Note
Click here to download the full example code
Basic Spatial Plotting¶
What it's for:
This example demonstrates the foundational SpatialPlot class, which provides a
consistent interface for creating map-based visualizations in MONET Plots.
When to use:
Use SpatialPlot when you need full control over a map-based visualization and want
to use standard Matplotlib or Cartopy commands on a pre-configured axes that
includes geographic features.
How to read: * Axes: The plot is in geographic coordinates (typically Latitude/Longitude). * Features: It automatically includes geographic context like coastlines and borders to provide spatial orientation. * Interpretation: Data is plotted directly onto the map; the location of colors or markers corresponds to their real-world geographical position.
Out:
Basic spatial plot saved successfully!
import numpy as np
from monet_plots import SpatialPlot
# Step 1: Prepare spatial data
# Create a 2D array representing spatial data
data = np.random.random((20, 30)) * 100
# Step 2: Initialize the plot
plot = SpatialPlot(figsize=(10, 8))
# Step 3: Plot the data
# SpatialPlot sets up the map axes. We use standard matplotlib/cartopy methods to plot.
plot.ax.pcolormesh(data)
plot.ax.set_title("Basic Spatial Plot")
# Step 4: Add labels and save
plot.ax.set_xlabel("Longitude")
plot.ax.set_ylabel("Latitude")
plot.save("basic_spatial.png")
# Step 5: Close the plot
plot.close()
print("Basic spatial plot saved successfully!")
Total running time of the script: ( 0 minutes 0.179 seconds)
Download Python source code: plot_basic_plotting.py