feat(plotnine): implement titration-curve

[github-actions]

Implementation: titration-curve - plotnine

Implements the plotnine version of titration-curve.

File: plots/titration-curve/implementations/plotnine.py

Parent Issue: #4407

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows an acid-base titration curve with a blue line (#306998) plotting pH (y-axis, 0–14) against Volume of NaOH added in mL (x-axis, 0–50). The characteristic S-shaped sigmoidal curve starts around pH 1, rises gradually, then shoots steeply upward near 25 mL before leveling off around pH 12–13. A vertical dashed gray line marks the equivalence volume at 25 mL. A red dot marks the equivalence point at (25 mL, pH 7) with a text annotation. The title reads "HCl + NaOH Titration · titration-curve · plotnine · pyplots.ai". Background is minimal with horizontal grid lines only (no vertical grid, no minor grid). Layout is 16:9.

Score: 80/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	10	20
Spec Compliance	13	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	5	10
Total	80	100

Visual Quality (28/30)

• VQ-01: Text Legibility (7/8) — Font sizes explicitly set (title=22, axis_title=20, axis_text=16). Title is 22pt, slightly below the 24pt guideline.
• VQ-02: No Overlap (6/6) — All text fully readable, no collisions.
• VQ-03: Element Visibility (5/6) — Line clearly visible at size=1.5. Equivalence point marker visible but could be slightly larger for emphasis.
• VQ-04: Color Accessibility (4/4) — Blue line with red marker and gray dashed line. Good contrast, no red-green-only distinction.
• VQ-05: Layout & Canvas (4/4) — Good 16:9 proportions, plot fills canvas well, balanced margins.
• VQ-06: Axis Labels & Title (2/2) — "Volume of NaOH added (mL)" with units, "pH" is unitless by nature.

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (4/8) — Well-configured default: Python Blue, theme_minimal, removed unnecessary grid lines. Looks clean but not exceptional.
• DE-02: Visual Refinement (3/6) — Some refinement: minor grid removed, x-axis major grid removed, theme_minimal provides clean base. But spines still present, no additional polish.
• DE-03: Data Storytelling (3/6) — The red equivalence point marker and annotation create a focal point. The dashed vertical line guides the eye. Some visual hierarchy present but could be stronger.

Spec Compliance (13/15)

• SC-01: Plot Type (5/5) — Correct line plot with sigmoidal titration curve shape.
• SC-02: Required Features (3/4) — Equivalence point marked with dashed line and annotation ✓. Derivative curve (dpH/dV) overlay missing (spec says "optional" but it would enhance the plot). Buffer region shading N/A for strong acid/base system.
• SC-03: Data Mapping (3/3) — X=volume, Y=pH. Full range shown (0–50 mL, 0–14 pH).
• SC-04: Title & Legend (2/3) — Title contains spec-id, library, and pyplots.ai but with extra descriptive prefix. No legend needed (single series).

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows the full S-curve with acidic plateau, steep transition zone (denser data points around equivalence), and basic plateau. Could benefit from derivative curve to show additional feature.
• DQ-02: Realistic Context (5/5) — HCl + NaOH titration is a standard analytical chemistry scenario. Real, neutral, comprehensible.
• DQ-03: Appropriate Scale (4/4) — 25 mL of 0.1 M HCl titrated with 0.1 M NaOH — realistic lab concentrations and volumes.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean Imports → Data → Plot → Save structure. No functions or classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic data from chemistry calculations (no randomness).
• CQ-03: Clean Imports (2/2) — All imports are used.
• CQ-04: Code Elegance (2/2) — Chemistry calculation loop is necessary and well-structured. Appropriate complexity.
• CQ-05: Output & API (1/1) — Saves as plot.png with dpi=300, current API.

Library Mastery (5/10)

• LM-01: Idiomatic Usage (4/5) — Good use of ggplot grammar: aes mapping, geom layers, labs, theme composition. Proper plotnine idioms.
• LM-02: Distinctive Features (1/5) — Generic ggplot usage. No plotnine-specific features that distinguish it from other libraries.

Score Caps Applied

• None

Strengths

• Realistic chemistry data with proper stoichiometric acid-base calculations
• Smart data density: more points around the equivalence point (40 points in 24–26 mL range) for a smooth steep transition
• Clean, readable layout with well-chosen axis ranges and tick intervals
• Perfect code quality — deterministic, KISS, clean imports

Weaknesses

• Missing derivative curve (dpH/dV) as secondary y-axis overlay — this would demonstrate the equivalence point more precisely and add visual interest
• Design is clean but not exceptional — needs stronger visual hierarchy and polish to reach publication quality
• Title format has extra prefix before the standard {spec-id} · {library} · pyplots.ai pattern
• No distinctive plotnine features used — could leverage plotnine's grammar more creatively

Issues Found

1. SC-02 PARTIAL: Missing derivative curve overlay mentioned in specification
   • Fix: Add dpH/dV calculation and plot as a second line (plotnine doesn't natively support twin axes, but can overlay with scaled data or use a separate geom)
2. DE-01 MODERATE: Clean but generic default styling
   • Fix: Add more sophisticated color palette, consider gradient shading in the steep transition region, refine typography
3. DE-02 LOW: Minimal visual refinement beyond theme_minimal
   • Fix: Remove remaining spines, add subtle y-axis-only grid styling, increase whitespace around title
4. SC-04 MINOR: Title has extra descriptive prefix
   • Fix: Use format titration-curve · plotnine · pyplots.ai as the title, or move descriptive text to subtitle

AI Feedback for Next Attempt

Add the derivative curve (dpH/dV) as a secondary overlay to satisfy the spec more fully — since plotnine lacks twin axes, consider scaling the derivative and adding it as a separate colored line with a legend. Improve design excellence: remove top/right spines via theme(axis_line_x/y), use a more refined annotation style (perhaps with an arrow), and consider shading the steep transition region to create stronger visual storytelling. Fix the title to match the standard format exactly.

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

The plot shows a titration curve with two series on a white background. A blue S-shaped line represents pH vs. volume of NaOH added, rising from ~1 at 0 mL to ~12.5 at 50 mL. An orange line shows the scaled dpH/dV derivative, which is flat near zero except for a sharp spike at 25 mL. A vertical dashed grey line marks the equivalence point at 25 mL. A red diamond marker sits at the equivalence point (25 mL, pH 7) with an italic annotation label to its right reading "Equivalence Point (25 mL, pH 7)". A light blue semi-transparent shaded band spans approximately 23-27 mL across the full height of the plot. The legend is positioned in the upper-left with a white semi-transparent background, showing "dpH/dV (scaled)" in orange and "pH" in blue, with a "series" header. X-axis reads "Volume of NaOH added (mL)" (0-50), Y-axis reads "pH" (0-14). Subtle horizontal grid lines are present; no vertical grid. Title is bold: "titration-curve · plotnine · pyplots.ai".

Score: 88/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	14	20
Spec Compliance	13	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 24pt, axis titles 20pt, tick labels 16pt, legend 16pt. All text is clearly readable.
• VQ-02: No Overlap (6/6) — No overlapping text. Annotation is well-positioned to the right of the marker. Legend doesn't overlap data.
• VQ-03: Element Visibility (5/6) — pH curve and equivalence point marker are clearly visible. The derivative curve is flat/invisible for most of the range (mathematically correct behavior) but this makes it hard to see as a "curve" — it reads more as a spike.
• VQ-04: Color Accessibility (4/4) — Blue (#306998) and orange (#E8A838) are colorblind-safe. Red diamond (#C0392B) provides strong contrast.
• VQ-05: Layout & Canvas (4/4) — 16:9 aspect ratio, plot fills canvas well with balanced margins.
• VQ-06: Axis Labels & Title (2/2) — "Volume of NaOH added (mL)" and "pH" are descriptive with units.

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom palette with Python blue and complementary orange. Thoughtful typography with italic annotation. Diamond marker for focal point. Above defaults but not publication-exceptional.
• DE-02: Visual Refinement (4/6) — theme_minimal with additional refinement: minor grid removed, x-grid removed, subtle y-grid (#E0E0E0), muted axis lines, legend with semi-transparent background. Good but not perfect polish.
• DE-03: Data Storytelling (4/6) — Clear visual hierarchy: blue pH curve as primary, orange derivative as secondary context, red diamond as focal point. Shaded transition region draws attention to the steep portion. The viewer's eye is guided to the equivalence point.

Spec Compliance (13/15)

• SC-01: Plot Type (5/5) — Correct S-shaped titration curve with derivative overlay.
• SC-02: Required Features (3/4) — Equivalence point marked with dashed line and annotation ✓, derivative curve overlay ✓, shaded transition region ✓, realistic HCl/NaOH data ✓, y-axis 0-14 ✓. Minor: spec requests "secondary y-axis" for derivative but plotnine lacks twin axes — scaling is a reasonable workaround but the derivative values are not interpretable without a second axis.
• SC-03: Data Mapping (3/3) — X = volume, Y = pH, all data visible across correct ranges.
• SC-04: Title & Legend (2/3) — Title format correct. Legend labels are descriptive but the legend header shows "series" (from the DataFrame column name) which is generic and should be suppressed or renamed.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows the complete S-curve with initial acidic region, steep transition, and post-equivalence buffered region. Derivative spike clearly marks the inflection point.
• DQ-02: Realistic Context (5/5) — 25 mL of 0.1 M HCl titrated with 0.1 M NaOH — a standard analytical chemistry scenario. Neutral, educational context.
• DQ-03: Appropriate Scale (4/4) — Concentrations, volumes, and resulting pH values are all chemically accurate. Equivalence at 25 mL with pH 7 is correct for strong acid/strong base.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → data generation → derivative calculation → DataFrame construction → plot → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic data from chemistry equations. No random elements.
• CQ-03: Clean Imports (2/2) — All imports are used in the implementation.
• CQ-04: Code Elegance (2/2) — Chemistry calculations are appropriately complex. DataFrame construction is clean. No fake UI or over-engineering.
• CQ-05: Output & API (1/1) — Saves as plot.png with dpi=300. No deprecated API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good grammar-of-graphics composition with multiple geom layers, scale_color_manual, guide_legend with override_aes, and comprehensive theme customization. The pH calculation loop is necessary but not plotnine-specific.
• LM-02: Distinctive Features (3/5) — Uses guide_legend with override_aes, geom_ribbon for region shading, element-based theme granularity (element_blank, element_line, element_rect). These are ggplot2/plotnine-characteristic features.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• Chemically accurate data generation with proper acid-base equilibrium calculations
• Excellent visual hierarchy with red diamond focal point, shaded transition region, and derivative overlay
• Clean grammar-of-graphics composition with well-layered geoms
• Strong color accessibility with blue/orange/red palette
• Perfect data quality — realistic, neutral, educational context

Weaknesses

• Legend header shows "series" (DataFrame column name) instead of being suppressed — use name="" in scale or rename the column
• Derivative curve lacks a secondary y-axis (plotnine limitation), making the scaled values uninterpretable — consider adding a note or adjusting the legend label
• Visual refinement could be pushed further — the shaded region spanning the full y-range (0-14) is heavy-handed

Issues Found

1. SC-04 MINOR: Legend header displays "series" — a generic DataFrame column name leaking into the visualization
   • Fix: The scale_color_manual already has name="" but the legend still shows "series" — verify the legend title suppression is working
2. DE-02 MINOR: Shaded transition region spans the full pH range (0-14) which is visually heavy
   • Fix: Consider narrowing the ymax to match the actual pH range of the curve in that region, or reduce the width

AI Feedback for Next Attempt

To reach 90+: (1) Fix the legend header — ensure "series" text is fully suppressed. (2) Consider adding a subtle annotation or label for the buffer/transition region. (3) Push DE-01 higher with a more distinctive design touch — perhaps a subtle background gradient or more refined annotation styling. The implementation is very close to approval with strong data quality and good visual hierarchy.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a strong acid-base titration curve (HCl + NaOH) on a white background with subtle horizontal grid lines. A blue sigmoidal pH curve rises from ~1 at 0 mL to ~13 at 50 mL, with the steep transition centered at 25 mL. An orange derivative curve (dpH/dV, scaled) shows a sharp spike reaching 12 at the equivalence point, flat near zero elsewhere. A red diamond marker sits at the equivalence point (25 mL, pH 7) with an italic annotation. A light blue semi-transparent ribbon shades the transition region (23–27 mL) around the pH curve. The title reads "titration-curve · plotnine · pyplots.ai" in bold. X-axis: "Volume of NaOH added (mL)" (0–50), Y-axis: "pH / dpH/dV (scaled)" (0–14). Legend in upper-left shows "dpH/dV (scaled)" in orange and "pH" in blue. An orange bold annotation at right reads "Peak dpH/dV = 57.5 at 25 mL".

Score: 88/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	14	20
Spec Compliance	14	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title=24, axis_title=20, axis_text=16, legend_text=16. All text clearly readable.
• VQ-02: No Overlap (6/6) — No overlapping text elements. Legend, annotations, and axis labels are well-separated.
• VQ-03: Element Visibility (5/6) — Lines are visible at size=1.5, diamond marker prominent. The derivative spike is inherently narrow but readable. Slight deduction for the ribbon being subtle.
• VQ-04: Color Accessibility (4/4) — Blue (#306998) and amber (#E8A838) are colorblind-safe with strong contrast.
• VQ-05: Layout & Canvas (4/4) — Good 16:9 proportions, balanced margins, nothing cut off.
• VQ-06: Axis Labels & Title (2/2) — X-axis has units "(mL)", y-axis descriptive "pH / dpH/dV (scaled)".

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom palette (Python Blue + amber), intentional typography with italic/bold annotations, styled legend with background. Clearly above defaults but not quite publication-flagship level.
• DE-02: Visual Refinement (4/6) — Minor grid removed, x-grid removed, subtle y-grid (#E0E0E0, 0.4), axis lines styled, white background. Good refinement visible.
• DE-03: Data Storytelling (4/6) — Clear focal point at the equivalence point via diamond marker + annotation. Derivative curve visually reinforces the transition. Peak annotation adds quantitative context. Good visual hierarchy.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct sigmoidal titration curve shape.
• SC-02: Required Features (3/4) — Has: equivalence point marking (dashed line + annotation + marker), derivative curve overlay, shaded transition region. Minor: derivative uses scaled single y-axis rather than secondary y-axis (plotnine limitation), and ribbon shades the transition region rather than a classical buffer region (appropriate for strong acid/base system).
• SC-03: Data Mapping (3/3) — Volume on x-axis, pH on y-axis, full pH scale 0–14 shown.
• SC-04: Title & Legend (3/3) — Title format "titration-curve · plotnine · pyplots.ai" correct. Legend labels "pH" and "dpH/dV (scaled)" match data series.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows pre-equivalence plateau, steep transition, post-equivalence plateau, derivative peak. Could show more buffer-region behavior with a weak acid system but strong acid choice is per spec.
• DQ-02: Realistic Context (5/5) — Real chemistry: 25 mL of 0.1 M HCl titrated with 0.1 M NaOH. Textbook scenario.
• DQ-03: Appropriate Scale (4/4) — pH 0–14 full scale, equivalence at 25 mL and pH 7, realistic concentrations.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → derivative calculation → dataframe → plot → save. No functions/classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic analytical calculation, no randomness.
• CQ-03: Clean Imports (2/2) — All imports used (verified: aes, annotate, element_blank, element_line, element_rect, element_text, geom_line, geom_point, geom_ribbon, geom_vline, ggplot, guide_legend, labs, scale_color_manual, scale_x_continuous, scale_y_continuous, theme, theme_minimal).
• CQ-04: Code Elegance (2/2) — Clean, appropriate complexity. The pH calculation loop is appropriate for the analytical chemistry.
• CQ-05: Output & API (1/1) — Saves as plot.png with dpi=300. Current plotnine API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good grammar-of-graphics composition with layered geoms, aesthetic mappings, scale_color_manual with guide_legend override_aes. Minor: the for-loop for pH calculation is outside plotnine's domain but necessary for the analytical computation.
• LM-02: Distinctive Features (3/5) — Uses plotnine-specific patterns: geom_ribbon for shading, guide_legend with override_aes, layered ggplot composition with multiple data sources. Could leverage more distinctive features like stat layers or custom scales.

Score Caps Applied

• None

Strengths

• Excellent analytical chemistry implementation with proper Henderson-Hasselbalch-style calculations
• Strong visual design with custom color palette, refined grid styling, and clear visual hierarchy
• Effective data storytelling through equivalence point highlighting and derivative overlay
• Clean, well-structured code with all font sizes explicitly set
• All imports are used and appropriate

Weaknesses

• Derivative curve uses scaled single y-axis rather than a true secondary axis (plotnine limitation)
• Ribbon shading around the curve is subtle and doesn't strongly convey "buffer region" concept
• Could leverage more plotnine-distinctive features (stat layers, custom geoms)

Issues Found

1. SC-02 MINOR: Derivative on scaled single axis instead of secondary y-axis — acceptable given plotnine's lack of twin-axis support
2. LM-01 MINOR: pH calculation uses explicit Python loop rather than vectorized approach — functional but slightly non-idiomatic

AI Feedback for Next Attempt

Strong implementation at 88/100. The main gaps are in library mastery (could use more distinctive plotnine features) and the derivative overlay approach. The visual design and data storytelling are good. For a higher score, consider using stat_function or other plotnine-native statistical layers, and explore more distinctive grammar-of-graphics patterns.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays an acid-base titration curve with a bold blue S-shaped pH line rising from ~1 at 0 mL to ~13 at 50 mL, with a sharp vertical transition around 25 mL. An amber/gold derivative curve (dpH/dV scaled) spikes sharply at 25 mL, with a faint amber area fill beneath it. A light blue semi-transparent ribbon shades the transition region (~22–28 mL). A red diamond marker sits at the equivalence point (25 mL, pH 7) with an italic annotation reading "Equivalence Point (25 mL, pH 7)." A bold amber label in the upper right reads "Peak dpH/dV = 57.5 at 25 mL." The legend in the upper left shows "pH" (blue) and "dpH/dV (scaled)" (amber) with a white background box. The title reads "titration-curve · plotnine · pyplots.ai" in bold. X-axis: "Volume of NaOH added (mL)" (0–50), Y-axis: "pH / dpH/dV (scaled)" (0–14). Background is a subtle off-white (#FAFAFA) with faint horizontal grid lines only. Clean, polished appearance.

Score: 93/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	16	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	9	10
Total	93	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title=24, axis_title=20, axis_text=16, legend_text=16. All text clearly readable.
• VQ-02: No Overlap (6/6) — No overlapping text. Annotations, legend, and labels are all well-separated.
• VQ-03: Element Visibility (5/6) — pH curve and derivative line are clearly visible at size=1.5. Diamond marker prominent. Minor deduction: derivative area fill at alpha=0.12 is extremely faint, nearly invisible.
• VQ-04: Color Accessibility (4/4) — Blue (#306998) and amber (#E8A838) provide excellent contrast and are colorblind-safe.
• VQ-05: Layout & Canvas (4/4) — 16:9 layout, plot fills canvas well with balanced margins.
• VQ-06: Axis Labels & Title (2/2) — "Volume of NaOH added (mL)" and "pH / dpH/dV (scaled)" are descriptive with units.

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom blue/amber palette, styled legend with white background and border, refined #FAFAFA panel background, intentional color hierarchy. Clearly above defaults.
• DE-02: Visual Refinement (5/6) — Minor grid removed, x major grid removed, subtle y-only grid lines (#E8E8E8), styled axis lines, generous whitespace. Well-polished.
• DE-03: Data Storytelling (5/6) — Clear focal point at equivalence point via red diamond marker and italic annotation. Derivative peak labeled. Transition region shaded. Viewer immediately sees the key chemical insight.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct S-shaped titration curve plotting pH vs volume of titrant.
• SC-02: Required Features (3/4) — Equivalence point marked with dashed vertical line and annotation ✓, derivative curve overlay ✓, transition region shading ✓. Minor: derivative is scaled onto primary y-axis rather than a true secondary y-axis, but plotnine lacks native twin-axes support, making this the best available approach.
• SC-03: Data Mapping (3/3) — X=volume (0–50 mL), Y=pH (0–14). Full pH scale shown, all data visible.
• SC-04: Title & Legend (3/3) — Title format "titration-curve · plotnine · pyplots.ai" correct. Legend labels "pH" and "dpH/dV (scaled)" match data.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows full S-curve with low-pH plateau, sharp transition, high-pH plateau, derivative peak, and equivalence point. All aspects of a titration curve represented.
• DQ-02: Realistic Context (5/5) — Real analytical chemistry scenario: 25 mL of 0.1 M HCl titrated with 0.1 M NaOH. Classic strong acid/strong base system.
• DQ-03: Appropriate Scale (4/4) — Realistic concentrations (0.1 M), correct equivalence at 25 mL and pH 7, sensible pH range 0–14.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → plot construction → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic analytical calculation (no randomness).
• CQ-03: Clean Imports (2/2) — All imports are used in the implementation.
• CQ-04: Code Elegance (2/2) — Clean vectorized NumPy calculations, appropriate complexity for the chemistry involved. No fake functionality.
• CQ-05: Output & API (1/1) — Saves as plot.png via plot.save("plot.png", dpi=300). Current API.

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) — Expertly uses plotnine's grammar of graphics: ggplot() + geom layers, aes mappings, long-format data for multi-series, scale_color_manual, guide_legend with override_aes, theme_minimal + theme customization.
• LM-02: Distinctive Features (4/5) — Uses geom_area for derivative fill, geom_ribbon for transition region, multiple data frames composed via grammar-of-graphics layering, guide_legend with override_aes. These patterns are distinctively plotnine/ggplot-style.

Score Caps Applied

• None — No caps triggered.

Strengths

• Excellent chemistry: vectorized analytical pH calculation produces a physically accurate titration curve
• Strong idiomatic plotnine usage with grammar-of-graphics layering (multiple data frames, geom_area, geom_ribbon)
• Clear visual hierarchy with red diamond equivalence point marker as focal point
• Polished theme with subtle grid, refined panel background, and styled legend
• Custom blue/amber palette with good colorblind accessibility

Weaknesses

• Derivative area fill at alpha=0.12 is nearly invisible — could be slightly more prominent
• No true secondary y-axis for the derivative (plotnine limitation, handled gracefully with scaling)

Issues Found

No blocking issues found.

AI Feedback for Next Attempt

Strong implementation that meets publication quality. The chemistry is accurate, the plotnine usage is idiomatic, and the design is polished with clear data storytelling. Minor refinement opportunity: the derivative area fill could be slightly more visible (alpha ~0.18-0.22).

Verdict: APPROVED