Aprenda Analyzing Generative Art Outputs | Creative Coding and Generative Art

When creating generative art, you are not just producing random images—you are building systems that generate visual outputs based on rules and parameters. Evaluating generative art outputs involves looking for patterns, balance, and visual interest. You should consider whether the generated images feel harmonious or chaotic, if there is enough variation to keep the viewer engaged, and if the outputs are visually appealing. Sometimes, subtle patterns emerge when you run your code multiple times or adjust the parameters, and spotting these can help you refine your creative process. Pay attention to the repetition, symmetry, randomness, and overall composition of your outputs. These observations will guide your decisions on how to tweak your code for more interesting results.


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import matplotlib.pyplot as plt
import numpy as np

# Parameter: number of elements controls complexity
num_elements = 30

angles = np.linspace(0, 2 * np.pi, num_elements, endpoint=False)
radii = np.linspace(0.2, 1, num_elements)
colors = plt.cm.viridis(np.linspace(0, 1, num_elements))

fig, ax = plt.subplots(figsize=(6, 6))
ax.set_aspect('equal')
ax.axis('off')

for angle, radius, color in zip(angles, radii, colors):
    x = radius * np.cos(angle)
    y = radius * np.sin(angle)
    circle = plt.Circle((x, y), 0.08, color=color, alpha=0.7)
    ax.add_patch(circle)

plt.show()

Adjusting the num_elements parameter in the code above has a significant impact on the pattern's appearance. With a lower value, the pattern is sparse and simple; increasing it creates more densely packed, intricate designs. By experimenting with this parameter, you can discover new visual effects and decide what level of complexity best suits your creative vision. This process of tweaking and observing is at the heart of generative art: each change can lead to unexpected and inspiring results, allowing you to explore a wide range of possibilities with just a few adjustments.


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import matplotlib.pyplot as plt
import numpy as np

num_elements = 30
angles = np.linspace(0, 2 * np.pi, num_elements, endpoint=False)
radii = np.linspace(0.2, 1, num_elements)

# Try a different colormap and increase spacing
colors = plt.cm.plasma(np.linspace(0, 1, num_elements))
spacing = 0.12  # Increased element spacing

fig, ax = plt.subplots(figsize=(6, 6))
ax.set_aspect('equal')
ax.axis('off')

for angle, radius, color in zip(angles, radii, colors):
    x = (radius + spacing) * np.cos(angle)
    y = (radius + spacing) * np.sin(angle)
    circle = plt.Circle((x, y), 0.08, color=color, alpha=0.8)
    ax.add_patch(circle)

plt.show()

1. Why is it important to analyze generative art outputs?

2. How can tweaking parameters lead to new creative discoveries?

3. Fill in the blank: To make a pattern more complex, you can increase the ________ parameter.

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 6

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Suggested prompts:

What other parameters can I adjust to change the visual output?

Can you explain how different colormaps affect the appearance?

How can I add more variation or complexity to the pattern?

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