Possibility to provide default map background/ elements to plot

[cwerner]

Hi.

Really enjoying xarray! Converting all my scripts and on the quest to simplify my plotting.

For my current project I need loads of maps and generally add a land/ ocean layer and country boundaries as well as an default extent to the plot axes...

I use for instance a extra layer like this:

import cartopy.crs as cars
import cartopy.feature as feature

# default map backgrounds    
countries = cfeature.NaturalEarthFeature(
    category='cultural',
    name='admin_0_countries',
    scale='50m',
    facecolor='none')

An example from a class I work on right now:

    def plot_monthly(self, figsize=(12,10)):
        _unit = 'n.d.'
        _name = 'var' 
        if 'units' in self._dataArray.attrs.keys():
            _unit = self._dataArray.attrs['units']


        self._fig, self._axes = plt.subplots(ncols=4, nrows=3, \
                                             figsize=figsize, subplot_kw={'projection': ccrs.PlateCarree()})

        _min = self._dataArray.min()
        _max = self._dataArray.max()

        print self._dataArray

        for i, ax in enumerate(self._axes.flatten()):
            ax.set_extent( self._extent )
            ax.add_feature(self._countries, edgecolor='gray')     
            ax.add_feature(self._ocean)                           
            ax.coastlines(resolution='50m')
            ax.set_title(self._monthNames[i+1])
            _plot = xr.plot.plot(self._dataArray.sel(month=i+1), ax=ax, \
                            vmin=_min, vmax=_max, \
                            transform=ccrs.PlateCarree(), \
                            add_labels=False,
                            robust=True, add_colorbar=False)


        self._fig.subplots_adjust(right=0.8)
        _cbar_ax = self._fig.add_axes([0.85, 0.15, 0.05, 0.7])

        _var_str = "%s [%s]" % (_name, _unit)
        self._fig.colorbar(_plot, cax=_cbar_ax, label=_var_str)

This is a bit clumsy at the moment - I basically define the axes for each subplot (say 1..12 month), loop over them, select the axis and add them with ax.set_extent(), ax.add_feature() etc...

This works, but I'd rather use the plot function within DataArray or DataSet... Also thinking about using FacetGrid instead of doing it manually.

I thought about using the new decorators to patch this onto a custom plot directive but my Python is not really advanced enough for decorators and the inner functioning of array it seems...

@xr.register_dataarray_accessor('map')
class MapDecorator(object):
    def __init__(self, xarray_obj):
        self._obj = xarray_obj

    def plot(self, **kwargs):
        """Plot data on a map."""

        print "my decorator"

        #ax.set_extent([102, 110, 8, 24])
        ## add map elements
        #ax.add_feature(countries, edgecolor='gray')     # country borders
        #ax.add_feature(ocean)                           # ocean
        #ax.coastlines(resolution='50m')                 # coastlines

        p = self._obj.plot( kwargs )
        return p

Not sure if this is a valid path or how one would do that? I would also like to have some default arguments (projection=)

Cheers,
Christian

[mathause]

I wrote a function that I call geocolormesh which automatically creates cartopy GeoAxes for single plots and for FacetGrids. This may go in the direction of what you would like to get. I find this very convenient for fast plotting - once I do the final plots some more work is needed. Unfortunately this is not possible without some changes* in the default plotting function (and also in facet grid). Thus, I added a pull request. Not sure what the developers will think of this.

*Because it is not possible to change the projection of axes once they are created.

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