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feat(pygal): implement line-arrhenius (#5171)
## Implementation: `line-arrhenius` - pygal Implements the **pygal** version of `line-arrhenius`. **File:** `plots/line-arrhenius/implementations/pygal.py` **Parent Issue:** #4408 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23389777964)* --------- 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/pygal.py‎

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""" pyplots.ai
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line-arrhenius: Arrhenius Plot for Reaction Kinetics
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Library: pygal 3.1.0 | Python 3.14.3
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Quality: 90/100 | Created: 2026-03-21
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"""
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import numpy as np
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import pygal
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from pygal.style import Style
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from scipy import stats
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# Data — First-order decomposition reaction rate constants at various temperatures
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temperature_K = np.array([300, 330, 360, 400, 440, 480, 520, 560, 600])
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np.random.seed(42)
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activation_energy = 75000 # J/mol (75 kJ/mol)
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R = 8.314 # Gas constant J/(mol·K)
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pre_exponential = 1.0e12 # s⁻¹
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rate_constant_k = pre_exponential * np.exp(-activation_energy / (R * temperature_K))
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# Add realistic experimental scatter (larger noise for visible deviation from fit)
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rate_constant_k *= np.exp(np.random.normal(0, 0.25, len(temperature_K)))
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# Transformed coordinates for Arrhenius plot
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inv_T = 1000.0 / temperature_K # 1000/T for readable axis values (×10⁻³ K⁻¹)
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ln_k = np.log(rate_constant_k)
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# Linear regression: ln(k) = ln(A) - Ea/R × (1/T)
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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 back (factor of 1000 from 1000/T scaling)
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# Regression line — extend slightly beyond data for visual clarity
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x_pad = 0.04
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inv_T_fit = np.linspace(float(min(inv_T)) - x_pad, float(max(inv_T)) + x_pad, 80)
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ln_k_fit = slope * inv_T_fit + intercept
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# Style — colorblind-safe palette: deep blue fit line, amber/orange data points
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custom_style = Style(
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background="white",
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plot_background="#f8f9fa",
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foreground="#2c3e50",
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foreground_strong="#1a252f",
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foreground_subtle="#dde1e4",
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colors=("#306998", "#d4760a", "#8b5cf6"),
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guide_stroke_color="#e8ecef",
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major_guide_stroke_color="#cfd4d8",
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guide_stroke_dasharray="6,3",
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title_font_size=68,
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label_font_size=44,
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major_label_font_size=40,
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legend_font_size=38,
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value_font_size=32,
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tooltip_font_size=34,
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stroke_width=4,
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opacity=0.92,
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opacity_hover=1.0,
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title_font_family="sans-serif",
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label_font_family="sans-serif",
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major_label_font_family="sans-serif",
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legend_font_family="sans-serif",
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value_font_family="sans-serif",
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)
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# Y-axis labels — tight to data range with minimal padding
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y_min_data, y_max_data = float(min(ln_k)), float(max(ln_k))
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y_floor = int(np.floor(y_min_data))
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y_ceil = int(np.ceil(y_max_data))
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y_labels_list = list(range(y_floor, y_ceil + 1, 2))
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if y_labels_list[-1] < y_ceil:
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y_labels_list.append(y_ceil)
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# Chart — pygal XY with tight axis range and polished config
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chart = pygal.XY(
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style=custom_style,
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width=4800,
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height=2700,
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title="line-arrhenius · pygal · pyplots.ai",
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x_title="1000/T (K⁻¹)",
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y_title="ln(k)",
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show_dots=True,
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dots_size=16,
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show_x_guides=False,
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show_y_guides=True,
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legend_at_bottom=True,
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legend_at_bottom_columns=3,
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legend_box_size=28,
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truncate_legend=-1,
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margin=40,
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margin_top=70,
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margin_bottom=160,
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margin_left=170,
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margin_right=80,
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tooltip_fancy_mode=True,
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tooltip_border_radius=10,
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x_value_formatter=lambda x: f"{x:.2f}",
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y_value_formatter=lambda y: f"{y:.1f}",
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range=(y_floor - 0.5, y_ceil + 0.5),
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xrange=(float(min(inv_T) - 0.1), float(max(inv_T) + 0.1)),
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y_labels=y_labels_list,
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y_labels_major_every=1,
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print_values=False,
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show_minor_x_labels=False,
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interpolate="cubic",
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css=[
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"file://style.css",
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"file://graph.css",
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"inline:"
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".axis > .line { stroke: transparent !important; } "
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".plot .background { rx: 14; ry: 14; } "
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".legends .legend text { font-weight: 500; } "
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".title { font-weight: 600; letter-spacing: 1px; }",
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],
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)
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# X-axis labels: 1000/T with temperature in parentheses
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x_label_temps = np.array([300, 360, 440, 520, 600])
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x_label_positions = sorted(1000.0 / x_label_temps)
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chart.x_labels = [float(x) for x in x_label_positions]
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chart.x_labels_major = [float(x) for x in x_label_positions]
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chart.x_label_rotation = 0
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chart.x_value_formatter = lambda x: f"{x:.2f} ({int(round(1000.0 / x))} K)"
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# Regression fit line — smooth blue line
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fit_points = [
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{"value": (float(x), float(y)), "label": f"Fit: ln(k) = {slope:.2f} × (1000/T) + {intercept:.2f}"}
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for x, y in zip(inv_T_fit, ln_k_fit, strict=False)
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]
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chart.add(
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f"Linear Fit (R² = {r_squared:.3f})",
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fit_points,
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show_dots=False,
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stroke_style={"width": 6, "linecap": "round", "linejoin": "round"},
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)
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# Experimental data points — amber markers with rich tooltips
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data_points = [
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{"value": (float(x), float(y)), "label": f"T = {int(t)} K\nk = {k:.3e} s⁻¹\nln(k) = {y:.2f}\n1000/T = {x:.3f}"}
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for x, y, t, k in zip(inv_T, ln_k, temperature_K, rate_constant_k, strict=False)
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]
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chart.add("Experimental Data", data_points, stroke=False, dots_size=20)
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# Activation energy annotation — dedicated legend entry for Eₐ result
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# Uses a zero-opacity point on the regression line to anchor the tooltip
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mid_x = float(np.median(inv_T))
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mid_y = float(slope * mid_x + intercept)
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chart.add(
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f"Eₐ = {Ea_extracted / 1000:.1f} kJ/mol (−Eₐ/R = {slope:.1f} K⁻¹)",
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[
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{
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"value": (mid_x, mid_y),
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"label": f"Activation Energy: Eₐ = {Ea_extracted / 1000:.1f} kJ/mol\n"
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f"Slope = {slope:.2f} · −Eₐ/R = {slope * 1000:.0f} K",
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}
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],
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dots_size=0,
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stroke=False,
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)
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# Save — PNG for static output, HTML with pygal's interactive SVG tooltips
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chart.render_to_png("plot.png")
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chart.render_to_file("plot.html")

‎plots/line-arrhenius/metadata/pygal.yaml‎

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library: pygal
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specification_id: line-arrhenius
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created: '2026-03-21T22:08:23Z'
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updated: '2026-03-21T22:42:22Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 23389777964
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issue: 4408
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python_version: 3.14.3
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library_version: 3.1.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/pygal/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/pygal/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/line-arrhenius/pygal/plot.html
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quality_score: 90
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review:
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strengths:
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- Excellent visual quality with all font sizes explicitly set and colorblind-safe
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blue/amber palette
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- Strong CSS customization leveraging pygal unique SVG styling capabilities (transparent
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axes, rounded corners, custom font weights)
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- Realistic chemistry data with proper Arrhenius equation parameters and experimental
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noise
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- Clean code structure with good reproducibility (seed=42)
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- Dual x-axis labels showing both 1000/T and temperature in K aid interpretation
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weaknesses:
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- Activation energy annotation conveyed through legend entry rather than on-plot
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annotation (pygal limitation)
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- Data storytelling could be enhanced with more emphasis on the key insight
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image_description: 'The plot displays an Arrhenius plot with "1000/T (K⁻¹)" on the
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x-axis and "ln(k)" on the y-axis. A blue linear regression line runs from the
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upper-left (~12.5 at 1.67 K⁻¹) to lower-right (~-2.5 at 3.33 K⁻¹). Nine amber/orange
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circular data points are scattered along the fit line showing realistic experimental
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scatter. The x-axis labels include both 1000/T values and corresponding temperatures
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in parentheses (e.g., "1.67 (600 K)", "3.33 (300 K)"). The y-axis shows integer
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values from -3 to 13 with subtle dashed horizontal grid lines. The plot background
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is light gray with a white page background, and the plot area has rounded corners.
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The title "line-arrhenius · pygal · pyplots.ai" appears at the top in bold. A
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bottom legend shows three entries: "Linear Fit (R² = 0.999)" (blue), "Experimental
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Data" (amber), and "Eₐ = 74.9 kJ/mol (−Eₐ/R = -9.0 K⁻¹)" (purple). Axis lines
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are hidden, giving a clean appearance.'
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criteria_checklist:
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visual_quality:
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score: 30
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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=68, label=44, major_label=40,
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legend=38). Clearly readable at full resolution.
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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. X-axis labels well-spaced. Legend fits cleanly
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at bottom.
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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: Amber data points at dots_size=20 are prominent. Well-suited for
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9 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: Blue and amber is an excellent colorblind-safe combination with strong
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contrast.
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- id: VQ-05
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name: Layout & Canvas
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score: 4
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max: 4
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passed: true
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comment: Plot fills canvas well with explicit margins. Good proportions at
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4800x2700.
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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: 1000/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 custom design: blue/amber palette, custom font weights, letter-spacing,
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distinct backgrounds. Above configured 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: Axis lines removed via CSS, dashed guide lines, rounded corners,
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custom guide stroke colors.
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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: 'Clear hierarchy: blue line as primary, amber scatter shows experimental
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noise, R² and Eₐ in legend. Dual x-axis labels aid interpretation.'
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spec_compliance:
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score: 14
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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 linear regression.'
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- id: SC-02
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name: Required Features
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score: 3
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max: 4
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passed: true
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comment: 'All features present. Minor: Eₐ annotation in legend rather than
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on-plot (pygal limitation).'
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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=1000/T, Y=ln(k) correctly mapped. Full data range displayed.
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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 format. Legend labels are descriptive and accurate.
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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: 9 data points spanning 300-600K, regression fit, scatter, R², and
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Eₐ extraction shown.
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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 physical 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, Eₐ=75 kJ/mol, rate constants spanning orders
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of magnitude — all 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 → Transform → Style → Chart → Save flow.
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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 before noise generation.
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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: numpy, pygal, Style, scipy.stats all 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: Well-organized with clear comments. Clever zero-opacity legend entry
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for Eₐ.
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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 plot.png and plot.html. Current pygal API.
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library_mastery:
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score: 7
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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 use of pygal.XY, Style, CSS injection, tooltip config, value
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formatters, legend positioning.
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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: CSS injection for axis removal and rounded corners, tooltip_fancy_mode,
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HTML export with interactive SVG tooltips.
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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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- custom-legend
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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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- grid-styling

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