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github-actions merged 2 commits into from
Dec 7, 2025
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feat(bokeh): implement pie-basic #365

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feat(bokeh): implement pie-basic
github-actions[bot] Dec 7, 2025
9a487a0
chore: merge selenium fix
MarkusNeusinger Dec 7, 2025
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300 changes: 101 additions & 199 deletions plots/bokeh/custom/pie-basic/default.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,18 +4,15 @@
"""

import math
from typing import TYPE_CHECKING

import pandas as pd
from bokeh.io import export_png
from bokeh.models import ColumnDataSource, Label, Legend, LegendItem
from bokeh.plotting import figure


if TYPE_CHECKING:
from bokeh.plotting import figure as Figure

# PyPlots.ai style colors
PYPLOTS_COLORS = [
COLORS = [
"#306998", # Python Blue
"#FFD43B", # Python Yellow
"#DC2626", # Signal Red
Expand All @@ -24,198 +21,103 @@
"#F97316", # Orange
]


def create_plot(
data: pd.DataFrame,
category: str,
value: str,
title: str | None = None,
colors: list[str] | None = None,
startangle: float = 90,
legend: bool = True,
legend_loc: str = "right",
**kwargs,
) -> "Figure":
"""
Create a basic pie chart using Bokeh wedge glyphs.

Bokeh does not have a native pie chart method, so this implementation
uses wedge glyphs to construct the pie chart manually.

Args:
data: Input DataFrame containing category and value columns
category: Column name for category labels (slice names)
value: Column name for numeric values (slice sizes)
title: Plot title (optional)
colors: Custom color palette for slices (defaults to PyPlots colors)
startangle: Starting angle for first slice in degrees (default: 90)
legend: Whether to display legend (default: True)
legend_loc: Legend location - 'right', 'left', 'above', 'below' (default: 'right')
**kwargs: Additional parameters passed to figure

Returns:
Bokeh figure object

Raises:
ValueError: If data is empty or values are all zero/negative
KeyError: If required columns not found in data

Example:
>>> data = pd.DataFrame({
... 'category': ['A', 'B', 'C'],
... 'value': [30, 50, 20]
... })
>>> fig = create_plot(data, 'category', 'value', title='Distribution')
"""
# Input validation
if data.empty:
raise ValueError("Data cannot be empty")

for col in [category, value]:
if col not in data.columns:
available = ", ".join(data.columns)
raise KeyError(f"Column '{col}' not found. Available: {available}")

# Validate numeric values
if not pd.api.types.is_numeric_dtype(data[value]):
raise ValueError(f"Column '{value}' must contain numeric values")

if (data[value] < 0).any():
raise ValueError("Pie chart values must be non-negative")

total = data[value].sum()
if total == 0:
raise ValueError("Sum of values cannot be zero")

# Prepare data
plot_data = data.copy()
plot_data["angle"] = plot_data[value] / total * 2 * math.pi
plot_data["percentage"] = plot_data[value] / total * 100

# Calculate cumulative angles for wedge positioning
plot_data["end_angle"] = plot_data["angle"].cumsum()
plot_data["start_angle"] = plot_data["end_angle"] - plot_data["angle"]

# Apply start angle offset (convert degrees to radians, adjust for Bokeh's coordinate system)
start_rad = math.radians(startangle - 90)
plot_data["start_angle"] = plot_data["start_angle"] + start_rad
plot_data["end_angle"] = plot_data["end_angle"] + start_rad

# Assign colors
if colors is None:
colors = PYPLOTS_COLORS
# Cycle through colors if more categories than colors
num_categories = len(plot_data)
plot_data["color"] = [colors[i % len(colors)] for i in range(num_categories)]

# Create ColumnDataSource
source = ColumnDataSource(plot_data)

# Create figure - use range to ensure circular aspect ratio
# Set frame dimensions to maintain 16:9 overall but circular pie
fig_width = kwargs.get("width", 1600)
fig_height = kwargs.get("height", 900)

p = figure(
width=fig_width,
height=fig_height,
title=title,
tools="hover",
tooltips=[(category.capitalize(), f"@{category}"), ("Value", f"@{value}"), ("Percentage", "@percentage{0.1}%")],
x_range=(-1.2, 2.0 if legend else 1.2),
y_range=(-1.2, 1.2),
)

# Draw wedges (pie slices)
renderers = p.wedge(
x=0,
y=0,
radius=0.9,
start_angle="start_angle",
end_angle="end_angle",
line_color="white",
line_width=2,
fill_color="color",
source=source,
)

# Add percentage labels inside slices
for _, row in plot_data.iterrows():
# Calculate label position at middle of wedge, 60% from center
mid_angle = (row["start_angle"] + row["end_angle"]) / 2
label_radius = 0.55

x = label_radius * math.cos(mid_angle)
y = label_radius * math.sin(mid_angle)

# Only show percentage label if slice is large enough
if row["percentage"] >= 5:
label = Label(
x=x,
y=y,
text=f"{row['percentage']:.1f}%",
text_font_size="14pt",
text_align="center",
text_baseline="middle",
text_color="white" if row["percentage"] >= 10 else "black",
)
p.add_layout(label)

# Configure legend
if legend:
legend_items = []
for i, cat in enumerate(plot_data[category]):
legend_items.append(LegendItem(label=str(cat), renderers=[renderers], index=i))

leg = Legend(
items=legend_items,
location="center",
label_text_font_size="16pt",
background_fill_color="white",
background_fill_alpha=1.0,
border_line_color="black",
border_line_width=1,
# Data
data = pd.DataFrame(
{"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
)

# Calculate angles for pie slices
total = data["value"].sum()
data["angle"] = data["value"] / total * 2 * math.pi
data["percentage"] = data["value"] / total * 100

# Calculate cumulative angles for wedge positioning
data["end_angle"] = data["angle"].cumsum()
data["start_angle"] = data["end_angle"] - data["angle"]

# Apply start angle offset (start from top, 90 degrees)
start_rad = math.radians(90 - 90) # Adjust for Bokeh coordinate system
data["start_angle"] = data["start_angle"] + start_rad
data["end_angle"] = data["end_angle"] + start_rad

# Assign colors (cycle if more categories than colors)
data["color"] = [COLORS[i % len(COLORS)] for i in range(len(data))]

# Create ColumnDataSource
source = ColumnDataSource(data)

# Create figure - 4800 x 2700 px as per style guide
p = figure(
width=4800,
height=2700,
title="Market Share Distribution",
tools="hover",
tooltips=[("Category", "@category"), ("Value", "@value"), ("Percentage", "@percentage{0.1}%")],
x_range=(-1.2, 2.0),
y_range=(-1.2, 1.2),
)

# Draw wedges (pie slices)
renderers = p.wedge(
x=0,
y=0,
radius=0.9,
start_angle="start_angle",
end_angle="end_angle",
line_color="white",
line_width=2,
fill_color="color",
source=source,
)

# Add percentage labels inside slices
for _, row in data.iterrows():
mid_angle = (row["start_angle"] + row["end_angle"]) / 2
label_radius = 0.55

x = label_radius * math.cos(mid_angle)
y = label_radius * math.sin(mid_angle)

# Only show label if slice is large enough
if row["percentage"] >= 5:
label = Label(
x=x,
y=y,
text=f"{row['percentage']:.1f}%",
text_font_size="48pt",
text_align="center",
text_baseline="middle",
text_color="white" if row["percentage"] >= 10 else "black",
)

p.add_layout(leg, legend_loc)

# Style configuration
p.axis.visible = False
p.grid.visible = False
p.outline_line_color = None

# Title styling
if title:
p.title.text_font_size = "20pt"
p.title.align = "center"

# Background
p.background_fill_color = "white"

return p


if __name__ == "__main__":
# Sample data for testing
sample_data = pd.DataFrame(
{"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
)

# Create plot
fig = create_plot(sample_data, "category", "value", title="Market Share Distribution")

# Save - try PNG first, fall back to HTML if selenium not available
try:
from bokeh.io import export_png

export_png(fig, filename="plot.png")
print("Plot saved to plot.png")
except RuntimeError as e:
if "selenium" in str(e).lower():
from bokeh.io import output_file, save

output_file("plot.html")
save(fig)
print("Plot saved to plot.html (selenium not available for PNG export)")
else:
raise
p.add_layout(label)

# Create legend
legend_items = []
for i, cat in enumerate(data["category"]):
legend_items.append(LegendItem(label=str(cat), renderers=[renderers], index=i))

leg = Legend(
items=legend_items,
location="center",
label_text_font_size="48pt",
background_fill_color="white",
background_fill_alpha=1.0,
border_line_color="black",
border_line_width=1,
)
p.add_layout(leg, "right")

# Style configuration
p.axis.visible = False
p.grid.visible = False
p.outline_line_color = None

# Title styling
p.title.text_font_size = "60pt"
p.title.align = "center"

# Background
p.background_fill_color = "white"

# Save
export_png(p, filename="plot.png")
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