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.

Practice

1. What does the cmap parameter do in plt.imshow() when displaying an image?

easy

A. It changes how numbers map to colors in the image.

B. It changes the image size.

C. It adds a title to the image.

D. It saves the image to a file.

Solution

1. Step 1: Understand the role of cmap in plt.imshow()

   The cmap parameter controls the colormap, which maps numeric values to colors in the image.

2. Step 2: Identify what cmap affects visually

   Changing cmap changes the colors shown, helping interpret data better.

3. Final Answer:

   It changes how numbers map to colors in the image. -> Option A

4. Quick Check:

   Colormap = color mapping [OK]

Hint: Remember: cmap controls colors, not size or titles [OK]

Common Mistakes:

• Thinking cmap changes image size
• Confusing cmap with adding titles
• Assuming cmap saves the image

2. Which of the following is the correct way to apply the 'viridis' colormap to an image using matplotlib?

easy

A. plt.imshow(image, color='viridis')

B. plt.imshow(image, cmap='viridis')

C. plt.imshow(image, cmap=viridis)

D. plt.imshow(image, colormap='viridis')

Solution

1. Step 1: Recall the correct parameter name for colormap

   The parameter to set colormap in plt.imshow() is cmap, and it expects a string name.

2. Step 2: Check the syntax for passing the colormap

   Passing cmap='viridis' is correct. Using color or colormap is incorrect, and omitting quotes causes an error.

3. Final Answer:

   plt.imshow(image, cmap='viridis') -> Option B

4. Quick Check:

   Use cmap='name' syntax [OK]

Hint: Use cmap='colormap_name' exactly [OK]

Common Mistakes:

• Using color instead of cmap
• Forgetting quotes around colormap name
• Using colormap instead of cmap

3. What will the following code display?

import matplotlib.pyplot as plt
import numpy as np
image = np.array([[0, 1], [2, 3]])
plt.imshow(image, cmap='gray')
plt.colorbar()
plt.show()

medium

A. A 2x2 image with shades of gray from black (0) to white (3) and a colorbar showing values 0 to 3.

B. A 2x2 image with random colors and no colorbar.

C. A 2x2 image with rainbow colors and a colorbar showing values 0 to 1.

D. An error because 'gray' is not a valid colormap.

Solution

1. Step 1: Understand the image data and colormap

   The image is a 2x2 array with values 0 to 3. The 'gray' colormap maps low values to black and high values to white.

2. Step 2: Analyze the colorbar and display

   The colorbar shows the numeric range from 0 to 3, matching the image values. The image colors range from black (0) to white (3).

3. Final Answer:

   A 2x2 image with shades of gray from black (0) to white (3) and a colorbar showing values 0 to 3. -> Option A

4. Quick Check:

   Gray cmap maps 0-3 to black-white [OK]

Hint: Gray cmap means black to white shades [OK]

Common Mistakes:

• Thinking 'gray' is invalid
• Ignoring the colorbar range
• Assuming rainbow colors with 'gray'

4. The code below is intended to show an image with the 'hot' colormap and a colorbar, but it raises an error. What is the problem?

import matplotlib.pyplot as plt
import numpy as np
image = np.random.rand(3,3)
plt.imshow(image, cmap=hot)
plt.colorbar()
plt.show()

medium

A. plt.colorbar() must be called before plt.imshow().

B. The image array shape is invalid for imshow.

C. The colormap name 'hot' should be a string: cmap='hot'.

D. np.random.rand() cannot be used for images.

Solution

1. Step 1: Check the cmap parameter usage

   The colormap name must be a string. Here, hot is used without quotes, causing a NameError.

2. Step 2: Verify other code parts

   The image shape is valid, and colorbar usage is correct. np.random.rand() is valid for image data.

3. Final Answer:

   The colormap name 'hot' should be a string: cmap='hot'. -> Option C

4. Quick Check:

   Colormap names need quotes [OK]

Hint: Always put colormap names in quotes [OK]

Common Mistakes:

• Forgetting quotes around colormap name
• Thinking image shape is wrong
• Misordering colorbar and imshow

5. You have a 5x5 numpy array with values from 0 to 24. You want to display it using plt.imshow() with the 'coolwarm' colormap but only show colors for values between 5 and 20. Which code snippet correctly applies this?

hard

A. plt.imshow(data, cmap='coolwarm'); plt.colorbar(vmin=5, vmax=20)

B. plt.imshow(data, cmap='coolwarm', min=5, max=20); plt.colorbar()

C. plt.imshow(data, cmap='coolwarm', clip=(5,20)); plt.colorbar()

D. plt.imshow(data, cmap='coolwarm', vmin=5, vmax=20); plt.colorbar()

Solution

1. Step 1: Understand how to limit color range in imshow

   Use vmin and vmax parameters to set the data range for colormap scaling.

2. Step 2: Check each option for correct syntax

   plt.imshow(data, cmap='coolwarm', vmin=5, vmax=20); plt.colorbar() uses vmin=5 and vmax=20, which is correct. Other options use invalid parameters or place them incorrectly.

3. Final Answer:

   plt.imshow(data, cmap='coolwarm', vmin=5, vmax=20); plt.colorbar() -> Option D

4. Quick Check:

   Use vmin and vmax to clip colors [OK]

Hint: Use vmin and vmax to set color limits [OK]

Common Mistakes:

• Using min/max instead of vmin/vmax
• Trying to clip with non-existent parameters
• Passing vmin/vmax to colorbar instead of imshow