Wind Rose Chart — Plotly

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 Plotly

Python source (Plotly)

""" anyplot.ai
windrose-basic: Wind Rose Chart
Library: plotly 6.7.0 | Python 3.13.13
Quality: 84/100 | Updated: 2026-05-07
"""

import os

import numpy as np
import plotly.graph_objects as go


# 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"
GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"

# Data - Simulated hourly wind measurements for one year
np.random.seed(42)
n_observations = 8760  # One year of hourly data

# Simulate wind direction with prevailing westerly and southwesterly winds
direction_weights = np.array([0.05, 0.05, 0.08, 0.10, 0.12, 0.20, 0.25, 0.15])  # N, NE, E, SE, S, SW, W, NW
directions_base = np.array([0, 45, 90, 135, 180, 225, 270, 315])
direction_idx = np.random.choice(8, size=n_observations, p=direction_weights)
directions = directions_base[direction_idx] + np.random.uniform(-20, 20, n_observations)
directions = directions % 360

# Simulate wind speeds with realistic distribution (Weibull-like)
speeds = np.random.weibull(2.0, n_observations) * 6  # Scale for realistic m/s values

# Define direction bins (8 sectors, 45 degrees each)
dir_bins = np.array([0, 45, 90, 135, 180, 225, 270, 315, 360])
dir_labels = ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]

# Define speed bins (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"]
speed_colors = ["#2ABCCD", "#4467A3", "#009E73", "#DDCC77", "#AE3030"]  # cyan→blue→green→amber→red

# Bin the data
dir_indices = np.digitize(directions, dir_bins[:-1]) - 1
dir_indices = np.clip(dir_indices, 0, 7)
speed_indices = np.digitize(speeds, speed_bins[:-1]) - 1

# Calculate frequencies for each direction and speed combination
frequencies = np.zeros((8, 5))
for d in range(8):
    for s in range(5):
        frequencies[d, s] = np.sum((dir_indices == d) & (speed_indices == s))

# Convert to percentages
frequencies_pct = frequencies / n_observations * 100

# Create wind rose using barpolar
fig = go.Figure()

# Add traces for each speed bin (stacked from inside to outside)
for s in range(5):
    # Calculate the radial values for stacking
    r_values = frequencies_pct[:, s]

    fig.add_trace(
        go.Barpolar(
            r=r_values,
            theta=dir_labels,
            name=speed_labels[s],
            marker_color=speed_colors[s],
            marker_line_color="white",
            marker_line_width=1,
            opacity=0.9,
        )
    )

# Update layout for proper stacking and styling
fig.update_layout(
    title=dict(
        text="windrose-basic · plotly · anyplot.ai", font=dict(size=28, color=INK), x=0.5, xanchor="center", y=0.95
    ),
    paper_bgcolor=PAGE_BG,
    plot_bgcolor=PAGE_BG,
    font=dict(color=INK),
    polar=dict(
        radialaxis=dict(
            visible=True,
            showticklabels=True,
            tickfont=dict(size=18, color=INK_SOFT),
            ticksuffix="%",
            angle=45,
            dtick=5,
            title=dict(text="Frequency (%)", font=dict(size=20, color=INK)),
            gridcolor=GRID,
        ),
        angularaxis=dict(
            tickfont=dict(size=22, color=INK),
            direction="clockwise",
            rotation=90,
            categoryorder="array",
            categoryarray=["N", "NE", "E", "SE", "S", "SW", "W", "NW"],
        ),
        bgcolor=PAGE_BG,
    ),
    legend=dict(
        title=dict(text="Wind Speed", font=dict(size=20, color=INK)),
        font=dict(size=18, color=INK_SOFT),
        x=1.05,
        y=0.5,
        xanchor="left",
        yanchor="middle",
        bgcolor=ELEVATED_BG,
        bordercolor=INK_SOFT,
        borderwidth=1,
    ),
    barmode="stack",
    margin=dict(l=80, r=180, t=120, b=80),
)

# Save as PNG and HTML
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")

Part of Wind Rose Chart on anyplot.ai.

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