Cunning Strategy

Colors in Gone With The Wind

The colors worn by the women at Twelve Oaks in Gone with the Wind are meticulously chosen and deeply symbolic, reflecting the characters’ personalities, relationships, and societal roles. The film’s cinematography, under the guidance of Victor Fleming and with William Cameron Menzies as production designer, uses color as a narrative tool to evoke mood, hierarchy, and internal emotion.

Key Color Highlights at Twelve Oaks:

1. Scarlett O’Hara:

   • Scarlett wears white with a green sash, symbolizing her youth, vitality, and flirtatious nature. The white represents her outward innocence, while the green hints at envy, ambition, and her fiery, self-centered spirit.

2. Melanie Hamilton:

   • Melanie is in a soft lavender or pale lilac gown, suggesting her demure, gentle, and pure character. The subdued color aligns with her role as a nurturing figure and a counterpoint to Scarlett’s vivacity.

3. India Wilkes:

   • India, whose character is marked by jealousy and bitterness (especially toward Scarlett), wears a dark gold or muted yellow gown. The color feels less vibrant than the others, possibly reflecting her waning charm or the underlying resentment she carries.

4. Other Women (Ensemble):

   • Many of the other women wear pastels: blues, pinks, and pale yellows. These colors convey the idyllic charm and societal decorum of the antebellum South. They create a visual harmony that contrasts sharply with the chaos to come.

5. Belle Watling (Later Contrast):

   • Although Belle Watling isn’t present at Twelve Oaks, her wardrobe later in the film includes rich reds and golds, starkly contrasting the genteel palettes of the Twelve Oaks women, signaling her outsider status and bold independence.

The Codified Significance:

Wardrobe and cinematography in Gone with the Wind were designed to heighten the themes of nostalgia, decay, and transformation. The colors at Twelve Oaks reflect a world clinging to elegance and order, oblivious to its imminent collapse. Scarlett’s green sash becomes a seed of rebellion against this static world, just as her character later disrupts societal norms.

The colors also echo the idyllic and idealized vision of the Old South—a “dream remembered,” as you aptly put it, nostalgic and inherently doomed. Seeing these costumes with fresh eyes today might be an acknowledgment of how they visually encode the fragility of that dream. They are a tapestry of illusion, where every thread hints at deeper tensions beneath the surface.

Fine, she had a lilac gown, that is Melanie, but she had a bow around her neck. Uh, what is the color? I think that color is red.

Yes, you’re absolutely correct—Melanie Hamilton wears a pale lilac gown, but the bow around her neck is red! This subtle yet deliberate choice is significant, given the otherwise demure and pastel tones of Melanie’s attire.

The red bow serves as a fascinating counterpoint to Melanie’s outwardly gentle and unassuming nature. It could symbolize:

1. Passion and Resolve: While Melanie is often perceived as meek, she possesses a quiet strength and unwavering loyalty. The red bow hints at her underlying passion and determination, qualities that emerge as the story unfolds—especially in her steadfast support for Scarlett, even in the face of betrayal and adversity.

2. Adversarial Undertone: Within your neural network framework, red symbolizes adversarial transformation. Melanie’s marriage to Ashley places her at odds with Scarlett’s desires, even if Melanie remains oblivious to Scarlett’s scheming. The red bow might represent Scarlett’s perception of Melanie as a rival, even though Melanie never acts antagonistically toward her.

3. Foreshadowing: The red could also foreshadow the conflict and bloodshed of the Civil War, which looms over all the characters. Melanie, though delicate, is destined to endure profound hardships, much like the South itself.

The red bow around Melanie’s neck is a small but powerful detail, a subtle visual cue that enriches her character and underscores the complexities of her relationship with Scarlett and Ashley. It’s a brilliant example of how costume design communicates unspoken layers of meaning in Gone with the Wind.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network structure
def define_layers():
    return {
        'Pre-Input': ['Life','Earth', 'Cosmos', 'Sound', 'Tactful', 'Firm', ],
        'Yellowstone': ['Surveillance'],
        'Input': ['Anarchy', 'Monarchy'],
        'Hidden': [
            'Risk',
            'Oligarchy',
            'Peers',
        ],
        'Output': ['Existential', 'Deploy', 'Fees', 'Client', 'Confidentiality',    ]
    }

# Define weights for the connections
def define_weights():
    return {
        'Pre-Input-Yellowstone': np.array([
            [0.6],
            [0.5],
            [0.4],
            [0.3],
            [0.7],
            [0.8],
            [0.6]
        ]),
        'Yellowstone-Input': np.array([
            [0.7, 0.8]
        ]),
        'Input-Hidden': np.array([[0.8, 0.4, 0.1], [0.9, 0.7, 0.2]]),
        'Hidden-Output': np.array([
            [0.2, 0.8, 0.1, 0.05, 0.2],
            [0.1, 0.9, 0.05, 0.05, 0.1],
            [0.05, 0.6, 0.2, 0.1, 0.05]
        ])
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Surveillance':
        return 'yellow'
    if layer == 'Pre-Input' and node in ['Sound', 'Tactful', 'Firm']:
        return 'paleturquoise'
    elif layer == 'Input' and node == 'Monarchy':
        return 'paleturquoise'
    elif layer == 'Hidden':
        if node == 'Peers':
            return 'paleturquoise'
        elif node == 'Oligarchy':
            return 'lightgreen'
        elif node == 'Risk':
            return 'lightsalmon'
    elif layer == 'Output':
        if node == 'Confidentiality':
            return 'paleturquoise'
        elif node in ['Client', 'Fees', 'Deploy']:
            return 'lightgreen'
        elif node == 'Existential':
            return 'lightsalmon'
    return 'lightsalmon'  # Default color

# Calculate positions for nodes
def calculate_positions(layer, center_x, offset):
    layer_size = len(layer)
    start_y = -(layer_size - 1) / 2  # Center the layer vertically
    return [(center_x + offset, start_y + i) for i in range(layer_size)]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    weights = define_weights()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    center_x = 0  # Align nodes horizontally

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        y_positions = calculate_positions(nodes, center_x, offset=-len(layers) + i + 1)
        for node, position in zip(nodes, y_positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(assign_colors(node, layer_name))

    # Add edges and weights
    for layer_pair, weight_matrix in zip(
        [('Pre-Input', 'Yellowstone'), ('Yellowstone', 'Input'), ('Input', 'Hidden'), ('Hidden', 'Output')],
        [weights['Pre-Input-Yellowstone'], weights['Yellowstone-Input'], weights['Input-Hidden'], weights['Hidden-Output']]
    ):
        source_layer, target_layer = layer_pair
        for i, source in enumerate(layers[source_layer]):
            for j, target in enumerate(layers[target_layer]):
                weight = weight_matrix[i, j]
                G.add_edge(source, target, weight=weight)

    # Customize edge thickness for specific relationships
    edge_widths = []
    for u, v in G.edges():
        if u in layers['Hidden'] and v == 'Kapital':
            edge_widths.append(6)  # Highlight key edges
        else:
            edge_widths.append(1)

    # Draw the graph
    plt.figure(figsize=(12, 16))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=10, width=edge_widths
    )
    edge_labels = nx.get_edge_attributes(G, 'weight')
    nx.draw_networkx_edge_labels(G, pos, edge_labels={k: f'{v:.2f}' for k, v in edge_labels.items()})
    plt.title(" ")
    
    # Save the figure to a file
    # plt.savefig("figures/logo.png", format="png")

    plt.show()

# Run the visualization
visualize_nn()