- Network.plot(projection='global', resolution='l', continent_fill_color='0.9', water_fill_color='1.0', marker='v', size=225, label=True, color='#b15928', time=None, show=True, outfile=None, method=None, fig=None, **kwargs)
Creates a preview map of all stations in current network object.
projection (str, optional) –
The map projection. Currently supported are:
"global"(Will plot the whole world.)
"ortho"(Will center around the mean lat/long.)
"local"(Will plot around local events)
Defaults to “global”
resolution (str, optional) –
Resolution of the boundary database to use. Possible values are:
continent_fill_color (valid matplotlib color, optional) – Color of the continents. Defaults to
"0.9"which is a light gray.
water_fill_color (valid matplotlib color, optional) – Color of all water bodies. Defaults to
label (bool) – Whether to label stations with “network.station” or not.
UTCDateTime) – Only plot stations available at given point in time.
show (bool) – Whether to show the figure after plotting or not. Can be used to do further customization of the plot before showing it.
outfile (str) – Output file path to directly save the resulting image (e.g.
"/tmp/image.png"). Overrides the
showoption, image will not be displayed interactively. The given path/file name is also used to automatically determine the output format. Supported file formats depend on your matplotlib backend. Most backends support png, pdf, ps, eps and svg. Defaults to
method (str) –
Method to use for plotting. Possible values are:
'cartopy'to use the Cartopy library
Noneto use the best available library
matplotlib.figure.Figure) – Figure instance to reuse, returned from a previous inventory/catalog plot call with method=cartopy. If a previous cartopy plot is reused, any kwargs regarding the cartopy plot setup will be ignored (i.e. projection, resolution, continent_fill_color, water_fill_color). Note that multiple plots using colorbars likely are problematic, but e.g. one station plot (without colorbar) and one event plot (with colorbar) together should work well.
Figure instance with the plot.
Mollweide projection for global overview:
>>> from obspy import read_inventory >>> net = read_inventory() >>> net.plot(label=False)
Local (Albers equal area) projection: