feat(matplotlib): implement spectrum-basic (#2950)

## Implementation: `spectrum-basic` - matplotlib

Implements the **matplotlib** version of `spectrum-basic`.

**File:** `plots/spectrum-basic/implementations/matplotlib.py`

**Parent Issue:** #2926

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612813068)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/spectrum-basic/implementations/matplotlib.py

@@ -0,0 +1,80 @@
+""" pyplots.ai
+spectrum-basic: Frequency Spectrum Plot
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Generate 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
+# 50 Hz fundamental, 120 Hz harmonic, 200 Hz component, plus noise
+signal = (
+    1.0 * np.sin(2 * np.pi * 50 * t)  # 50 Hz fundamental
+    + 0.5 * np.sin(2 * np.pi * 120 * t)  # 120 Hz harmonic
+    + 0.3 * np.sin(2 * np.pi * 200 * t)  # 200 Hz component
+    + 0.1 * 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  # Normalized amplitude
+
+# Convert to dB scale (with floor to avoid log of zero)
+amplitude_db = 20 * np.log10(amplitude + 1e-10)
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot spectrum
+ax.plot(frequencies, amplitude_db, linewidth=2.5, color="#306998", alpha=0.9)
+
+# Fill under the curve for visual emphasis
+ax.fill_between(frequencies, amplitude_db, alpha=0.3, color="#306998")
+
+# Mark peak frequencies
+peaks = [50, 120, 200]
+for peak_freq in peaks:
+    idx = np.argmin(np.abs(frequencies - peak_freq))
+    ax.axvline(x=peak_freq, color="#FFD43B", linestyle="--", linewidth=2, alpha=0.8)
+    ax.scatter(
+        [frequencies[idx]], [amplitude_db[idx]], s=200, color="#FFD43B", zorder=5, edgecolors="black", linewidths=1.5
+    )
+    ax.annotate(
+        f"{peak_freq} Hz",
+        xy=(frequencies[idx], amplitude_db[idx]),
+        xytext=(10, 10),
+        textcoords="offset points",
+        fontsize=14,
+        fontweight="bold",
+        color="#306998",
+    )
+
+# Styling
+ax.set_xlabel("Frequency (Hz)", fontsize=20)
+ax.set_ylabel("Amplitude (dB)", fontsize=20)
+ax.set_title("spectrum-basic · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Set axis limits
+ax.set_xlim(0, 300)
+ax.set_ylim(-60, 10)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/spectrum-basic/metadata/matplotlib.yaml

@@ -0,0 +1,27 @@
+library: matplotlib
+specification_id: spectrum-basic
+created: '2025-12-31T05:33:30Z'
+updated: '2025-12-31T05:45:00Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612813068
+issue: 2926
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent signal processing implementation with proper FFT computation and normalization
+  - Clear visualization of frequency peaks with effective use of annotations and vertical
+    reference lines
+  - Professional styling with appropriate font sizes meeting 4800×2700 resolution
+    requirements
+  - dB scale properly implemented with floor to avoid log(0)
+  - Fill under curve provides good visual emphasis of the spectrum shape
+  weaknesses:
+  - Line width (2.5) could be increased to 3 for better visibility at full resolution
+  - No legend present, though for this single-series plot it is acceptable
+  - Could use more distinctive matplotlib features like spine customization or logarithmic
+    frequency scale option