fcst_month, fcst_year, target_months, target_year, hind_period, outdir
)
below, normal, above, f_signal, f_std, o_pad, fcst_sig_anom = cs.compute_probability(fcst, hind, obs)
cn.create_netcdf_probs(below, normal, above, fcst_lats, fcst_lons, fileout=file_out)
figout = "{5}/neb_precip_persistida_{1}_{2}-{3}_{4}_{0}_rsm97_" "1dg_{6}_tercilmaisprovavel.png".format(
fcst_months[fcst_month], hind_period_name, target_year, target_months, fcst_year, outdir, obs_base
)
figtitle = "RSM97 - {0}/{1} - {2}/{3}\nPROB PREC (%) ({4}) ({5})".format(
fcst_month.upper(), fcst_year, target_months, target_year, hind_period_name, obs_base.upper()
)
pm.maptercisrsm97(file_out, figtitle, figout, maskocean=1)
background = Image.open(figout)
foreground = Image.open("FUNCEME_LOGO.png")
foreground = foreground.resize((90, 70), Image.ANTIALIAS)
background.paste(foreground, (bx, by), foreground)
background.save(figout, optimize=True, quality=95)
########## Curva para o CE ##########
# Retorna matriz com os pontos sobre o Ceará
# shapef = 'pontos_ce.txt'
# TODO: Usar Thiessen
pointsgrid, lonlatgrid, mymatriz = dg.pointinside(fcst_lats, fcst_lons, shapefile="pontos_ce.txt")
# Aplica máscara para os pontos sobre o Ceará
points_over_ce_fcst = np.ma.array(fcst, mask=mymatriz)
