Plotting#
In this exercise we will try making some of our own visualizations using hvPlot.
Loading the data#
We will be building a new visualization based on the same data we have cleaned and filtered in the rest of the tutorial. First we load the earthquakes DataFrame and filter to those with >=7 magnitude:
import pathlib
import pandas as pd
import xarray as xr
import hvplot.pandas # noqa: adds hvplot method to pandas objects
import hvplot.xarray # noqa: adds hvplot method to xarray objects
df = pd.read_parquet(pathlib.Path('../../data/earthquakes-projected.parq'))
most_severe = df[df.mag >= 7]
And next we load the population density raster data:
ds = xr.open_dataarray(pathlib.Path('../../data/raster/gpw_v4_population_density_rev11_2010_2pt5_min.nc'))
cleaned_ds = ds.where(ds.values != ds.nodatavals).sel(band=1)
cleaned_ds.name = 'population'
Visualizing the raster data#
Start by using hvplot.image to visualize the whole of the population density data using the Datashader rasterize support in hvPlot. Grab a handle on this Image HoloViews object called pop_density and customize it in the .hvplot.image call, enabling a logarithmic color scale and a blue colormap (specifically the 'Blues' colormap). At the end of the cell, display this object.
Hint
Don’t forget to include rasterize=True in the hvplot.image call. You can also use logz=True (or cnorm="log") with clim=(1, np.nan), then add cmap='Blues'.
pop_density = ... # Use hvplot here to visualize the data in cleaned_ds
pop_density # Display it
Ellipsis
pop_density = cleaned_ds.hvplot.image(rasterize=True, logz=True, clim=(1, np.nan), cmap='Blues')
pop_density
Visualizing the earthquake positions#
Now visualize the tabular data in most_severe by building a hv.Points object with .hvplot. This will be very similar to the approach shown in the tutorial but this time we want all the earthquakes to be marked with red crosses of size 100 at their geographic location. As above, get a handle on the resulting object and display it where the handle is now called quake_points.
Hint: To figure out which arguments you need to pass, try help(most_severe.hvplot.points), then if you can find the right option for marking the points but don’t know what value to pass, try passing an invalid value to see from the error message what the valid options are.
Hint
Don’t forget to map the longitude and latitude dimensions to x and y in the call to hvplot.points.
quake_points = ... # Use hvplot here to visualize the data in most_severe
quake_points
Ellipsis
quake_points = most_severe.hvplot.points(x='longitude', y='latitude', marker='+', size=100, color='red')
quake_points
Composing these visualizations together#
Now overlay quake_points over pop_density to check everything looks correct. Make sure the box select tool is working as expected.
pop_density * quake_points
