feat(altair): implement spectrum-basic (#2966)

## Implementation: `spectrum-basic` - altair

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

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

**Parent Issue:** #2926

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612831682)*

---------

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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/altair.py

@@ -0,0 +1,68 @@
+""" pyplots.ai
+spectrum-basic: Frequency Spectrum Plot
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 98/100 | Created: 2025-12-31
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - 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)
+
+# Create time-domain signal with multiple frequency components
+t = np.linspace(0, duration, n_samples, endpoint=False)
+
+# Signal components: 50 Hz (dominant), 120 Hz (harmonic), 200 Hz (weak), plus noise
+signal = (
+    1.0 * np.sin(2 * np.pi * 50 * t)  # 50 Hz - dominant frequency
+    + 0.5 * np.sin(2 * np.pi * 120 * t)  # 120 Hz - secondary component
+    + 0.25 * np.sin(2 * np.pi * 200 * t)  # 200 Hz - weak 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  # Normalize
+
+# Convert to dB scale for better visualization
+amplitude_db = 20 * np.log10(amplitude + 1e-10)  # Add small value to avoid log(0)
+
+# Create DataFrame
+df = pd.DataFrame({"Frequency (Hz)": frequencies, "Amplitude (dB)": amplitude_db})
+
+# Filter to show relevant frequency range (0-300 Hz)
+df = df[df["Frequency (Hz)"] <= 300]
+
+# Create chart
+chart = (
+    alt.Chart(df)
+    .mark_line(color="#306998", strokeWidth=2)
+    .encode(
+        x=alt.X("Frequency (Hz):Q", title="Frequency (Hz)", scale=alt.Scale(domain=[0, 300])),
+        y=alt.Y("Amplitude (dB):Q", title="Amplitude (dB)", scale=alt.Scale(domain=[-80, 10])),
+        tooltip=[alt.Tooltip("Frequency (Hz):Q", format=".1f"), alt.Tooltip("Amplitude (dB):Q", format=".1f")],
+    )
+    .properties(
+        width=1600, height=900, title=alt.Title("spectrum-basic · altair · pyplots.ai", fontSize=28, anchor="middle")
+    )
+    .configure_axis(labelFontSize=18, titleFontSize=22, grid=True, gridColor="#cccccc", gridOpacity=0.3)
+    .configure_view(strokeWidth=0)
+    .configure_title(fontSize=28)
+)
+
+# Save as PNG and HTML
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/spectrum-basic/metadata/altair.yaml

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+library: altair
+specification_id: spectrum-basic
+created: '2025-12-31T05:35:59Z'
+updated: '2025-12-31T05:48:53Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612831682
+issue: 2926
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/spectrum-basic/altair/plot.html
+quality_score: 98
+review:
+  strengths:
+  - Excellent FFT computation with proper normalization and dB conversion
+  - Clear visualization of multiple frequency components (50 Hz dominant, 120 Hz secondary,
+    200 Hz weak)
+  - Appropriate use of Altair declarative encoding with typed columns
+  - Interactive tooltips with number formatting
+  - Proper scale domains ensure all data is visible
+  - Clean KISS code structure
+  weaknesses:
+  - Could add mark_rule() or mark_point() to highlight peak frequencies
+  - No use of Altair selection/interactivity features (zoom/pan)