Add stippling to maps

[xenct]

It may be useful to have stippling available for anomaly maps to show how many models agree with the direction of change.
This link might be helpful for contour hatching contourf_hatching
The required code might look like this:

ax1.contourf(x, y, z, hatches=['..',], cmap='gray', alpha=0.5)

It probably only needs to work on a True/False mask.

[xenct]

I have added this line to acs_plotting_maps.py to apply stippling to the maps. stippling is a True/False mask.

ax.contourf(stippling.lon, stippling.lat, stippling, 
            alpha=0, 
            hatches = ["",".."],
            zorder=4, 
            transform=ccrs.PlateCarree(),
            )

This is an an example of its use.

Note that stippling_mask = ds[var]>42 is a bit of a dummy mask. The intended use is for indicating significance or agreement across models.

import numpy as np
from matplotlib.colors import ListedColormap
from acs_plotting_maps import plot_acs_hazard, regions_dict, cmap_dict, tick_dict
import xarray as xr

filename = "/g/data/ia39/ncra/heat/data/HWAtx/bias-corrected/ensemble/GWL-average/\
HWAtx_AGCD-05i_MME50_ssp370_v1-r1-ACS-QME-AGCD-1960-2022_GWL12.nc"
ds = xr.open_dataset(filename)
var = "HWAtx"
stippling_mask = ds[var]>42

plot_acs_hazard(data = ds[var],
                stippling=stippling_mask,
                regions = regions_dict['ncra_regions'],
                title = "title",
                date_range = "GWL12",
                cmap = cmap_dict["tasmax"],
                ticks = np.arange(18,53,2),
                cbar_label = "degC",
                cbar_extend = "both",                
                dataset_name = "",
                outfile = "figures/out.png",
               watermark_color="k");