Is it possible to adjust a colormap color scheme to maintain the same color past a specific point?
For example, if my current colormap is:
import palettable
cmap = palettable.colorbrewer.sequential.YlGn_9.mpl_colormap
How can I alter this colormap so that when plotting a range from 0 to 100, the colors remain consistent up to 50 and then transition to red beyond that point?
If you need to create a colormap for a specified range (0 to 100), one approach is to layer two different colormaps on top of each other. Here's an example of how it can be done:
Visual Example:
import numpy as np
import matplotlib.pyplot as plt
import palettable
import matplotlib.colors as mcolors
# Setting a random seed
np.random.seed(42)
# Generating random values in the shape of 10x10
data = np.random.rand(10, 10) * 100
# Using a colormap that ranges from 0 to 50
colors1 = palettable.colorbrewer.sequential.YlGn_9.mpl_colormap(np.linspace(0, 1, 256))
# Utilizing a red colormap that ranges from 50 to 100
colors2 = plt.cm.Reds(np.linspace(0, 1, 256))
# Stacking the two arrays row-wise
colors = np.vstack((colors1, colors2))
# Creating a smoothly-varying LinearSegmentedColormap
cmap = mcolors.LinearSegmentedColormap.from_list('colormap', colors)
plt.pcolor(data, cmap=cmap)
plt.colorbar()
# Adjusting the lower and upper limits of the colorbar
plt.clim(0, 100)
plt.show()
https://i.stack.imgur.com/568SD.png
If you prefer the upper section to have a single color without spreading across the entire length of the colormap, you can make this adjustment:
colors2 = plt.cm.Reds(np.linspace(1, 1, 256))
cmap.set_over("blue")
Consider utilizing a different norm function to customize the bounds as per your requirements. Additionally, when employing imshow, remember that you can specify vmin=50 to designate it as the maximum value.
To create a brand new colormap from an existing one, follow these steps:
newcmap = cmap.from_list('newcmap',list(map(cmap,range(50))), N=50)This fresh map will utilize the final color value in the colormap for values exceeding 50. To change the last color to red, simply include red as the final color in the list defining the colormap.
newcmap = cmap.from_list('newcmap',list(map(cmap,range(50)))+[(1,0,0,1)], N=51)import palettable
from matplotlib import pyplot as plt
cmap = palettable.colorbrewer.sequential.YlGn_9.mpl_colormap
newcmap = cmap.from_list('newcmap',list(map(cmap,range(50))), N=50)
for x in range(80):
plt.bar(x,1, width=1, edgecolor='none',facecolor=newcmap(x))
plt.show()
https://i.stack.imgur.com/DJ2aW.png
newcmap = cmap.from_list('newcmap',list(map(cmap,range(50)))+[(1,0,0,1)], N=51)
for x in range(80):
plt.bar(x,1, width=1, edgecolor='none',facecolor=newcmap(x))
plt.show()
To access the color variations, you can use this code:
color_data = colors._segmentdata
After running this code, you will get a dictionary containing various color data. Next, extract specific colors using the following lines of code:
red = color_data["red"]
green = color_data["green"]
blue = color_data["blue"]
alpha = color_data["alpha"]
You can then add a new color to the existing list like so:
red.append(red[1])
Combine these colors back into a dictionary with the 4 respective keys:
new_color_data["red"] = red
Finally, generate a new color palette using the updated color dictionary:
updated_palette = custom_palette.ListedColormap(new_color_data)
In my opinion, it's best to adjust the coloring of the element rather than changing the entire color scheme. I had a similar query not too long ago, which led me to seek guidance from various sources. One valuable discussion I found was about modifying colors for specific levels in contour plots. You can explore more on this topic by visiting this thread and also refer to insightful insights shared here: Alter default color settings in Python matplotlib for values beyond colorbar range
Suppose you wish to customize the colors of your contours. In that case, consider implementing the following approach:
cs = pyplot.contourf(x,y,z, cmap=your_cmap)
cs.cmap.set_over('r') # Assign 'red' color
cs.set_clim(0, 50) # Specify threshold above which items turn red
cb = pyplot.colorbar(cs) # Display color scale (if necessary)
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