feat(plotnine): implement line-arrhenius (#5165)

## Implementation: `line-arrhenius` - plotnine

Implements the **plotnine** version of `line-arrhenius`.

**File:** `plots/line-arrhenius/implementations/plotnine.py`

**Parent Issue:** #4408

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23389777968)*

---------

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/line-arrhenius/implementations/plotnine.py

@@ -0,0 +1,139 @@
+""" pyplots.ai
+line-arrhenius: Arrhenius Plot for Reaction Kinetics
+Library: plotnine 0.15.3 | Python 3.14.3
+Quality: 90/100 | Created: 2026-03-21
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    annotate,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_line,
+    geom_point,
+    geom_ribbon,
+    ggplot,
+    labs,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+from scipy.stats import t as t_dist
+
+
+# Data — first-order decomposition with slight experimental scatter
+temperature_K = np.array([300, 350, 400, 450, 500, 550, 600])
+rate_constant_k = np.array([0.0013, 0.0091, 0.054, 0.19, 0.72, 1.75, 4.2])
+
+inv_T = 1.0 / temperature_K
+ln_k = np.log(rate_constant_k)
+
+# Compute regression statistics for annotations
+coeffs = np.polyfit(inv_T, ln_k, 1)
+slope, intercept = coeffs
+ln_k_pred = np.polyval(coeffs, inv_T)
+ss_res = np.sum((ln_k - ln_k_pred) ** 2)
+ss_tot = np.sum((ln_k - ln_k.mean()) ** 2)
+r_squared = 1 - ss_res / ss_tot
+
+R = 8.314
+Ea_kJ = -slope * R / 1000
+
+df = pd.DataFrame({"inv_T": inv_T, "ln_k": ln_k})
+
+# Regression line data for manual ribbon + line (tighter control than geom_smooth)
+inv_T_fine = np.linspace(inv_T.min() - 0.0001, inv_T.max() + 0.0001, 200)
+ln_k_fit = np.polyval(coeffs, inv_T_fine)
+
+# Compute residual SE for narrow confidence band
+n = len(inv_T)
+se_residual = np.sqrt(ss_res / (n - 2))
+inv_T_mean = inv_T.mean()
+inv_T_ss = np.sum((inv_T - inv_T_mean) ** 2)
+se_fit = se_residual * np.sqrt(1.0 / n + (inv_T_fine - inv_T_mean) ** 2 / inv_T_ss)
+t_val = t_dist.ppf(0.975, n - 2)
+ci_lower = ln_k_fit - t_val * se_fit
+ci_upper = ln_k_fit + t_val * se_fit
+
+df_fit = pd.DataFrame({"inv_T": inv_T_fine, "ln_k_fit": ln_k_fit, "ci_lower": ci_lower, "ci_upper": ci_upper})
+
+# Tick labels with temperature reference — select subset for clean spacing
+tick_temps = [300, 400, 500, 600]
+tick_positions = [1.0 / t for t in tick_temps]
+tick_labels = [f"{1.0 / t:.2e}\n({t} K)" for t in tick_temps]
+
+# Annotation placement — upper-left region for better balance
+anno_x = inv_T.min() + 0.35 * (inv_T.max() - inv_T.min())
+anno_y_top = ln_k.max() - 0.15
+
+# Combined annotation text block for polished typography
+anno_line1 = f"R² = {r_squared:.4f}"
+anno_line2 = f"Eₐ = {Ea_kJ:.1f} kJ/mol"
+anno_line3 = f"slope = −Eₐ/R = {slope:.0f} K"
+
+# Plot — manual ribbon + line for tight CI, geom_point for markers
+plot = (
+    ggplot(df, aes(x="inv_T", y="ln_k"))
+    # Confidence ribbon — narrow band from manual calculation
+    + geom_ribbon(
+        aes(x="inv_T", ymin="ci_lower", ymax="ci_upper"), data=df_fit, fill="#4a90d9", alpha=0.12, inherit_aes=False
+    )
+    # Regression line
+    + geom_line(aes(x="inv_T", y="ln_k_fit"), data=df_fit, color="#4a90d9", size=2.0, inherit_aes=False)
+    # Data points with filled markers
+    + geom_point(color="#0d2240", fill="#306998", size=7, stroke=1.4, shape="o")
+    + scale_x_continuous(breaks=tick_positions, labels=tick_labels)
+    + scale_y_continuous(expand=(0.08, 0))
+    # Annotation block — stacked text with visual hierarchy
+    + annotate(
+        "label",
+        x=anno_x,
+        y=anno_y_top,
+        label=anno_line1,
+        size=17,
+        color="#0d2240",
+        fontweight="bold",
+        ha="center",
+        fill="#ffffff",
+        alpha=0.75,
+        label_padding=0.4,
+        label_size=0,
+    )
+    + annotate(
+        "text", x=anno_x, y=anno_y_top - 0.9, label=anno_line2, size=15, color="#1a3a5c", fontweight="bold", ha="center"
+    )
+    + annotate(
+        "text",
+        x=anno_x,
+        y=anno_y_top - 1.65,
+        label=anno_line3,
+        size=13,
+        color="#667788",
+        fontstyle="italic",
+        ha="center",
+    )
+    + labs(x="1/T (K⁻¹)", y="ln(k)", title="line-arrhenius · plotnine · pyplots.ai")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=26, weight="bold", color="#0d2240", margin={"b": 15}),
+        axis_title_x=element_text(size=20, color="#333333", margin={"t": 10}),
+        axis_title_y=element_text(size=20, color="#333333", margin={"r": 10}),
+        axis_text=element_text(size=16, color="#444444"),
+        axis_ticks=element_line(color="#cccccc", size=0.5),
+        plot_background=element_rect(fill="#f7f7f7", color="none"),
+        panel_background=element_rect(fill="#f7f7f7", color="none"),
+        panel_grid_major_x=element_blank(),
+        panel_grid_minor=element_blank(),
+        panel_grid_major_y=element_line(color="#dcdcdc", size=0.3, linetype="dashed"),
+        plot_margin=0.05,
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/line-arrhenius/metadata/plotnine.yaml

@@ -0,0 +1,223 @@
+library: plotnine
+specification_id: line-arrhenius
+created: '2026-03-21T22:06:52Z'
+updated: '2026-03-21T22:25:09Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 23389777968
+issue: 4408
+python_version: 3.14.3
+library_version: 0.15.3
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 90
+review:
+  strengths:
+  - Excellent spec compliance — all five required features present and well-executed
+  - Strong visual hierarchy in annotations with typography differentiation (bold/italic)
+  - Custom blue color palette with intentional hierarchy across all elements
+  - Confidence ribbon adds statistical depth beyond minimum requirements
+  - Dual x-axis labels (1/T + temperature in K) greatly aid interpretation
+  - Clean, deterministic code with appropriate complexity
+  weaknesses:
+  - Minor empty space on right side of canvas between last two x-axis ticks
+  - Data is extremely clean (R²=0.9968) — slightly more scatter would better showcase
+    the regression fit
+  image_description: 'The plot displays an Arrhenius plot with 1/T (K⁻¹) on the x-axis
+    and ln(k) on the y-axis. Seven dark navy-blue circular data points are plotted
+    along a medium-blue regression line (size 2.0) with a subtle light-blue confidence
+    ribbon. The title "line-arrhenius · plotnine · pyplots.ai" appears in bold dark
+    navy at the top. Three stacked annotations in the upper-left show: "R² = 0.9968"
+    in a white semi-transparent label box (bold), "Eₐ = 40.6 kJ/mol" (bold, dark blue),
+    and "slope = −Eₐ/R = -4878 K" (italic, gray). The x-axis has 4 tick labels showing
+    both scientific notation and temperature in K (e.g., "1.67e-03 (600 K)"). Background
+    is a warm light gray (#f7f7f7) with subtle dashed horizontal grid lines on the
+    y-axis only. No x-axis grid. The overall design is clean and polished.'
+  criteria_checklist:
+    visual_quality:
+      score: 29
+      max: 30
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 8
+        max: 8
+        passed: true
+        comment: 'All font sizes explicitly set: title 26pt, axis titles 20pt, tick
+          text 16pt'
+      - id: VQ-02
+        name: No Overlap
+        score: 6
+        max: 6
+        passed: true
+        comment: No overlapping text, annotations well-spaced, 4 x-axis ticks avoids
+          crowding
+      - id: VQ-03
+        name: Element Visibility
+        score: 6
+        max: 6
+        passed: true
+        comment: 7 data points at size=7 with stroke=1.4 clearly visible above regression
+          line
+      - id: VQ-04
+        name: Color Accessibility
+        score: 4
+        max: 4
+        passed: true
+        comment: Single-series blue palette, no colorblind issues
+      - id: VQ-05
+        name: Layout & Canvas
+        score: 3
+        max: 4
+        passed: true
+        comment: Good 16:9 proportions, slight gap on right side
+      - id: VQ-06
+        name: Axis Labels & Title
+        score: 2
+        max: 2
+        passed: true
+        comment: 'Descriptive with units: 1/T (K⁻¹) and ln(k)'
+    design_excellence:
+      score: 15
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 6
+        max: 8
+        passed: true
+        comment: Custom blue palette with intentional hierarchy, typography differentiation
+          in annotations
+      - id: DE-02
+        name: Visual Refinement
+        score: 5
+        max: 6
+        passed: true
+        comment: 'theme_minimal customized: x-grid removed, y-grid subtle dashed,
+          warm gray background'
+      - id: DE-03
+        name: Data Storytelling
+        score: 4
+        max: 6
+        passed: true
+        comment: Annotation hierarchy guides viewer from R² to Ea to slope, confidence
+          band communicates fit quality
+    spec_compliance:
+      score: 15
+      max: 15
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 5
+        max: 5
+        passed: true
+        comment: 'Correct Arrhenius plot: ln(k) vs 1/T with linear regression'
+      - id: SC-02
+        name: Required Features
+        score: 4
+        max: 4
+        passed: true
+        comment: 'All spec features present: regression line, R², Ea/R slope, secondary
+          temp labels, visible points'
+      - id: SC-03
+        name: Data Mapping
+        score: 3
+        max: 3
+        passed: true
+        comment: X=1/T, Y=ln(k), correctly assigned
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
+        passed: true
+        comment: Title format correct, no legend needed for single series
+    data_quality:
+      score: 14
+      max: 15
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 5
+        max: 6
+        passed: true
+        comment: 7 data points spanning 300-600K showing clear linear Arrhenius relationship
+      - id: DQ-02
+        name: Realistic Context
+        score: 5
+        max: 5
+        passed: true
+        comment: First-order decomposition reaction, genuine chemistry scenario
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 4
+        passed: true
+        comment: Temperature 300-600K, rate constants 0.0013-4.2, Ea=40.6 kJ/mol all
+          realistic
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: Clean imports-data-computation-plot-save flow, no functions/classes
+      - id: CQ-02
+        name: Reproducibility
+        score: 2
+        max: 2
+        passed: true
+        comment: Fully deterministic data, no random components
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: All imports used including scipy.stats.t for CI
+      - id: CQ-04
+        name: Code Elegance
+        score: 2
+        max: 2
+        passed: true
+        comment: Well-organized, appropriate complexity
+      - id: CQ-05
+        name: Output & API
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png at 300 DPI, current API
+    library_mastery:
+      score: 7
+      max: 10
+      items:
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 4
+        max: 5
+        passed: true
+        comment: Good grammar-of-graphics layering with inherit_aes=False for separate
+          data frames
+      - id: LM-02
+        name: Distinctive Features
+        score: 3
+        max: 5
+        passed: true
+        comment: Uses annotate label type, grammar-of-graphics composition, extensive
+          theme customization
+  verdict: APPROVED
+impl_tags:
+  dependencies:
+  - scipy
+  techniques:
+  - annotations
+  - layer-composition
+  - manual-ticks
+  patterns:
+  - data-generation
+  dataprep:
+  - regression
+  styling:
+  - alpha-blending
+  - grid-styling