Visualization¶

MONET provides a variety of plotting functions for atmospheric and air quality data, ranging from quick map plots to specialized diagrams like Taylor diagrams.

Map Plots¶

While the .monet accessor provides quick map methods (quick_map, quick_imshow, quick_contourf), MONET also includes standalone plotting functions in monet.plots.

Wind Plots¶

Visualize wind fields using quivers or barbs.

from monet.plots.plots import wind_quiver, wind_barbs

# Create a wind quiver plot
fig, ax = wind_quiver(u_da, v_da, thin=10)

# Create a wind barbs plot
fig, ax = wind_barbs(u_da, v_da, thin=15)

Bias Scatter Plots¶

Visualize the spatial distribution of model bias.

from monet.plots.plots import spatial_bias_scatter

# ds must contain 'obs' and 'model' variables
fig, ax = spatial_bias_scatter(ds, cmap='RdBu_r')

Point Scatter Bias¶

Create a spatial scatter plot showing the bias between two columns in a DataFrame.

from monet.plots import sp_scatter_bias

ax = sp_scatter_bias(df, col1='obs', col2='mod')

Statistical Plots¶

Time Series with Error Shading¶

Plot time series from a DataFrame with automatic grouping and ±1 SD shading.

from monet.plots.plots import timeseries

# df should have 'time', 'obs', 'variable', and 'units'
ax = timeseries(df, y='obs', title='Ozone Time Series')

Kernel Density Estimate (KDE)¶

from monet.plots.plots import kdeplot

ax = kdeplot(df['obs'], title='Distribution of Observations')

Taylor Diagrams¶

Taylor diagrams are used to concisely summarize multiple aspects of model performance (correlation, RMS error, and standard deviation) in a single plot.

from monet.plots import taylordiagram_function

# Create a Taylor diagram for a model
dia = taylordiagram_function(obs_series, model_series, model_label='Model 1')

# Add a second model to the same diagram
taylordiagram_function(obs_series, model2_series, model_label='Model 2', dia=dia)

Plot Customization¶

Many MONET plotting functions return Matplotlib Figure and Axes objects, allowing for further customization using standard Matplotlib and Cartopy commands.

fig, ax = ds.O3.monet.quick_map()
ax.set_extent([-130, -60, 20, 50]) # Zoom to CONUS
fig.set_size_inches(12, 6)

Saving Figures with Logos¶

MONET provides a savefig utility that can automatically add the MONET logo (or a custom logo) to your plots.

from monet.plots import savefig

fig, ax = ds.O3.monet.quick_map()
savefig("my_plot.png", fig=fig, loc=1) # loc=1 is bottom left