"#CCFFCC",
"#99FFCC",
"#66FF66",
"#00CC00",
"#009900",
"#003300",
) # 11
result_aux, lon_aux = shiftgrid(180.0, result, lon, start=False)
pm.map_shaded(
result_aux,
lat - 2.5 / 2.0,
lon_aux - 2.5 / 2.0,
meridians=np.arange(-160.0, 161.0, 10.0),
fig_name="sub.cmap.fma.2015.accum.png",
barlevs=lev,
barcolor=my_colors,
latsouthpoint=-60.0,
latnorthpoint=15.0,
fig_title="SUB CMAP - FMA 2015 - ACUM",
lonwestpoint=-90.0,
loneastpoint=-30.0,
barinf="both",
ocean_mask=1,
)
print result
print "original.shape", original.shape
print "myvar.shape", myvar.shape
print "result.shape", result.shape
print "np.min(result)", np.min(result)
print "np.max(result)", np.max(result)
clim_pcp_obs = clim_obs_data.variables['PRECIP'][:]
clim_obs_data.close()
# exit()
# pcp_obs = np.mean(pcp_obs, axis=0)
# pcp_obs = pcp_obs * 89. # Fev 28, Mar 31, Abr 30
pcp_obs = pcp_obs * 89
clim_pcp_obs = clim_pcp_obs * 89
mytercis = cs.tercil_verification(clim_pcp_obs, pcp_obs)
# Plot categorias
lev = [-1, 0., 1., 2.]
my_colors = ('#F75026', '#57F255', '#4143F2')
pm.map_shaded(mytercis, lats-1./2., lons-1./2.,
meridians=np.arange(-160., 161., 10.),
fig_name="cmap.fma.2015.cat.png", barlevs=lev, barcolor=my_colors,
latsouthpoint=-60., latnorthpoint=15., fig_title="CMAP - FMA 2015 - CAT",
lonwestpoint=-90., loneastpoint=-30., ocean_mask=1)
# Plot precip acumulada
lev = [0., 50., 100., 200., 300., 500., 700., 900., 1000.]
my_colors = ('#CC3333', '#FF6633', '#FF9933', '#FFFF99', '#FFFFCC', '#CCFFCC', '#99FFCC', '#66FF66', '#00CC00', '#009900', '#003300') #11
pm.map_shaded(pcp_obs, lats-1./2., lons-1./2.,
meridians=np.arange(-160., 161., 10.),
fig_name="cmap.fma.2015.accum.png", barlevs=lev, barcolor=my_colors,
latsouthpoint=-60., latnorthpoint=15., fig_title="CMAP - FMA 2015 - ACUM",
lonwestpoint=-90., loneastpoint=-30., barinf="max", ocean_mask=1)
# Plot map correlacao globo # pm.correl_map_shaded_echam_nmme(correl, lons-1.0/2., lats-1.0/2., # fig_name="correl_echam46+echam46_issue_jan_fma.png", # fig_title="ECHAM4.6 + NMME x CMAP\nCorrel de Prec Acum (mm) - FMA 89-08") # # Plot map correlacao globo sem oceano # pm.correl_map_shaded(correl, lons-1.0/2., lats-1.0/2., # fig_name="correl_echam46+echam46_issue_jan_fma_noocean.png", # fig_title="ECHAM4.6 + NMME x CMAP\nCorrel de Prec Acum (mm) - FMA 89-08", ocean_mask=1) # # Plot map correlacao Brasil # pm.correl_map_shaded(correl, lons-1.0/2., lats-1.0/2., # fig_name="correl_echam46+echam46_issue_jan_fma_br.png", # fig_title="ECHAM4.6 + NMME x CMAP\nCorrel de Prec Acum (mm) - FMA 89-08", br_country=1) # # # Plot map correlacao Brasil sem oceano # pm.correl_map_shaded(correl, lons-1.0/2., lats-1.0/2., # fig_name="correl_echam46+echam46_issue_jan_fma_noocean_br.png", # fig_title="ECHAM4.6 + NMME x CMAP\nCorrel de Prec Acum (mm) - FMA 89-08", ocean_mask=1, br_country=1) # Cria máscara com valores menores 0.3 mymask = np.where(correl < 0.3, True, False) # Aplica a máscara na váriavel fsct fsct_mask = ma.masked_array(fsct, mymask) # Plota o mapa com a máscara aplicada pm.map_shaded(fsct_mask[0,:,:],lons,lats)
figura, ax = plot_reliability(above[:,0], above[:,1], maintitle=mytitle, first=False, fig=figura, cor='red')
name_fig = "bra_precip_persistida_{0}_null-{1}_null_{2}_echam46_1dg_cmap_reliability.png".format(hind_period, target_months, n_fcst_month)
plt.savefig(name_fig)
plt.close()
correl = cs.compute_pearson(pcp_model, pcp_obs, timelen=14.)
# correl_aux, lons_aux = shiftgrid(180., correl, lons, start=False)
figtitle = u'RSM97 x PRECL - FMA - 02-15\nPersistida - 0.54x0.54 - Correlação - Precip Acum'
figname = "correl_rsm97per_x_precl_jfm_0215.png"
levs = (-1.0, -0.9, -0.7, -0.5, -0.3,
0.3, 0.5, 0.7, 0.9, 1.0) #10
my_colors = ('#2372c9', '#3498ed', '#4ba7ef', '#76bbf3', '#93d3f6',
'#b0f0f7', '#ffffff', '#fbe78a', '#ff9d37', '#ff5f26',
'#ff2e1b', '#ff0219', '#ae000c') #13
print "\n... salvando figura ..."
pm.map_shaded(correl, lats-0.54/2., lons-0.54/2., latsouthpoint=-16., latnorthpoint=5.,
lonwestpoint=-54., loneastpoint=-30., fig_name=figname, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=figtitle, barinf='neither', ocean_mask=1)
# pm.map_shaded(correl_aux, lats-2.8125/2., lons_aux-2.8125/2., fig_name=figname, barloc='right',
# barcolor=my_colors, barlevs=levs, fig_title=figtitle, barinf='neither', ocean_mask=0)
obs = read_obs_file.variables['pcp'][:, :, :]
read_obs_file.close()
except:
print "Erro ao ler arquivo observado"
exit()
# RMSE
print "... RMSE ..."
error_rmse = cs.rmse(hind, obs)
rmse_title = u'ECHAM4.6 x CMAP - {0}/{1} ({2})\nRMSE (mm) - Precip Acum'.format(fcst_month, target_months, hind_period)
fig_rmse = 'bra_precip_persistida_{0}_null-{1}_null_{2}_echam46_1dg_cmap_rmse.png'.format(hind_period, target_months, n_fcst_month)
levs = (0., 50., 100., 200., 400., 600., 800.) #7
my_colors = ('#ffffff', '#E1FFFF', '#B4F0FA', '#96D2FA', '#78B9FA',
'#50A5F5', '#3C96F5', '#2882F0') #8
pm.map_shaded(error_rmse, lats-1./2., lons-1./2., latsouthpoint=-60., latnorthpoint=15.,
lonwestpoint=-90., loneastpoint=-30., fig_name=fig_rmse, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=rmse_title, barinf='max', ocean_mask=1)
# Desvio padrão
print u"... desvio padrão ..."
hind_std = np.nanstd(hind, axis=0)
hind_std_title = u'ECHAM4.6 x CMAP - {0}/{1} ({2})\nDesvio Padrão (mm) - Precip Acum'.format(fcst_month, target_months, hind_period)
fig_hind_std = 'bra_precip_persistida_{0}_null-{1}_null_{2}_echam46_1dg_cmap_std.png'.format(hind_period, target_months, n_fcst_month)
levs = (0., 25., 50., 75., 100., 150., 200.) #7
my_colors = ('#ffffff', '#E1FFFF', '#B4F0FA', '#96D2FA', '#78B9FA',
'#50A5F5', '#3C96F5', '#2882F0') #8
pm.map_shaded(hind_std, lats-1./2., lons-1./2., latsouthpoint=-60., latnorthpoint=15.,
lonwestpoint=-90., loneastpoint=-30., fig_name=fig_hind_std, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=hind_std_title, barinf='max', ocean_mask=1)
# Viés
print clim_pcp_obs.shape
clim_obs_data.close()
# clim_pcp_obs_aux, lons_clim_aux = shiftgrid(180., clim_pcp_obs, lons, start=False)
mytercis, tercilinf, tercilupp = cs.tercil_verification(clim_pcp_obs, pcp_obs)
print mytercis.shape
lons -= 1./2.
lats -= 1./2.
# Plot precip acumulada
lev = [0., 50., 100., 200., 300., 500., 700., 900., 1000.]
my_colors = ('#CC3333', '#FF6633', '#FF9933', '#FFFF99', '#FFFFCC', '#CCFFCC', '#99FFCC', '#66FF66', '#00CC00', '#009900', '#003300') #11
pm.map_shaded(pcp_obs, lats, lons,
meridians=np.arange(-160., 161., 10.),
fig_name="cmap.merge.funceme.fma.2015.accum.1dg.png", barlevs=lev, barcolor=my_colors,
latsouthpoint=-60., latnorthpoint=15., fig_title="CMAP/FUNCEME - FMA 2015 - ACUM",
lonwestpoint=-90., loneastpoint=-30., barinf="max", ocean_mask=1)
# Plot categorias
lev = [-1, 0., 1., 2.]
my_colors = ('#F75026', '#57F255', '#4143F2')
pm.map_shaded_tercis(mytercis, lats, lons,
meridians=np.arange(-160., 161., 10.),
fig_name="cmap.merge.funceme.fma.2015.catobs.1dg.png", barlevs=lev, barcolor=my_colors,
latsouthpoint=-60., latnorthpoint=15., fig_title="CMAP/FUNCEME - FMA 2015 - CAT OBS",
lonwestpoint=-90., loneastpoint=-30., ocean_mask=1)
# Tercil inferior
