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feat(letsplot): implement line-arrhenius (#5168)
## Implementation: `line-arrhenius` - letsplot Implements the **letsplot** version of `line-arrhenius`. **File:** `plots/line-arrhenius/implementations/letsplot.py` **Parent Issue:** #4408 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23389777945)* --------- 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>
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‎plots/line-arrhenius/implementations/letsplot.py‎

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""" pyplots.ai
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line-arrhenius: Arrhenius Plot for Reaction Kinetics
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Library: letsplot 4.9.0 | Python 3.14.3
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Quality: 91/100 | Created: 2026-03-21
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"""
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import numpy as np
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import pandas as pd
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from lets_plot import * # noqa: F403
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from lets_plot.export import ggsave as export_ggsave
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from scipy import stats
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LetsPlot.setup_html() # noqa: F405
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# Data - First-order decomposition reaction rate constants at various temperatures
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np.random.seed(42)
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temperature_K = np.array([300, 325, 350, 375, 400, 425, 450, 475, 500, 550, 600])
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R = 8.314 # Gas constant (J/(mol·K))
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Ea_true = 75000 # Activation energy (J/mol) ~75 kJ/mol
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A_true = 1e12 # Pre-exponential factor (s⁻¹)
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rate_constant_k = A_true * np.exp(-Ea_true / (R * temperature_K))
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noise = np.random.normal(0, 0.15, len(temperature_K))
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ln_k = np.log(rate_constant_k) + noise
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inv_T = 1.0 / temperature_K
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# Linear regression for annotation values only
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slope, intercept, r_value, p_value, std_err = stats.linregress(inv_T, ln_k)
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r_squared = r_value**2
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Ea_extracted = -slope * R / 1000 # Convert to kJ/mol
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# Main DataFrame
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df_points = pd.DataFrame(
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{
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"inv_T": inv_T,
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"ln_k": ln_k,
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"temp_K": [f"{t} K" for t in temperature_K],
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"k_val": [f"{np.exp(lk):.2e}" for lk in ln_k],
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}
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)
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# Annotation
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eq_text = f"Slope = \u2212Ea/R = {slope:.0f} K\nEa = {Ea_extracted:.1f} kJ/mol\nR\u00b2 = {r_squared:.4f}"
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y_range = ln_k.max() - ln_k.min()
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annot_x = inv_T.max() - (inv_T.max() - inv_T.min()) * 0.02
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annot_y = ln_k.min() + y_range * 0.25
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# Secondary x-axis: temperature labels at top of plot
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temp_ticks = np.array([600, 500, 450, 400, 350, 300])
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inv_T_ticks = 1.0 / temp_ticks
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y_top = ln_k.max() + y_range * 0.08
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df_sec_axis = pd.DataFrame(
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{
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"inv_T": np.concatenate([inv_T_ticks, [np.mean(inv_T_ticks)]]),
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"y_label": np.concatenate([[y_top] * len(temp_ticks), [y_top + y_range * 0.06]]),
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"y_tick_start": np.concatenate([[y_top - y_range * 0.02] * len(temp_ticks), [np.nan]]),
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"y_tick_end": np.concatenate([[y_top - y_range * 0.04] * len(temp_ticks), [np.nan]]),
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"label": [f"{t} K" for t in temp_ticks] + ["Temperature (K)"],
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"is_title": [False] * len(temp_ticks) + [True],
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}
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)
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df_ticks = df_sec_axis[~df_sec_axis["is_title"]].copy()
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df_title = df_sec_axis[df_sec_axis["is_title"]].copy()
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# Plot with lets-plot specific features: geom_smooth, tooltips, flavor
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plot = (
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ggplot()
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# Regression line using lets-plot's geom_smooth
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+ geom_smooth(
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aes(x="inv_T", y="ln_k"), data=df_points, method="lm", color="#306998", size=1.8, alpha=0.4, se=True, level=0.95
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)
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+ geom_point(
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aes(x="inv_T", y="ln_k"),
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data=df_points,
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fill="#306998",
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color="white",
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size=8,
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shape=21,
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stroke=1.2,
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tooltips=layer_tooltips().line("@temp_K").line("1/T = @inv_T").line("ln(k) = @ln_k").line("k = @k_val"),
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)
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# Annotation with regression parameters
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+ geom_text(
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aes(x="x", y="y", label="label"),
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data=pd.DataFrame({"x": [annot_x], "y": [annot_y], "label": [eq_text]}),
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size=12,
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color="#333333",
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hjust=1,
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)
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# Secondary x-axis: temperature labels at top
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+ geom_text(aes(x="inv_T", y="y_label", label="label"), data=df_ticks, size=11, color="#777777")
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+ geom_segment(
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aes(x="inv_T", y="y_tick_start", xend="inv_T", yend="y_tick_end"), data=df_ticks, color="#BBBBBB", size=0.5
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)
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+ geom_text(aes(x="inv_T", y="y_label", label="label"), data=df_title, size=13, color="#555555", fontface="italic")
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+ labs(x="1/T (K\u207b\u00b9)", y="ln(k)", title="line-arrhenius \u00b7 letsplot \u00b7 pyplots.ai")
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+ scale_x_continuous(breaks=inv_T_ticks.tolist(), labels=[f"{v:.2e}" for v in inv_T_ticks])
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+ scale_y_continuous(limits=[ln_k.min() - y_range * 0.08, y_top + y_range * 0.10])
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+ coord_cartesian(xlim=[inv_T.min() * 0.95, inv_T.max() * 1.05])
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+ ggsize(1600, 900)
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+ flavor_solarized_light()
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+ theme(
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axis_text=element_text(size=16, color="#555555"),
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axis_title=element_text(size=20, color="#333333"),
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plot_title=element_text(size=24, color="#222222", face="bold"),
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panel_grid_major_x=element_blank(),
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panel_grid_major_y=element_line(color="#D5D0C8", size=0.4),
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panel_grid_minor=element_blank(),
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axis_ticks=element_blank(),
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axis_ticks_length=0,
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plot_margin=[40, 40, 20, 20],
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)
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)
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# Save
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export_ggsave(plot, filename="plot.png", path=".", scale=3)
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export_ggsave(plot, filename="plot.html", path=".")

‎plots/line-arrhenius/metadata/letsplot.yaml‎

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library: letsplot
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specification_id: line-arrhenius
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created: '2026-03-21T22:07:01Z'
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updated: '2026-03-21T22:35:07Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 23389777945
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issue: 4408
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python_version: 3.14.3
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library_version: 4.9.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/letsplot/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/letsplot/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/letsplot/plot.html
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quality_score: 91
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review:
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strengths:
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- Full spec compliance with all required features (regression line, R², Ea annotation,
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secondary axis)
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- Strong design with solarized light flavor creating a distinctive warm aesthetic
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- Effective use of lets-plot distinctive features (geom_smooth, layer_tooltips,
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flavor)
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- Clean well-structured code with proper reproducibility
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weaknesses:
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- Secondary axis constructed via manual geom_text/geom_segment rather than native
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support (lets-plot limitation)
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- Slight extra vertical space to accommodate secondary axis labels
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image_description: The plot displays an Arrhenius plot on a warm solarized light
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background. The title "line-arrhenius · letsplot · pyplots.ai" appears bold in
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the top-left. The x-axis shows "1/T (K⁻¹)" with scientific notation tick labels
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(1.67e-03 to 3.33e-03), and the y-axis shows "ln(k)" ranging from about -4 to
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18. A secondary temperature axis at the top displays temperature values from 600
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K down to 300 K with small tick marks and an italic "Temperature (K)" header.
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Eleven dark blue filled circles with white edge strokes are plotted along a blue
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regression line. A light gray 95% confidence band surrounds the regression line.
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An annotation box in the lower-right region displays "Slope = −Ea/R = -8983 K",
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"Ea = 74.7 kJ/mol", and "R² = 0.9995". The plot uses subtle horizontal grid lines
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only, with no vertical grid, and the overall composition is clean and polished.
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criteria_checklist:
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visual_quality:
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score: 29
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max: 30
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items:
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- id: VQ-01
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name: Text Legibility
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score: 8
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max: 8
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passed: true
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comment: 'All font sizes explicitly set: title=24, axis_title=20, axis_text=16,
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annotation=12'
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- id: VQ-02
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name: No Overlap
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score: 6
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max: 6
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passed: true
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comment: No overlapping text elements
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- id: VQ-03
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name: Element Visibility
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score: 6
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max: 6
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passed: true
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comment: Points size=8 with white stroke, perfectly visible for 11 data points
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- id: VQ-04
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name: Color Accessibility
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score: 4
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max: 4
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passed: true
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comment: Single-color scheme with good contrast against solarized light background
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- id: VQ-05
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name: Layout & Canvas
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score: 3
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max: 4
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passed: true
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comment: Good layout with minor extra vertical space for secondary axis accommodation
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- id: VQ-06
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name: Axis Labels & Title
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score: 2
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max: 2
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passed: true
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comment: 'Descriptive labels with units: 1/T (K⁻¹) and ln(k)'
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design_excellence:
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score: 15
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max: 20
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items:
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- id: DE-01
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name: Aesthetic Sophistication
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score: 6
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max: 8
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passed: true
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comment: Strong design with solarized light flavor, Python Blue with white-stroked
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points, intentional hierarchy
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- id: DE-02
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name: Visual Refinement
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score: 5
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max: 6
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passed: true
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comment: Horizontal grid only, ticks removed, generous margins, clean composition
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- id: DE-03
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name: Data Storytelling
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score: 4
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max: 6
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passed: true
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comment: Annotation communicates activation energy and fit quality, secondary
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axis provides physical context
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spec_compliance:
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score: 15
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max: 15
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items:
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- id: SC-01
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name: Plot Type
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score: 5
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max: 5
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passed: true
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comment: 'Correct Arrhenius plot: ln(k) vs 1/T'
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- id: SC-02
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name: Required Features
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score: 4
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max: 4
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passed: true
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comment: 'All spec features present: regression line, R², Ea annotation, secondary
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axis, visible data points'
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- id: SC-03
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name: Data Mapping
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score: 3
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max: 3
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passed: true
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comment: X=1/T, Y=ln(k), correct mapping
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- id: SC-04
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name: Title & Legend
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score: 3
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max: 3
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passed: true
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comment: Title matches required format, no legend needed for single series
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data_quality:
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score: 14
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max: 15
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 5
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max: 6
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passed: true
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comment: Clear linear relationship with realistic scatter, 11 points spanning
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300-600 K
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 5
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passed: true
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comment: First-order decomposition reaction with Ea ≈ 75 kJ/mol, realistic
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chemistry
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 4
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passed: true
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comment: Temperature range and activation energy are physically realistic
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: Clean imports → data → plot → save structure
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- id: CQ-02
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name: Reproducibility
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score: 2
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max: 2
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passed: true
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comment: np.random.seed(42) set
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: All imports used
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- id: CQ-04
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name: Code Elegance
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score: 2
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max: 2
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passed: true
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comment: Clean, well-organized code
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- id: CQ-05
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name: Output & API
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score: 1
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max: 1
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passed: true
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comment: Saves via export_ggsave with scale=3
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library_mastery:
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score: 8
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max: 10
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items:
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- id: LM-01
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name: Idiomatic Usage
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score: 4
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max: 5
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passed: true
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comment: 'Good grammar of graphics usage: geom_smooth, layer_tooltips, flavor
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themes'
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- id: LM-02
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name: Distinctive Features
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score: 4
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max: 5
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passed: true
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comment: Uses flavor_solarized_light, layer_tooltips, geom_smooth with confidence
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band, HTML export
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verdict: APPROVED
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impl_tags:
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dependencies:
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- scipy
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techniques:
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- annotations
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- layer-composition
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- hover-tooltips
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- html-export
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patterns:
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- data-generation
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dataprep:
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- regression
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styling:
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- edge-highlighting
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- grid-styling
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- alpha-blending

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