A scatter plot displayed in polar coordinates where data points are positioned using angle (theta) and radius (r) rather than Cartesian x and y coordinates. This visualization is particularly effective for cyclical or directional data where the angular component carries meaningful information.
""" anyplot.ai
polar-scatter: Polar Scatter Plot
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-09
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, Legend, LegendItem
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Okabe-Ito palette (categorical)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data - Wind measurements with prevailing directions
np.random.seed(42)
n_points = 120
# Create realistic wind data with prevailing directions (NE and SW)
# Morning winds (prevailing from NE ~45°)
n_morning = 50
morning_angles = np.random.normal(45, 30, n_morning) % 360
morning_speeds = np.random.gamma(3, 3, n_morning) + 5
# Afternoon winds (prevailing from SW ~225°)
n_afternoon = 45
afternoon_angles = np.random.normal(225, 35, n_afternoon) % 360
afternoon_speeds = np.random.gamma(2.5, 4, n_afternoon) + 3
# Evening winds (variable)
n_evening = 25
evening_angles = np.random.uniform(0, 360, n_evening)
evening_speeds = np.random.gamma(2, 2, n_evening) + 2
# Combine data
angles_deg = np.concatenate([morning_angles, afternoon_angles, evening_angles])
speeds = np.concatenate([morning_speeds, afternoon_speeds, evening_speeds])
categories = ["Morning"] * n_morning + ["Afternoon"] * n_afternoon + ["Evening"] * n_evening
# Convert to radians for polar coordinates
angles_rad = np.radians(angles_deg)
# Convert polar to Cartesian for Bokeh (Bokeh doesn't have native polar support)
x = speeds * np.cos(angles_rad)
y = speeds * np.sin(angles_rad)
# Calculate max radius for gridlines
max_speed = np.ceil(np.max(speeds) / 5) * 5
# Create figure
p = figure(
width=3600,
height=3600,
title="polar-scatter · bokeh · anyplot.ai",
x_range=(-max_speed * 1.2, max_speed * 1.2),
y_range=(-max_speed * 1.35, max_speed * 1.2),
tools="",
toolbar_location=None,
)
# Draw polar grid - radial circles
for r in np.arange(5, max_speed + 1, 5):
theta_circle = np.linspace(0, 2 * np.pi, 100)
circle_x = r * np.cos(theta_circle)
circle_y = r * np.sin(theta_circle)
p.line(circle_x, circle_y, line_color=INK_SOFT, line_width=2, line_alpha=0.15)
# Add radius labels at 45 degrees
label_x = r * np.cos(np.radians(45)) + 0.5
label_y = r * np.sin(np.radians(45)) + 0.5
p.text([label_x], [label_y], text=[f"{int(r)}"], text_font_size="20pt", text_color=INK_SOFT, text_alpha=0.8)
# Draw angular gridlines (spokes) at 30° intervals
for angle in range(0, 360, 30):
rad = np.radians(angle)
p.line(
[0, max_speed * 1.05 * np.cos(rad)],
[0, max_speed * 1.05 * np.sin(rad)],
line_color=INK_SOFT,
line_width=2,
line_alpha=0.15,
)
# Add cardinal direction labels
directions = {0: "E", 90: "N", 180: "W", 270: "S"}
for angle, label in directions.items():
rad = np.radians(angle)
label_r = max_speed * 1.1
p.text(
[label_r * np.cos(rad)],
[label_r * np.sin(rad)],
text=[label],
text_font_size="24pt",
text_font_style="bold",
text_color=INK,
text_align="center",
text_baseline="middle",
)
# Add intermediate direction labels
intermediate = {45: "NE", 135: "NW", 225: "SW", 315: "SE"}
for angle, label in intermediate.items():
rad = np.radians(angle)
label_r = max_speed * 1.1
p.text(
[label_r * np.cos(rad)],
[label_r * np.sin(rad)],
text=[label],
text_font_size="20pt",
text_color=INK_SOFT,
text_align="center",
text_baseline="middle",
)
# Add label for radius axis
p.text(
[0],
[-max_speed * 1.25],
text=["Wind Speed (m/s)"],
text_font_size="22pt",
text_color=INK_SOFT,
text_align="center",
text_baseline="top",
)
# Create scatter plot for each category with hover tooltips
legend_items = []
renderers = []
for i, cat in enumerate(["Morning", "Afternoon", "Evening"]):
mask = np.array(categories) == cat
source = ColumnDataSource(
data={
"x": x[mask],
"y": y[mask],
"angle": angles_deg[mask],
"speed": speeds[mask],
"category": [cat] * np.sum(mask),
}
)
# Add hover tool for this renderer
hover = HoverTool(tooltips=[("Direction", "@angle{0.0}°"), ("Speed", "@speed{0.0} m/s"), ("Category", "@category")])
r = p.scatter("x", "y", source=source, size=24, color=IMPRINT[i], alpha=0.75, line_color=PAGE_BG, line_width=2)
p.add_tools(hover)
renderers.append(r)
legend_items.append(LegendItem(label=cat, renderers=[r]))
# Add legend
legend = Legend(
items=legend_items,
location="top_right",
label_text_font_size="20pt",
spacing=15,
glyph_height=28,
glyph_width=28,
background_fill_color=ELEVATED_BG,
background_fill_alpha=0.9,
border_line_color=INK_SOFT,
padding=15,
margin=15,
border_line_width=2,
)
p.add_layout(legend, "right")
# Styling
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.title.align = "center"
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
# Hide axes (using polar grid instead)
p.xaxis.visible = False
p.yaxis.visible = False
p.xgrid.visible = False
p.ygrid.visible = False
# Save as HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome
W, H = 3600, 3600
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Polar Scatter Plot on anyplot.ai.