Note
Click here to download the full example code
Bivariate Polar Plot¶
What it's for: A Bivariate Polar plot (sometimes called a polar plot or bivariate plot) visualizes how a variable (e.g., pollutant concentration) varies as a function of wind speed and wind direction.
When to use: Use this to identify the potential source directions and distances (using wind speed as a proxy for distance/source type) of observed concentrations at a monitoring site. High concentrations at low wind speeds often suggest local sources, while high concentrations at high wind speeds suggest long-range transport.
How to read: * Angle: Represents the Wind Direction (0-360 degrees). * Radial Distance: Represents the Wind Speed. * Color: Represents the magnitude of the variable (e.g., concentration). * Interpretation: Bright clusters indicate specific wind conditions (direction and speed) associated with high values of the variable.
Out:
Bivariate polar plot saved to polar_example.png
import numpy as np
import pandas as pd
from monet_plots.plots.polar import BivariatePolarPlot
# Create dummy wind dependent data
n = 1000
ws = np.random.gamma(2, 2, n)
wd = np.random.uniform(0, 360, n)
# Concentration higher when wind is from the East (90 deg) and low speed
conc = 10 * np.exp(-((wd - 90) ** 2) / 1000) * np.exp(-ws / 5) + np.random.rand(n)
df = pd.DataFrame({"ws": ws, "wd": wd, "conc": conc})
# Initialize and plot
plot = BivariatePolarPlot(df, ws_col="ws", wd_col="wd", val_col="conc")
plot.plot(n_bins_ws=15, n_bins_wd=36)
plot.save("polar_example.png")
print("Bivariate polar plot saved to polar_example.png")
Total running time of the script: ( 0 minutes 0.413 seconds)
Download Python source code: plot_polar.py