feat(matplotlib): implement spectrum-basic

[github-actions]

Implementation: spectrum-basic - matplotlib

Implements the matplotlib version of spectrum-basic.

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

Parent Issue: #2926

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a frequency spectrum with amplitude (dB) on the Y-axis ranging from -60 to 10, and frequency (Hz) on the X-axis ranging from 0 to 300. The spectrum line is rendered in a blue color (#306998) with a semi-transparent fill beneath the curve. Three prominent peaks are clearly visible at 50 Hz, 120 Hz, and 200 Hz, each marked with yellow circular markers and annotated with their frequency values. Vertical yellow dashed lines highlight each peak frequency. The noise floor fluctuates around -40 to -50 dB. The title follows the correct format "spectrum-basic · matplotlib · pyplots.ai". The overall layout is clean with balanced margins and a subtle grid.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, annotations positioned with offset to avoid collision
• VQ-03: Element Visibility (7/8) - Line width of 2.5 is good, markers at s=200 are visible, fill provides good emphasis. Slight deduction as line could be slightly thicker for 4800×2700 canvas
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) combination is colorblind-safe
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with tight_layout(), balanced margins
• VQ-06: Axis Labels (2/2) - "Frequency (Hz)" and "Amplitude (dB)" - descriptive with units
• VQ-07: Grid & Legend (0/2) - Grid is subtle at alpha=0.3. No legend present but none is required for this single-series plot

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct frequency spectrum plot
• SC-02: Data Mapping (5/5) - Frequency on X-axis, amplitude (dB) on Y-axis as specified
• SC-03: Required Features (5/5) - Shows multiple frequency components, uses dB scale, peak annotation as suggested in Notes
• SC-04: Data Range (3/3) - All data visible, axis limits appropriately set (0-300 Hz, -60 to 10 dB)
• SC-05: Legend Accuracy (2/2) - No legend needed for single data series
• SC-06: Title Format (2/2) - Correct format: "spectrum-basic · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows fundamental frequency (50 Hz), harmonic (120 Hz), additional component (200 Hz), and noise floor. Could show wider frequency range or more complex harmonics
• DQ-02: Realistic Context (7/7) - Synthetic signal with multiple frequency components is a realistic signal processing scenario
• DQ-03: Appropriate Scale (4/5) - Values are realistic (1000 Hz sample rate, dB range -60 to 10), though peak amplitudes could vary more

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Imports → Data → Plot → Save, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy used
• CQ-04: No Deprecated API (1/1) - No deprecated functions
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses standard matplotlib features (plot, fill_between, scatter, annotate, axvline). Could leverage more advanced features like twin axes or custom tick formatting

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

Verdict: APPROVED