feat(seaborn): implement spectrum-basic

plots/spectrum-basic/implementations/seaborn.py

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+""" pyplots.ai
+spectrum-basic: Frequency Spectrum Plot
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+# 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)
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Use seaborn lineplot for the spectrum
+sns.lineplot(x=frequencies, y=amplitude_db, ax=ax, color="#306998", linewidth=2.5)
+
+# Fill under the curve for better visualization
+ax.fill_between(frequencies, amplitude_db, alpha=0.3, color="#306998")
+
+# Mark peak frequencies
+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="#FFD43B", alpha=0.5, linestyle="--", linewidth=1.5)
+
+# Styling
+ax.set_xlabel("Frequency (Hz)", fontsize=20)
+ax.set_ylabel("Amplitude (dB)", fontsize=20)
+ax.set_title("spectrum-basic · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.set_xlim(0, 300)  # Focus on the frequency range of interest
+ax.set_ylim(-60, 10)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/spectrum-basic/metadata/seaborn.yaml

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+library: seaborn
+specification_id: spectrum-basic
+created: '2025-12-31T05:34:05Z'
+updated: '2025-12-31T05:45:42Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612813037
+issue: 2926
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent visual clarity with properly sized text elements and good color contrast
+  - Realistic machinery vibration scenario with fundamental frequency and harmonics
+  - Effective use of fill_between to enhance the spectrum visualization
+  - Peak frequency highlighting with vertical dashed lines aids interpretation
+  - Proper use of dB scale as recommended in the specification
+  - Clean KISS code structure with reproducible random seed
+  weaknesses:
+  - Uses seaborn primarily for lineplot which is relatively basic; could leverage
+    more distinctive seaborn features
+  - Grid styling uses alpha=0.3 which is acceptable but slightly at the edge of the
+    0.2-0.4 recommendation