feat(bokeh): implement cat-box-strip

plots/cat-box-strip/implementations/bokeh.py

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+""" pyplots.ai
+cat-box-strip: Box Plot with Strip Overlay
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, Whisker
+from bokeh.plotting import figure
+from bokeh.transform import jitter
+
+
+# Data - Plant growth measurements across different soil types
+np.random.seed(42)
+
+categories = ["Sandy", "Clay", "Loamy", "Silty"]
+n_per_group = [35, 40, 45, 38]
+
+# Generate data with different distributions per group
+data = []
+for cat, n in zip(categories, n_per_group, strict=True):
+    if cat == "Sandy":
+        values = np.random.normal(25, 6, n)  # Lower growth, moderate variance
+    elif cat == "Clay":
+        values = np.random.normal(32, 8, n)  # Medium growth, high variance
+        values = np.append(values, [55, 58])  # Add outliers
+    elif cat == "Loamy":
+        values = np.random.normal(42, 5, n)  # High growth, low variance
+    else:  # Silty
+        values = np.random.normal(35, 7, n)  # Medium-high growth
+        values = np.append(values, [12, 14])  # Add low outliers
+
+    for v in values:
+        data.append({"category": cat, "value": v})
+
+df = pd.DataFrame(data)
+
+# Calculate box plot statistics for each category
+box_data = {"category": [], "q1": [], "q2": [], "q3": [], "upper": [], "lower": []}
+
+for cat in categories:
+    group = df[df["category"] == cat]["value"]
+    q1 = group.quantile(0.25)
+    q2 = group.quantile(0.50)
+    q3 = group.quantile(0.75)
+    iqr = q3 - q1
+    upper_whisker = group[group <= q3 + 1.5 * iqr].max()
+    lower_whisker = group[group >= q1 - 1.5 * iqr].min()
+
+    box_data["category"].append(cat)
+    box_data["q1"].append(q1)
+    box_data["q2"].append(q2)
+    box_data["q3"].append(q3)
+    box_data["upper"].append(upper_whisker)
+    box_data["lower"].append(lower_whisker)
+
+box_source = ColumnDataSource(data=box_data)
+
+# Create figure with categorical x-axis
+p = figure(
+    width=4800,
+    height=2700,
+    x_range=categories,
+    title="cat-box-strip · bokeh · pyplots.ai",
+    x_axis_label="Soil Type",
+    y_axis_label="Plant Growth (cm)",
+    tools="",
+    toolbar_location=None,
+)
+
+# Styling - scaled for 4800x2700 canvas
+p.title.text_font_size = "36pt"
+p.xaxis.axis_label_text_font_size = "28pt"
+p.yaxis.axis_label_text_font_size = "28pt"
+p.xaxis.major_label_text_font_size = "24pt"
+p.yaxis.major_label_text_font_size = "22pt"
+p.xaxis.axis_label_standoff = 25
+p.yaxis.axis_label_standoff = 25
+
+# Grid styling
+p.grid.grid_line_alpha = 0.3
+p.grid.grid_line_dash = [6, 4]
+p.xgrid.grid_line_color = None
+
+# Background
+p.background_fill_color = "#fafafa"
+
+# Draw whiskers using the Whisker annotation
+upper_whisker = Whisker(
+    source=box_source, base="category", upper="upper", lower="q3", line_color="#306998", line_width=2.5
+)
+upper_whisker.upper_head.size = 30
+upper_whisker.upper_head.line_color = "#306998"
+upper_whisker.upper_head.line_width = 2.5
+upper_whisker.lower_head.size = 0
+p.add_layout(upper_whisker)
+
+lower_whisker = Whisker(
+    source=box_source, base="category", upper="q1", lower="lower", line_color="#306998", line_width=2.5
+)
+lower_whisker.lower_head.size = 30
+lower_whisker.lower_head.line_color = "#306998"
+lower_whisker.lower_head.line_width = 2.5
+lower_whisker.upper_head.size = 0
+p.add_layout(lower_whisker)
+
+# Draw boxes (IQR range) - upper half
+p.vbar(
+    x="category",
+    top="q3",
+    bottom="q2",
+    width=0.5,
+    source=box_source,
+    fill_color="#306998",
+    fill_alpha=0.4,
+    line_color="#306998",
+    line_width=3,
+)
+
+# Draw boxes (IQR range) - lower half
+p.vbar(
+    x="category",
+    top="q2",
+    bottom="q1",
+    width=0.5,
+    source=box_source,
+    fill_color="#306998",
+    fill_alpha=0.4,
+    line_color="#306998",
+    line_width=3,
+)
+
+# Median line (horizontal segment across the box)
+p.segment(x0="category", x1="category", y0="q2", y1="q2", source=box_source, line_color="#1a3d5c", line_width=4)
+
+# Strip plot overlay with jitter
+strip_source = ColumnDataSource(data={"category": df["category"], "value": df["value"]})
+
+p.scatter(
+    x=jitter("category", width=0.3, range=p.x_range),
+    y="value",
+    source=strip_source,
+    size=16,
+    fill_color="#FFD43B",
+    fill_alpha=0.75,
+    line_color="#b8860b",
+    line_width=2,
+)
+
+# Save PNG
+export_png(p, filename="plot.png")
+
+# Save HTML for interactive version
+output_file("plot.html")
+save(p)

plots/cat-box-strip/metadata/bokeh.yaml

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+library: bokeh
+specification_id: cat-box-strip
+created: '2025-12-30T16:31:45Z'
+updated: '2025-12-30T16:44:16Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20601057657
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/cat-box-strip/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/cat-box-strip/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/cat-box-strip/bokeh/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent manual box plot construction using Bokeh primitives (Whisker, vbar,
+    segment)
+  - Good use of jitter transform for strip points to reduce overlap
+  - Proper colorblind-safe color scheme (blue/yellow contrast)
+  - Well-calculated IQR-based whiskers with proper outlier handling
+  - Clear demonstration of different distributions and outliers across categories
+  - Font sizes properly scaled for 4800x2700 canvas
+  weaknesses:
+  - No legend explaining what box/strip elements represent (minor for this plot type)
+  - Could add HoverTool to show exact values on hover for the HTML version
+  - The median line segment has zero width (x0=x1) which works but is technically
+    a point not a line