Demonstrating different types of colormaps

 

import matplotlib.pyplot as plt

import numpy as np

# Generate sample data

data = np.random.rand(10, 10)

# List of colormaps to demonstrate

colormaps = [

    'viridis',      # Sequential

    'plasma',       # Sequential

    'inferno',      # Sequential

    'magma',        # Sequential

    'cividis',      # Sequential

    'PiYG',         # Diverging

    'PRGn',         # Diverging

    'BrBG',         # Diverging

    'PuOr',         # Diverging

    'Set1',         # Qualitative

    'Set2',         # Qualitative

    'tab20',        # Qualitative

    'hsv',          # Cyclic

    'twilight',     # Cyclic

    'twilight_shifted' # Cyclic

]

# Create subplots to display colormaps

fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15, 20))

# Flatten axes array for easy iteration

axes = axes.flatten()

# Loop through colormaps and plot data

for ax, cmap in zip(axes, colormaps):

    im = ax.imshow(data, cmap=cmap)

    ax.set_title(cmap)

    plt.colorbar(im, ax=ax)

# Adjust layout to prevent overlap

plt.tight_layout()

# Show the plot

plt.show()

Explanation:

1. Generate Sample Data:

   data = np.random.rand(10, 10)

   This creates a 10x10 array of random numbers.

2. List of Colormaps:

   • A list of colormap names is defined. Each name corresponds to a different colormap in Matplotlib.

3. Create Subplots:

   fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15, 20))

   This creates a 5x3 grid of subplots to display multiple colormaps.

4. Loop Through Colormaps:

   • The loop iterates through each colormap, applying it to the sample data and plotting it in a subplot.

5. Add Colorbar:

   plt.colorbar(im, ax=ax)
   

   This adds a colorbar to each subplot to show the mapping of data values to colors.

6. Adjust Layout and Show Plot:

   plt.tight_layout() plt.show()

   These commands adjust the layout to prevent overlap and display the plot.

Choosing Colormaps

• Sequential: Good for data with a clear order or progression.
• Diverging: Best for data with a critical midpoint.
• Qualitative: Suitable for categorical data.
• Cyclic: Ideal for data that wraps around, such as angles.

By selecting appropriate colormaps, you can enhance the visual representation of your data, making it easier to understand and interpret.