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feat(altair): implement line-arrhenius (#5169)
## Implementation: `line-arrhenius` - altair Implements the **altair** version of `line-arrhenius`. **File:** `plots/line-arrhenius/implementations/altair.py` **Parent Issue:** #4408 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23389778092)* --------- 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/altair.py‎

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""" pyplots.ai
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line-arrhenius: Arrhenius Plot for Reaction Kinetics
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Library: altair 6.0.0 | Python 3.14.3
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Quality: 90/100 | Created: 2026-03-21
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"""
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import altair as alt
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import numpy as np
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import pandas as pd
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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, 525, 550, 600])
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R = 8.314 # J/(mol·K)
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Ea_true = 75000 # J/mol (75 kJ/mol)
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A_true = 1.5e12 # Pre-exponential factor (s⁻¹)
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# Arrhenius equation: k = A * exp(-Ea / (R*T))
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ln_k_true = np.log(A_true) - Ea_true / (R * temperature_K)
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ln_k_measured = ln_k_true + np.random.normal(0, 0.15, len(temperature_K))
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inv_T = 1.0 / temperature_K # 1/T in K⁻¹
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# Compute regression parameters for annotations
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coeffs = np.polyfit(inv_T, ln_k_measured, 1)
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slope_fit, intercept_fit = coeffs
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y_pred = slope_fit * inv_T + intercept_fit
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ss_res = np.sum((ln_k_measured - y_pred) ** 2)
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ss_tot = np.sum((ln_k_measured - np.mean(ln_k_measured)) ** 2)
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r_squared = 1 - ss_res / ss_tot
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Ea_fit = -slope_fit * R # Activation energy in J/mol
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# DataFrame
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data_df = pd.DataFrame({"inv_T": inv_T, "ln_k": ln_k_measured, "T_K": temperature_K})
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# Shared scales
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x_scale = alt.Scale(domain=[inv_T.min() - 0.0001, inv_T.max() + 0.0001], nice=False)
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y_scale = alt.Scale(domain=[ln_k_measured.min() - 1.2, ln_k_measured.max() + 1.2])
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# Regression line using Altair's native transform_regression
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reg_line = (
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alt.Chart(data_df)
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.mark_line(strokeWidth=3, color="#306998")
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.transform_regression("inv_T", "ln_k", extent=[inv_T.min() - 0.00005, inv_T.max() + 0.00005])
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.encode(x=alt.X("inv_T:Q", scale=x_scale), y=alt.Y("ln_k:Q", scale=y_scale))
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)
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# Data points with interactive highlight
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highlight = alt.selection_point(on="pointerover", nearest=True, empty=False)
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points = (
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alt.Chart(data_df)
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.mark_point(filled=True, color="#306998", stroke="white", strokeWidth=1.5)
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.encode(
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x=alt.X("inv_T:Q", scale=x_scale, title="1/T (K⁻¹)"),
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y=alt.Y("ln_k:Q", scale=y_scale, title="ln(k)"),
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size=alt.condition(highlight, alt.value(500), alt.value(350)),
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tooltip=[
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alt.Tooltip("T_K:Q", title="Temperature", format=".0f"),
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alt.Tooltip("inv_T:Q", title="1/T", format=".5f"),
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alt.Tooltip("ln_k:Q", title="ln(k)", format=".2f"),
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],
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)
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.add_params(highlight)
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)
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# Annotation: Ea and R² value
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ea_kj = Ea_fit / 1000
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annotation_text = f"Eₐ = {ea_kj:.1f} kJ/mol R² = {r_squared:.4f}"
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slope_text = f"slope = −Eₐ/R = {slope_fit:.0f} K"
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annotation_df = pd.DataFrame(
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{"inv_T": [inv_T.min() + 0.0002], "ln_k": [ln_k_measured.max() + 0.7], "text": [annotation_text]}
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)
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slope_ann_df = pd.DataFrame(
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{"inv_T": [inv_T.min() + 0.0002], "ln_k": [ln_k_measured.max() + 0.15], "text": [slope_text]}
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)
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ea_label = (
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alt.Chart(annotation_df)
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.mark_text(fontSize=20, align="left", fontWeight="bold", color="#306998")
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.encode(x=alt.X("inv_T:Q", scale=x_scale), y=alt.Y("ln_k:Q", scale=y_scale), text="text:N")
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)
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slope_label = (
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alt.Chart(slope_ann_df)
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.mark_text(fontSize=17, align="left", fontStyle="italic", color="#666666")
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.encode(x=alt.X("inv_T:Q", scale=x_scale), y=alt.Y("ln_k:Q", scale=y_scale), text="text:N")
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)
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# Secondary x-axis: temperature labels at data point positions
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temp_labels_df = pd.DataFrame(
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{
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"inv_T": inv_T[::2],
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"ln_k": [ln_k_measured.min() - 0.5] * len(inv_T[::2]),
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"text": [f"{int(t)} K" for t in temperature_K[::2]],
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}
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)
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temp_tick_labels = (
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alt.Chart(temp_labels_df)
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.mark_text(fontSize=14, color="#888888", angle=0)
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.encode(x=alt.X("inv_T:Q", scale=x_scale), y=alt.Y("ln_k:Q", scale=y_scale), text="text:N")
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)
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# Temperature axis label
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temp_axis_label_df = pd.DataFrame(
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{"inv_T": [(inv_T.min() + inv_T.max()) / 2], "ln_k": [ln_k_measured.min() - 0.9], "text": ["Temperature (K)"]}
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)
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temp_axis_label = (
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alt.Chart(temp_axis_label_df)
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.mark_text(fontSize=16, color="#888888", fontStyle="italic")
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.encode(x=alt.X("inv_T:Q", scale=x_scale), y=alt.Y("ln_k:Q", scale=y_scale), text="text:N")
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)
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# Combine all layers
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chart = (
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alt.layer(reg_line, points, ea_label, slope_label, temp_tick_labels, temp_axis_label)
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.properties(
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width=1600,
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height=900,
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title=alt.Title(
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"line-arrhenius · altair · pyplots.ai",
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fontSize=28,
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anchor="middle",
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color="#2C3E50",
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subtitle="First-Order Decomposition · Rate Constants vs Inverse Temperature",
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subtitleFontSize=18,
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subtitleColor="#7f8c8d",
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subtitlePadding=8,
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),
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)
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.configure_axis(
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labelFontSize=18,
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titleFontSize=22,
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titleFont="Helvetica Neue, Arial, sans-serif",
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labelFont="Helvetica Neue, Arial, sans-serif",
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titleColor="#333333",
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labelColor="#555555",
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grid=False,
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domain=False,
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tickColor="#aaaaaa",
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tickSize=5,
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tickWidth=0.6,
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)
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.configure_title(font="Helvetica Neue, Arial, sans-serif", color="#222222")
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.configure_view(strokeWidth=0)
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.interactive()
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)
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# Save
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chart.save("plot.png", scale_factor=3.0)
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chart.save("plot.html")

‎plots/line-arrhenius/metadata/altair.yaml‎

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library: altair
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specification_id: line-arrhenius
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created: '2026-03-21T22:07:37Z'
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updated: '2026-03-21T22:20:06Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 23389778092
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issue: 4408
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python_version: 3.14.3
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library_version: 6.0.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/altair/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/altair/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/altair/plot.html
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quality_score: 90
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review:
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strengths:
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- Complete spec compliance with all required features (regression line, R², Ea annotation,
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temperature labels)
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- Strong design with removed spines/domain, clean typography, and effective annotation
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hierarchy
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- Idiomatic Altair usage with native transform_regression and interactive features
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- Realistic physical chemistry data with appropriate parameters
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- Clean, well-organized code structure
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weaknesses:
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- Temperature reference labels (14pt) could be slightly larger for better readability
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- Minor whitespace imbalance on the right edge of the canvas
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image_description: The plot displays an Arrhenius plot with "line-arrhenius · altair
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· pyplots.ai" as the title and a subtitle "First-Order Decomposition · Rate Constants
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vs Inverse Temperature". The x-axis shows "1/T (K⁻¹)" ranging from 0.00160 to
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0.00340, and the y-axis shows "ln(k)" ranging from approximately -3 to 14. A blue
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regression line (#306998 Python Blue) extends across the full data range with
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12 filled blue circular data points (white stroke) plotted on top. In the upper-left
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area, a bold blue annotation reads "Eₐ = 74.5 kJ/mol R² = 0.9995" and below it
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in gray italic "slope = −Eₐ/R = -8963 K". Along the bottom of the plot area, gray
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temperature labels (550 K, 500 K, 450 K, 400 K, 350 K, 300 K) provide secondary
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x-axis reference with a centered "Temperature (K)" label beneath them. The design
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is clean with no grid lines, no spines/domain lines, and a white background. Layout
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fills the canvas well in landscape format.
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criteria_checklist:
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visual_quality:
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score: 28
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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: 7
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max: 8
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passed: true
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comment: All font sizes explicitly set (title 28pt, subtitle 18pt, axis titles
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22pt, ticks 18pt, annotations 20/17pt). Temperature labels at 14pt slightly
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small.
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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 elements anywhere in the plot.
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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: 12 data points with size 350/500, clearly visible with white stroke
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separation.
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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 (Python Blue), no colorblind concerns.
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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 landscape layout. Minor whitespace imbalance on right side.
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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 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: Thoughtful design with Python Blue, Helvetica Neue, removed spines/domain.
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Clearly above defaults.
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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: Grid disabled, domain/spines removed, view stroke removed, custom
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tick colors. Well-polished.
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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: Annotations display key insight (Ea and R²), slope annotation provides
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interpretation, subtitle frames experiment.
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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 with scatter and regression.'
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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/R annotation,
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temperature labels, 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 (K⁻¹), Y=ln(k) correctly mapped.
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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 format correct. 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: 12 data points spanning 300-600K with realistic scatter. Good coverage
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but uniform scatter.
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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 with Ea=75 kJ/mol, realistic chemistry
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scenario.
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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 300-600K and Ea=75 kJ/mol are realistic for reaction
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kinetics.
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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 linear flow: imports, data, plot layers, combine, save.'
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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: Appropriate complexity, well-organized layer composition.
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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 as plot.png and plot.html with current API.
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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: 5
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max: 5
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passed: true
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comment: 'Expert Altair patterns: layered composition, encoding types, alt.Title,
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configure methods.'
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- id: LM-02
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name: Distinctive Features
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score: 3
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max: 5
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passed: true
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comment: Uses transform_regression, selection_point, tooltips, .interactive(),
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HTML export.
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verdict: APPROVED
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impl_tags:
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dependencies: []
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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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