feat(matplotlib): implement area-mountain-panorama

[github-actions]

Implementation: area-mountain-panorama - python/matplotlib

Implements the python/matplotlib version of area-mountain-panorama.

File: plots/area-mountain-panorama/implementations/python/matplotlib.py

Parent Issue: #5365

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): Warm off-white background (#FAF8F1). The plot shows a mountain panorama silhouette filled solid in brand green (#009E73) from the baseline up to the synthetic ridgeline, which is composed of Gaussian-shaped summit bumps. All 17 Wallis Alps peaks are annotated at three staggered vertical levels (5050 / 5320 / 5590 m) with thin vertical leader lines. The Matterhorn label is in bold dark ink; all other labels are in INK_SOFT gray. Title, y-axis label "Elevation (m)", and compass bearing x-axis labels (W, SW, S, SE, E) are all clearly readable in dark text. Subtle horizontal grid lines at 10% opacity are visible. All text is legible against the light background, though the peak annotation labels (11–13pt) are smaller than the 16pt recommended minimum for this canvas size.

Dark render (plot-dark.png): Warm near-black background (#1A1A17). The mountain silhouette fill is identical brand green (#009E73) — data color unchanged from the light render, as required. Title and Matterhorn label are rendered in #F0EFE8 (INK for dark theme), clearly readable. All other labels and tick text are in #B8B7B0 (INK_SOFT), readable against the near-black surface. No dark-on-dark text failures observed anywhere. Brand green is fully visible on the dark background. Chrome (background, title, ticks, spines, grid) has flipped correctly while data colors remain constant. Both renders pass the theme-readability check.

Both paragraphs are required. A review that only describes one render is invalid.

Score: 82/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	12	20
Spec Compliance	14	15
Data Quality	14	15
Code Quality	9	10
Library Mastery	7	10
Total	82	100

Visual Quality (26/30)

• VQ-01: Text Legibility (6/8) — Title 24pt, axis labels 20pt, ticks 16pt all explicitly set; peak annotation labels at 11–13pt are below the 16pt minimum recommended for a 4800×2700 canvas
• VQ-02: No Overlap (5/6) — 3-level stagger prevents collisions among 17 peaks; minor crowding risk in dense sections but no visible overlap
• VQ-03: Element Visibility (5/6) — Mountain silhouette is prominent; leader lines (0.7–1.2 linewidth) are visible but thin
• VQ-04: Color Accessibility (2/2) — Single brand green (#009E73), CVD-safe on both surfaces
• VQ-05: Layout & Canvas (4/4) — 16:9 landscape fills canvas well; nothing cut off; label space well-utilized
• VQ-06: Axis Labels & Title (2/2) — Y: "Elevation (m)" with units; X: compass bearings
• VQ-07: Palette Compliance (2/2) — First/only series is #009E73; backgrounds #FAF8F1 / #1A1A17; all chrome tokens applied in both renders

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) — Above default: the staggered annotation system and Matterhorn focal point show intentional design; however, the spec calls for a "dark solid color (photo-like silhouette, evening/dusk feel)" — brand green is palette-compliant but lacks the dramatic alpine mood; missing sky gradient limits visual impact
• DE-02: Visual Refinement (4/6) — Top/right spines removed, subtle 10% opacity grid, zero x-tick length, variable leader-line opacity (0.45 vs 0.85); clearly above library defaults
• DE-03: Data Storytelling (3/6) — Matterhorn emphasis (bold text, thicker line) creates a focal point; panoramic W-to-E sweep is a clear geographic narrative; stronger visual differentiation between major and minor peaks would improve storytelling

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct: filled area silhouette chart with annotated peaks
• SC-02: Required Features (3/4) — Leader-line annotations, staggered labels, y-axis in meters, compass x-axis all present; spec requested "dark solid color" for the fill but brand green is used (justified by palette rules)
• SC-03: Data Mapping (3/3) — X: viewing angle in degrees; Y: elevation in meters; all 17 peaks correctly positioned
• SC-04: Title & Legend (3/3) — Title includes area-mountain-panorama · matplotlib · anyplot.ai; no legend needed for single series

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Skyline shape, annotated peaks with leader lines, staggered labels, compass orientation; sky gradient absent (optional per spec)
• DQ-02: Realistic Context (5/5) — Real Wallis Alps peaks with authentic names and accurate elevations (Matterhorn 4478 m, Monte Rosa 4634 m, Dom 4545 m, etc.)
• DQ-03: Appropriate Scale (4/4) — 17 peaks in 262° sweep; elevations 4027–4634 m for 4000 m+ summits; 2500 m lower bound appropriate

Code Quality (9/10)

• CQ-01: KISS Structure (2/3) — gaussian_smooth() function defined in script violates the no-functions rule
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set before all random operations
• CQ-03: Clean Imports (2/2) — Only os, matplotlib.pyplot, numpy — all used
• CQ-04: Code Elegance (2/2) — Clean and readable; no fake UI elements
• CQ-05: Output & API (1/1) — Saves as plot-{THEME}.png with facecolor=PAGE_BG; current API

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Consistent axes-method usage (ax.fill_between, ax.plot, ax.text), environment-variable theming, correct subplot creation
• LM-02: Distinctive Features (3/5) — fill_between for the mountain area; the staggered multi-level annotation system composed from ax.plot + ax.text is a creative matplotlib-specific technique

Score Caps Applied

• None

Strengths

• Real Wallis Alps data with authentic peak names and accurate elevations — highly realistic and geographically credible
• Well-designed 3-level stagger system keeps 17 peak labels readable throughout the panorama
• Both themes fully and correctly adapted: all chrome tokens (background, title, ticks, spines, grid, leader lines) properly flip while brand green data color stays constant
• Reproducible synthetic skyline from Gaussian summit bumps + seeded noise creates a convincing mountain horizon
• Matterhorn correctly designated as focal point with bold text and more prominent leader line

Weaknesses

• Peak annotation labels (fontsize=11 non-anchor, 13 Matterhorn) are below the 16pt minimum for a 4800×2700 canvas — increase to at least 14pt non-anchor / 16pt Matterhorn
• gaussian_smooth() function definition violates the KISS/no-functions rule — inline the np.convolve call directly in the script
• Spec explicitly requests "dark solid color (photo-like silhouette, evening/dusk feel)" for the mountain fill; compensate for the brand-green palette constraint by adding a sky-gradient background (LinearSegmentedColormap from light blue/dusk orange above the ridgeline) to recover the atmospheric mood
• All peaks share the same flat green fill with no elevation-based differentiation — consider a subtle alpha or saturation gradient that makes higher peaks feel more prominent
• DE-03 storytelling is limited: beyond bold Matterhorn text, no visual hierarchy guides the viewer through the panorama

Issues Found

1. CQ-01 LOW: gaussian_smooth() function defined in script violates KISS/no-functions rule
   • Fix: Inline the convolution as np.convolve(arr, kernel, mode='same') directly where called, or use scipy.ndimage.gaussian_filter1d if scipy is acceptable
2. VQ-01 MINOR: Peak annotation labels (fontsize=11–13) below 16pt recommended minimum for large canvas
   • Fix: Increase non-anchor labels to fontsize=14, Matterhorn to fontsize=16
3. DE-01 / DE-03: Missing sky gradient reduces panoramic atmosphere and visual hierarchy
   • Fix: Add a LinearSegmentedColormap gradient fill above the ridgeline (e.g., fill_between from ridge to ax.get_ylim()[1] with a sky colormap ranging from light blue to PAGE_BG); this will also differentiate the sky from the mountain and make the silhouette read more dramatically

AI Feedback for Next Attempt

Increase peak annotation label sizes to 14pt (non-anchor) and 16pt (Matterhorn). Remove the gaussian_smooth() function — inline the numpy convolution. Add a sky-gradient background above the ridgeline using fill_between with a LinearSegmentedColormap (e.g., light blue fading to PAGE_BG) to recover the photographic alpine mood the spec describes. Consider a subtle alpha or brightness variation on the mountain fill to give taller peaks more visual weight. These changes will lift DE-01, DE-03, and VQ-01 scores.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): The plot renders on a warm off-white (#FAF8F1) background. A sky gradient flows from the background color up to a warm peach tone (#E8C8A0) — creating a dusk/alpenglow mood. The mountain silhouette is filled solid in brand green (#009E73) from the plot floor to the synthetic ridgeline. 17 Wallis Alps summits are annotated at three staggered height levels (5050 / 5320 / 5590 m) with thin vertical leader lines. The Matterhorn label is bold at 16pt with a thicker, more opaque leader line; other peaks are 14pt regular weight. The title "Wallis Alps · area-mountain-panorama · matplotlib · anyplot.ai" appears in dark ink at 24pt. Y-axis label "Elevation (m)" in dark ink at 20pt; compass bearing x-ticks (W, SW, S, SE, E) in INK_SOFT gray. All text is clearly readable against the warm off-white background — no light-on-light failures.

Dark render (plot-dark.png): The background is warm near-black (#1A1A17). The sky gradient shifts to a deep navy tone (#252D40), creating a moody night-alpine atmosphere. The mountain silhouette is identical brand green (#009E73) — data colors unchanged from the light render, as required. The title and y-axis label render in #F0EFE8 (INK for dark theme), clearly readable. Tick labels and compass bearings use #B8B7B0 (INK_SOFT), readable against the near-black background. Peak annotation text uses INK / INK_SOFT tokens appropriately — no dark-on-dark failures observed. Leader lines use theme-adaptive ink with appropriate alpha. The subtle grid at 10% opacity reads correctly on the dark surface. Brand green #009E73 remains well-visible against #1A1A17.

Both paragraphs are required. A review that only describes one render is invalid.

Score: 91/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	15	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	9	10
Total	91	100

Visual Quality (28/30)

• VQ-01: Text Legibility (7/8) — Title 24pt, axis label 20pt, ticks 16pt explicitly set; Matterhorn label 16pt; non-anchor peak labels 14pt — a step below the 16pt canvas minimum, keeps from a perfect 8
• VQ-02: No Overlap (5/6) — 3-level stagger (5050/5320/5590 m) prevents collisions across all 17 peaks; minor crowding risk in the dense W–SW cluster but no visible collisions in either render
• VQ-03: Element Visibility (6/6) — Mountain silhouette prominently visible; sky gradient adds depth; leader lines at 0.8–1.4 linewidth visible at full resolution
• VQ-04: Color Accessibility (2/2) — Single brand green on warm backgrounds — excellent contrast, CVD-safe
• VQ-05: Layout & Canvas (4/4) — 16:9 landscape perfectly suits the panoramic subject; label zone above 5000 m makes good use of upper canvas
• VQ-06: Axis Labels & Title (2/2) — Y-axis: "Elevation (m)" with units; X-axis: compass bearings (W, SW, S, SE, E)
• VQ-07: Palette Compliance (2/2) — First/only series is #009E73; backgrounds #FAF8F1 (light) / #1A1A17 (dark); sky gradient uses theme-adaptive PAGE_BG and SKY_TOP (not data colors); all chrome tokens correctly applied in both renders

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) — Strong design: theme-adaptive sky gradient (warm peach dusk in light, deep navy in dark) elevates the panoramic mood significantly; Matterhorn focal point with intentional bold/opacity hierarchy; composition is clearly above configured defaults
• DE-02: Visual Refinement (5/6) — Top/right spines removed; x-axis tick marks hidden (length=0); y-grid at 10% opacity; variable leader line alpha (0.45 non-anchor / 0.85 Matterhorn); sky gradient fills background meaningfully — well-refined
• DE-03: Data Storytelling (4/6) — The sky gradient establishes the panorama-from-a-viewpoint narrative; Matterhorn is the clear focal point through bold weight and stronger leader; W→E geographic sweep provides orientation; elevation-based differentiation between minor and major summits could push this higher

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct: filled area mountain silhouette from angular panoramic vantage point
• SC-02: Required Features (3/4) — Staggered leader-line annotations ✓, sky gradient ✓ (was absent in attempt 1), y-axis in meters ✓, compass x-axis ✓, Matterhorn as focal point ✓; spec requested "dark solid color" silhouette but brand green is used — justified by palette rule (first series = #009E73)
• SC-03: Data Mapping (3/3) — X: horizontal viewing angle 0–262°; Y: elevation in meters; all 17 Wallis Alps peaks correctly positioned
• SC-04: Title & Legend (3/3) — Title contains "area-mountain-panorama · matplotlib · anyplot.ai"; no legend needed for single series

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Skyline curve with Gaussian peak construction, sky gradient background, annotated summits with leader lines, staggered labels, compass orientation — all spec features present
• DQ-02: Realistic Context (5/5) — Real Wallis Alps peaks with authentic names and accurate elevations (Matterhorn 4478 m, Monte Rosa 4634 m, Weisshorn 4506 m, etc.)
• DQ-03: Appropriate Scale (4/4) — 17 peaks spanning 0–262° panorama; elevations 4027–4634 m; 2500 m lower bound appropriate for high alpine scene

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Flat script: imports → tokens → data → ridge construction → plot → save; gaussian_smooth() function from attempt 1 correctly inlined
• CQ-02: Reproducibility (2/2) — np.random.seed(42) before all random calls
• CQ-03: Clean Imports (2/2) — os, matplotlib.pyplot, numpy, matplotlib.colors.LinearSegmentedColormap — all actively used
• CQ-04: Code Elegance (2/2) — Clean, readable; Gaussian peak construction with np.maximum is elegant; no fake UI elements
• CQ-05: Output & API (1/1) — Saves as plot-{THEME}.png with facecolor=PAGE_BG; current matplotlib API

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) — Consistent axes-object API (ax.fill_between, ax.plot, ax.text, ax.imshow); proper subplot creation; environment-variable theming; tight_layout + bbox_inches='tight'
• LM-02: Distinctive Features (4/5) — LinearSegmentedColormap.from_list() for custom sky gradient; ax.imshow() with custom extent as a gradient background layer — sophisticated matplotlib-specific technique; Gaussian kernel convolution for ridgeline smoothing

Score Caps Applied

• None — all cap conditions clear (VQ-02=5, VQ-03=6, SC-01=5, DQ-02=5, DE-01=6>2, DE-02=5>2, CQ-04=2)

Strengths

• Sky gradient (LinearSegmentedColormap + imshow) added in attempt 2 correctly addresses the prior review's top weakness and genuinely elevates the panoramic mood — theme-adaptive (warm dusk in light, deep navy in dark)
• gaussian_smooth() function correctly inlined, fixing the KISS violation from attempt 1
• Peak label sizes raised to 14pt/16pt — a significant improvement over the previous 11pt/13pt
• All 17 real Wallis Alps summits with authentic elevations, geographically ordered W→E
• Both themes fully correct: all chrome tokens threaded through, no dark-on-dark or light-on-light failures
• Matterhorn focal point (bold, 16pt, 1.4 linewidth leader, 0.85 alpha) is intentional and effective

Weaknesses

• Non-anchor peak labels at 14pt remain slightly below the 16pt canvas minimum — raising all labels to 16pt would fully satisfy VQ-01
• Spec calls for "dark solid color (photo-like silhouette, evening/dusk feel)" — brand green is palette-justified but produces a stylized illustration rather than a dramatic photo-like silhouette; sky gradient partially compensates
• DE-03 could improve with elevation-based visual differentiation (e.g., subtle highlight on the top 3 summits by elevation) to guide the viewer beyond the Matterhorn focal point

Issues Found

1. VQ-01 MINOR: Non-anchor peak labels at 14pt fall below the 16pt canvas minimum
   • Fix: Set fsize = 16 for all peaks; use bold weight for Matterhorn differentiation instead of size

AI Feedback for Next Attempt

The implementation is strong and crossed the 90-point threshold. Sky gradient and inlined convolution correctly addressed attempt 1's two main weaknesses. Minor remaining issue: raise all peak annotation labels to 16pt (use bold weight as the Matterhorn differentiator instead of size). The overall composition — theme-adaptive panoramic gradient + brand green silhouette + staggered annotations — is publication-quality.

Verdict: APPROVED