A frequency spectrum plot displays signal amplitude or power across a range of frequencies, showing the frequency domain representation of time-series data. This visualization reveals the frequency components present in a signal, making it essential for identifying dominant frequencies, harmonics, and noise characteristics. It is fundamental in signal processing, audio engineering, and vibration analysis.
""" anyplot.ai
spectrum-basic: Frequency Spectrum Plot
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-14
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Okabe-Ito palette
BRAND = "#009E73" # First series - machinery signal
ACCENT = "#C475FD" # Peak markers
# Data - Create a synthetic signal with multiple frequency components
np.random.seed(42)
# Sampling parameters
sample_rate = 1000 # Hz
duration = 1.0 # seconds
n_samples = int(sample_rate * duration)
t = np.linspace(0, duration, n_samples, endpoint=False)
# Create signal with multiple frequency components (simulating machinery vibration)
# Fundamental frequency at 50 Hz, harmonics at 100 Hz and 150 Hz, plus some noise
signal = (
2.0 * np.sin(2 * np.pi * 50 * t) # 50 Hz fundamental
+ 1.2 * np.sin(2 * np.pi * 100 * t) # 100 Hz harmonic
+ 0.8 * np.sin(2 * np.pi * 150 * t) # 150 Hz harmonic
+ 0.3 * np.sin(2 * np.pi * 220 * t) # 220 Hz component
+ 0.4 * np.random.randn(n_samples) # noise
)
# Compute FFT
fft_result = np.fft.fft(signal)
frequencies = np.fft.fftfreq(n_samples, 1 / sample_rate)
# Take only positive frequencies
positive_mask = frequencies >= 0
frequencies = frequencies[positive_mask]
amplitude = np.abs(fft_result[positive_mask]) * 2 / n_samples # Normalize amplitude
# Convert to dB scale for better visualization
amplitude_db = 20 * np.log10(amplitude + 1e-10) # Add small value to avoid log(0)
# Plot
sns.set_context("talk", font_scale=1.2)
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.10,
},
)
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Use seaborn lineplot for the spectrum with Okabe-Ito brand color
sns.lineplot(x=frequencies, y=amplitude_db, ax=ax, color=BRAND, linewidth=2.5)
# Fill under the curve for better visualization
ax.fill_between(frequencies, amplitude_db, alpha=0.3, color=BRAND)
# Mark peak frequencies with Okabe-Ito accent color
peak_indices = np.where((amplitude_db > -20) & (frequencies > 10))[0]
for idx in peak_indices:
if amplitude_db[idx] > amplitude_db[max(0, idx - 5) : min(len(amplitude_db), idx + 6)].mean() + 5:
ax.axvline(x=frequencies[idx], color=ACCENT, alpha=0.5, linestyle="--", linewidth=1.5)
# Styling
ax.set_xlabel("Frequency (Hz)", fontsize=20, color=INK)
ax.set_ylabel("Amplitude (dB)", fontsize=20, color=INK)
ax.set_title("spectrum-basic · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax.set_xlim(0, 300) # Focus on the frequency range of interest
ax.set_ylim(-60, 10)
# Subtle grid on y-axis only
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
ax.xaxis.grid(False)
# Spine styling
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Frequency Spectrum Plot on anyplot.ai.