Topic: Python Matplotlib – From Easy to Top: Part 5 of 6: Images, Heatmaps, and Colorbars

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### 1. Introduction

Matplotlib can handle images, heatmaps, and color mapping effectively, making it a great tool for visualizing:

• Image data (grayscale or color)
• Matrix-like data with heatmaps
• Any data that needs a gradient of colors

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### 2. Displaying Images with `imshow()`

import matplotlib.pyplot as plt
import numpy as np

# Create a random grayscale image
img = np.random.rand(10, 10)

plt.imshow(img, cmap='gray')
plt.title("Grayscale Image")
plt.colorbar()
plt.show()

Key parameters:

• cmap – color map (gray, hot, viridis, coolwarm, etc.)
• interpolation – for smoothing pixelation (nearest, bilinear, bicubic)

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### 3. Displaying Color Images

import matplotlib.image as mpimg

img = mpimg.imread('example.png')  # image must be in your directory
plt.imshow(img)
plt.title("Color Image")
plt.axis('off')  # Hide axes
plt.show()

Note: Image should be PNG or JPG. For real projects, use PIL or OpenCV for more control.

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### 4. Creating a Heatmap from a 2D Matrix

matrix = np.random.rand(6, 6)

plt.imshow(matrix, cmap='viridis', interpolation='nearest')
plt.title("Heatmap Example")
plt.colorbar(label="Intensity")
plt.xticks(range(6), ['A', 'B', 'C', 'D', 'E', 'F'])
plt.yticks(range(6), ['P', 'Q', 'R', 'S', 'T', 'U'])
plt.show()

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### 5. Customizing Color Maps

You can reverse or customize color maps:

plt.imshow(matrix, cmap='coolwarm_r')  # Reversed coolwarm

You can also create custom color ranges using vmin and vmax:

plt.imshow(matrix, cmap='hot', vmin=0.2, vmax=0.8)

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### 6. Using `matshow()` for Matrix-Like Data

matshow() is optimized for visualizing 2D arrays:

plt.matshow(matrix)
plt.title("Matrix View with matshow()")
plt.colorbar()
plt.show()

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### 7. Annotating Heatmaps

fig, ax = plt.subplots()
cax = ax.imshow(matrix, cmap='plasma')

# Add text annotations
for i in range(matrix.shape[0]):
    for j in range(matrix.shape[1]):
        ax.text(j, i, f'{matrix[i, j]:.2f}', ha='center', va='center', color='white')

plt.title("Annotated Heatmap")
plt.colorbar(cax)
plt.show()

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### 8. Displaying Multiple Images in Subplots

fig, axs = plt.subplots(1, 2, figsize=(10, 4))

axs[0].imshow(matrix, cmap='Blues')
axs[0].set_title("Blues")

axs[1].imshow(matrix, cmap='Greens')
axs[1].set_title("Greens")

plt.tight_layout()
plt.show()

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### 9. Saving Heatmaps and Figures

plt.imshow(matrix, cmap='magma')
plt.title("Save This Heatmap")
plt.colorbar()
plt.savefig("heatmap.png", dpi=300)
plt.close()

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### 10. Summary

• imshow() and matshow() visualize 2D data or images
• Heatmaps are great for matrix or correlation data
• Use colorbars and annotations to add context
• Customize colormaps with cmap, vmin, vmax
• Save your visualizations easily using savefig()

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### Exercise

• Load a grayscale image using NumPy and display it.
• Create a 10×10 heatmap with annotations.
• Display 3 subplots of the same matrix using 3 different colormaps.
• Save one of the heatmaps with high resolution.

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#Python #Matplotlib #Heatmaps #DataVisualization #Images #ColorMapping

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