Wind Rose Chart — Pygal

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.

Wind Rose Chart rendered with Pygal

Python source (Pygal)

""" anyplot.ai
windrose-basic: Wind Rose Chart
Library: pygal 3.1.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-07
"""

import os
import sys

import numpy as np


# Avoid import shadowing: remove script directory and cwd from path
_script_dir = os.path.dirname(os.path.abspath(__file__))
_cwd = os.getcwd()
sys.path = [p for p in sys.path if os.path.abspath(p) not in (_script_dir, _cwd, "")]

import pygal
from pygal.style import Style


# Restore path for later operations
sys.path.insert(0, _cwd)

# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

# Okabe-Ito palette (positions 1-7)
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")

# Data generation
np.random.seed(42)
n_observations = 8760  # ~1 year of hourly measurements

# Simulate prevailing winds from SW (225°) and W (270°) with variation
directions = np.concatenate(
    [
        np.random.normal(225, 30, int(n_observations * 0.35)),  # SW dominant
        np.random.normal(270, 25, int(n_observations * 0.25)),  # W secondary
        np.random.normal(180, 40, int(n_observations * 0.15)),  # S occasional
        np.random.uniform(0, 360, int(n_observations * 0.25)),  # Random variation
    ]
)
directions = directions % 360  # Normalize to 0-360

# Generate corresponding wind speeds (Weibull-like distribution)
speeds = np.concatenate(
    [
        np.random.weibull(2, int(n_observations * 0.35)) * 8,  # SW: moderate-strong
        np.random.weibull(2.2, int(n_observations * 0.25)) * 9,  # W: stronger
        np.random.weibull(1.8, int(n_observations * 0.15)) * 6,  # S: lighter
        np.random.weibull(1.5, int(n_observations * 0.25)) * 5,  # Others: light
    ]
)

# Define 8 direction sectors (N, NE, E, SE, S, SW, W, NW)
direction_labels = ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]

# Define wind speed ranges (m/s)
speed_bins = [0, 3, 6, 9, 12, np.inf]
speed_labels = ["0-3 m/s", "3-6 m/s", "6-9 m/s", "9-12 m/s", ">12 m/s"]

# Calculate frequencies for each direction and speed bin
frequencies = {label: [] for label in speed_labels}

for dir_center in [0, 45, 90, 135, 180, 225, 270, 315]:
    if dir_center == 0:
        # North spans 337.5-360 and 0-22.5
        mask = (directions >= 337.5) | (directions < 22.5)
    else:
        low = dir_center - 22.5
        high = dir_center + 22.5
        mask = (directions >= low) & (directions < high)

    dir_speeds = speeds[mask]

    # Count frequencies in each speed bin
    for j, (low_speed, high_speed) in enumerate(zip(speed_bins[:-1], speed_bins[1:], strict=True)):
        count = np.sum((dir_speeds >= low_speed) & (dir_speeds < high_speed))
        freq_pct = (count / len(directions)) * 100
        frequencies[speed_labels[j]].append(round(freq_pct, 2))

# Build cumulative values for proper stacked rendering
cumulative = {}
for i, label in enumerate(speed_labels):
    cumulative[label] = [sum(frequencies[speed_labels[k]][j] for k in range(i + 1)) for j in range(8)]

# Custom style for large canvas with theme-adaptive colors
custom_style = Style(
    background=PAGE_BG,
    plot_background=PAGE_BG,
    foreground=INK,
    foreground_strong=INK,
    foreground_subtle=INK_MUTED,
    colors=IMPRINT,
    title_font_size=32,
    label_font_size=24,
    major_label_font_size=22,
    legend_font_size=20,
    value_font_size=18,
    stroke_width=2.5,
    opacity=0.95,
    guide_stroke_width=1,
)

# Create radar chart (wind rose)
chart = pygal.Radar(
    width=3600,
    height=3600,
    style=custom_style,
    title="windrose-basic · pygal · anyplot.ai",
    y_title="Frequency (%)",
    show_legend=True,
    legend_at_bottom=False,
    legend_box_size=16,
    fill=True,
    stroke=True,
    show_dots=False,
    inner_radius=0.05,
    truncate_legend=-1,
    margin=120,
    spacing=40,
    show_y_guides=True,
    show_x_guides=False,
    range=(0, None),
)

# Set direction labels
chart.x_labels = direction_labels

# Add series from strongest to calmest (drawing order)
# This creates proper visual stacking with each layer visible
reversed_labels = list(reversed(speed_labels))  # [">12 m/s", "9-12 m/s", ...]
for label in reversed_labels:
    chart.add(label, cumulative[label])

# Save outputs
chart.render_to_file(f"plot-{THEME}.html")
chart.render_to_png(f"plot-{THEME}.png")

Part of Wind Rose Chart on anyplot.ai.

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