I’m working with Matplotlib to create a heightmap visualization using pcolormesh, but I’m encountering an issue where the edges of the mesh grid are visible in the output image, despite setting edgecolors=’none’. This results in an unwanted wireframe-like appearance on the plot near the edge of the colorplot. I’m trying to achieve a smooth, uninterrupted surface visualization.
image

Despite setting edgecolors=’none’ and testing with rasterized=True, the wireframe still appear in the saved figure. I’ve also tried increasing the resolution and adjusting the colormap. When I set color map at 50% opacity i see that the meshgrid comes through my colormap. Like this pic shows (with viridis and alpha=0.5) image

How can I remove or hide these mesh edges to achieve a completely smooth surface visualization?

Additional Information:
Python version: 3.x
Matplotlib version: latest

Any suggestions or insights would be greatly appreciated!

Here’s a simplified version of my code that shows the same artifact if you zoom in to the edge:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from scipy.interpolate import griddata
from os.path import join

# Sample data for demonstration
x = np.random.rand(1000)
y = np.random.rand(1000) * 100  # Y values scaled to simulate height data
z = np.random.rand(1000)

# Convert data to NumPy arrays
x_np = np.array(x)
y_np = np.array(y)
z_np = np.array(z)

# Define grid resolution
heightmap_resolution = (512, 512)

# Create a grid
grid_x, grid_z = np.meshgrid(np.linspace(np.min(x_np), np.max(x_np), heightmap_resolution[0]),
                             np.linspace(np.min(z_np), np.max(z_np), heightmap_resolution[1]))

# Interpolate Y values on the grid
points = np.column_stack((x_np, z_np))
grid_y = griddata(points, y_np, (grid_x, grid_z), method='cubic')

# Plot setup
fig_width, fig_height = 210 / 25.4, 297 / 25.4  # Dimensions in inches
dpi = 1440  # High DPI for detailed output

fig = plt.figure(figsize=(fig_width, fig_height), dpi=dpi)
ax = fig.add_axes([0, 0, 1, 1])  # Use full area of the figure
cmap = LinearSegmentedColormap.from_list('mycmap', ['white', 'black'])
c = ax.pcolormesh(grid_x, grid_z, grid_y, cmap=cmap, shading='gouraud', edgecolors='none', rasterized=True)
ax.axis('off')  # Turn off the axis

# Save the figure
output_dir = r"C:test" #CHANGE THIS PATH 
heightmap_name = 'out_plot_height_map.png'
plt.savefig(join(output_dir, heightmap_name), format='png', dpi=dpi, bbox_inches='tight', pad_inches=0)
plt.close(fig)

I tried this code, but this ‘pixelates’ the output. I need the gradient that comes with shading=’gouraud’, as this heightmap will be used in a manufacturing step and the steps form the pixels are a no-go

fig, ax = plt.subplots(figsize=(fig_width, fig_height), dpi=dpi)
im = ax.imshow(grid_y, extent=[np.min(x_np), np.max(x_np), np.min(z_np), np.max(z_np)], interpolation='bicubic', cmap='viridis', aspect='auto')
ax.axis('off')
plt.savefig(join(output_dir, heightmap_name), format='png', dpi=dpi, bbox_inches='tight', pad_inches=0)
plt.close(fig)