Unexpected color mapping in Matplotlib scatter plot with colormap and alpha blending
I'm working on a project and hit a roadblock... I'm trying to create a scatter plot using Matplotlib where each point is colored according to a colormap based on a separate data variable, and I'd like to apply alpha blending based on another variable as well... However, I'm encountering unexpected behavior where the colors don't seem to reflect the colormap properly, and the points are not blending as expected. Here’s a simplified version of what I’m doing: ```python import numpy as np import matplotlib.pyplot as plt from matplotlib import cm # Sample data x = np.random.rand(100) y = np.random.rand(100) colors = np.random.rand(100) # Values for colormap alpha_values = np.random.rand(100) # Alpha values between 0 and 1 # Create scatter plot scatter = plt.scatter(x, y, c=colors, cmap='viridis', alpha=alpha_values) plt.colorbar(scatter) plt.title('Scatter Plot with Color and Alpha') plt.show() ``` The scatter points appear too bright and don't seem to reflect the intended colormap from 'viridis'. I expected the colors to blend based on the alpha values, but instead, they seem to be rendered at full opacity for some points and very faint for others. I've tried setting the alpha directly in the scatter function, but it appears that Matplotlib may not be applying the alpha mapping correctly when using the `c=` parameter. I've also checked the version I'm using (Matplotlib 3.6.2) and ensured that the alpha values are indeed between 0 and 1. Is there a better way to apply both color mapping and alpha blending in this scenario? Any insights or alternative approaches would be greatly appreciated! Has anyone dealt with something similar? I'm working on a desktop app that needs to handle this. Hoping someone can shed some light on this. My development environment is Debian. What's the correct way to implement this? My development environment is Windows 10. What are your experiences with this?