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name: skill-122 description: "Create high-quality visualizations of data using JAX. Supports various plotting techniques and customization options." license: Proprietary. LICENSE.txt has complete terms

Requirements for Outputs

General Guidelines

Visualizations

• All visualizations MUST be compatible with JAX data structures.
• Ensure visual clarity and appropriateness for data types.
• Provide detailed error messages for invalid configurations.

JAX Data Visualization

1. Basic Line Plot

line_plot(x, y, title='Line Plot', xlabel='X-axis', ylabel='Y-axis')

Description: Generate a basic line plot of y against x.
Parameters:

• x (array): Data for the x-axis.
• y (array): Data for the y-axis.
• title (str, optional): Title of the plot.
• xlabel (str, optional): Label for the x-axis.
• ylabel (str, optional): Label for the y-axis.

Returns: Visualization displayed using JAX-compatible plotting library.

import jax.numpy as jnp
import jax_data_visualization as jx
import matplotlib.pyplot as plt

x = jnp.array([0, 1, 2, 3, 4])
y = jnp.array([0, 1, 4, 9, 16])
jx.line_plot(x, y, title='Quadratic Growth')

2. Scatter Plot

scatter_plot(x, y, title='Scatter Plot')

Description: Generate a scatter plot of data points.
Parameters:

• x (array): Data for the x-axis.
• y (array): Data for the y-axis.
• title (str, optional): Title of the plot.

Returns: Visualization displayed using JAX-compatible plotting library.

def scatter_plot(x, y, title='Scatter Plot'):
    plt.scatter(x, y)
    plt.title(title)
    plt.xlabel('X-axis')
    plt.ylabel('Y-axis')
    plt.show()

scatter_plot(x, y)

3. Histogram Plot

histogram_plot(data, bins=10)

Description: Generate a histogram of the data.
Parameters:

• data (array): Data for histogram.
• bins (int, optional): Number of bins in the histogram.

Returns: Visualization displayed using JAX-compatible plotting library.

def histogram_plot(data, bins=10):
    plt.hist(data, bins=bins)
    plt.title('Histogram')
    plt.xlabel('Value')
    plt.ylabel('Frequency')
    plt.show()

histogram_plot(jnp.array([1, 2, 2, 3, 4, 4, 4, 5]))

4. Customization Options

custom_plot(x, y, style='default')

Description: Generates a customizable plot based on specified style.
Parameters:

• x (array): Data for the x-axis.
• y (array): Data for the y-axis.
• style (str, optional): Custom style for the plot.

Returns: Visualization displayed with custom style.

def custom_plot(x, y, style='default'):
    plt.style.use(style)
    plt.plot(x, y)
    plt.title('Custom Style Plot')
    plt.xlabel('X-axis')
    plt.ylabel('Y-axis')
    plt.show()

custom_plot(x, y, style='ggplot')

5. Save Plot

save_plot(filename)

Description: Save the current plot to a specified file.
Parameters:

• filename (str): Filename to save the plot as.

Returns: None.

def save_plot(filename):
    plt.savefig(filename)

save_plot('my_plot.png')

Notes:

• Visualizations can be saved in multiple formats (e.g., PNG, PDF).
• Ensure appropriate labeling for all axes and titles.