legendtkl 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.