Examples

contour_boxplot_example

curve_boxplot_example

functional_boxplot_example

probabilistic_marching_cubes_example

probabilistic_marching_squares_example

probabilistic_marching_tet_example

probabilistic_marching_triangles_example

squid_glyphs_2D_example

squid_glyphs_3D_example

uncertainty_lobes_2D_example

uncertainty_tube_example

vsup_example

Example script demonstrating the ColorTree colormap for visualizing value and uncertainty. A ColorTree is inspired by VSUP (Value-Suppressing Uncertainty Palettes) and creates a tree-based colormap. Value is suppressed based on uncertainty, allowing for more effective visualization of uncertain data.

VSUP:https://medium.com/@uwdata/value-suppressing-uncertainty-palettes-426130122ce9

This script creates a sample 2D image where the x-axis represents value (0 to 1) and the y-axis represents uncertainty (0 to 1). It generates three visualizations: - Discrete: Uses tree nodes for discrete levels. - Continuous: Interpolates colormap colors with uncertainty. - Continuous Leaves: Uses colormap for leaf levels in discrete mode.

Run this script to see the differences between the modes.

Important necessary libraries

import numpy as np
import matplotlib.pyplot as plt
from uvisbox.Core.Colors.colortree import ColorTree

Set up the figure with three subplots

fig, ax = plt.subplots(1, 3, figsize=(12, 6))

Create a sample image where x (columns) is value (0 to 1), y (rows) is uncertainty (0 to 1)

height, width = 100, 100
value_grid = np.linspace(0, 1, width)[None, :]  # Shape (1, 100), broadcasted to (100, 100)
uncertainty_grid = np.linspace(0, 1, height)[:, None]  # Shape (100, 1), broadcasted to (100, 100)

# Create image array with shape (100, 100, 2) where last dim is [uncertainty, value]
image = np.stack([uncertainty_grid * np.ones((height, width)), value_grid * np.ones((height, width))], axis=-1)
# Initialize ColorTree with depth=4 and default settings
colormap = ColorTree(depth=4, cmap="viridis")
# Generate colors for discrete mode (uses tree nodes)
colors = colormap.get_colors(image, discrete=True)

# Plot the discrete color map
ax[0].imshow(colors, origin='lower', extent=(0, 1, 0, 1))
ax[0].set_title("Discrete Color Map")
ax[0].set_xlabel("Value")
ax[0].set_ylabel("Uncertainty")
# Generate colors for continuous mode (interpolates colormap with uncertainty)
continuous_color = colormap.get_colors(image, discrete=False)

# Plot the continuous color map
ax[1].imshow(continuous_color, origin='lower', extent=(0, 1, 0, 1))
ax[1].set_title("Continuous Color Map")
ax[1].set_xlabel("Value")
ax[1].set_ylabel("Uncertainty")

# Generate colors for continuous leaves mode (discrete with colormap at leaves)
continuous_leaves_color = colormap.get_colors(image, discrete=True, continuous_leaves=True)
# Plot the continuous leaves color map
ax[2].imshow(continuous_leaves_color, origin='lower', extent=(0, 1, 0, 1))
ax[2].set_title("Continuous Leaves Color Map")
ax[2].set_xlabel("Value")
ax[2].set_ylabel("Uncertainty")
# Display the plot
plt.show()