feat(pygal): implement spectrum-basic (#2973)

## Implementation: `spectrum-basic` - pygal

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

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

**Parent Issue:** #2926

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612855025)*

---------

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

Author: github-actions[bot]

plots/spectrum-basic/implementations/pygal.py

@@ -0,0 +1,95 @@
+""" pyplots.ai
+spectrum-basic: Frequency Spectrum Plot
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data - Generate synthetic signal with multiple frequency components
+np.random.seed(42)
+
+# Create a synthetic time-domain signal with known frequency components
+fs = 1000  # Sampling frequency (Hz)
+t = np.linspace(0, 1, fs)  # 1 second of data
+
+# Signal with 3 frequency components: 50 Hz (strong), 120 Hz (medium), 200 Hz (weak)
+signal = (
+    3.0 * np.sin(2 * np.pi * 50 * t)  # 50 Hz - dominant frequency
+    + 1.5 * np.sin(2 * np.pi * 120 * t)  # 120 Hz - secondary
+    + 0.8 * np.sin(2 * np.pi * 200 * t)  # 200 Hz - tertiary
+    + 0.3 * np.random.randn(len(t))  # Noise floor
+)
+
+# Compute FFT
+n = len(signal)
+fft_result = np.fft.fft(signal)
+frequencies = np.fft.fftfreq(n, 1 / fs)
+
+# Take only positive frequencies
+positive_mask = frequencies >= 0
+frequencies = frequencies[positive_mask]
+amplitude = np.abs(fft_result[positive_mask]) / n * 2  # Normalize
+
+# Convert to dB scale for better visualization
+amplitude_db = 20 * np.log10(amplitude + 1e-10)
+
+# Limit to 0-300 Hz for clarity (where our signal components are)
+freq_limit_mask = frequencies <= 300
+frequencies = frequencies[freq_limit_mask]
+amplitude_db = amplitude_db[freq_limit_mask]
+
+# Downsample for pygal (it works better with fewer points)
+step = 2
+frequencies = frequencies[::step]
+amplitude_db = amplitude_db[::step]
+
+# Create custom style for large canvas
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=("#306998",),  # Python Blue
+    title_font_size=72,
+    label_font_size=48,
+    major_label_font_size=40,
+    legend_font_size=40,
+    value_font_size=32,
+    stroke_width=4,
+    opacity=0.9,
+    opacity_hover=1.0,
+)
+
+# Create XY chart (line chart with custom x values)
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="spectrum-basic · pygal · pyplots.ai",
+    x_title="Frequency (Hz)",
+    y_title="Amplitude (dB)",
+    show_dots=False,
+    fill=True,
+    stroke_style={"width": 4},
+    show_x_guides=True,
+    show_y_guides=True,
+    x_label_rotation=0,
+    show_legend=False,
+    range=(-60, 20),  # dB range
+    dots_size=0,
+    margin=60,
+    spacing=40,
+)
+
+# Add data as XY points
+xy_data = [(float(f), float(a)) for f, a in zip(frequencies, amplitude_db, strict=True)]
+chart.add("Amplitude", xy_data)
+
+# Save as PNG and HTML
+chart.render_to_png("plot.png")
+chart.render_to_file("plot.html")

plots/spectrum-basic/metadata/pygal.yaml

@@ -0,0 +1,30 @@
+library: pygal
+specification_id: spectrum-basic
+created: '2025-12-31T05:38:16Z'
+updated: '2025-12-31T05:52:55Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612855025
+issue: 2926
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/pygal/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent visualization of frequency spectrum with clear, distinct peaks at 50
+    Hz, 120 Hz, and 200 Hz matching the synthetic signal
+  - Proper use of dB scale for amplitude representation, making the spectrum analysis
+    realistic
+  - Well-configured canvas size (4800x2700) with appropriately scaled font sizes for
+    readability
+  - Clean, professional appearance with subtle grid lines and good color contrast
+  - Appropriate data preprocessing including FFT computation, normalization, and frequency
+    limiting
+  - Good use of pygal XY chart with fill for area-under-curve visualization
+  weaknesses:
+  - Missing peak annotations or markers to highlight the dominant frequency components
+    as suggested in the specification
+  - Font sizes in the custom style are quite large (title_font_size=72) which may
+    be excessive; could be slightly reduced for better proportions