Image colormaps in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of applying 'viridis' colormap to a grayscale image

What is the shape and data type of the output array after applying the 'viridis' colormap to a 2D grayscale image using matplotlib's cm.viridis?

Matplotlib

import numpy as np
import matplotlib.cm as cm

gray_image = np.array([[0, 128], [192, 255]], dtype=np.uint8)
colored_image = cm.viridis(gray_image / 255.0)
print(colored_image.shape, colored_image.dtype)

A (2, 2, 4) float32

B (2, 2, 4) float64

C (2, 2, 3) float32

D (2, 2) uint8

💡 Hint

Remember that matplotlib colormaps return RGBA values as floats between 0 and 1.

The 'viridis' colormap converts a 2D grayscale image into an RGBA image with shape (height, width, 4). The output dtype is float32 by default.

❓ data_output

intermediate

Number of unique colors after applying 'gray' colormap

Given a 1D numpy array with values [0, 0.5, 1.0], what is the number of unique colors in the output after applying the 'gray' colormap from matplotlib?

Matplotlib

import numpy as np
import matplotlib.cm as cm

values = np.array([0, 0.5, 1.0])
colors = cm.gray(values)
unique_colors = np.unique(colors, axis=0)
print(len(unique_colors))

A 3

B 2

C 1

D 4

💡 Hint

Check if the colormap maps different input values to distinct RGBA colors.

Each input value maps to a distinct grayscale RGBA color, so there are 3 unique colors.

❓ visualization

advanced

Visualizing the effect of 'hot' colormap on a gradient image

Which option shows the correct matplotlib code to display a horizontal gradient image with the 'hot' colormap applied?

A

import numpy as np
import matplotlib.pyplot as plt
img = np.linspace(0, 1, 256).reshape(-1, 1)
plt.imshow(img, cmap='hot')
plt.axis('on')
plt.show()

B

import numpy as np
import matplotlib.pyplot as plt
img = np.linspace(0, 1, 256)
plt.imshow(img, cmap='hot')
plt.axis('off')
plt.show()

C

import numpy as np
import matplotlib.pyplot as plt
img = np.linspace(0, 1, 256).reshape(1, -1)
plt.imshow(img, cmap='hot')
plt.axis('on')
plt.show()

D

import numpy as np
import matplotlib.pyplot as plt
img = np.linspace(0, 1, 256).reshape(1, -1)
plt.imshow(img, cmap='hot', aspect='auto')
plt.axis('off')
plt.show()

💡 Hint

A horizontal gradient image should have shape (1, width) and axis turned off for clean display.

Option D correctly creates a 1-row gradient, applies 'hot' colormap, sets aspect ratio to auto, and hides axes.

🔧 Debug

advanced

Identify the error when applying colormap to integer image

What error will occur when running this code snippet?

import numpy as np
import matplotlib.cm as cm
img = np.array([[0, 255], [128, 64]], dtype=np.uint8)
colored = cm.plasma(img)
print(colored)

A TypeError: float argument required, not numpy.uint8

B ValueError: Input values must be in the range 0 to 1

C No error, prints RGBA array

D AttributeError: module 'matplotlib.cm' has no attribute 'plasma'

💡 Hint

Colormaps expect input values normalized between 0 and 1.

The colormap expects float inputs between 0 and 1. Passing uint8 values causes a ValueError.

🚀 Application

expert

Combining two colormaps to create a custom colormap

Which code correctly creates a new colormap by combining the first half of 'Blues' and the second half of 'Reds' colormaps?

A

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

blues = plt.cm.Blues(np.linspace(0, 1, 128))
reds = plt.cm.Reds(np.linspace(0, 1, 128))
newcolors = np.vstack((blues[:128], reds[128:]))
newcmp = LinearSegmentedColormap.from_list('BlueRed', newcolors)
print(newcmp(0.5))

B

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

blues = plt.cm.Blues(np.linspace(0, 1, 128))
reds = plt.cm.Reds(np.linspace(0, 1, 128))
newcolors = np.vstack((blues[:64], reds[64:]))
newcmp = LinearSegmentedColormap.from_list('BlueRed', newcolors)
print(newcmp(0.75))

C

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

blues = plt.cm.Blues(np.linspace(0, 1, 256))
reds = plt.cm.Reds(np.linspace(0, 1, 256))
newcolors = np.vstack((blues[:128], reds[128:]))
newcmp = LinearSegmentedColormap.from_list('BlueRed', newcolors)
print(newcmp(0.5))

D

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

blues = plt.cm.Blues(np.linspace(0, 1, 128))
reds = plt.cm.Reds(np.linspace(0, 1, 128))
newcolors = np.vstack((blues[:64], reds[64:]))
newcmp = LinearSegmentedColormap.from_list('BlueRed', newcolors)
print(newcmp(0.5))

💡 Hint

Ensure the slices cover half of the arrays and the arrays have matching sizes before stacking.

Option C correctly uses 256 samples, slices half (128) from each colormap, stacks them, and creates a new colormap.