如何创建28°-34°坡度倾角矩阵并实现Python可视化绘图？

Hey there! Let's walk through how to build that slope angle matrix and visualize it with Python—super straightforward once you break it down. I'll cover both a smooth gradient matrix (for gradual transitions) and a random distribution (for real-world scattered slopes), plus a clean visualization using matplotlib.

Step 1: Generate the Slope Angle Matrix #

We'll use numpy to handle matrix creation efficiently. Pick the option that fits your use case:

Option 1: Gradient Matrix (Smooth Transition) #

This creates a matrix where angles transition evenly from 28° (top-left) to 34° (bottom-right). Adjust the matrix size to whatever you need!

import numpy as np

# Define your angle bounds
min_angle = 28
max_angle = 34

# Set matrix dimensions (e.g., 20x20—tweak this to your needs)
matrix_shape = (20, 20)

# Generate a linear range of angles and reshape into the matrix
slope_matrix = np.linspace(min_angle, max_angle, num=matrix_shape[0]*matrix_shape[1]).reshape(matrix_shape)

Option 2: Random Distribution Matrix #

If you want angles scattered randomly within the 28-34° range (great for simulating natural terrain), use this instead:

# Create a matrix with random angles between 28° and 34°
slope_matrix = np.random.uniform(low=min_angle, high=max_angle, size=matrix_shape)

Step 2: Visualize the Matrix #

We'll use matplotlib to plot the matrix as a heatmap—this makes it easy to see exactly where each slope angle falls. Here's the full visualization code:

import matplotlib.pyplot as plt

# Set up the plot window
plt.figure(figsize=(8, 6))

# Plot the matrix as a heatmap (use 'viridis' for a colorblind-friendly scheme)
im = plt.imshow(slope_matrix, cmap='viridis', vmin=min_angle, vmax=max_angle)

# Add clear labels and a title
plt.title('Slope Angle Distribution (28° to 34°)', fontsize=14, pad=15)
plt.xlabel('Column Position', fontsize=12)
plt.ylabel('Row Position', fontsize=12)

# Add a color bar that directly maps colors to angles
color_bar = plt.colorbar(im)
color_bar.set_label('Slope Angle (°)', fontsize=12)

# Optional: Add faint grid lines to make individual matrix cells easier to spot
plt.grid(visible=True, linestyle='--', alpha=0.5, color='white')

# Display the plot
plt.show()

Quick Tweaks You Might Want #

• Resize the matrix: Change matrix_shape to (50,50) for a denser grid or (10,10) for something simpler.
• Change the color scheme: Swap 'viridis' for 'plasma', 'inferno', or 'coolwarm' to get a different look.
• Reverse the gradient: Flip min_angle and max_angle in np.linspace() to make the highest angles start at the top-left.

内容的提问来源于stack exchange，提问作者Steren