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ViewsHow to modify intervals on matplotlib's qualitative color maps?
import matplotlib.pyplot as plt
import numpy as np
I have a 2D numpy array:
mock=\
np.array([[0.1,0.2,0.3],
[0.2,0.3,0.4],
[0.3,0.4,0.5],
[0.4,0.5,0.6],
[0.5,0.6,0.7],
[0.6,0.7,0.8],
[0.7,0.8,0.9]])
I plot these values using the tab20c colormap, from the so-called "Qualitative" colormaps:
plt.figure(figsize=(6,6))
im = plt.imshow(mock,cmap = "tab20c", vmin=0, vmax=1)
plt.colorbar(im)
How would I go about this if I wanted all the values below a certain value, let's say 0.25 having the same color as 0.25 has, replacing not only the colors in the plot itself, but also in the colorbar? The expected output would be then: all 3 squares in the top left corner to be red, bottom 5 rectangles in the colormap are also all red.
By doing this, I am essentially modifying the interval for the color red in the barchart: instead of showing values in the interval between 0.2 and 0.25 as red, the new version will show everything in the interval between 0 and 0.25 red.
2 answers
Answer question0
What about using numpy.clip to set the data below 0.25 to 0.25?
im = plt.imshow(np.clip(mock, 0.25, np.inf), cmap = "tab20c", vmin=0, vmax=1)
This seems to be the most straightforward solution to me as it doesn't require to mess with the plotting parameters.
output:
0
You can adapt an existing colourmap:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
tab20c = cm.get_cmap('tab20c', 256)
newcolors = tab20c(np.linspace(0, 1, 256))
red = np.array([1, 0, 0, 1])
newcolors[:int(256/4), :] = red
newcmp = ListedColormap(newcolors)
mock=\
np.array([[0.1,0.2,0.3],
[0.2,0.3,0.4],
[0.3,0.4,0.5],
[0.4,0.5,0.6],
[0.5,0.6,0.7],
[0.6,0.7,0.8],
[0.7,0.8,0.9]])
plt.figure(figsize=(6,6))
im = plt.imshow(mock,cmap = newcmp, vmin=0.0, vmax=1)
plt.colorbar(im)
