Closed xenct closed 1 month ago
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");
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:
It probably only needs to work on a True/False mask.