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feat(altair): implement scatter-matrix (#2252)
## Implementation: `scatter-matrix` - altair Implements the **altair** version of `scatter-matrix`. **File:** `plots/scatter-matrix/implementations/altair.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20525446227)* --------- 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/scatter-matrix/implementations/altair.py‎

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""" pyplots.ai
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scatter-matrix: Scatter Plot Matrix
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Library: altair 6.0.0 | Python 3.13.11
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Quality: 85/100 | 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 - Iris dataset (classic multivariate data)
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np.random.seed(42)
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# Generate realistic iris-like data with 4 variables and 3 species
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n_per_species = 50
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data = []
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# Setosa - smaller flowers
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for _ in range(n_per_species):
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data.append(
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{
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"Sepal Length (cm)": np.random.normal(5.0, 0.35),
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"Sepal Width (cm)": np.random.normal(3.4, 0.38),
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"Petal Length (cm)": np.random.normal(1.5, 0.17),
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"Petal Width (cm)": np.random.normal(0.25, 0.1),
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"Species": "Setosa",
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}
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)
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# Versicolor - medium flowers
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for _ in range(n_per_species):
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data.append(
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{
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"Sepal Length (cm)": np.random.normal(5.9, 0.52),
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"Sepal Width (cm)": np.random.normal(2.8, 0.31),
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"Petal Length (cm)": np.random.normal(4.3, 0.47),
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"Petal Width (cm)": np.random.normal(1.3, 0.2),
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"Species": "Versicolor",
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}
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)
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# Virginica - larger flowers
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for _ in range(n_per_species):
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data.append(
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{
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"Sepal Length (cm)": np.random.normal(6.6, 0.64),
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"Sepal Width (cm)": np.random.normal(3.0, 0.32),
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"Petal Length (cm)": np.random.normal(5.5, 0.55),
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"Petal Width (cm)": np.random.normal(2.0, 0.27),
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"Species": "Virginica",
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}
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)
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df = pd.DataFrame(data)
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# Variables for the scatter matrix
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variables = ["Sepal Length (cm)", "Sepal Width (cm)", "Petal Length (cm)", "Petal Width (cm)"]
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# Color scheme - Distinct colorblind-safe palette (blue, orange, green)
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color_scale = alt.Scale(domain=["Setosa", "Versicolor", "Virginica"], range=["#306998", "#E69F00", "#009E73"])
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# Use Altair's native repeat() for declarative scatter matrix construction
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# This is the idiomatic Altair approach for creating SPLOM (scatter plot matrix)
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scatter_matrix = (
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alt.Chart(df)
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.mark_circle(size=100, opacity=0.7)
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.encode(
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alt.X(alt.repeat("column"), type="quantitative", axis=alt.Axis(labelFontSize=18, titleFontSize=22)),
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alt.Y(alt.repeat("row"), type="quantitative", axis=alt.Axis(labelFontSize=18, titleFontSize=22)),
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alt.Color(
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"Species:N",
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scale=color_scale,
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legend=alt.Legend(
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title="Species",
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titleFontSize=28,
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labelFontSize=24,
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symbolSize=400,
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orient="right",
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titlePadding=15,
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labelPadding=10,
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),
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),
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tooltip=[
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"Species:N",
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alt.Tooltip(alt.repeat("column"), type="quantitative"),
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alt.Tooltip(alt.repeat("row"), type="quantitative"),
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],
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)
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.properties(width=320, height=320)
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.repeat(row=variables, column=variables)
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)
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# Apply configuration and title
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chart = (
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scatter_matrix.properties(
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title=alt.Title(text="scatter-matrix · altair · pyplots.ai", fontSize=36, anchor="middle", offset=25)
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)
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.configure_axis(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 us ~3840x3840 for square output close to 3600x3600 target)
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chart.save("plot.png", scale_factor=3.0)
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# Save interactive HTML version
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chart.save("plot.html")

‎plots/scatter-matrix/metadata/altair.yaml‎

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library: altair
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specification_id: scatter-matrix
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created: '2025-12-26T16:13:43Z'
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updated: '2025-12-26T16:37:44Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20525446227
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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/scatter-matrix/altair/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix/altair/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix/altair/plot.html
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quality_score: 85
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review:
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strengths:
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- Excellent use of Altair's declarative repeat() pattern for idiomatic SPLOM construction
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- Colorblind-safe palette (blue/orange/green) provides clear species distinction
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- Clean data generation with realistic Iris-like measurements and proper random
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seed
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- All axis labels include units (cm) for clarity
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- Interactive tooltips enhance usability in HTML version
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weaknesses:
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- Diagonal cells show scatter (x=y lines) instead of required histograms/KDE for
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univariate distributions as specified
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- Legend could be larger or more prominently positioned for better visual balance

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