Visualizing the Data

Overview

Teaching: 15 min
Exercises: 30 min

Questions

• How can I use classes to make code reusable?

Objectives

At the end of the last lesson we wrote a new program that calculated the 49 city’s contribution to the overall emissions. An example of what this program might look like is available here.

Although we have completed the analysis that we set out to do, it would also be valuable to visualize the emissions data with an overlay of the city locations. To do this, we’re going to use the Matplotlib basemap toolkit.

The Matplotlib basemap toolkit is a library for plotting 2D data on maps in Python. It is similar in functionality to the MATLAB mapping toolbox, the IDL mapping facilities, GrADS, or the Generic Mapping Tools. PyNGL and CDAT are other libraries that provide similar capabilities in Python.

Basemap does not do any plotting on it’s own, but provides the facilities to transform coordinates to one of 25 different map projections (using the PROJ.4 C library). Matplotlib is then used to plot contours, images, vectors, lines or points in the transformed coordinates.

Here is how to set up a basic plot. You can see there are various high-level methods available to configure how the map is drawn, such as drawcoastlines(), fillcontinents(), etc. The drawparallels() and drawmeridians() methods take an array specifying which parallels or meridians to draw (in degrees):

from mpl_toolkits.basemap import Basemap

year = 2005

if __name__ == '__main__':
    # Setup the plot
    city_map = Basemap(projection='robin', lon_0=0)
    city_map.drawcoastlines()
    city_map.fillcontinents(color='0.75', lake_color='1')
    city_map.drawparallels(np.arange(-80.,81.,20.))
    city_map.drawmeridians(np.arange(-180.,181.,20.))
    city_map.drawmapboundary(fill_color='white')

Now we can load the emissions data for a particular year and calculate the global emissions in MtCO2e as we did before.

    # Load global emissions data
    emissions = HistoricalCO2Emissions('CMIP5_gridcar_CO2_emissions_fossil_fuel_Andres_1751-2007_monthly_SC_mask11.nc')

    # Find the global emissions for the given year
    monthly_global_emissions = emissions.get_total_monthly_emissions_grid(year)
    
    # Sum and convert to MtC02
    global_emissions = monthly_global_emissions.sum(level=[1,2]) * 1.0e-12
    
    # Convert to 2-D NumPy array
    global_emissions_values = global_emissions.unstack(level=1).values 

Next we plot contours representing the emissions values. Calling a Basemap class instance with the arguments longitude and latitude will convert lon/lat (in degrees) to x/y map projection coordinates (in meters). These can then be passed to the contourf method to plot the contours. The argument are the same as the Matplolib contourf method.

    lat_grid, lon_grid = np.meshgrid(emissions.latitude, emissions.longitude, indexing='ij')
    x_grid, y_grid = city_map(lon_grid, lat_grid)

    clevs = [0.1] + np.linspace(0.0,85,18)
    cont = city_map.contourf(x_grid, y_grid, global_emissions_values, clevs, cmap='Reds', zorder=100)

Once we have the emissions data, the next step is to load the city data and add it to the plot. This will simply create a scatter plot of the city locations:

    # Get city data 
    city_data = pd.read_csv('CityCO2Emissions.csv')

    x, y = city_map(np.array(city_data['Longitude'].tolist()), 
                    np.array(city_data['Latitude'].tolist()))
    
    city_map.scatter(x, y, 30, marker='o', edgecolors='m', facecolors='none', zorder=200)

Finally, add a title, color bar, and label, then plot it!

    # Finish plot
     plt.title('The top 49 $CO_2$ emitting cities in 2005 [Hoornweg, 2010], \n ' +
            'located within the globally gridded $CO_2$ emissions (Mt;  '+
            '>1e-4) during ' + year)
    
    cbar = city_map.colorbar(cont, location='bottom', pad='5%')
    cbar.set_label('Mt $CO_2$')
    plt.show()

Challenge

Create a new Python program called visualize_city_emissions.py and add the above code to it. Test the program to make sure that it creates the plot as expected.

Solution

You can download a version of this program here

Challenge

Notice that we’ve used the same code to find the global emissions for a year in MtCO2 in two separate programs. It seems like this might be a good candidate for a new method in the HistoricalCO2Emissions class. Add a new method to this class called get_total_emissions_Mt that takes a string year as argument. It can return either a DataFrame or a NumPy array.

Once you have added this method, update the city_emissions_contribution.py and visualize_city_emissions.py programs to use the new method. Test both programs to make sure they still work correctly.

Solution

You can download final versions of all three programs here:

• historical_co2_emissions.py
• city_emissions_contribution.py
• visualize_city_emissions.py

Key Points