Note
Click here to download the full example code
Trajectory Plot¶
What it's for: A Trajectory plot visualizes the path of a parcel of air or a mobile platform (e.g., aircraft, ship, or research balloon) over time. It typically combines a horizontal map view of the path with a time-series view of variables measured along that path.
When to use: Use this when you need to understand the history of an air mass (back-trajectories) or visualize data collected by mobile sensors. It is essential for source-receptor analysis in air quality studies.
How to read: * Map View (Top/Main): Shows the geographic path (Longitude/Latitude). Markers or colors along the path often represent time or a measured variable. * Time-Series View (Bottom): Shows how one or more variables changed as the platform moved along the trajectory. * Interpretation: Allows you to correlate specific geographical locations or events along the path with observed changes in the measured data.
Out:
Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.
/home/runner/work/monet-plots/monet-plots/src/monet_plots/plots/timeseries.py:151: UserWarning: The figure layout has changed to tight
self.fig.tight_layout()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from monet_plots.plots.trajectory import TrajectoryPlot
# 1. Prepare sample data
n_points = 50
time = pd.date_range("2023-01-01", periods=n_points, freq="h")
lat = np.linspace(30, 40, n_points) + np.random.normal(0, 0.5, n_points)
lon = np.linspace(-120, -110, n_points) + np.random.normal(0, 0.5, n_points)
data = np.sin(np.linspace(0, 5, n_points)) * 10 + 20
ts_data = data + np.random.normal(0, 2, n_points)
# 2. Initialize and plot
# TrajectoryPlot(longitude, latitude, data, time, ts_data, ...)
plot = TrajectoryPlot(lon, lat, data, time, ts_data, figsize=(12, 6))
plot.plot()
plt.show()
Total running time of the script: ( 0 minutes 0.252 seconds)
Download Python source code: plot_trajectory.py