A wind rose displays wind speed and direction data as a polar stacked histogram showing the frequency distribution of wind across compass directions. Each spoke represents a direction sector (typically 8-16 bins), with stacked colored segments indicating different wind speed ranges. This specialized meteorological visualization reveals dominant wind patterns, prevailing directions, and speed distributions simultaneously, making it essential for site assessment and environmental analysis.
""" anyplot.ai
windrose-basic: Wind Rose Chart
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 96/100 | Updated: 2026-05-07
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Okabe-Ito palette for speed bins (cool to warm progression)
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
# Data - Generate realistic wind data for a coastal weather station
np.random.seed(42)
n_observations = 5000
# Direction distribution favoring SW and W winds (common for coastal areas)
direction_weights = [0.08, 0.06, 0.05, 0.08, 0.10, 0.18, 0.25, 0.20] # N, NE, E, SE, S, SW, W, NW
directions_idx = np.random.choice(8, size=n_observations, p=direction_weights)
direction_noise = np.random.uniform(-22.5, 22.5, n_observations)
directions = directions_idx * 45 + direction_noise
directions = directions % 360
# Wind speed with Weibull distribution (realistic for wind data)
speeds = np.random.weibull(2.2, n_observations) * 6 # Scale for m/s
# Define bins
direction_bins = np.linspace(0, 360, 9) # 8 direction sectors
direction_labels = ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]
speed_bins = [0, 3, 6, 9, 12, np.inf] # m/s ranges
speed_labels = ["0-3 m/s", "3-6 m/s", "6-9 m/s", "9-12 m/s", ">12 m/s"]
# Aggregate data into direction/speed bins
dir_indices = np.digitize(directions, direction_bins) - 1
dir_indices = np.clip(dir_indices, 0, 7)
speed_indices = np.digitize(speeds, speed_bins) - 1
speed_indices = np.clip(speed_indices, 0, len(speed_bins) - 2)
# Calculate frequencies for each direction/speed combination
frequencies = np.zeros((8, len(speed_bins) - 1))
for d_idx in range(8):
for s_idx in range(len(speed_bins) - 1):
frequencies[d_idx, s_idx] = np.sum((dir_indices == d_idx) & (speed_indices == s_idx))
# Convert to percentages
frequencies = frequencies / n_observations * 100
# Create figure - square format for polar-like display
p = figure(
width=3600,
height=3600,
title="windrose-basic · bokeh · anyplot.ai",
x_range=(-35, 35),
y_range=(-35, 35),
tools="",
toolbar_location=None,
)
# Style title and overall appearance
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.title.align = "center"
# Theme-adaptive background
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = None
# Hide axes for polar plot
p.xaxis.visible = False
p.yaxis.visible = False
p.xgrid.visible = False
p.ygrid.visible = False
# Draw concentric circles for reference
for radius in [5, 10, 15, 20, 25]:
theta_circle = np.linspace(0, 2 * np.pi, 100)
x_circle = radius * np.cos(theta_circle)
y_circle = radius * np.sin(theta_circle)
p.line(x_circle, y_circle, line_color=INK_SOFT, line_width=1.5, line_alpha=0.4)
# Add frequency labels on the right side
p.text(
x=[radius + 0.5],
y=[0.5],
text=[f"{radius}%"],
text_font_size="20pt",
text_color=INK_SOFT,
text_baseline="bottom",
)
# Draw direction lines and labels
sector_width = 2 * np.pi / 8 # 45 degrees in radians
for i, label in enumerate(direction_labels):
angle = np.pi / 2 - i * sector_width # Start from North (top), go clockwise
# Draw spoke lines
x_spoke = [0, 28 * np.cos(angle)]
y_spoke = [0, 28 * np.sin(angle)]
p.line(x_spoke, y_spoke, line_color=INK_SOFT, line_width=1.5, line_alpha=0.3)
# Add direction labels outside
label_radius = 30
x_label = label_radius * np.cos(angle)
y_label = label_radius * np.sin(angle)
p.text(
x=[x_label],
y=[y_label],
text=[label],
text_font_size="22pt",
text_font_style="bold",
text_color=INK,
text_align="center",
text_baseline="middle",
)
# Draw stacked wedges for each direction
legend_items = []
renderers_by_speed = {i: [] for i in range(len(speed_labels))}
for dir_idx in range(8):
# Angle for this direction (North = up, clockwise)
center_angle = np.pi / 2 - dir_idx * sector_width
# Draw wedges for each speed bin (stacked from center outward)
cumulative_radius = 0
for speed_idx in range(len(speed_labels)):
freq = frequencies[dir_idx, speed_idx]
if freq > 0.1: # Only draw if significant
inner_radius = cumulative_radius
outer_radius = cumulative_radius + freq
# Create wedge shape
n_points = 30
angles = np.linspace(
center_angle - sector_width / 2 + 0.02, center_angle + sector_width / 2 - 0.02, n_points
)
# Build polygon: inner arc, outer arc (reversed)
x_inner = inner_radius * np.cos(angles)
y_inner = inner_radius * np.sin(angles)
x_outer = outer_radius * np.cos(angles[::-1])
y_outer = outer_radius * np.sin(angles[::-1])
x_wedge = np.concatenate([x_inner, x_outer])
y_wedge = np.concatenate([y_inner, y_outer])
source = ColumnDataSource(data={"x": [x_wedge.tolist()], "y": [y_wedge.tolist()]})
renderer = p.patches(
xs="x",
ys="y",
source=source,
fill_color=IMPRINT[speed_idx],
fill_alpha=0.85,
line_color=PAGE_BG,
line_width=1.5,
)
renderers_by_speed[speed_idx].append(renderer)
cumulative_radius = outer_radius
# Create legend items
for speed_idx in range(len(speed_labels)):
if renderers_by_speed[speed_idx]:
legend_items.append(LegendItem(label=speed_labels[speed_idx], renderers=[renderers_by_speed[speed_idx][0]]))
# Add legend with theme-adaptive styling
legend = Legend(
items=legend_items,
location="center",
label_text_font_size="22pt",
label_text_color=INK_SOFT,
spacing=10,
padding=15,
background_fill_alpha=0.95,
background_fill_color=ELEVATED_BG,
border_line_color=INK_SOFT,
border_line_width=2,
)
p.add_layout(legend, "right")
# Add subtitle with data info
p.text(
x=[0],
y=[-33],
text=["Wind Speed (m/s)"],
text_font_size="20pt",
text_color=INK_SOFT,
text_align="center",
text_baseline="top",
)
# Save as HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome — Selenium 4 / Selenium Manager auto-resolves a working driver
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) # let bokeh's JS render the canvas
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Wind Rose Chart on anyplot.ai.