POF Plotter

import matplotlib
import matplotlib.pyplot as plt
from matplotlib import colors
from matplotlib import cm
from matplotlib.colors import Normalize
import numpy as np
import xarray as xr
import matplotlib as mpl
from cartopy import config
import cartopy.crs as ccrs
import cartopy as cart
import sys
import datetime
from matplotlib.colors import ListedColormap,LinearSegmentedColormap

year=2019
month=12


fire= xr.open_dataset(f"./outputs/POF_prediction_{year}_{month:02d}.nc")
fire = fire.assign_coords(longitude=((fire.longitude + 360) % 360)).sortby('longitude')
lats = fire.latitude.values
lons = fire.longitude.values
fire

lat1 = -10
lat2 = -45
lon1 = 110
lon2 = 155

sub_ds = fire.sel(
    latitude=slice(lat1, lat2), 
    longitude=slice(lon1, lon2)
)
# x1 = (np.argmin(np.abs(lons - lon1)))
# x2 = (np.argmin(np.abs(lons - lon2)))
# y1 = (np.argmin(np.abs(lats - lat1)))
# y2 = (np.argmin(np.abs(lats - lat2)))

# sub_lons = lons[x1:x2]
# sub_lats = lats[y2:y1]
# print(y2)
# print(y1)
# print(sub_lats)
sub_ds

cols = ['#4a5a60','#edcc00','#edcc00','#e57d0f','#e57d0f','#e57d0f','#e57d0f','#e57d0f','#e57d0f','#e21819','#e21819','#e21819','#e21819','#e21819','#000000','#000000']
test = ListedColormap(cols)

index=0 # Which index to plot (1st of the month)

fires = sub_ds.fire_probability[index].values
plot_title = f"Probability of Fire Forecast for {sub_ds.time.values[index].strftime('%d %B %Y') }"

fires[fires>0.006]=0.006 # 1km

levels = np.arange(16)/2500 # 1km

print(np.shape(fires))
print(np.nanmax(fire.fire_probability.values))
print(np.nanmin(fire.fire_probability.values))
print(np.nanmean(fire.fire_probability.values))

ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines(linewidth=1)

fig = ax.contourf(sub_ds.longitude, sub_ds.latitude, fires,levels=levels,vmin=0,vmax=0.006,transform=ccrs.PlateCarree(),cmap=test)

ax.add_feature(cart.feature.OCEAN,zorder=1,edgecolor='k',color='white')
ax.add_feature(cart.feature.BORDERS,linewidth=0.5,color='white')
ax.add_feature(cart.feature.LAKES,color='white', edgecolor='k', linewidth=0.2, zorder=2)

states_provinces = cart.feature.NaturalEarthFeature(
        category='cultural',
        name='admin_1_states_provinces_lines',
        scale='50m',
        edgecolor='black'
)

for spine in ax.spines.values():
   spine.set_visible(False)

cbar = plt.colorbar(fig,ticks=[0.0002,0.0008,0.0022,0.0044,0.0056],fraction=0.025, pad=0.04)
cbar.ax.set_yticklabels(['Low', 'Medium','High', 'Very High', 'Extreme'])

plt.title(plot_title, fontsize=16)

plt.tight_layout()

plt.savefig('./outputs/My_PoF_Plot.png', dpi=300)