# ds.sel(time=core_pp.get_subdates(pd.to_datetime(ds.time.values), start_end_date=('06-01', '08-31'))).mean(dim='time').plot()
#%%
import make_country_mask
orography = os.path.join(user_dir, 'surfdrive/ERA5/input_raw/Orography.nc')
xarray, Country = make_country_mask.create_mask(var_filename,
kwrgs_load={'selbox': selbox},
level='Countries')
if domain == 'USCA':
mask_US_CA = np.logical_or(xarray.values == Country.US,
xarray.values == Country.CA)
elif domain == 'US':
mask_US_CA = xarray.values == Country.US
# xr_mask = xarray.where(mask_US_CA)
xr_mask = xarray.where(make_country_mask.binary_erosion(mask_US_CA))
# xr_mask = xarray.where(make_country_mask.binary_erosion(np.nan_to_num(xr_mask)))
xr_mask.values[~np.isnan(xr_mask)] = 1
xr_mask = find_precursors.xrmask_by_latlon(xr_mask, upper_right=(270, 63))
# mask small Western US Island
xr_mask = find_precursors.xrmask_by_latlon(xr_mask, bottom_left=(228, 58))
# add Rocky mask
geo_surf_height = core_pp.import_ds_lazy(
orography, var='z_NON_CDM', selbox=selbox) / 9.81
geo_surf_height = geo_surf_height.drop('time').drop('realization')
plot_maps.plot_corr_maps(geo_surf_height,
cmap=plt.cm.Oranges,
clevels=np.arange(0, 2600, 500))
max_height = 1500
mask_Rockies = geo_surf_height < max_height
plot_maps.plot_labels(mask_Rockies)
'color': colors,
'edgecolors': 'black'
}
]
]]
# regions= list(np.unique(xrclustered)[~np.isnan(np.unique(xrclustered))])
# if region == 'USCA':
# dic = {4:3, 3:4}
# else:
# dic = {2:3, 3:2}
# xrclustered = find_precursors.view_or_replace_labels(xrclustered.copy(), regions,
# [int(dic.get(n, n)) for n in regions])
if region == 'USCA':
mask_cl_n = find_precursors.view_or_replace_labels(xrclustered.copy(),
[1, 5])
mask_cl_n = make_country_mask.binary_erosion(~np.isnan(mask_cl_n))
mask_cl_s = ~np.isnan(
find_precursors.view_or_replace_labels(xrclustered.copy(), [2]))
mask_cl = ~np.logical_or(mask_cl_n, mask_cl_s)
elif region == 'USCAnew':
mask_cl = find_precursors.view_or_replace_labels(xrclustered.copy(),
[1, 4])
mask_cl = np.isnan(mask_cl)
elif region == 'init':
mask_cl_e = find_precursors.view_or_replace_labels(xrclustered.copy(), [3])
mask_cl_e = make_country_mask.binary_erosion(~np.isnan(mask_cl_e))
mask_cl_w = ~np.isnan(
find_precursors.view_or_replace_labels(xrclustered.copy(), [1]))
mask_cl = ~np.logical_or(mask_cl_w, mask_cl_e)
title = np.array([['Clustered simultaneous high temperature events']])
# xarray, Country = make_country_mask.create_mask(var_filename, kwrgs_load={'selbox':selbox}, level='Countries')
# mask_US = (xarray.values == Country.US)
# mask_US = make_country_mask.binary_erosion(mask_US)
# mask_US = make_country_mask.binary_erosion(mask_US)
# mask_US = make_country_mask.binary_opening(mask_US)
# xr_mask = xarray.where(mask_US)
# xr_mask.values[mask_US] = 1
# xr_mask = cl.mask_latlon(xr_mask, latmax=63, lonmax=270)
selbox = (232, 295, 25, 50)
xr_mask = core_pp.import_ds_lazy(
user_dir +
'/surfdrive/Scripts/rasterio/mask_North_America_0.25deg_orig.nc',
var='lsm',
selbox=selbox)
xr_mask.values = make_country_mask.binary_erosion(xr_mask.values)
plot_maps.plot_labels(xr_mask)
# In[9]:
# =============================================================================
# Clustering co-occurence of anomalies
# =============================================================================
q = [80, 85, 90, 95]
n_clusters = [2, 3, 4, 5, 6, 7, 8]
tfreq = 1
from time import time
t0 = time()
xrclustered, results = cl.dendogram_clustering(var_filename,
mask=xr_mask,
kwrgs_load={
'tfreq': tfreq,
