feat(bokeh): implement spectrum-basic (#2974)

## Implementation: `spectrum-basic` - bokeh

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

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

**Parent Issue:** #2926

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612829247)*

---------

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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/bokeh.py

@@ -0,0 +1,108 @@
+""" pyplots.ai
+spectrum-basic: Frequency Spectrum Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+from bokeh.io import export_png, save
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure
+
+
+# Data: Generate synthetic signal with multiple frequency components
+np.random.seed(42)
+sample_rate = 8192  # Hz
+duration = 1.0  # seconds
+n_samples = int(sample_rate * duration)
+t = np.linspace(0, duration, n_samples, endpoint=False)
+
+# Create composite signal: 50 Hz base, 150 Hz harmonic, 400 Hz component, plus noise
+signal = (
+    1.0 * np.sin(2 * np.pi * 50 * t)  # Fundamental at 50 Hz
+    + 0.5 * np.sin(2 * np.pi * 150 * t)  # Harmonic at 150 Hz
+    + 0.3 * np.sin(2 * np.pi * 400 * t)  # Component at 400 Hz
+    + 0.1 * np.random.randn(n_samples)  # Noise
+)
+
+# Compute FFT
+fft_result = np.fft.rfft(signal)
+frequencies = np.fft.rfftfreq(n_samples, 1 / sample_rate)
+amplitude = np.abs(fft_result) / n_samples  # Normalize amplitude
+
+# Convert to dB scale (with floor to avoid log(0))
+amplitude_db = 20 * np.log10(np.maximum(amplitude, 1e-10))
+
+# Limit to 500 Hz for better visualization
+mask = frequencies <= 500
+frequencies = frequencies[mask]
+amplitude_db = amplitude_db[mask]
+
+# Create data source
+source = ColumnDataSource(data={"frequency": frequencies, "amplitude": amplitude_db})
+
+# Create figure (4800 x 2700 px for 16:9)
+p = figure(
+    width=4800,
+    height=2700,
+    title="spectrum-basic · bokeh · pyplots.ai",
+    x_axis_label="Frequency (Hz)",
+    y_axis_label="Amplitude (dB)",
+    tools="pan,wheel_zoom,box_zoom,reset,save",
+)
+
+# Plot spectrum as line with area fill
+p.line(x="frequency", y="amplitude", source=source, line_width=4, line_color="#306998", legend_label="Signal Spectrum")
+
+# Add subtle fill under the curve
+p.varea(x="frequency", y1="amplitude", y2=-80, source=source, fill_color="#306998", fill_alpha=0.2)
+
+# Mark peak frequencies with vertical lines and circles
+peak_freqs = [50, 150, 400]
+peak_labels = ["50 Hz", "150 Hz", "400 Hz"]
+for freq, label in zip(peak_freqs, peak_labels, strict=True):
+    idx = np.argmin(np.abs(frequencies - freq))
+    peak_amp = amplitude_db[idx]
+
+    # Vertical line at peak
+    p.line(x=[freq, freq], y=[-80, peak_amp], line_width=3, line_dash="dashed", line_color="#FFD43B", line_alpha=0.8)
+
+    # Circle marker at peak
+    p.scatter(
+        x=[freq],
+        y=[peak_amp],
+        size=25,
+        color="#FFD43B",
+        line_color="#306998",
+        line_width=3,
+        legend_label=f"Peak: {label}",
+    )
+
+# Styling - text sizes for large canvas (4800x2700)
+p.title.text_font_size = "36pt"
+p.title.text_color = "#333333"
+p.xaxis.axis_label_text_font_size = "28pt"
+p.yaxis.axis_label_text_font_size = "28pt"
+p.xaxis.major_label_text_font_size = "20pt"
+p.yaxis.major_label_text_font_size = "20pt"
+
+# Grid styling
+p.xgrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_alpha = 0.3
+p.xgrid.grid_line_dash = "dashed"
+p.ygrid.grid_line_dash = "dashed"
+
+# Legend styling
+p.legend.label_text_font_size = "20pt"
+p.legend.location = "top_right"
+p.legend.background_fill_alpha = 0.8
+p.legend.border_line_color = "#cccccc"
+p.legend.padding = 10
+p.legend.spacing = 5
+
+# Background
+p.background_fill_color = "#fafafa"
+
+# Save as PNG and HTML
+export_png(p, filename="plot.png")
+save(p, filename="plot.html", title="spectrum-basic · bokeh · pyplots.ai")

plots/spectrum-basic/metadata/bokeh.yaml

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+library: bokeh
+specification_id: spectrum-basic
+created: '2025-12-31T05:38:40Z'
+updated: '2025-12-31T05:53:30Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612829247
+issue: 2926
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/bokeh/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent visual clarity with proper text sizing for the 4800x2700 canvas
+  - Clear peak highlighting with dashed vertical lines and circle markers at 50 Hz,
+    150 Hz, and 400 Hz
+  - Good use of varea for the subtle fill under the curve which aids visual interpretation
+  - Proper FFT implementation with correct normalization and dB conversion
+  - Colorblind-safe color scheme with good contrast
+  weaknesses:
+  - Legend text appears small relative to the canvas size; could benefit from larger
+    legend text
+  - Missing HoverTool which would showcase Bokeh interactivity for showing exact frequency/amplitude
+    values