feat(bokeh): implement box-basic (#315)

## Summary
Implements `box-basic` for **bokeh** library.

**Parent Issue:** #204
**Sub-Issue:** #233
**Base Branch:** `plot/box-basic`
**Attempt:** 2/3

## Implementation
- `plots/bokeh/custom/box-basic/default.py`

## Features
- Clean KISS-style implementation (no functions, no classes)
- Uses spec-defined data structure with `group` and `value` columns
- Displays Q1, median, Q3 boxes with whiskers extending to 1.5×IQR
- Shows outliers as individual red points (#DC2626)
- Correct figure dimensions (4800×2700 px)
- Font sizes match default-style-guide.md recommendations (20pt labels,
16pt ticks)
- Uses style guide color palette (#306998, #FFD43B, #DC2626, #059669)

## Changes in This Attempt
- Updated to use the official style guide color palette instead of
Category10_4
- Selenium dependencies already added for PNG export

---------

Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>

Author: claude[bot]

plots/bokeh/custom/box-basic/default.py

@@ -1,248 +1,137 @@
 """
 box-basic: Basic Box Plot
-Implementation for: bokeh
-Variant: default
-Python: 3.10+
+Library: bokeh
 """
 
-from typing import TYPE_CHECKING, Optional
-
 import numpy as np
 import pandas as pd
-from bokeh.models import ColumnDataSource, FixedTicker, Label, Whisker
+from bokeh.io import export_png
+from bokeh.models import ColumnDataSource, FixedTicker, Whisker
 from bokeh.plotting import figure
 
 
-if TYPE_CHECKING:
-    from bokeh.plotting import Figure
-
-
-def create_plot(
-    data: pd.DataFrame,
-    values: str,
-    groups: str,
-    title: Optional[str] = None,
-    xlabel: Optional[str] = None,
-    ylabel: Optional[str] = None,
-    colors: Optional[list] = None,
-    width: int = 1600,
-    height: int = 900,
-    **kwargs,
-) -> Figure:
-    """
-    Create a basic box plot showing statistical distribution of multiple groups using bokeh.
-
-    Args:
-        data: Input DataFrame with required columns
-        values: Column name containing numeric values
-        groups: Column name containing group categories
-        title: Plot title (optional)
-        xlabel: Custom x-axis label (optional, defaults to groups column name)
-        ylabel: Custom y-axis label (optional, defaults to values column name)
-        colors: List of colors for each box (optional)
-        width: Figure width in pixels (default: 1600)
-        height: Figure height in pixels (default: 900)
-        **kwargs: Additional parameters
-
-    Returns:
-        Bokeh Figure object
-
-    Raises:
-        ValueError: If data is empty
-        KeyError: If required columns not found
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
-        ...     'Value': [1, 2, 2, 3, 3, 4]
-        ... })
-        >>> fig = create_plot(data, values='Value', groups='Group')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    # Check required columns
-    for col in [values, groups]:
-        if col not in data.columns:
-            available = ", ".join(data.columns)
-            raise KeyError(f"Column '{col}' not found. Available columns: {available}")
-
-    # Calculate box plot statistics for each group
-    group_names = sorted(data[groups].unique())
-    n_groups = len(group_names)
-
-    # Prepare data for box plot
-    stats = {"x": [], "q1": [], "q2": [], "q3": [], "upper": [], "lower": [], "group": []}
-    outliers = {"x": [], "y": []}
-
-    for i, group in enumerate(group_names):
-        group_data = data[data[groups] == group][values].dropna()
-
-        q1 = group_data.quantile(0.25)
-        q2 = group_data.quantile(0.5)
-        q3 = group_data.quantile(0.75)
-        iqr = q3 - q1
-        upper = min(group_data.max(), q3 + 1.5 * iqr)
-        lower = max(group_data.min(), q1 - 1.5 * iqr)
-
-        stats["x"].append(i)
-        stats["q1"].append(q1)
-        stats["q2"].append(q2)
-        stats["q3"].append(q3)
-        stats["upper"].append(upper)
-        stats["lower"].append(lower)
-        stats["group"].append(group)
-
-        # Find outliers
-        outlier_data = group_data[(group_data < lower) | (group_data > upper)]
-        for val in outlier_data:
-            outliers["x"].append(i)
-            outliers["y"].append(val)
-
-    # Set colors
-    if not colors:
-        from bokeh.palettes import Set2_8
-
-        colors = Set2_8[:n_groups]
-
-    # Create figure with numeric x-axis
-    p = figure(
-        width=width,
-        height=height,
-        title=title or "Box Plot Distribution",
-        toolbar_location="above",
-        tools="pan,wheel_zoom,box_zoom,reset,save",
+# Data
+np.random.seed(42)
+data = pd.DataFrame(
+    {
+        "group": ["A"] * 50 + ["B"] * 50 + ["C"] * 50 + ["D"] * 50,
+        "value": np.concatenate(
+            [
+                np.random.normal(50, 10, 50),
+                np.random.normal(60, 15, 50),
+                np.random.normal(45, 8, 50),
+                np.random.normal(70, 20, 50),
+            ]
+        ),
+    }
+)
+
+# Calculate box plot statistics for each group
+group_names = sorted(data["group"].unique())
+n_groups = len(group_names)
+
+stats = {"x": [], "q1": [], "q2": [], "q3": [], "upper": [], "lower": []}
+outliers = {"x": [], "y": []}
+
+for i, group in enumerate(group_names):
+    group_data = data[data["group"] == group]["value"].dropna()
+
+    q1 = group_data.quantile(0.25)
+    q2 = group_data.quantile(0.50)
+    q3 = group_data.quantile(0.75)
+    iqr = q3 - q1
+
+    upper_fence = q3 + 1.5 * iqr
+    lower_fence = q1 - 1.5 * iqr
+    upper = group_data[group_data <= upper_fence].max()
+    lower = group_data[group_data >= lower_fence].min()
+
+    stats["x"].append(i)
+    stats["q1"].append(q1)
+    stats["q2"].append(q2)
+    stats["q3"].append(q3)
+    stats["upper"].append(upper)
+    stats["lower"].append(lower)
+
+    # Find outliers
+    outlier_data = group_data[(group_data < lower_fence) | (group_data > upper_fence)]
+    for val in outlier_data:
+        outliers["x"].append(i)
+        outliers["y"].append(val)
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="Basic Box Plot",
+    x_axis_label="Group",
+    y_axis_label="Value",
+    toolbar_location="above",
+    tools="pan,wheel_zoom,box_zoom,reset,save",
+)
+
+source = ColumnDataSource(data=stats)
+box_width = 0.5
+# Style guide color palette
+colors = ["#306998", "#FFD43B", "#DC2626", "#059669"]
+
+# Draw boxes (Q1 to Q3)
+for i, color in enumerate(colors):
+    p.vbar(
+        x=i,
+        width=box_width,
+        bottom=stats["q1"][i],
+        top=stats["q3"][i],
+        fill_color=color,
+        fill_alpha=0.7,
+        line_color="#333333",
+        line_width=2,
     )
 
-    source = ColumnDataSource(data=stats)
-
-    # Draw boxes (Q1 to Q3)
-    box_width = 0.5
-    for i, color in enumerate(colors):
-        p.vbar(
-            x=i,
-            width=box_width,
-            bottom=stats["q1"][i],
-            top=stats["q3"][i],
-            fill_color=color,
-            line_color="black",
-            alpha=0.7,
-        )
-
-    # Draw median lines
-    for i in range(n_groups):
-        p.segment(
-            x0=i - box_width / 2,
-            y0=stats["q2"][i],
-            x1=i + box_width / 2,
-            y1=stats["q2"][i],
-            line_color="red",
-            line_width=2,
-        )
-
-    # Draw whiskers
-    upper_whisker = Whisker(base="x", upper="upper", lower="q3", source=source, line_color="black")
-    upper_whisker.upper_head.size = 10
-    upper_whisker.lower_head.size = 0
-    p.add_layout(upper_whisker)
-
-    lower_whisker = Whisker(base="x", upper="q1", lower="lower", source=source, line_color="black")
-    lower_whisker.upper_head.size = 0
-    lower_whisker.lower_head.size = 10
-    p.add_layout(lower_whisker)
-
-    # Draw outliers
-    if outliers["x"]:
-        outlier_source = ColumnDataSource(data=outliers)
-        p.scatter(x="x", y="y", source=outlier_source, size=8, color="red", alpha=0.5, line_color="black", line_width=1)
-
-    # Set x-axis to show group names
-    p.xaxis.ticker = FixedTicker(ticks=list(range(n_groups)))
-    p.xaxis.major_label_overrides = dict(enumerate(group_names))
-
-    # Labels
-    p.xaxis.axis_label = xlabel or groups
-    p.yaxis.axis_label = ylabel or values
-
-    # Styling
-    p.title.text_font_size = "14pt"
-    p.title.align = "center"
-    p.ygrid.grid_line_alpha = 0.3
-    p.ygrid.grid_line_dash = [6, 4]
-    p.xgrid.visible = False
-
-    # Add sample size annotations
-    group_counts = data.groupby(groups)[values].count()
-    y_min = data[values].min()
-    y_range = data[values].max() - y_min
-    for i, group in enumerate(group_names):
-        count = group_counts[group]
-        label = Label(
-            x=i, y=y_min - y_range * 0.08, text=f"n={count}", text_align="center", text_font_size="9pt", text_alpha=0.7
-        )
-        p.add_layout(label)
-
-    return p
-
-
-if __name__ == "__main__":
-    # Sample data for testing with different distributions per group
-    np.random.seed(42)
-
-    data_dict = {"Group": [], "Value": []}
-
-    # Group A: Normal distribution
-    group_a_data = np.random.normal(50, 10, 40)
-    group_a_data = np.append(group_a_data, [80, 85, 15])
-
-    # Group B: Normal distribution
-    group_b_data = np.random.normal(60, 15, 35)
-    group_b_data = np.append(group_b_data, [100, 10])
-
-    # Group C: Normal distribution
-    group_c_data = np.random.normal(45, 8, 45)
-
-    # Group D: Skewed distribution
-    group_d_data = np.random.gamma(2, 2, 30) + 40
-    group_d_data = np.append(group_d_data, [75, 78, 20])
-
-    # Combine all data
-    for group, values in zip(
-        ["Group A", "Group B", "Group C", "Group D"],
-        [group_a_data, group_b_data, group_c_data, group_d_data],
-        strict=False,
-    ):
-        data_dict["Group"].extend([group] * len(values))
-        data_dict["Value"].extend(values)
-
-    data = pd.DataFrame(data_dict)
-
-    # Create plot
-    fig = create_plot(
-        data,
-        values="Value",
-        groups="Group",
-        title="Statistical Distribution Comparison Across Groups",
-        ylabel="Measurement Value",
-        xlabel="Categories",
+# Draw median lines
+for i in range(n_groups):
+    p.segment(
+        x0=i - box_width / 2,
+        y0=stats["q2"][i],
+        x1=i + box_width / 2,
+        y1=stats["q2"][i],
+        line_color="#333333",
+        line_width=3,
     )
 
-    # Save as PNG using webdriver-manager for automatic chromedriver
-    from bokeh.io import export_png
-    from selenium import webdriver
-    from selenium.webdriver.chrome.options import Options
-    from selenium.webdriver.chrome.service import Service
-    from webdriver_manager.chrome import ChromeDriverManager
-
-    chrome_options = Options()
-    chrome_options.add_argument("--headless")
-    chrome_options.add_argument("--no-sandbox")
-    chrome_options.add_argument("--disable-dev-shm-usage")
-
-    service = Service(ChromeDriverManager().install())
-    driver = webdriver.Chrome(service=service, options=chrome_options)
+# Draw whiskers
+upper_whisker = Whisker(base="x", upper="upper", lower="q3", source=source, line_color="#333333", line_width=2)
+upper_whisker.upper_head.size = 20
+upper_whisker.upper_head.line_width = 2
+upper_whisker.lower_head.size = 0
+p.add_layout(upper_whisker)
+
+lower_whisker = Whisker(base="x", upper="q1", lower="lower", source=source, line_color="#333333", line_width=2)
+lower_whisker.upper_head.size = 0
+lower_whisker.lower_head.size = 20
+lower_whisker.lower_head.line_width = 2
+p.add_layout(lower_whisker)
+
+# Draw outliers
+if outliers["x"]:
+    outlier_source = ColumnDataSource(data=outliers)
+    p.scatter(
+        x="x", y="y", source=outlier_source, size=12, color="#DC2626", alpha=0.7, line_color="#333333", line_width=1
+    )
 
-    export_png(fig, filename="plot.png", webdriver=driver)
-    driver.quit()
-    print("Plot saved to plot.png")
+# Set x-axis to show group names
+p.xaxis.ticker = FixedTicker(ticks=list(range(n_groups)))
+p.xaxis.major_label_overrides = dict(enumerate(group_names))
+
+# Styling
+p.title.text_font_size = "20pt"
+p.title.align = "center"
+p.xaxis.axis_label_text_font_size = "20pt"
+p.yaxis.axis_label_text_font_size = "20pt"
+p.xaxis.major_label_text_font_size = "16pt"
+p.yaxis.major_label_text_font_size = "16pt"
+p.ygrid.grid_line_alpha = 0.3
+p.xgrid.visible = False
+
+# Save as PNG (requires selenium + webdriver)
+export_png(p, filename="plot.png")

pyproject.toml

@@ -75,7 +75,7 @@ plotting = [
 lib-matplotlib = ["matplotlib>=3.9.0", "numpy>=1.26.0", "pandas>=2.2.0"]
 lib-seaborn = ["seaborn>=0.13.0", "matplotlib>=3.9.0", "numpy>=1.26.0", "pandas>=2.2.0"]
 lib-plotly = ["plotly>=5.18.0", "kaleido>=0.2.1", "numpy>=1.26.0", "pandas>=2.2.0"]
-lib-bokeh = ["bokeh>=3.4.0", "numpy>=1.26.0", "pandas>=2.2.0"]
+lib-bokeh = ["bokeh>=3.4.0", "numpy>=1.26.0", "pandas>=2.2.0", "selenium>=4.15.0", "webdriver-manager>=4.0.0"]
 lib-altair = ["altair>=5.2.0", "vl-convert-python>=1.3.0", "numpy>=1.26.0", "pandas>=2.2.0"]
 lib-plotnine = ["plotnine>=0.13.0", "numpy>=1.26.0", "pandas>=2.2.0"]
 lib-pygal = ["pygal>=3.0.0", "cairosvg>=2.7.0", "pandas>=2.2.0"]