animation for pathway: spiritual\n",
            "Created 3D animation for pathway: prayer\n",
            "Created 3D animation for pathway: sad\n",
            "Created 3D animation for pathway: precise\n",
            "Created 3D animation for pathway: fem\n",
            "Created 3D animation for pathway: masc\n",
            "Created 3D animation for pathway: direct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas numpy scikit-learn nltk gensim spacy textblob english-words\n",
        "!python -m spacy download en_core_web_sm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wMWgbxfxzxB",
        "outputId": "7692ddd3-4482-456a-fe0c-be6cc8ccb7a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting english-words\n",
            "  Downloading english-words-2.0.1.tar.gz (8.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: english-words\n",
            "  Building wheel for english-words (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for english-words: filename=english_words-2.0.1-py3-none-any.whl size=8196236 sha256=1e3e97b9512b04f1f38beead0f1f2a880a932dc95dcb4d08095e742e4f35dfff\n",
            "  Stored in directory: /root/.cache/pip/wheels/f0/e6/d9/16a123647999fe535f03a36e7af23eef203736d84c7ca25b0b\n",
            "Successfully built english-words\n",
            "Installing collected packages: english-words\n",
            "Successfully installed english-words-2.0.1\n",
            "Collecting en-core-web-sm==3.7.1\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)\n",
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            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0)\n",
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            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.18.0)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.16.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.2)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
            "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
            "order to load all the package's dependencies. You can do this by selecting the\n",
            "'Restart kernel' or 'Restart runtime' option.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import nltk\n",
        "from nltk.corpus import wordnet as wn\n",
        "from gensim.models import Word2Vec\n",
        "import spacy\n",
        "from textblob import TextBlob\n",
        "from english_words import get_english_words_set\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Download required NLTK data\n",
        "nltk.download('wordnet')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "\n",
        "# Load spaCy model\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "\n",
        "# Get English words set\n",
        "english_words_set = get_english_words_set(['web2'], lower=True)\n",
        "\n",
        "def load_and_prepare_data(file_path):\n",
        "    df = pd.read_csv(file_path)\n",
        "    string_columns = df.select_dtypes(include=['object']).columns\n",
        "    for col in string_columns:\n",
        "        df[col] = df[col].astype(str)\n",
        "    return df\n",
        "\n",
        "def normalize_rgb(df):\n",
        "    rgb_vectors = df[['r', 'g', 'b']].values.astype(float)\n",
        "    return rgb_vectors / 255.0\n",
        "\n",
        "def get_color_similarity(normalized_rgb):\n",
        "    return cosine_similarity(normalized_rgb)\n",
        "\n",
        "def calculate_word_color_similarity(word, color_name):\n",
        "    # Using spaCy for word similarity\n",
        "    word_doc = nlp(str(word))\n",
        "    color_doc = nlp(str(color_name))\n",
        "    semantic_similarity = word_doc.similarity(color_doc)\n",
        "\n",
        "    return semantic_similarity\n",
        "\n",
        "def get_word_properties(word):\n",
        "    word = str(word)\n",
        "    blob = TextBlob(word)\n",
        "    sentiment = blob.sentiment.polarity\n",
        "    doc = nlp(word)\n",
        "    pos_tag = doc[0].pos_\n",
        "    length = len(word)\n",
        "    return sentiment, pos_tag, length\n",
        "\n",
        "def find_replacement_words(color_name, rgb_vector, existing_words, n=5):\n",
        "    potential_words = set()\n",
        "    filtered_words = {word for word in english_words_set if len(word) > 2}\n",
        "    sampled_words = set(np.random.choice(list(filtered_words), 1000))\n",
        "\n",
        "    existing_words = set(str(word) for word in existing_words)\n",
        "    for word in sampled_words:\n",
        "        if word not in existing_words:\n",
        "            potential_words.add(word)\n",
        "\n",
        "    word_scores = []\n",
        "    for word in potential_words:\n",
        "        sentiment, pos_tag, length = get_word_properties(word)\n",
        "        color_similarity = calculate_word_color_similarity(word, color_name)\n",
        "\n",
        "        # Scoring system incorporating both sentiment and color similarity\n",
        "        score = (0.5 * sentiment) + (0.5 * color_similarity) + (0.1 / length)\n",
        "        word_scores.append((word, score, sentiment, color_similarity))\n",
        "\n",
        "    # Sort by overall score and get top n\n",
        "    word_scores.sort(key=lambda x: x[1], reverse=True)\n",
        "    return [(word, score, sent, color_sim) for word, score, sent, color_sim in word_scores[:n]]\n",
        "\n",
        "def main():\n",
        "    df = load_and_prepare_data('la matrice.csv')\n",
        "    normalized_rgb = normalize_rgb(df)\n",
        "    color_similarity = get_color_similarity(normalized_rgb)\n",
        "\n",
        "    results = []\n",
        "    for i, row in df.iterrows():\n",
        "        color = str(row['color'])\n",
        "        rgb_vector = normalized_rgb[i]\n",
        "        existing_words = df['matrice'].tolist() + df['matrice1'].tolist()\n",
        "\n",
        "        replacement_words_with_scores = find_replacement_words(color, rgb_vector, existing_words)\n",
        "\n",
        "        # Format replacement words with scores\n",
        "        formatted_replacements = [\n",
        "            {\n",
        "                'word': word,\n",
        "                'overall_score': round(score, 3),\n",
        "                'sentiment_score': round(sent, 3),\n",
        "                'color_similarity': round(color_sim, 3)\n",
        "            }\n",
        "            for word, score, sent, color_sim in replacement_words_with_scores\n",
        "        ]\n",
        "\n",
        "        results.append({\n",
        "            'color': color,\n",
        "            'rgb': f\"({row['r']}, {row['g']}, {row['b']})\",\n",
        "            'original_matrice': str(row['matrice']),\n",
        "            'original_matrice1': str(row['matrice1']),\n",
        "            'replacement_suggestions': formatted_replacements\n",
        "        })\n",
        "\n",
        "    results_df = pd.DataFrame(results)\n",
        "    return results_df, color_similarity\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    results_df, color_similarity = main()\n",
        "\n",
        "    pd.set_option('display.max_columns', None)\n",
        "    pd.set_option('display.width', 1000)\n",
        "    pd.set_option('display.max_colwidth', None)\n",
        "\n",
        "    print(\"Color Word Replacement Analysis:\")\n",
        "    for _, row in results_df.iterrows():\n",
        "        print(f\"\\nColor: {row['color']} {row['rgb']}\")\n",
        "        print(f\"Original matrice words: {row['original_matrice']} / {row['original_matrice1']}\")\n",
        "        print(\"Replacement suggestions:\")\n",
        "        for suggestion in row['replacement_suggestions']:\n",
        "            print(f\"  - {suggestion['word']}\")\n",
        "            print(f\"    Overall Score: {suggestion['overall_score']}\")\n",
        "            print(f\"    Sentiment Score: {suggestion['sentiment_score']}\")\n",
        "            print(f\"    Color Similarity: {suggestion['color_similarity']}\")\n",
        "        print(\"-\" * 50)\n",
        "\n",
        "    print(\"\\nColor Similarity Matrix:\")\n",
        "    color_sim_df = pd.DataFrame(color_similarity, columns=results_df['color'], index=results_df['color'])\n",
        "    print(color_sim_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KW4pSy1YxnxA",
        "outputId": "6bb28954-f5f1-411b-eeaf-ac8559f1a32a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Color Word Replacement Analysis:\n",
            "\n",
            "Color: grey (170, 170, 170)\n",
            "Original matrice words: lose / Cover \n",
            "Replacement suggestions:\n",
            "  - redoubtable\n",
            "    Overall Score: 0.649\n",
            "    Sentiment Score: 0.6\n",
            "    Color Similarity: 0.68\n",
            "  - respectable\n",
            "    Overall Score: 0.464\n",
            "    Sentiment Score: 0.5\n",
            "    Color Similarity: 0.409\n",
            "  - eurite\n",
            "    Overall Score: 0.443\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.854\n",
            "  - otolaryngologist\n",
            "    Overall Score: 0.437\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.862\n",
            "  - nitrosulphate\n",
            "    Overall Score: 0.436\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.857\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: pink (250, 0, 90)\n",
            "Original matrice words: place / Category \n",
            "Replacement suggestions:\n",
            "  - cushy\n",
            "    Overall Score: 0.768\n",
            "    Sentiment Score: 0.9\n",
            "    Color Similarity: 0.596\n",
            "  - exotic\n",
            "    Overall Score: 0.505\n",
            "    Sentiment Score: 0.5\n",
            "    Color Similarity: 0.476\n",
            "  - uninfiniteness\n",
            "    Overall Score: 0.386\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.758\n",
            "  - parasitotrope\n",
            "    Overall Score: 0.386\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.757\n",
            "  - grippy\n",
            "    Overall Score: 0.383\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.733\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: gold (250, 200, 0)\n",
            "Original matrice words: hide / Pull \n",
            "Replacement suggestions:\n",
            "  - cushy\n",
            "    Overall Score: 0.716\n",
            "    Sentiment Score: 0.9\n",
            "    Color Similarity: 0.491\n",
            "  - welcomely\n",
            "    Overall Score: 0.511\n",
            "    Sentiment Score: 0.8\n",
            "    Color Similarity: 0.201\n",
            "  - coburgess\n",
            "    Overall Score: 0.433\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.845\n",
            "  - picket\n",
            "    Overall Score: 0.43\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.828\n",
            "  - pregnant\n",
            "    Overall Score: 0.422\n",
            "    Sentiment Score: 0.333\n",
            "    Color Similarity: 0.486\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: nude (250, 180, 120)\n",
            "Original matrice words: jump / Approach \n",
            "Replacement suggestions:\n",
            "  - skittishly\n",
            "    Overall Score: 0.508\n",
            "    Sentiment Score: 0.7\n",
            "    Color Similarity: 0.296\n",
            "  - uplift\n",
            "    Overall Score: 0.401\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.768\n",
            "  - cabble\n",
            "    Overall Score: 0.391\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.749\n",
            "  - ungild\n",
            "    Overall Score: 0.385\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.736\n",
            "  - nutational\n",
            "    Overall Score: 0.384\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.749\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: orange (250, 110, 0)\n",
            "Original matrice words: drop / Hit \n",
            "Replacement suggestions:\n",
            "  - resourcefully\n",
            "    Overall Score: 0.437\n",
            "    Sentiment Score: 0.6\n",
            "    Color Similarity: 0.258\n",
            "  - bushi\n",
            "    Overall Score: 0.419\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.799\n",
            "  - flaglike\n",
            "    Overall Score: 0.391\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.758\n",
            "  - atony\n",
            "    Overall Score: 0.387\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.733\n",
            "  - refont\n",
            "    Overall Score: 0.38\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.727\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: white (255, 255, 255)\n",
            "Original matrice words: block / Hope \n",
            "Replacement suggestions:\n",
            "  - elect\n",
            "    Overall Score: 0.655\n",
            "    Sentiment Score: 0.8\n",
            "    Color Similarity: 0.469\n",
            "  - brilliantly\n",
            "    Overall Score: 0.609\n",
            "    Sentiment Score: 0.9\n",
            "    Color Similarity: 0.3\n",
            "  - medicable\n",
            "    Overall Score: 0.422\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.821\n",
            "  - whereanent\n",
            "    Overall Score: 0.416\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.812\n",
            "  - bathygraphic\n",
            "    Overall Score: 0.416\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.815\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: blue (0, 0, 255)\n",
            "Original matrice words: glean / Collect \n",
            "Replacement suggestions:\n",
            "  - ideal\n",
            "    Overall Score: 0.861\n",
            "    Sentiment Score: 0.9\n",
            "    Color Similarity: 0.782\n",
            "  - enthusiastic\n",
            "    Overall Score: 0.68\n",
            "    Sentiment Score: 0.6\n",
            "    Color Similarity: 0.744\n",
            "  - innovative\n",
            "    Overall Score: 0.582\n",
            "    Sentiment Score: 0.5\n",
            "    Color Similarity: 0.643\n",
            "  - inspiring\n",
            "    Overall Score: 0.498\n",
            "    Sentiment Score: 0.5\n",
            "    Color Similarity: 0.474\n",
            "  - photopathy\n",
            "    Overall Score: 0.43\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.84\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: green (0, 255, 0)\n",
            "Original matrice words: grow / Transition \n",
            "Replacement suggestions:\n",
            "  - exquisitely\n",
            "    Overall Score: 0.686\n",
            "    Sentiment Score: 1.0\n",
            "    Color Similarity: 0.354\n",
            "  - unsalted\n",
            "    Overall Score: 0.522\n",
            "    Sentiment Score: 0.4\n",
            "    Color Similarity: 0.619\n",
            "  - sandy\n",
            "    Overall Score: 0.395\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.751\n",
            "  - aristotelic\n",
            "    Overall Score: 0.386\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.754\n",
            "  - ultratotal\n",
            "    Overall Score: 0.385\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.75\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: red (255, 0, 0)\n",
            "Original matrice words: cry / Limit \n",
            "Replacement suggestions:\n",
            "  - magnificently\n",
            "    Overall Score: 0.632\n",
            "    Sentiment Score: 1.0\n",
            "    Color Similarity: 0.249\n",
            "  - incredibly\n",
            "    Overall Score: 0.609\n",
            "    Sentiment Score: 0.9\n",
            "    Color Similarity: 0.299\n",
            "  - floppy\n",
            "    Overall Score: 0.384\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.734\n",
            "  - becloud\n",
            "    Overall Score: 0.383\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.738\n",
            "  - impositive\n",
            "    Overall Score: 0.376\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.731\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: black (0, 0, 0)\n",
            "Original matrice words: scale / Order \n",
            "Replacement suggestions:\n",
            "  - mild\n",
            "    Overall Score: 0.536\n",
            "    Sentiment Score: 0.333\n",
            "    Color Similarity: 0.689\n",
            "  - redoubtably\n",
            "    Overall Score: 0.499\n",
            "    Sentiment Score: 0.6\n",
            "    Color Similarity: 0.381\n",
            "  - interdigital\n",
            "    Overall Score: 0.437\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.857\n",
            "  - entogenous\n",
            "    Overall Score: 0.435\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.851\n",
            "  - odorous\n",
            "    Overall Score: 0.427\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.826\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: brown (180, 50, 0)\n",
            "Original matrice words: shrink / Learn\n",
            "Replacement suggestions:\n",
            "  - impressively\n",
            "    Overall Score: 0.674\n",
            "    Sentiment Score: 1.0\n",
            "    Color Similarity: 0.332\n",
            "  - vallar\n",
            "    Overall Score: 0.417\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.8\n",
            "  - multiplex\n",
            "    Overall Score: 0.416\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.81\n",
            "  - monozygotic\n",
            "    Overall Score: 0.408\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.799\n",
            "  - sonatina\n",
            "    Overall Score: 0.407\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.789\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: yellow (255, 255, 0)\n",
            "Original matrice words: array / Structure \n",
            "Replacement suggestions:\n",
            "  - superb\n",
            "    Overall Score: 0.728\n",
            "    Sentiment Score: 1.0\n",
            "    Color Similarity: 0.424\n",
            "  - apt\n",
            "    Overall Score: 0.633\n",
            "    Sentiment Score: 0.6\n",
            "    Color Similarity: 0.6\n",
            "  - compensative\n",
            "    Overall Score: 0.421\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.826\n",
            "  - semifatalistic\n",
            "    Overall Score: 0.415\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.816\n",
            "  - polythalamous\n",
            "    Overall Score: 0.413\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.81\n",
            "--------------------------------------------------\n",
            "\n",
            "Color: purple (180, 50, 255)\n",
            "Original matrice words: cheer / Free \n",
            "Replacement suggestions:\n",
            "  - daintily\n",
            "    Overall Score: 0.582\n",
            "    Sentiment Score: 0.9\n",
            "    Color Similarity: 0.24\n",
            "  - abundant\n",
            "    Overall Score: 0.525\n",
            "    Sentiment Score: 0.6\n",
            "    Color Similarity: 0.424\n",
            "  - straightforward\n",
            "    Overall Score: 0.467\n",
            "    Sentiment Score: 0.375\n",
            "    Color Similarity: 0.547\n",
            "  - paedotrophic\n",
            "    Overall Score: 0.397\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.778\n",
            "  - conjugateness\n",
            "    Overall Score: 0.389\n",
            "    Sentiment Score: 0.0\n",
            "    Color Similarity: 0.762\n",
            "--------------------------------------------------\n",
            "\n",
            "Color Similarity Matrix:\n",
            "color       grey      pink      gold      nude    orange     white      blue     green       red  black     brown    yellow    purple\n",
            "color                                                                                                                                \n",
            "grey    1.000000  0.738781  0.811503  0.960488  0.760979  1.000000  0.577350  0.577350  0.577350    0.0  0.710812  0.816497  0.885817\n",
            "pink    0.738781  1.000000  0.734710  0.834433  0.861208  0.738781  0.338719  0.000000  0.940887    0.0  0.906562  0.665308  0.809003\n",
            "gold    0.811503  0.734710  1.000000  0.930603  0.966330  0.811503  0.000000  0.624695  0.780869    0.0  0.919577  0.993884  0.543455\n",
            "nude    0.960488  0.834433  0.930603  1.000000  0.911424  0.960488  0.362970  0.544456  0.756188    0.0  0.874321  0.919694  0.809512\n",
            "orange  0.760979  0.861208  0.966330  0.911424  1.000000  0.760979  0.000000  0.402739  0.915315    0.0  0.989713  0.932005  0.584904\n",
            "white   1.000000  0.738781  0.811503  0.960488  0.760979  1.000000  0.577350  0.577350  0.577350    0.0  0.710812  0.816497  0.885817\n",
            "blue    0.577350  0.338719  0.000000  0.362970  0.000000  0.577350  1.000000  0.000000  0.000000    0.0  0.000000  0.000000  0.806683\n",
            "green   0.577350  0.000000  0.624695  0.544456  0.402739  0.577350  0.000000  1.000000  0.000000    0.0  0.267644  0.707107  0.158173\n",
            "red     0.577350  0.940887  0.780869  0.756188  0.915315  0.577350  0.000000  0.000000  1.000000    0.0  0.963518  0.707107  0.569424\n",
            "black   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000    0.0  0.000000  0.000000  0.000000\n",
            "brown   0.710812  0.906562  0.919577  0.874321  0.989713  0.710812  0.000000  0.267644  0.963518    0.0  1.000000  0.870563  0.590984\n",
            "yellow  0.816497  0.665308  0.993884  0.919694  0.932005  0.816497  0.000000  0.707107  0.707107    0.0  0.870563  1.000000  0.514489\n",
            "purple  0.885817  0.809003  0.543455  0.809512  0.584904  0.885817  0.806683  0.158173  0.569424    0.0  0.590984  0.514489  1.000000\n"
          ]
        }
      ]
    }
  ]
}