Image colormaps in Matplotlib - Step-by-Step Execution

Concept Flow - Image colormaps

Load Image Data

↓

Select Colormap

↓

Apply Colormap to Data

↓

Display Image with Colormap

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Interpret Colors as Data Values

The flow shows loading image data, choosing a colormap, applying it, displaying the image, and interpreting colors as data values.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.random.rand(5,5)
plt.imshow(image, cmap='viridis')
plt.colorbar()
plt.show()

This code creates a 5x5 random image and displays it using the 'viridis' colormap with a colorbar.

Execution Table

Step	Action	Data Shape/Value	Colormap Applied	Output Description
1	Generate random 5x5 image	(5,5) array with values 0-1	None	Raw grayscale data matrix
2	Select colormap 'viridis'	N/A	'viridis'	Colormap chosen for mapping values to colors
3	Apply colormap to image data	(5,5) array	'viridis'	Each value mapped to a color in 'viridis' scale
4	Display image with plt.imshow	(5,5) colored image	'viridis'	Image shown with colors representing data values
5	Add colorbar	N/A	N/A	Colorbar shows mapping from data values to colors
6	Show plot	N/A	N/A	Final image with colormap and colorbar displayed

💡 All steps complete, image displayed with colormap applied.

Variable Tracker

Variable	Start	After Step 1	After Step 3	Final
image	None	5x5 array of floats 0-1	5x5 array unchanged	5x5 array unchanged
cmap	None	None	'viridis'	'viridis'

Key Moments - 2 Insights

Why do we need to select a colormap before displaying the image?

Because the image data is numeric, the colormap translates these numbers into colors so we can visually interpret the data. See execution_table step 2 and 3.

What does the colorbar represent in the image display?

The colorbar shows how numeric values map to colors in the colormap, helping us understand which colors correspond to which data values. See execution_table step 5.

Visual Quiz - 3 Questions

Look at the execution table, what is the shape of the image data after step 1?

A (5,5) array with integer values

B (5,5) array with values between 0 and 1

C Single number

D Empty array

💡 Hint

Check the 'Data Shape/Value' column in row for step 1.

At which step is the colormap actually applied to the image data?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Look at the 'Colormap Applied' column and 'Action' column in the execution table.

If we change the colormap from 'viridis' to 'gray', how would the output description in step 4 change?

A Image colors would be grayscale instead of 'viridis' colors

B Image size would change

C Image data values would change

D Colorbar would disappear

💡 Hint

Refer to the 'Output Description' in step 4 and understand what colormaps do.

Concept Snapshot

Image colormaps map numeric image data to colors.
Use plt.imshow(data, cmap='name') to apply.
Common colormaps: 'viridis', 'gray', 'plasma'.
Add plt.colorbar() to show value-color mapping.
Colormaps help visually interpret data values.

Full Transcript

This visual execution traces how image colormaps work in matplotlib. First, numeric image data is created as a 5x5 array of random values between 0 and 1. Then, a colormap named 'viridis' is selected. This colormap maps each numeric value to a specific color. The colormap is applied to the image data, and the image is displayed with colors representing the data values. A colorbar is added to show the mapping from data values to colors. The process ends with the image shown on screen. Key points include why colormaps are needed to translate numbers to colors and how the colorbar helps interpret the image. Changing the colormap changes the colors but not the data or image size.