feat(plotnine): implement scatter-matrix-interactive

plots/scatter-matrix-interactive/implementations/plotnine.py

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+""" pyplots.ai
+scatter-matrix-interactive: Interactive Scatter Plot Matrix (SPLOM)
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 72/100 | Created: 2026-01-10
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_line,
+    element_rect,
+    element_text,
+    facet_grid,
+    geom_point,
+    geom_ribbon,
+    ggplot,
+    labs,
+    scale_color_manual,
+    scale_fill_manual,
+    theme,
+    theme_minimal,
+)
+from sklearn.datasets import load_iris
+
+
+# Data: Iris dataset for multivariate analysis
+np.random.seed(42)
+iris = load_iris()
+df = pd.DataFrame(iris.data, columns=["Sepal Length (cm)", "Sepal Width (cm)", "Petal Length (cm)", "Petal Width (cm)"])
+df["Species"] = pd.Categorical([iris.target_names[i] for i in iris.target])
+
+# Variables for the matrix (already include units)
+variables = ["Sepal Length (cm)", "Sepal Width (cm)", "Petal Length (cm)", "Petal Width (cm)"]
+
+# Colorblind-safe palette (Dark2 inspired - teal, orange, purple)
+colors = ["#1B9E77", "#D95F02", "#7570B3"]
+
+# Create long-form data for scatter matrix
+scatter_data = []
+density_data = []
+
+for i, var_y in enumerate(variables):
+    for j, var_x in enumerate(variables):
+        if i == j:
+            # Diagonal: Create normalized density data that fits within the variable's range
+            var_min, var_max = df[var_x].min(), df[var_x].max()
+            var_range = var_max - var_min
+            # Use baseline slightly above min for visual clarity
+            baseline = var_min
+
+            for species in df["Species"].unique():
+                species_vals = df[df["Species"] == species][var_x].values
+                # Simple histogram-based density
+                hist, edges = np.histogram(species_vals, bins=20, range=(var_min, var_max), density=True)
+                # Normalize to fit within the y-axis range (scale to var_range * 0.5)
+                max_density = hist.max() if hist.max() > 0 else 1
+                hist_scaled = hist / max_density * var_range * 0.5 + baseline
+                bin_centers = (edges[:-1] + edges[1:]) / 2
+
+                for k in range(len(bin_centers)):
+                    density_data.append(
+                        {
+                            "x": bin_centers[k],
+                            "ymin": baseline,
+                            "ymax": hist_scaled[k],
+                            "Species": species,
+                            "var_x": var_x,
+                            "var_y": var_y,
+                        }
+                    )
+        else:
+            # Off-diagonal: scatter data
+            for _, row in df.iterrows():
+                scatter_data.append(
+                    {"x": row[var_x], "y": row[var_y], "Species": row["Species"], "var_x": var_x, "var_y": var_y}
+                )
+
+scatter_df = pd.DataFrame(scatter_data)
+density_df = pd.DataFrame(density_data)
+
+# Set factor levels for proper ordering
+scatter_df["var_x"] = pd.Categorical(scatter_df["var_x"], categories=variables, ordered=True)
+scatter_df["var_y"] = pd.Categorical(scatter_df["var_y"], categories=variables[::-1], ordered=True)
+density_df["var_x"] = pd.Categorical(density_df["var_x"], categories=variables, ordered=True)
+density_df["var_y"] = pd.Categorical(density_df["var_y"], categories=variables[::-1], ordered=True)
+
+# Sort density data for proper ribbon rendering
+density_df = density_df.sort_values(["var_x", "var_y", "Species", "x"])
+
+# Create scatter plot matrix with density ribbons on diagonal
+plot = (
+    ggplot(mapping=aes(x="x"))
+    + geom_point(data=scatter_df, mapping=aes(y="y", color="Species"), size=3.5, alpha=0.7)
+    + geom_ribbon(data=density_df, mapping=aes(ymin="ymin", ymax="ymax", fill="Species"), alpha=0.5)
+    + facet_grid("var_y ~ var_x", scales="free")
+    + scale_color_manual(values=colors)
+    + scale_fill_manual(values=colors)
+    + labs(title="scatter-matrix-interactive · plotnine · pyplots.ai", x="", y="")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 16),
+        plot_title=element_text(size=24, weight="bold", ha="left"),
+        strip_text_x=element_text(size=14),
+        strip_text_y=element_text(size=14, angle=0),
+        axis_text=element_text(size=11),
+        axis_title_x=element_text(size=16),
+        axis_title_y=element_text(size=16),
+        legend_title=element_text(size=16),
+        legend_text=element_text(size=14),
+        legend_position="bottom",
+        legend_background=element_rect(fill="white", alpha=0.9),
+        panel_spacing=0.03,
+        panel_grid_major=element_line(color="#cccccc", alpha=0.3),
+        panel_grid_minor=element_line(color="#eeeeee", alpha=0.2),
+        panel_background=element_rect(fill="white"),
+    )
+)
+
+# Save plot
+plot.save("plot.png", dpi=300, width=16, height=16, verbose=False)

plots/scatter-matrix-interactive/metadata/plotnine.yaml

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+library: plotnine
+specification_id: scatter-matrix-interactive
+created: '2026-01-10T01:57:41Z'
+updated: '2026-01-10T02:24:36Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20870868420
+issue: 3604
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix-interactive/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix-interactive/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 72
+review:
+  strengths:
+  - Excellent use of plotnine grammar of graphics with facet_grid for matrix layout
+  - Creative solution using geom_ribbon for histogram-based density distributions
+    on diagonal
+  - Colorblind-safe palette (Dark2-inspired teal, orange, purple)
+  - Clean data transformation from wide to long format for faceting
+  - Good visual separation of the three Iris species clusters
+  - Proper title format following specification
+  weaknesses:
+  - Missing interactive features (brushing, linked selection, zoom/pan) - plotnine
+    is static library
+  - Axis labels are empty strings - variable names only shown in strip text
+  - Diagonal density plots y-axis scale does not match the variable natural scale
+  image_description: 'The plot displays a 4×4 scatter plot matrix (SPLOM) using the
+    Iris dataset with four variables: Sepal Length (cm), Sepal Width (cm), Petal Length
+    (cm), and Petal Width (cm). The matrix uses a colorblind-safe Dark2-inspired palette
+    with teal (#1B9E77) for setosa, orange (#D95F02) for versicolor, and purple (#7570B3)
+    for virginica. Off-diagonal cells show scatter plots with points colored by species
+    at alpha=0.7. Diagonal cells display histogram-based density distributions as
+    filled ribbon areas with alpha=0.5, showing the univariate distribution of each
+    variable per species. The title "scatter-matrix-interactive · plotnine · pyplots.ai"
+    appears at top-left in bold. Strip labels on top show variable names for x-axis,
+    and strip labels on right show variable names for y-axis. A legend at the bottom
+    indicates species color mapping. The grid is subtle with light gray lines. The
+    layout is clean with minimal spacing between panels.'
+  criteria_checklist:
+    visual_quality:
+      score: 32
+      max: 40
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 8
+        max: 10
+        passed: true
+        comment: Title and labels readable, strip text slightly small but acceptable
+      - id: VQ-02
+        name: No Overlap
+        score: 8
+        max: 8
+        passed: true
+        comment: No overlapping text elements
+      - id: VQ-03
+        name: Element Visibility
+        score: 6
+        max: 8
+        passed: true
+        comment: Points visible with good alpha, density ribbons clear but could use
+          more contrast
+      - id: VQ-04
+        name: Color Accessibility
+        score: 5
+        max: 5
+        passed: true
+        comment: Colorblind-safe palette (Dark2 teal/orange/purple)
+      - id: VQ-05
+        name: Layout Balance
+        score: 3
+        max: 5
+        passed: true
+        comment: Good 4x4 grid but some wasted space around edges
+      - id: VQ-06
+        name: Axis Labels
+        score: 0
+        max: 2
+        passed: false
+        comment: Axis labels are empty (shown in strip text instead)
+      - id: VQ-07
+        name: Grid & Legend
+        score: 2
+        max: 2
+        passed: true
+        comment: Subtle grid, legend well-placed at bottom
+    spec_compliance:
+      score: 17
+      max: 25
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 8
+        max: 8
+        passed: true
+        comment: Correct scatter matrix with density diagonals
+      - id: SC-02
+        name: Data Mapping
+        score: 5
+        max: 5
+        passed: true
+        comment: All 4 variables correctly mapped to pairwise scatter
+      - id: SC-03
+        name: Required Features
+        score: 0
+        max: 5
+        passed: false
+        comment: 'Missing interactive features: no brushing/linked selection, no zoom/pan
+          (plotnine is static - spec notes limitations)'
+      - id: SC-04
+        name: Data Range
+        score: 3
+        max: 3
+        passed: true
+        comment: All data visible within axes
+      - id: SC-05
+        name: Legend Accuracy
+        score: 1
+        max: 2
+        passed: false
+        comment: Species legend correct but no interactive indicator
+      - id: SC-06
+        name: Title Format
+        score: 2
+        max: 2
+        passed: true
+        comment: 'Correct format: scatter-matrix-interactive · plotnine · pyplots.ai'
+    data_quality:
+      score: 18
+      max: 20
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 7
+        max: 8
+        passed: true
+        comment: Shows correlations, clusters separation visible, density distributions
+          on diagonal
+      - id: DQ-02
+        name: Realistic Context
+        score: 7
+        max: 7
+        passed: true
+        comment: Classic Iris dataset - real botanical data
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 5
+        max: 5
+        passed: true
+        comment: Real measurements in centimeters
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: 'Linear flow: imports -> data -> plot -> save'
+      - id: CQ-02
+        name: Reproducibility
+        score: 3
+        max: 3
+        passed: true
+        comment: np.random.seed(42) set
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: All imports used
+      - id: CQ-04
+        name: No Deprecated API
+        score: 1
+        max: 1
+        passed: true
+        comment: Current plotnine syntax
+      - id: CQ-05
+        name: Output Correct
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png
+    library_features:
+      score: 5
+      max: 5
+      items:
+      - id: LF-01
+        name: Distinctive Features
+        score: 5
+        max: 5
+        passed: true
+        comment: 'Excellent use of ggplot2 grammar: facet_grid, geom_ribbon for density,
+          aes mapping, scale_color_manual, theme customization'
+  verdict: APPROVED
+impl_tags:
+  dependencies:
+  - sklearn
+  techniques:
+  - faceting
+  - layer-composition
+  patterns:
+  - dataset-loading
+  - wide-to-long
+  - iteration-over-groups
+  dataprep:
+  - binning
+  styling:
+  - alpha-blending
+  - grid-styling