bolides.BolideDataFrame.plot_density

BolideDataFrame.plot_density(*args, **kwargs)

Plot bolide detection density.

Density is computed using scikit-learn’s KernelDensity using the haversine distance metric (as the data is in longitude and latitude) and gaussian kernel by default. It is then gridded, projected, and plotted.

Parameters:

crsCRS

The map projection to use. Refer to https://scitools.org.uk/cartopy/docs/latest/reference/projections.html.

bandwidthfloat

The bandwidth of the Kernel Density Estimator, in degrees.

boundarystr or list of str

The boundaries to plot and clip the density by. Refer to get_boundary.

n_levelsint

Number of discrete density levels to plot.

lat_resolution, lon_resolutionints

The number of discrete latitude and longitude levels when gridding the density.

**kwargs

Keyword arguments passed through to contourf.

Returns:

figfigure

axGeoAxesSubplot

Other Parameters:

coastlinesbool

Whether or not to draw coastlines.

stylestr

The matplotlib style to use. Refer to https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html

boundary_styledict

The kwargs to use when plotting the boundary. Refer to add_geometries.

kde_paramsdict

The kwargs to pass to KernelDensity. Note that ‘metric’ is not allowed to be specified, as haversine is the only valid metric.

figsizetuple

The size (width, height) of the plotted figure.