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feat(altair): implement line-confidence (#2253)
## Implementation: `line-confidence` - altair Implements the **altair** version of `line-confidence`. **File:** `plots/line-confidence/implementations/altair.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20525434309)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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‎plots/line-confidence/implementations/altair.py‎

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""" pyplots.ai
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line-confidence: Line Plot with Confidence Interval
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Library: altair 6.0.0 | Python 3.13.11
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Quality: 78/100 | Created: 2025-12-26
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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: Simulating a time series forecast with 95% confidence interval
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np.random.seed(42)
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# Generate time points (50 days of daily data)
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n_points = 50
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days = np.arange(n_points)
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# Create a trend with some curvature (e.g., a model prediction)
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trend = 100 + 0.5 * days + 0.02 * days**2 + np.sin(days / 5) * 5
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# Add noise for the central line
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noise = np.random.normal(0, 3, n_points)
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y_mean = trend + noise
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# Confidence interval that widens over time (typical for forecasts)
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uncertainty = 5 + 0.15 * days # Uncertainty grows with time
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y_lower = y_mean - 1.96 * uncertainty / 2
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y_upper = y_mean + 1.96 * uncertainty / 2
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# Create DataFrame
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df = pd.DataFrame({"Day": days, "Value": y_mean, "Lower": y_lower, "Upper": y_upper})
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# Calculate appropriate y-axis domain with padding (avoid unnecessary whitespace from 0)
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y_min = df["Lower"].min()
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y_max = df["Upper"].max()
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y_padding = (y_max - y_min) * 0.1
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y_domain = [y_min - y_padding, y_max + y_padding]
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# Create shared y-scale for consistent rendering across all layers
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y_scale = alt.Scale(domain=y_domain)
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# Create the confidence band (area) with fill encoding for legend
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band = (
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alt.Chart(df)
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.transform_calculate(legend_label='"95% Confidence Interval"')
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.mark_area(opacity=0.3)
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.encode(
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x=alt.X("Day:Q", title="Day", axis=alt.Axis(values=list(range(0, 51, 5)))),
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y=alt.Y("Lower:Q", title="Predicted Value", scale=y_scale),
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y2="Upper:Q",
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fill=alt.Fill(
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"legend_label:N",
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scale=alt.Scale(domain=["95% Confidence Interval"], range=["#306998"]),
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legend=alt.Legend(
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title="Legend",
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orient="right",
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labelFontSize=16,
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titleFontSize=18,
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symbolType="square",
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symbolSize=300,
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symbolOpacity=0.3,
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),
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),
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)
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)
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# Create the central line with stroke encoding for legend
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line = (
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alt.Chart(df)
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.transform_calculate(legend_label='"Predicted Mean"')
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.mark_line(strokeWidth=4)
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.encode(
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x="Day:Q",
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y=alt.Y("Value:Q", scale=y_scale),
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stroke=alt.Stroke(
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"legend_label:N",
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scale=alt.Scale(domain=["Predicted Mean"], range=["#306998"]),
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legend=alt.Legend(
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title="Legend", orient="right", labelFontSize=16, titleFontSize=18, symbolStrokeWidth=4, symbolSize=300
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),
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),
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)
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)
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# Add point markers on the line for clarity (increased size for better visibility)
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points = (
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alt.Chart(df)
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.mark_point(size=100, filled=True, color="#306998")
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.encode(x="Day:Q", y=alt.Y("Value:Q", scale=y_scale))
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)
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# Combine band, line, and points with resolved legends
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chart = (
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alt.layer(band, line, points)
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.resolve_legend(fill="independent", stroke="independent")
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.properties(
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width=1600, height=900, title=alt.Title("line-confidence · altair · pyplots.ai", fontSize=28, anchor="middle")
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)
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.configure_axis(labelFontSize=18, titleFontSize=22, gridOpacity=0.3)
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.configure_view(strokeWidth=0)
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)
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# Save as PNG (scale_factor=3 gives 4800x2700)
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chart.save("plot.png", scale_factor=3.0)
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# Save as HTML for interactivity
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interactive_band = (
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alt.Chart(df)
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.mark_area(opacity=0.3, color="#306998")
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.encode(
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x=alt.X("Day:Q", title="Day"),
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y=alt.Y("Lower:Q", title="Predicted Value", scale=y_scale),
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y2="Upper:Q",
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tooltip=[
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alt.Tooltip("Day:Q", title="Day"),
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alt.Tooltip("Lower:Q", title="Lower Bound", format=".1f"),
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alt.Tooltip("Upper:Q", title="Upper Bound", format=".1f"),
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],
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)
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)
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interactive_line = (
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alt.Chart(df).mark_line(strokeWidth=3, color="#306998").encode(x="Day:Q", y=alt.Y("Value:Q", scale=y_scale))
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)
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interactive_points = (
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alt.Chart(df)
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.mark_point(size=60, filled=True, color="#306998")
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.encode(
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x="Day:Q",
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y=alt.Y("Value:Q", scale=y_scale),
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tooltip=[alt.Tooltip("Day:Q", title="Day"), alt.Tooltip("Value:Q", title="Predicted Value", format=".1f")],
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)
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)
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interactive_chart = (
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alt.layer(interactive_band, interactive_line, interactive_points)
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.properties(
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width=800, height=450, title=alt.Title("line-confidence · altair · pyplots.ai", fontSize=20, anchor="middle")
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)
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.configure_axis(labelFontSize=14, titleFontSize=16, gridOpacity=0.3)
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.configure_view(strokeWidth=0)
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.interactive()
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)
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interactive_chart.save("plot.html")

‎plots/line-confidence/metadata/altair.yaml‎

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library: altair
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specification_id: line-confidence
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created: '2025-12-26T16:14:06Z'
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updated: '2025-12-26T16:33:33Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20525434309
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issue: 0
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python_version: 3.13.11
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library_version: 6.0.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/line-confidence/altair/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-confidence/altair/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/line-confidence/altair/plot.html
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quality_score: 78
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review:
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strengths:
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- Clean code structure following KISS principles with clear data generation logic
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- Confidence band appropriately widens over time showing realistic forecast uncertainty
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behavior
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- Uses Altair layered chart composition effectively (band + line + points)
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- Interactive HTML export includes useful tooltips for exploration
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- Consistent color scheme with good contrast between line and band
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weaknesses:
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- Legend is not visible in the rendered PNG despite code attempting to create one
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using resolve_legend - this violates spec requirement for legend that clearly
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identifies both the central line and confidence band
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- Y-axis starts at 0 but data ranges ~90-180, wasting nearly half the vertical canvas
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space (the code calculates y_domain but Altair appears to ignore it or it is not
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applied correctly to all layers)

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