Colormap Selection¶

Choosing the right colormap is critical for effective data visualization. This guide covers colormap categories, selection criteria, and best practices.

Colormap Categories¶

Matplotlib provides several categories of colormaps:

Category	Use Case	Examples
Sequential	Data with natural ordering (low→high)	`viridis`, `plasma`, `Blues`
Diverging	Data with meaningful center point	`coolwarm`, `RdBu`, `seismic`
Cyclic	Periodic data (angles, phases)	`twilight`, `hsv`
Qualitative	Categorical data	`Set1`, `tab10`, `Paired`

Setup¶

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)

Sequential Colormaps¶

Best for data that progresses from low to high values.

Perceptually Uniform¶

These colormaps have uniform perceptual brightness changes, making them ideal for scientific visualization.

Z = np.exp(-(X**2 + Y**2))

fig, axes = plt.subplots(2, 3, figsize=(15, 9))

cmaps = ['viridis', 'plasma', 'inferno', 'magma', 'cividis', 'turbo']

for ax, cmap in zip(axes.flat, cmaps):
    im = ax.imshow(Z, cmap=cmap, origin='lower')
    ax.set_title(f"'{cmap}'")
    ax.axis('off')
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.suptitle('Perceptually Uniform Sequential Colormaps', fontsize=14)
plt.tight_layout()
plt.show()

Single-Hue Sequential¶

fig, axes = plt.subplots(2, 3, figsize=(15, 9))

cmaps = ['Blues', 'Greens', 'Reds', 'Purples', 'Oranges', 'Greys']

for ax, cmap in zip(axes.flat, cmaps):
    im = ax.imshow(Z, cmap=cmap, origin='lower')
    ax.set_title(f"'{cmap}'")
    ax.axis('off')
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.suptitle('Single-Hue Sequential Colormaps', fontsize=14)
plt.tight_layout()
plt.show()

Multi-Hue Sequential¶

fig, axes = plt.subplots(2, 3, figsize=(15, 9))

cmaps = ['YlOrRd', 'YlGnBu', 'BuPu', 'GnBu', 'PuBuGn', 'OrRd']

for ax, cmap in zip(axes.flat, cmaps):
    im = ax.imshow(Z, cmap=cmap, origin='lower')
    ax.set_title(f"'{cmap}'")
    ax.axis('off')
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.suptitle('Multi-Hue Sequential Colormaps', fontsize=14)
plt.tight_layout()
plt.show()

When to Use Sequential¶

Density plots
Probability distributions
Temperature (single direction)
Elevation maps
Any monotonically increasing data

# Example: Probability density
from scipy import stats

rv = stats.multivariate_normal([0, 0], [[1, 0], [0, 1]])
Z_pdf = rv.pdf(np.dstack((X, Y)))

fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(Z_pdf, extent=[-3, 3, -3, 3], origin='lower', cmap='viridis')
ax.set_title('Bivariate Normal PDF')
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.colorbar(im, ax=ax, label='Density')
plt.show()

Diverging Colormaps¶

Best for data with a meaningful center point (often zero).

Standard Diverging¶

Z_div = X**2 - Y**2  # Saddle function with positive and negative values

fig, axes = plt.subplots(2, 3, figsize=(15, 9))

cmaps = ['coolwarm', 'RdBu', 'seismic', 'bwr', 'RdYlBu', 'PiYG']

for ax, cmap in zip(axes.flat, cmaps):
    im = ax.imshow(Z_div, cmap=cmap, origin='lower')
    ax.set_title(f"'{cmap}'")
    ax.axis('off')
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.suptitle('Diverging Colormaps', fontsize=14)
plt.tight_layout()
plt.show()

Centered Normalization¶

For diverging data, center the colormap at zero:

from matplotlib.colors import TwoSlopeNorm

Z_asym = X**2 - Y**2 + 2  # Asymmetric around zero

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Without centering
im1 = axes[0].imshow(Z_asym, cmap='RdBu', origin='lower')
axes[0].set_title('Default Normalization')
plt.colorbar(im1, ax=axes[0])

# With centering at zero
norm = TwoSlopeNorm(vmin=Z_asym.min(), vcenter=0, vmax=Z_asym.max())
im2 = axes[1].imshow(Z_asym, cmap='RdBu', norm=norm, origin='lower')
axes[1].set_title('Centered at Zero (TwoSlopeNorm)')
plt.colorbar(im2, ax=axes[1])

plt.tight_layout()
plt.show()

When to Use Diverging¶

Correlation matrices
Temperature anomalies
Profit/loss data
Deviations from mean
Any data with positive and negative values around a center

# Example: Correlation matrix
np.random.seed(42)
data = np.random.randn(5, 100)
corr = np.corrcoef(data)

fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(corr, cmap='RdBu', vmin=-1, vmax=1)
ax.set_title('Correlation Matrix')
ax.set_xticks(range(5))
ax.set_yticks(range(5))
plt.colorbar(im, ax=ax, label='Correlation')
plt.show()

Cyclic Colormaps¶

Best for periodic data like angles or phases.

Available Cyclic Colormaps¶

theta = np.arctan2(Y, X)  # Angle in radians

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

cmaps = ['twilight', 'twilight_shifted', 'hsv']

for ax, cmap in zip(axes, cmaps):
    im = ax.imshow(theta, cmap=cmap, origin='lower')
    ax.set_title(f"'{cmap}'")
    ax.axis('off')
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.suptitle('Cyclic Colormaps', fontsize=14)
plt.tight_layout()
plt.show()

When to Use Cyclic¶

Angular data (directions, phases)
Time of day (circular)
Hue values
Any data that wraps around

# Example: Phase angle
Z_complex = X + 1j * Y
phase = np.angle(Z_complex)

fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(phase, extent=[-3, 3, -3, 3], origin='lower', cmap='twilight')
ax.set_title('Complex Phase Angle')
ax.set_xlabel('Re(z)')
ax.set_ylabel('Im(z)')
plt.colorbar(im, ax=ax, label='Phase (radians)')
plt.show()

Qualitative Colormaps¶

Best for categorical data with no natural ordering.

Available Qualitative Colormaps¶

fig, axes = plt.subplots(2, 3, figsize=(15, 8))

cmaps = ['Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'Paired']

# Create categorical-like data
np.random.seed(42)
Z_cat = np.random.randint(0, 8, (20, 20))

for ax, cmap in zip(axes.flat, cmaps):
    im = ax.imshow(Z_cat, cmap=cmap, origin='lower')
    ax.set_title(f"'{cmap}'")
    ax.axis('off')
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.suptitle('Qualitative Colormaps', fontsize=14)
plt.tight_layout()
plt.show()

When to Use Qualitative¶

Segmentation maps
Cluster labels
Land use categories
Any discrete, unordered categories

# Example: Cluster visualization
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

X_data, _ = make_blobs(n_samples=300, centers=4, random_state=42)
kmeans = KMeans(n_clusters=4, random_state=42).fit(X_data)

fig, ax = plt.subplots(figsize=(8, 6))
scatter = ax.scatter(X_data[:, 0], X_data[:, 1], c=kmeans.labels_, cmap='Set1')
ax.set_title('K-Means Clustering')
plt.colorbar(scatter, ax=ax, label='Cluster')
plt.show()

Reversed Colormaps¶

Add _r suffix to reverse any colormap.

fig, axes = plt.subplots(2, 3, figsize=(15, 9))

Z = np.exp(-(X**2 + Y**2))

pairs = [
    ('viridis', 'viridis_r'),
    ('Blues', 'Blues_r'),
    ('coolwarm', 'coolwarm_r')
]

for i, (cmap1, cmap2) in enumerate(pairs):
    im1 = axes[0, i].imshow(Z, cmap=cmap1, origin='lower')
    axes[0, i].set_title(f"'{cmap1}'")
    axes[0, i].axis('off')
    plt.colorbar(im1, ax=axes[0, i], shrink=0.8)

    im2 = axes[1, i].imshow(Z, cmap=cmap2, origin='lower')
    axes[1, i].set_title(f"'{cmap2}'")
    axes[1, i].axis('off')
    plt.colorbar(im2, ax=axes[1, i], shrink=0.8)

plt.suptitle('Original vs Reversed Colormaps', fontsize=14)
plt.tight_layout()
plt.show()

Color Vision Deficiency¶

Consider colorblind-friendly options.

Colorblind-Safe Colormaps¶

fig, axes = plt.subplots(2, 3, figsize=(15, 9))

# Colorblind-friendly options
cmaps = ['viridis', 'plasma', 'cividis', 'inferno', 'magma', 'YlOrBr']

for ax, cmap in zip(axes.flat, cmaps):
    im = ax.imshow(Z, cmap=cmap, origin='lower')
    ax.set_title(f"'{cmap}' (colorblind-safe)")
    ax.axis('off')
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.suptitle('Colorblind-Friendly Colormaps', fontsize=14)
plt.tight_layout()
plt.show()

Avoid These for Colorblind Users¶

jet (rainbow) - poor perceptual uniformity
hsv - hard to distinguish regions
rainbow - misleading intensity perception

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

# Problematic colormaps
cmaps = ['jet', 'rainbow', 'hsv']

for ax, cmap in zip(axes, cmaps):
    im = ax.imshow(Z, cmap=cmap, origin='lower')
    ax.set_title(f"'{cmap}' (avoid for scientific use)")
    ax.axis('off')
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.suptitle('Colormaps to Avoid', fontsize=14)
plt.tight_layout()
plt.show()

Custom Colormaps¶

Create custom colormaps for specific needs.

From Listed Colors¶

from matplotlib.colors import ListedColormap

colors = ['#2ecc71', '#3498db', '#9b59b6', '#e74c3c', '#f39c12']
custom_cmap = ListedColormap(colors)

fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(np.random.randint(0, 5, (20, 20)), cmap=custom_cmap)
ax.set_title('Custom ListedColormap')
plt.colorbar(im, ax=ax)
plt.show()

Linear Segmented¶

from matplotlib.colors import LinearSegmentedColormap

# White to blue gradient
colors = ['white', 'lightblue', 'blue', 'darkblue']
custom_cmap = LinearSegmentedColormap.from_list('custom_blues', colors, N=256)

fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(Z, cmap=custom_cmap, origin='lower')
ax.set_title('Custom LinearSegmentedColormap')
plt.colorbar(im, ax=ax)
plt.show()

Truncated Colormap¶

def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=256):
    new_cmap = LinearSegmentedColormap.from_list(
        f'trunc({cmap.name},{minval:.2f},{maxval:.2f})',
        cmap(np.linspace(minval, maxval, n))
    )
    return new_cmap

# Use only middle portion of viridis
cmap_full = plt.cm.viridis
cmap_trunc = truncate_colormap(cmap_full, 0.2, 0.8)

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

im1 = axes[0].imshow(Z, cmap=cmap_full, origin='lower')
axes[0].set_title('Full viridis')
plt.colorbar(im1, ax=axes[0])

im2 = axes[1].imshow(Z, cmap=cmap_trunc, origin='lower')
axes[1].set_title('Truncated viridis (0.2-0.8)')
plt.colorbar(im2, ax=axes[1])

plt.tight_layout()
plt.show()

Selection Decision Tree¶

Is your data...
│
├── Ordered/Sequential (low → high)?
│   ├── Need colorblind-safe? → viridis, cividis, plasma
│   ├── Single hue preferred? → Blues, Greens, Greys
│   └── Multi-hue preferred? → YlOrRd, YlGnBu
│
├── Diverging (center is meaningful)?
│   ├── Center at zero? → coolwarm, RdBu, seismic
│   └── Asymmetric data? → Use TwoSlopeNorm
│
├── Cyclic (wraps around)?
│   └── Use twilight or twilight_shifted
│
└── Categorical (no order)?
    ├── Few categories (<10)? → Set1, tab10
    └── Many categories? → tab20, Set3

Quick Reference Table¶

Data Type	Recommended	Avoid
Density/PDF	`viridis`, `plasma`	`jet`, `rainbow`
Elevation	`terrain`, `gist_earth`	`hsv`
Temperature	`coolwarm`, `RdBu`	`jet`
Correlation	`RdBu`, `coolwarm`	sequential
Binary	`Greys`, `binary`	diverging
Categories	`Set1`, `tab10`	sequential
Phase/Angle	`twilight`, `hsv`	sequential
Heatmap	`viridis`, `YlOrRd`	`jet`

List All Available Colormaps¶

import matplotlib.pyplot as plt

cmaps = plt.colormaps()
print(f"Total colormaps: {len(cmaps)}")
print("\nFirst 20:", cmaps[:20])

Visual Catalog¶

import matplotlib.pyplot as plt
import numpy as np

gradient = np.linspace(0, 1, 256).reshape(1, -1)

categories = {
    'Perceptually Uniform': ['viridis', 'plasma', 'inferno', 'magma', 'cividis'],
    'Sequential': ['Blues', 'Greens', 'Reds', 'YlOrRd', 'PuBuGn'],
    'Diverging': ['coolwarm', 'RdBu', 'seismic', 'bwr', 'RdYlBu'],
    'Cyclic': ['twilight', 'twilight_shifted', 'hsv'],
}

for category, cmap_list in categories.items():
    fig, axes = plt.subplots(len(cmap_list), 1, figsize=(8, len(cmap_list) * 0.4))
    fig.suptitle(category, fontsize=12, fontweight='bold')

    for ax, cmap in zip(axes, cmap_list):
        ax.imshow(gradient, aspect='auto', cmap=cmap)
        ax.set_ylabel(cmap, rotation=0, ha='right', va='center', fontsize=9)
        ax.set_xticks([])
        ax.set_yticks([])

    plt.tight_layout()
    plt.show()