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update(heatmap-basic): altair — comprehensive quality review (#4256)
## Summary Updated **altair** implementation for **heatmap-basic**. **Changes:** Comprehensive quality review — fix weaknesses from prior reviews, preserve strengths, improve quality across all dimensions. ### Changes - Addressed review weaknesses from prior quality assessment - Improved data choice and visual design - Enhanced library-specific feature usage - Updated to current library and Python versions - Quality self-assessment: pending CI review ## Test Plan - [x] Preview images uploaded to GCS staging - [x] Implementation file passes ruff format/check - [x] Metadata YAML updated with current versions - [ ] Automated review triggered --- Generated with [Claude Code](https://claude.com/claude-code) `/update` command --------- 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/heatmap-basic/implementations/altair.py‎

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""" pyplots.ai
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heatmap-basic: Basic Heatmap
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Library: altair 6.0.0 | Python 3.13.11
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Quality: 91/100 | Created: 2025-12-23
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Library: altair 6.0.0 | Python 3.14.3
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Quality: 88/100 | Updated: 2026-02-16
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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 - create a matrix with patterns
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# Data - correlation matrix with realistic weather variables
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np.random.seed(42)
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rows = ["Row A", "Row B", "Row C", "Row D", "Row E", "Row F", "Row G", "Row H"]
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cols = ["Col 1", "Col 2", "Col 3", "Col 4", "Col 5", "Col 6", "Col 7", "Col 8"]
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variables = [
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"Temperature",
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"Humidity",
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"Wind Speed",
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"Pressure",
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"Visibility",
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"Cloud Cover",
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"Precipitation",
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"UV Index",
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]
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# Generate values with some patterns (diagonal pattern + noise)
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values = []
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for i, row in enumerate(rows):
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for j, col in enumerate(cols):
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# Create pattern: higher values near diagonal, add noise
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base = 100 - abs(i - j) * 12
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noise = np.random.randn() * 10
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values.append({"x": col, "y": row, "value": base + noise})
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n_samples = 200
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raw = np.random.randn(n_samples, len(variables))
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df = pd.DataFrame(values)
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# Inject realistic correlations with stronger relationships
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raw[:, 1] += raw[:, 0] * 0.6 # Humidity ~ Temperature
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raw[:, 5] += raw[:, 1] * 0.7 # Cloud Cover ~ Humidity
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raw[:, 6] += raw[:, 5] * 0.65 # Precipitation ~ Cloud Cover
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raw[:, 4] -= raw[:, 5] * 0.9 # Visibility inversely ~ Cloud Cover
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raw[:, 7] -= raw[:, 5] * 0.7 # UV Index inversely ~ Cloud Cover
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raw[:, 7] += raw[:, 0] * 0.5 # UV Index ~ Temperature
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raw[:, 3] -= raw[:, 0] * 0.4 # Pressure inversely ~ Temperature
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raw[:, 2] += raw[:, 3] * 0.3 # Wind Speed ~ Pressure
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# Plot - heatmap base
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corr = np.corrcoef(raw.T)
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# Build long-form dataframe using list comprehension
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df = pd.DataFrame(
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[
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{"Row": row_var, "Column": col_var, "value": round(corr[i, j], 2)}
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for i, row_var in enumerate(variables)
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for j, col_var in enumerate(variables)
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]
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)
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# Axis ordering
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axis_order = list(variables)
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# Plot - heatmap with colorblind-safe diverging color centered at 0
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heatmap = (
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alt.Chart(df)
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.mark_rect()
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.mark_rect(stroke="#ffffff", strokeWidth=1.5, cornerRadius=2)
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.encode(
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x=alt.X("x:N", title="Column", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
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y=alt.Y("y:N", title="Row", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
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x=alt.X(
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"Column:N",
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title="Weather Variable",
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sort=axis_order,
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axis=alt.Axis(
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labelFontSize=15, titleFontSize=18, labelAngle=-45, orient="top", labelPadding=8, titlePadding=10
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),
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),
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y=alt.Y(
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"Row:N",
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title="Weather Variable",
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sort=axis_order,
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axis=alt.Axis(labelFontSize=15, titleFontSize=18, labelPadding=8, titlePadding=10),
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),
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color=alt.Color(
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"value:Q",
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scale=alt.Scale(scheme="blueorange", domainMid=50),
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legend=alt.Legend(title="Value", titleFontSize=18, labelFontSize=16),
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scale=alt.Scale(scheme="blueorange", domain=[-1, 1], domainMid=0),
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legend=alt.Legend(
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title="Correlation",
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titleFontSize=16,
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labelFontSize=14,
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gradientLength=300,
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gradientThickness=16,
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titlePadding=6,
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offset=8,
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direction="vertical",
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),
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),
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tooltip=["x:N", "y:N", "value:Q"],
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tooltip=[
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alt.Tooltip("Column:N", title="Column"),
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alt.Tooltip("Row:N", title="Row"),
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alt.Tooltip("value:Q", title="Correlation", format=".2f"),
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],
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)
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)
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# Add text annotations
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# Highlight cells with strong correlations using thicker borders
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highlight = (
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alt.Chart(df)
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.transform_filter((alt.datum.value >= 0.7) | (alt.datum.value <= -0.7))
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.mark_rect(stroke="#2a2a2a", strokeWidth=2.5, filled=False, cornerRadius=2)
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.encode(x=alt.X("Column:N", sort=axis_order), y=alt.Y("Row:N", sort=axis_order))
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)
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# Text annotations with adaptive color
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text = (
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alt.Chart(df)
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.mark_text(fontSize=18)
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.mark_text(fontSize=15, fontWeight="bold")
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.encode(
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x=alt.X("x:N"),
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y=alt.Y("y:N"),
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text=alt.Text("value:Q", format=".0f"),
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color=alt.condition(alt.datum.value > 70, alt.value("white"), alt.value("black")),
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x=alt.X("Column:N", sort=axis_order),
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y=alt.Y("Row:N", sort=axis_order),
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text=alt.Text("value:Q", format=".2f"),
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color=alt.when((alt.datum.value > 0.55) | (alt.datum.value < -0.55))
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.then(alt.value("#ffffff"))
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.otherwise(alt.value("#333333")),
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)
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)
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# Combine heatmap and text, then apply configuration
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# Combine layers and configure
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chart = (
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(heatmap + text)
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.properties(width=1400, height=800, title=alt.Title("heatmap-basic · altair · pyplots.ai", fontSize=28))
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.configure_axis(labelFontSize=16, titleFontSize=20, grid=False)
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(heatmap + highlight + text)
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.properties(
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width=740,
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height=766,
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title=alt.Title(
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"heatmap-basic · altair · pyplots.ai",
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subtitle=[
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"Pairwise Pearson correlation coefficients for 8 weather metrics.",
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"Bold borders highlight strong relationships (|r| ≥ 0.7).",
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],
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fontSize=26,
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subtitleFontSize=16,
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subtitleColor="#666666",
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anchor="start",
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offset=16,
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),
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padding={"left": 20, "right": 20, "top": 20, "bottom": 20},
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)
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.configure_axis(grid=False)
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.configure_view(strokeWidth=0)
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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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# Save — target: 3600×3600 square format
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chart.save("plot.png", scale_factor=3.6)
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chart.save("plot.html")

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