Note
Click here to download the full example code
Spatial Contour Plot¶
What it's for: A Spatial Contour plot visualizes continuous 2D fields (like temperature, pressure, or pollutant concentrations) as line or filled contours on a map.
When to use: Use this to show the spatial distribution and gradients of a variable over a geographical region. It is ideal for identifying centers of high/low values and observing the overall structure of a field.
How to read: * Contours: Lines (or boundaries between colors) represent points of equal value. * Color Scale: Indicates the magnitude of the variable in each region. * Interpretation: Closely spaced contour lines indicate a steep gradient (rapid change over distance), while widely spaced lines indicate a flat field.
Out:
/home/runner/work/monet-plots/monet-plots/src/monet_plots/plots/spatial_contour.py:320: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
self.fig.tight_layout()
import matplotlib.pyplot as plt
import numpy as np
from monet_plots.plots.spatial_contour import SpatialContourPlot
# 1. Create dummy data
class GridObj:
def __init__(self, lat, lon):
self.variables = {
"LAT": lat[np.newaxis, np.newaxis, :, :],
"LON": lon[np.newaxis, np.newaxis, :, :],
}
lats = np.linspace(30, 50, 100)
lons = np.linspace(-125, -70, 100)
lon_grid, lat_grid = np.meshgrid(lons, lats)
gridobj = GridObj(lat_grid, lon_grid)
# Create a 2D variable to contour
data = np.sin(lat_grid / 10.0) * np.cos(lon_grid / 10.0)
# 2. Initialize and plot
plot = SpatialContourPlot(data, gridobj, figsize=(10, 8))
plot.plot(levels=15, cmap="viridis")
plot.ax.set_title("Spatial Contour Example")
plt.show()
Total running time of the script: ( 0 minutes 0.304 seconds)
Download Python source code: plot_spatial_contour.py