feat(plotnine): implement spectrum-basic (#2968)

## Implementation: `spectrum-basic` - plotnine

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

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

**Parent Issue:** #2926

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612836373)*

---------

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

Author: github-actions[bot]

plots/spectrum-basic/implementations/plotnine.py

@@ -0,0 +1,78 @@
+""" pyplots.ai
+spectrum-basic: Frequency Spectrum Plot
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    annotation_logticks,
+    element_text,
+    geom_line,
+    ggplot,
+    labs,
+    scale_x_log10,
+    theme,
+    theme_minimal,
+)
+
+
+# Data: Create a synthetic signal with multiple frequency components
+np.random.seed(42)
+
+# Sampling parameters
+sample_rate = 4096  # 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 a mechanical vibration signal with fundamental and harmonics
+fundamental_freq = 50  # Hz (e.g., motor rotation)
+signal = (
+    1.0 * np.sin(2 * np.pi * fundamental_freq * t)  # Fundamental
+    + 0.5 * np.sin(2 * np.pi * 2 * fundamental_freq * t)  # 2nd harmonic
+    + 0.25 * np.sin(2 * np.pi * 3 * fundamental_freq * t)  # 3rd harmonic
+    + 0.15 * np.sin(2 * np.pi * 500 * t)  # High frequency 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 positive frequencies only
+positive_mask = frequencies > 0
+frequencies = frequencies[positive_mask]
+amplitudes = np.abs(fft_result[positive_mask]) * 2 / n_samples
+
+# Convert to dB scale for better visualization
+amplitudes_db = 20 * np.log10(amplitudes + 1e-10)
+
+# Create DataFrame for plotnine
+df = pd.DataFrame({"frequency": frequencies, "amplitude": amplitudes_db})
+
+# Filter to relevant frequency range (10 Hz to 1000 Hz)
+df = df[(df["frequency"] >= 10) & (df["frequency"] <= 1000)]
+
+# Create plot
+plot = (
+    ggplot(df, aes(x="frequency", y="amplitude"))
+    + geom_line(color="#306998", size=1.2, alpha=0.9)
+    + scale_x_log10()
+    + annotation_logticks(sides="b")
+    + labs(x="Frequency (Hz)", y="Amplitude (dB)", title="spectrum-basic · plotnine · pyplots.ai")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, face="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        panel_grid_major=element_text(alpha=0.3),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/spectrum-basic/metadata/plotnine.yaml

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+library: plotnine
+specification_id: spectrum-basic
+created: '2025-12-31T05:36:25Z'
+updated: '2025-12-31T05:50:10Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612836373
+issue: 2926
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent signal processing implementation with realistic mechanical vibration
+    scenario showing fundamental and harmonics
+  - Proper use of logarithmic frequency scale with annotation_logticks for professional
+    axis presentation
+  - Clear axis labels with appropriate units (Hz, dB)
+  - Good use of dB scale for amplitude visualization as recommended in spec
+  - Clean KISS code structure with proper FFT computation
+  weaknesses:
+  - Grid lines not visible because panel_grid_major=element_text(alpha=0.3) uses wrong
+    element type - should be element_line not element_text