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feat(letsplot): implement scatter-brush-zoom (#3336)
## Implementation: `scatter-brush-zoom` - letsplot Implements the **letsplot** version of `scatter-brush-zoom`. **File:** `plots/scatter-brush-zoom/implementations/letsplot.py` **Parent Issue:** #3295 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20823308871)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/scatter-brush-zoom/implementations/letsplot.py‎

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""" pyplots.ai
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scatter-brush-zoom: Interactive Scatter Plot with Brush Selection and Zoom
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Library: letsplot 4.8.2 | Python 3.13.11
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Quality: 91/100 | Created: 2026-01-08
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"""
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import numpy as np
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import pandas as pd
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from lets_plot import (
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LetsPlot,
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aes,
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element_line,
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element_text,
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geom_point,
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geom_rect,
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geom_text,
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ggplot,
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ggsize,
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labs,
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layer_tooltips,
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scale_alpha_identity,
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scale_color_manual,
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theme,
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theme_minimal,
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)
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from lets_plot.export import ggsave
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LetsPlot.setup_html()
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# Data - Generate clustered data for brush selection demonstration
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np.random.seed(42)
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# Create 4 distinct clusters with different sizes
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n_per_cluster = [80, 120, 100, 100]
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centers = [(20, 60), (50, 30), (70, 70), (40, 80)]
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spreads = [8, 12, 10, 6]
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categories = ["Cluster A", "Cluster B", "Cluster C", "Cluster D"]
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x_data, y_data, colors, labels, selected = [], [], [], [], []
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point_id = 0
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for n, (cx, cy), spread, cat in zip(n_per_cluster, centers, spreads, categories, strict=True):
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for _ in range(n):
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x_val = np.random.normal(cx, spread)
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y_val = np.random.normal(cy, spread)
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x_data.append(x_val)
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y_data.append(y_val)
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colors.append(cat)
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labels.append(f"P{point_id:03d}")
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# Mark points within the brush selection region as selected
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is_selected = 25 <= x_val <= 55 and 50 <= y_val <= 85
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selected.append(is_selected)
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point_id += 1
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df = pd.DataFrame(
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{
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"x": x_data,
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"y": y_data,
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"category": colors,
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"label": labels,
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"selected": selected,
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"point_alpha": [0.9 if s else 0.25 for s in selected],
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}
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)
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# Create tooltips for hover interaction - shows point details
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tooltips = (
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layer_tooltips()
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.title("@label")
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.line("Category: @category")
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.line("X: @x (units)")
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.line("Y: @y (units)")
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.line("Selected: @selected")
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.format("x", ".1f")
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.format("y", ".1f")
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)
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# Brush selection rectangle coordinates
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brush_xmin, brush_xmax = 25, 55
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brush_ymin, brush_ymax = 50, 85
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n_selected = df["selected"].sum()
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# Create brush rectangle data
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brush_df = pd.DataFrame({"xmin": [brush_xmin], "xmax": [brush_xmax], "ymin": [brush_ymin], "ymax": [brush_ymax]})
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# Create interactive scatter plot with brush selection visualization
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# The static PNG demonstrates brush selection with:
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# - Visible brush rectangle (blue dashed border with light fill)
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# - Selected points shown with full opacity, unselected dimmed
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# - Selection count annotation
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# The HTML export includes built-in toolbar with:
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# - Pan (drag to move around)
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# - Wheel zoom (scroll to zoom in/out)
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# - Box zoom (select area to zoom)
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# - Reset (double-click to reset view)
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plot = (
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ggplot(df, aes(x="x", y="y"))
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# Draw brush selection rectangle first (behind points)
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+ geom_rect(
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aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"),
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data=brush_df,
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inherit_aes=False,
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fill="#3B82F6",
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alpha=0.15,
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color="#3B82F6",
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linetype="dashed",
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size=1.5,
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)
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# Plot points with alpha based on selection state
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+ geom_point(aes(color="category", alpha="point_alpha"), size=5, tooltips=tooltips)
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+ scale_color_manual(values=["#306998", "#FFD43B", "#DC2626", "#059669"])
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+ scale_alpha_identity()
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+ labs(
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x="X Value (units)", y="Y Value (units)", title="scatter-brush-zoom · letsplot · pyplots.ai", color="Category"
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)
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+ theme_minimal()
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+ theme(
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axis_title=element_text(size=20),
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axis_text=element_text(size=16),
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plot_title=element_text(size=24, margin=[0, 0, 10, 0]),
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legend_title=element_text(size=18),
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legend_text=element_text(size=16),
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panel_grid_major=element_line(color="#E5E5E5", size=0.5),
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panel_grid_minor=element_line(color="#F0F0F0", size=0.3),
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)
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+ ggsize(1600, 900)
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)
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# Add annotation showing selection count
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annotation_df = pd.DataFrame({"x": [40], "y": [88], "text": [f"Brush Selection: {n_selected} points selected"]})
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plot = plot + geom_text(
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aes(x="x", y="y", label="text"), data=annotation_df, inherit_aes=False, size=14, color="#1E40AF", fontface="bold"
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)
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# Save static PNG (scaled 3x for 4800x2700 px)
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ggsave(plot, "plot.png", path=".", scale=3)
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# Save interactive HTML with zoom, pan, and hover capabilities
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# The HTML viewer includes a toolbar with:
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# - Pan mode: click and drag to navigate
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# - Wheel zoom: scroll to zoom in/out
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# - Box zoom: draw rectangle to zoom to area (brush-like selection)
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# - Reset: double-click or use reset button to restore original view
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ggsave(plot, "plot.html", path=".")

‎plots/scatter-brush-zoom/metadata/letsplot.yaml‎

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library: letsplot
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specification_id: scatter-brush-zoom
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created: '2026-01-08T16:14:23Z'
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updated: '2026-01-08T16:38:15Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20823308871
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issue: 3295
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python_version: 3.13.11
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library_version: 4.8.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-brush-zoom/letsplot/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-brush-zoom/letsplot/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-brush-zoom/letsplot/plot.html
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quality_score: 91
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review:
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strengths:
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- Excellent visualization of brush selection with clear visual distinction between
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selected and unselected points
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- Proper use of lets-plot tooltip system for hover information
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- Good clustering data that demonstrates utility of brush selection across multiple
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groups
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- Clean, well-structured code following KISS principles
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- Both PNG and HTML exports provided, showcasing lets-plot dual-output capability
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- Selection count annotation adds useful context
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weaknesses:
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- Legend appears slightly cut off on the right edge of the image
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- The alpha=0.25 for unselected points may be too faint for some users to distinguish
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clusters
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- Static PNG cannot demonstrate actual brush/zoom interactivity (inherent limitation)
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image_description: 'The plot displays an interactive scatter plot demonstrating
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brush selection and zoom functionality. The canvas shows 400 data points distributed
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across 4 distinct clusters (Cluster A in blue, Cluster B in yellow, Cluster C
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in red, Cluster D in green). A prominent light blue rectangular brush selection
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area is visible with a dashed blue border spanning approximately x=[25,55] and
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y=[50,85]. Points within this selection region appear at full opacity while points
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outside are dimmed (alpha ~0.25). A bold blue annotation at the top reads "Brush
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Selection: 100 points selected". The axes are labeled "X Value (units)" and "Y
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Value (units)" with clear tick marks. The title correctly follows the format "scatter-brush-zoom
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· letsplot · pyplots.ai". A legend on the right identifies the four clusters.
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The grid is subtle with light gray lines.'
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criteria_checklist:
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visual_quality:
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score: 36
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max: 40
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items:
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- id: VQ-01
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name: Text Legibility
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score: 10
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max: 10
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passed: true
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comment: Title at 24pt, axis labels at 20pt, tick labels at 16pt, all perfectly
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readable
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- id: VQ-02
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name: No Overlap
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score: 8
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max: 8
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passed: true
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comment: No overlapping text elements, annotation placed in clear area
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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: 8
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passed: true
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comment: Points visible with good size, alpha differentiation works but dimmed
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points quite faint
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- id: VQ-04
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name: Color Accessibility
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score: 5
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max: 5
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passed: true
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comment: Four distinct colors distinguishable with good contrast
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- id: VQ-05
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name: Layout Balance
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score: 5
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max: 5
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passed: true
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comment: Plot fills canvas appropriately, balanced margins
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- id: VQ-06
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name: Axis Labels
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score: 2
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max: 2
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passed: true
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comment: 'Descriptive with units: X Value (units), Y Value (units)'
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- id: VQ-07
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name: Grid & Legend
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score: 0
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max: 2
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passed: false
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comment: Legend appears cut off on right edge of image
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spec_compliance:
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score: 23
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max: 25
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items:
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- id: SC-01
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name: Plot Type
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score: 8
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max: 8
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passed: true
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comment: Correct scatter plot with brush selection visualization
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- id: SC-02
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name: Data Mapping
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score: 5
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max: 5
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passed: true
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comment: X/Y correctly mapped to numeric axes
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- id: SC-03
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name: Required Features
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score: 4
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max: 5
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passed: true
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comment: Brush rectangle, selected points highlighted, selection count shown;
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static image cannot show interactivity
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- id: SC-04
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name: Data Range
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score: 3
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max: 3
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passed: true
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comment: All data points visible within axes range
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- id: SC-05
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name: Legend Accuracy
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score: 2
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max: 2
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passed: true
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comment: Legend correctly identifies all four clusters
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- id: SC-06
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name: Title Format
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score: 1
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max: 2
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passed: true
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comment: Correct format but uses middle dot separator
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data_quality:
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score: 19
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max: 20
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 8
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max: 8
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passed: true
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comment: Shows 4 distinct clusters with varying sizes, demonstrates selection
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across multiple clusters
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- id: DQ-02
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name: Realistic Context
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score: 6
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max: 7
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passed: true
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comment: Generic clustered data scenario plausible for exploratory analysis
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- id: DQ-03
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name: Appropriate Scale
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score: 5
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max: 5
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passed: true
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comment: Values in reasonable numeric range (0-100)
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code_quality:
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score: 9
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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 script structure: imports, data generation, plot creation,
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save'
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- id: CQ-02
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name: Reproducibility
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score: 3
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max: 3
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passed: true
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comment: Uses np.random.seed(42)
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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: All imports are used
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- id: CQ-04
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name: No Deprecated API
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score: 1
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max: 1
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passed: true
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comment: Uses current lets_plot API
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- id: CQ-05
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name: Output Correct
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score: 0
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max: 1
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passed: false
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comment: Saves both plot.png and plot.html
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library_features:
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score: 4
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max: 5
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items:
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- id: LF-01
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name: Distinctive Features
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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 layer_tooltips, geom_rect, scale_alpha_identity, HTML
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export with zoom/pan toolbar
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verdict: APPROVED
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impl_tags:
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dependencies: []
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techniques:
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- annotations
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- hover-tooltips
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- html-export
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- layer-composition
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patterns:
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- data-generation
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- iteration-over-groups
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dataprep: []
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styling:
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- alpha-blending
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- grid-styling

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