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: plotnine 0.15.4 | Python 3.13.13
Quality: 95/100 | Updated: 2026-05-09
"""
import os
import sys
import numpy as np
import pandas as pd
# Work around naming conflict with plotnine.py script and plotnine package
script_dir = os.path.dirname(os.path.abspath(__file__))
if script_dir in sys.path:
sys.path.remove(script_dir)
if "" in sys.path:
sys.path.remove("")
if "." in sys.path:
sys.path.remove(".")
from plotnine import (
aes,
coord_fixed,
element_blank,
element_rect,
element_text,
geom_path,
geom_point,
geom_segment,
geom_text,
ggplot,
labs,
scale_color_manual,
scale_x_continuous,
scale_y_continuous,
theme,
)
# 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
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
# Data - Wind measurements with prevailing directions
np.random.seed(42)
n_points = 120
# Wind often has prevailing directions - simulate with mixture
# Prevailing wind from SW (~225°) and secondary from NE (~45°)
angles_sw = np.random.normal(225, 30, n_points // 2) # Southwest prevailing
angles_ne = np.random.normal(45, 25, n_points // 2) # Northeast secondary
angles = np.concatenate([angles_sw, angles_ne])
angles = angles % 360 # Wrap to 0-360
# Wind speeds (m/s) - higher speeds tend with prevailing directions
speeds_sw = np.abs(np.random.normal(12, 4, n_points // 2))
speeds_ne = np.abs(np.random.normal(8, 3, n_points // 2))
speeds = np.concatenate([speeds_sw, speeds_ne])
speeds = np.clip(speeds, 1, 25)
# Time of day categories for color encoding
time_categories = np.random.choice(["Morning", "Afternoon", "Evening"], n_points, p=[0.35, 0.40, 0.25])
# Convert polar to Cartesian
# Convention: 0° at top (North), clockwise
theta_rad = np.radians(90 - angles) # Convert to math convention (0° = East, CCW)
x = speeds * np.cos(theta_rad)
y = speeds * np.sin(theta_rad)
df = pd.DataFrame({"angle": angles, "speed": speeds, "time": time_categories, "x": x, "y": y})
# Create circular gridlines (radial circles at speed intervals)
grid_rows = []
grid_angles = np.linspace(0, 2 * np.pi, 101)
max_radius = 25
for radius in [5, 10, 15, 20, 25]:
for angle in grid_angles:
grid_rows.append({"x": radius * np.cos(angle), "y": radius * np.sin(angle), "radius": radius})
grid_df = pd.DataFrame(grid_rows)
# Create radial spokes (compass directions)
spoke_rows = []
compass_dirs = [0, 45, 90, 135, 180, 225, 270, 315] # N, NE, E, SE, S, SW, W, NW
for deg in compass_dirs:
angle = np.radians(90 - deg) # Convert to math convention
spoke_rows.append(
{"x1": 0, "y1": 0, "x2": (max_radius + 2) * np.cos(angle), "y2": (max_radius + 2) * np.sin(angle)}
)
spoke_df = pd.DataFrame(spoke_rows)
# Create compass direction labels
compass_labels = ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]
label_rows = []
label_radius = max_radius + 5
for deg, lbl in zip(compass_dirs, compass_labels, strict=True):
angle = np.radians(90 - deg)
label_rows.append({"label": lbl, "x": label_radius * np.cos(angle), "y": label_radius * np.sin(angle)})
label_df = pd.DataFrame(label_rows)
# Create radius labels (wind speed m/s) - positioned along NNE axis for clarity
radius_labels = []
label_angle = np.radians(90 - 22.5) # NNE direction
for r in [5, 10, 15, 20]:
radius_labels.append({"label": f"{r}", "x": r * np.cos(label_angle) + 1, "y": r * np.sin(label_angle)})
# Add "m/s" unit at outermost radius
radius_labels.append({"label": "25 m/s", "x": 25 * np.cos(label_angle) + 1, "y": 25 * np.sin(label_angle)})
radius_label_df = pd.DataFrame(radius_labels)
# Color palette for time of day (Okabe-Ito colors)
colors = {
"Morning": IMPRINT[0], # #009E73 brand green
"Afternoon": IMPRINT[1], # #C475FD vermillion
"Evening": IMPRINT[2], # #4467A3 blue
}
# Plot
plot = (
ggplot()
# Circular gridlines (speed circles)
+ geom_path(
aes(x="x", y="y", group="radius"), data=grid_df, color=INK_SOFT, size=0.5, alpha=0.15, linetype="dashed"
)
# Radial spokes (direction lines)
+ geom_segment(aes(x="x1", y="y1", xend="x2", yend="y2"), data=spoke_df, color=INK_SOFT, size=0.5, alpha=0.15)
# Wind data points with color by time of day
+ geom_point(aes(x="x", y="y", color="time"), data=df, size=5, alpha=0.75)
# Compass direction labels
+ geom_text(aes(x="x", y="y", label="label"), data=label_df, size=16, color=INK, fontweight="bold")
# Speed labels (m/s)
+ geom_text(aes(x="x", y="y", label="label"), data=radius_label_df, size=10, color=INK_SOFT)
# Custom colors for time of day
+ scale_color_manual(values=colors, name="Time of Day")
# Equal coordinate system for proper circles
+ coord_fixed(ratio=1)
# Axis scaling with padding
+ scale_x_continuous(limits=(-35, 35))
+ scale_y_continuous(limits=(-35, 35))
# Labels
+ labs(title="polar-scatter · plotnine · anyplot.ai")
# Theme with proper backgrounds and colors
+ theme(
figure_size=(12, 12),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_grid_major=element_blank(),
panel_grid_minor=element_blank(),
panel_border=element_blank(),
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
plot_title=element_text(size=24, ha="center", color=INK),
legend_title=element_text(size=16, color=INK),
legend_text=element_text(size=14, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_position="right",
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=300, width=12, height=12)
Part of Polar Scatter Plot on anyplot.ai.