advanced patterns when using custom colormaps in Matplotlib 3.6.0 with imshow()
I can't seem to get Hey everyone, I'm running into an issue that's driving me crazy... I'm trying to customize the colormap for an image displayed using `imshow()` in Matplotlib 3.6.0, but I'm working with an scenario where my custom colormap does not appear to apply correctly. I created a custom colormap using `LinearSegmentedColormap`, but when I apply it, the colors do not reflect the expected gradient. Here’s the code snippet I’m using: ```python import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap # Generate some random data np.random.seed(0) data = np.random.rand(10, 10) # Create a custom colormap colors = [(1, 1, 1), (0, 0, 1)] # White to Blue custom_cmap = LinearSegmentedColormap.from_list('custom_blue', colors, N=256) # Display the image with imshow plt.imshow(data, cmap=custom_cmap) plt.colorbar() plt.show() ``` The scenario I'm working with is that the data values seem to be mapped incorrectly to the colormap. Even though the `data` values range from 0 to 1, the color representation doesn't reflect this range properly, and I’m seeing a lot of unexpected white areas in the output. I've checked the normalization settings of `imshow()`, but I need to find a way to specify a normalization that fits my data range. I also tried using `Normalize()` from `matplotlib.colors`, but it still didn't fix the scenario. Is there something I'm missing in my colormap setup or in the way I'm applying it? Any suggestions for resolving this would be greatly appreciated! I'm using Python stable in this project. Thanks in advance! For reference, this is a production CLI tool. The project is a mobile app built with Python. Any ideas what could be causing this?