def mapa_show(data3d, lat, lon):
y1, y2, x1, x2 = -25, 0, -52, -34
cor1 = ('#750000', '#ff0000', '#ff8000', '#fcd17d', '#ffff00', '#ffffff',
'#00ffff', '#7dd1fa', '#0080ff', '#0000ff', '#000075') # Colors
lev1 = (-2., -1.6, -1.3, -0.8, -0.5, 0.5, 0.8, 1.3, 1.6, 2.)
pm.plotmap(data3d[0, :, :],
lat,
lon,
latsouthpoint=y1,
latnorthpoint=y2,
lonwestpoint=x1,
loneastpoint=x2,
ocean_mask=0,
fig_name="figou1_teste.png.",
barcolor=cor1,
barlevs=lev1,
barinf='both',
barloc='right')
caso_shape_so_plot('/estados/sergipe/sergipe.txt')
y1, y2, x1, x2 = -20, 0, -50, -34
cor1 = ('#750000', '#ff0000', '#ff8000', '#fcd17d', '#ffff00',
'#ffffff', '#00ffff', '#7dd1fa', '#0080ff', '#0000ff',
'#000075') # Paleta
lev1 = (-2., -1.6, -1.3, -0.8, -0.5, 0.5, 0.8, 1.3, 1.6, 2.)
figou1 = 'ESI_Regiao_NEB_4WK_{0}_{1}.png'.format(mes[i], ANO)
title1 = u'Índice de Estresse Evaporativo \n{0} - {1}'.format(
mes[i], ANO)
pm.plotmap(Dmasked[0, :, :],
lats,
lons,
latsouthpoint=y1,
latnorthpoint=y2,
lonwestpoint=x1,
loneastpoint=x2,
ocean_mask=1,
shapefile=None,
fig_name=figou1,
fig_title=title1,
barcolor=cor1,
barlevs=lev1,
barinf='both',
barloc='right')
plt.close('all')
plt.cla()
# y1, y2, x1, x2 = -40., 40., -150., 70. # Globo
#~ y1, y2, x1, x2 = -89., 89., -178., 178. # Globo
#~ y1, y2, x1, x2 = -23., 9., -75., -34. # Região Reliability
y1, y2, x1, x2 = -21.3, 7., -55.6, -34 # América do sul
########### CORRELAÇÃO ###########
correl = cs.compute_pearson(hind, obs, n_years)
figtitle = u'RSM97 x UTEXAS - {0}/{1} ({2})\nCorrelação - Precip Acum'.format(fcst_month_name, target_months, hind_period_name)
figname = "{3}/bra_precip_persistida_{0}_null-{1}_null_{2}_rsm97_1dg_utexas_correlacao.png".format(hind_period_name, target_months, n_fcst_month, outdir)
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
pm.plotmap(correl, obs_lats-0.54/2., obs_lons-0.54/2., latsouthpoint=y1, latnorthpoint=y2,
lonwestpoint=x1, loneastpoint=x2, fig_name=figname, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=figtitle, barinf='neither', ocean_mask=1)
background = Image.open(figname)
foreground = Image.open("/home/marcelo/FSCT-ECHAM46/FUNCEME_LOGO.png")
foreground = foreground.resize((90, 70), Image.ANTIALIAS)
background.paste(foreground, (313, 460), foreground)
background.save(figname, optimize=True, quality=95)
# ########### RMSE ###########
# error_rmse = cs.compute_rmse(hind, obs)
# rmse_title = u'ECHAM4.6 x CMAP - {0}/{1} ({2})\nRMSE (mm) - Precip Acum'.format(fcst_month_name, target_months, hind_period_name)
# fig_rmse = '{3}/bra_precip_persistida_{0}_null-{1}_null_{2}_echam46_1dg_cmap_rmse.png'.format(hind_period_name, target_months, n_fcst_month, outdir)
# levs = (0., 50., 100., 200., 400., 600., 800.) #7
# my_colors = ('#ffffff', '#E1FFFF', '#B4F0FA', '#96D2FA', '#78B9FA',
# '#50A5F5', '#3C96F5', '#2882F0') #8
# pm.plotmap(error_rmse, newlats-1./2., newlons-1./2., latsouthpoint=y1, latnorthpoint=y2,
#~ y1, y2, x1, x2 = -89., 89., -178., 178. # Globo
#~ y1, y2, x1, x2 = -23., 9., -75., -34. # Região Reliability
y1, y2, x1, x2 = -60., 15., -90., -30. # América do sul
########### CORRELAÇÃO ###########
correl = cs.compute_pearson(hind, obs, n_years)
figtitle = u'ECHAM4.6 x CMAP - {0}/{1} ({2})\nCorrelação - Precip Acum'.format(fcst_month_name, target_months, hind_period_name)
#~ figtitle = u'ECHAM4.6 x CMAP - {0}/{1} ({2})\nCorrelation - Precip Accum'.format(fcst_month_name, target_months, hind_period_name)
figname = "{3}/bra_precip_persistida_{0}_null-{1}_null_{2}_echam46_1dg_cmap_correlacao.png".format(hind_period_name, target_months, n_fcst_month, outdir)
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
pm.plotmap(correl, newlats-1./2., newlons-1./2., latsouthpoint=y1, latnorthpoint=y2,
lonwestpoint=x1, loneastpoint=x2, fig_name=figname, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=figtitle, barinf='neither', ocean_mask=1)
background = Image.open(figname)
foreground = Image.open("FUNCEME_LOGO.png")
foreground = foreground.resize((90, 70), Image.ANTIALIAS)
background.paste(foreground, (334, 461), foreground)
background.save(figname, optimize=True, quality=95)
########### RMSE ###########
error_rmse = cs.compute_rmse(hind, obs)
rmse_title = u'ECHAM4.6 x CMAP - {0}/{1} ({2})\nRMSE (mm) - Precip Acum'.format(fcst_month_name, target_months, hind_period_name)
fig_rmse = '{3}/bra_precip_persistida_{0}_null-{1}_null_{2}_echam46_1dg_cmap_rmse.png'.format(hind_period_name, target_months, n_fcst_month, outdir)
levs = (0., 50., 100., 200., 400., 600., 800.) #7
my_colors = ('#ffffff', '#E1FFFF', '#B4F0FA', '#96D2FA', '#78B9FA',
'#50A5F5', '#3C96F5', '#2882F0') #8
pm.plotmap(error_rmse, newlats-1./2., newlons-1./2., latsouthpoint=y1, latnorthpoint=y2,
print zi.shape
# np.savetxt("zi.txt", zi)
for ilon in range(0, 109):
for ilat in range(0, 72):
print lonrsm[ilon], latrsm[ilat], zi[ilat, ilon]
exit()
# print zi
levs = [-2., -1.8, -1.3, -0.8, -0.5, 0.]
my_colors = ('#730000', '#E60000', '#FFAA00', '#FCD37F', '#FFFF00')
pm.plotmap(zi, latrsm-0.54/2., lonrsm-0.54/2., latnorthpoint=np.max(latrsm), latsouthpoint=np.min(latrsm),
loneastpoint=np.max(lonrsm), lonwestpoint=np.min(lonrsm), barlevs=levs, barcolor=my_colors, barinf='min')
exit()
# inc = netCDF4.Dataset('/home/funceme/Downloads/pcp-seasonacc-echam46-hind8908-en51-jan2014_2014AMJ.ctl.nc')
# time = inc.variables['time'][:]
# latt42 = inc.variables['latitude'][:]
# lont42_360 = inc.variables['longitude'][:]
# pcpt42 = inc.variables['pcp'][0, :, :]
# pcpt42 , lont42_360 = addcyclic(pcpt42, lont42_360)
# pcpt42, lont42 = shiftgrid(180., pcpt42, lont42_360, start=False)
#
# inc = netCDF4.Dataset('/home/funceme/Downloads/pcp-seasonacc-echam46-hind8908-en51-jan2014_2014AMJ.ctl.1dg.nc')
# lat1dg = inc.variables['LAT'][:]
# lon1dg = inc.variables['LON'][:]
# pcp1dg = inc.variables['pcp'][0, :, :]
ltime = 5
levs = (270., 275., 280., 285., 290., 295., 300.) #7
my_colors = ('#ffffff', '#E5E5E5', '#CBCBCB', '#B2B2B2',
'#989898', '#7F7F7F', '#666666', '#4E4E4E') #8
# LIQIANG
f1 = NetCDFFile('/home/marcelo/Downloads/sst-test/liq/obs.sst.2013.nc', 'r')
sstliq = f1.variables['sst'][:]
lons_sstliq = f1.variables['lon'][:]
lats_sstliq = f1.variables['lat'][:]
f1.close()
sstliq, lons_sstliq = shiftgrid(180., sstliq, lons_sstliq, start=False)
sstliq_title = u'TSM LIQ'
fig_sstliq = '/home/marcelo/Downloads/sst-test/tsmliq.png'
pm.plotmap(sstliq[ltime, :, :], lats_sstliq-2.8125/2., lons_sstliq-2.8125/2., latsouthpoint=y1, latnorthpoint=y2,
lonwestpoint=x1, loneastpoint=x2, fig_name=fig_sstliq, barloc='bottom', maptype='fillc',
barcolor=my_colors, barlevs=levs, fig_title=sstliq_title, barinf='max', ocean_mask=0,
meridians=np.arange(-160., 161., 30.), parallels=np.arange(-90., 91., 20.))
# NÃO INVERTIDO USANDO A LAT DO MENOR PARA O MAIOR
# f4 = NetCDFFile('/home/marcelo/Downloads/sst-test/nc3/obs.sst.2013.nc', 'r')
# sstnc3 = f4.variables['sst'][:, :, :]
# lons_sstnc3 = f4.variables['lon'][:]
# lats_sstnc3 = f4.variables['lat'][:]
# f4.close()
# sstnc3, lons_sstnc3 = shiftgrid(180., sstnc3, lons_sstnc3, start=False)
# sstliq_title = u'NÃO INVERTIDO USANDO A LAT DO MENOR PARA O MAIOR'
# fig_sstliq = '/home/marcelo/Downloads/sst-test/n3.png'
# pm.plotmap(sstnc3[ltime, :, :], lats_sstnc3-2.8125/2., lons_sstnc3-2.8125/2., latsouthpoint=y1, latnorthpoint=y2,
# lonwestpoint=x1, loneastpoint=x2, fig_name=fig_sstliq, barloc='bottom', maptype='fill',
# barcolor=my_colors, barlevs=levs, fig_title=sstliq_title, barinf='max', ocean_mask=0,
#~ lat,lon = grb.latlons()
#~
#~ figtitle = 'TITULO DA FIGURA'
#~ figname_anom = 'atub1.png'
#~ levs = range(100, 420, 20)
#~ my_colors = ('#340003', '#ae000c', '#ff2e1b', '#ff5f26', '#ff9d37', '#fbe78a',
#~ '#ffffff', '#b0f0f7', '#93d3f6', '#76bbf3', '#4ba7ef', '#3498ed', '#2372c9') #13
#~ pm.plotmap(aux1[0,217:231,130:280], lat[217:231,0], lon[0,130:280], fig_name=figname_anom, barloc='bottom',
#~ barcolor=my_colors, barlevs=levs, fig_title=figtitle,
#~ barinf='both', ocean_mask=0)
print '---'
grb = grbs.select(name='Potential temperature')[0]
data = grb.values
print data[217:231,130:280]
exit()
figtitle = 'TITULO DA FIGURA 2'
figname_anom = 'atub2.png'
levs = range(100, 420, 20)
my_colors = ('#340003', '#ae000c', '#ff2e1b', '#ff5f26', '#ff9d37', '#fbe78a',
'#ffffff', '#b0f0f7', '#93d3f6', '#76bbf3', '#4ba7ef', '#3498ed', '#2372c9') #13
pm.plotmap(data[217:231,130:280], lat[217:231,0], lon[0,130:280], fig_name=figname_anom, barloc='bottom',
barcolor=my_colors, barlevs=levs, fig_title=figtitle,
barinf='both', ocean_mask=0)
for i in range(30):
obs[i, ...] = interp(obs_aux[i, ...], lons_obs, lats_obs, x, y, order=1)
print(obs.shape)
########### CORRELAÇÃO ###########
correl = cs.compute_pearson(hind, obs)
figtitle = u'RSM2008 x INMET - JAN/{0} (8110)\nCORRELAÇÃO - PRECIP ACUM'.format(tri.upper())
figname = 'rsm.correl.{0}.png'.format(tri)
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
pm.plotmap(correl, lats_hind, lons_hind, latsouthpoint=-38.27, latnorthpoint=13.63,
lonwestpoint=-84.3, loneastpoint=-33., fig_name=figname, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=figtitle, barinf='neither', ocean_mask=1)
########### VIÉS ###########
print(hind.shape)
print(obs.shape)
exit()
error_bias = cs.compute_bias(hind, obs)
error_bias[np.isinf(error_bias)] = 0
print(np.min(error_bias))
print(np.max(error_bias))
figtitle =u'RSM2008 x INMET - JAN/{0} (8110)\nVIÉS (mm) - PRECIP ACUM'.format(tri.upper())
figname = 'rsm.vies.{0}.png'.format(tri)
levs = (-4, -3, -2., -1., -0.5, 0.5, 1, 2, 3, 4)
anom_diff = anom_exp - anom
figtitle = u'Anom Diff (mm) (EXP - CONTROl)'
figname_anom = '{0}/diff_anom_{1}.png'.format(directory, y)
levs = (-300., -100., -50., -30., -15., -5.,
5., 15., 30., 50., 100., 300.) # 12
my_colors = ('#340003', '#ae000c', '#ff2e1b', '#ff5f26', '#ff9d37',
'#fbe78a', '#ffffff', '#b0f0f7', '#93d3f6', '#76bbf3',
'#4ba7ef', '#3498ed', '#2372c9') # 13
pm.plotmap(anom_diff, nla, nlo,
latsouthpoint=y1, latnorthpoint=y2, lonwestpoint=x1,
loneastpoint=x2, fig_name=figname_anom, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=figtitle,
barinf='both', ocean_mask=1)
# diff das anomalias percentual
anom_diff_p = ((anom_exp - anom) / anom) * 100
print(np.max(anom_diff_p))
print(np.min(anom_diff_p))
figtitle = u'Anom Diff Percentual (mm) (EXP - CONTROl)'
figname_anom = '{0}/diff_anom_{1}_p.png'.format(directory, y)
# 10
data = netCDF4.Dataset(path_in + name)
var = data.variables[
'eto'][:, :, :] # Declaring variable under study to calculate the
lats = data.variables['lat'][:] # Declaring latitude
lons = data.variables['lon'][:] # Declaring longitude
for m in range(0, var.shape[0]):
print m, mes[m], dic[mes[m]]
title1 = u'Evapotranspiração Potencial\n Desvio Padrão - {0}'.format(
mes[m])
figou1 = 'plot_STD_CRU_{0}.png'.format(mes[m])
pm.plotmap(var[m, :, :] * dic[mes[m]],
lats,
lons,
latsouthpoint=y1,
latnorthpoint=y2,
lonwestpoint=x1,
loneastpoint=x2,
ocean_mask=1,
fig_name=figou1,
fig_title=title1,
barcolor=cor1,
barlevs=lev1,
barinf='max',
barloc='right')
print np.nanmax(var), np.nanmin(var)
"#FFFFCC",
"#CCFFCC",
"#99FFCC",
"#66FF66",
"#00CC00",
"#009900",
"#003300",
) # 11
pm.plotmap(
diff,
lat1dg - 1 / 2.0,
lon1dg - 1 / 2.0,
meridians=np.arange(-180.0, 180.0, 30.0),
fig_name="cdobilinterp.png",
barlevs=lev,
barcolor=my_colors,
latsouthpoint=-90.0,
latnorthpoint=90.0,
fig_title="cdo bili interp",
lonwestpoint=-180.0,
loneastpoint=180.0,
barinf="max",
ocean_mask=0,
)
exit()
lev = [0.0, 50.0, 100.0, 200.0, 300.0, 500.0, 700.0, 900.0, 1000.0]
my_colors = (
"#CC3333",
"#FF6633",
"#FF9933",
correl = cs.compute_pearson(hind, obs)
figtitle = u'RSM97 x {3} - {0}/{1} ({2})\nCorrelação - Precip Acum' \
.format(fcst_month.upper(), target_months,
hind_period_name, obs_base.upper())
figname = '{3}/neb_precip_persistida_{0}_null-{1}_null_{2}' \
'_rsm97_1dg_{4}_correlacao.png' \
.format(hind_period_name, target_months,
fcst_months[fcst_month], outdir, obs_base)
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
pm.plotmap(correl, hind_lats, hind_lons,
latsouthpoint=y1, latnorthpoint=y2,
lonwestpoint=x1, loneastpoint=x2, fig_name=figname,
barloc='right', barcolor=my_colors, barlevs=levs,
fig_title=figtitle, barinf='neither', ocean_mask=1,
meridians=np.arange(-160., 161., 2.),
parallels=np.arange(-90., 91., 2.))
# TODO: Colocar em uma funcao
bx, by = 408, 460
background = Image.open(figname)
foreground = Image.open("FUNCEME_LOGO.png")
foreground = foreground.resize((90, 70), Image.ANTIALIAS)
background.paste(foreground, (bx, by), foreground)
background.save(figname, optimize=True, quality=95)
########### RMSE ###########
error_rmse = cs.compute_rmse(hind, obs)
rmse_title = u'RSM97 x {3} - {0}/{1} ({2})\nRMSE (mm) - Precip Acum' \
.format(fcst_month.upper(), target_months,
"#ffffff",
"#fbe78a",
"#ff9d37",
"#ff5f26",
"#ff2e1b",
"#ff0219",
"#ae000c",
) # 13
pm.plotmap(
correl,
newlats,
newlons,
latsouthpoint=y1,
latnorthpoint=y2,
lonwestpoint=x1,
loneastpoint=x2,
fig_name=figname,
barloc="right",
barcolor=my_colors,
barlevs=levs,
fig_title=figtitle,
barinf="neither",
ocean_mask=1,
)
background = Image.open(figname)
foreground = Image.open("FUNCEME_LOGO.png")
foreground = foreground.resize((90, 70), Image.ANTIALIAS)
background.paste(foreground, (334, 461), foreground)
background.save(figname, optimize=True, quality=95)
########### RMSE ###########
error_rmse = cs.compute_rmse(hind, obs)
img22.save("img22.png", quality=23)
os.remove(figname)
figname = "ABOVE.png"
my_colors = ('#b2b2f2', '#8484f2', '#4143f2', '#0006f2')
xlabel = 'ACIMA'
levs = (40., 50., 60, 70.)
myplot(abovep[0, :, :]-1000, lat-1./2., lon-1./2., my_colors, levs, xlabel, figname, y1, y2, x1, x2)
img3 = Image.open(figname)
img33 = img3.crop((250, 455, 700, 575))
# img33.show()
img33.save("img33.png", quality=23)
os.remove(figname)
levs = ( 0., 40., 50., 60, 70.,
100., 140., 150., 160., 170.,
1000., 1040., 1050., 1060., 1070., 1100 )
my_colors = ('#ffffff', '#ffed4c', '#f9bb34', '#f75026', '#dd2b28',
'#ffffff', '#ccf2cc', '#abf2aa', '#57f255', '#16f200',
'#ffffff', '#b2b2f2', '#8484f2', '#4143f2', '#0006f2')
figname, figtitle = "ECHAM4p6.png", "ECHAM4.6 - OUT/2015 - OND/2015\nPROB PREC (%) (89-08)"
pm.plotmap(finalp[0, :, :], lat-1./2., lon-1./2., latsouthpoint=y1, latnorthpoint=y2,
lonwestpoint=x1, loneastpoint=x2, fig_name=figname, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=figtitle, barinf='neither', ocean_mask=1)
concat("img11.png", "img22.png", "img33.png", figname, "echamprob.png")
'#ffffff', '#00ffff', '#7dd1fa', '#0080ff', '#0000ff',
'#000075') # Paleta
lev1 = (-2., -1.6, -1.3, -0.8, -0.5, 0.5, 0.8, 1.3, 1.6, 2.)
fig = plt.figure()
figou1 = 'ESI_Regiao_SA_4WK_{0}_{1}.png'.format(mes[i], ANO)
title1 = u'Índice de Estresse Evaporativo \n{0} - {1}'.format(
mes[i], ANO)
txtbox(u'Fonte: NOAA/ESSIC and USDA-ARS\nElaboração: FUNCEME', 0.46,
0.06, 8, (1, 1, 1))
pm.plotmap(aux_in[0, :, :],
lat,
lon,
latsouthpoint=y1,
latnorthpoint=y2,
lonwestpoint=x1,
loneastpoint=x2,
ocean_mask=1,
fig_name=figou1,
fig_title=title1,
barcolor=cor1,
barlevs=lev1,
barinf='both',
barloc='right')
plt.close('all')
plt.cla()
lonlat = np.array(polig)
elif isinstance(polig, MultiPolygon):
# Cria dicionario para os multipoligonos onde a chave do dicionário
# é o número de vérticies do polígono
poly = {}
for poli in polig:
xy = np.array(poli.exterior.coords)
poly[xy.shape[0]] = xy
lonlat = poly[max(poly)]
# Salvar lonlat das regiões
# fstr = "{0}.dat".format(sigla)
# np.savetxt(fstr, lonlat, fmt='%15.6f', delimiter=' ', newline='\n')
# Plotar mapas
# pl.plot(lonlat[:, 0], lonlat[:, 1])
# pl.title(sigla)
# pl.show()
points_grid, lonlat_grid, array_bool = pointinsidev2(lats1, lons1, lonlat)
pm.plotmap(array_bool, lats1-0.5, lons1-0.5)
def maptercisrsm97(filein, figtitle, figout, maskocean=1):
# TODO: Tirar leitura do netcdf e passar dados via cabeçalho da função
ncfile = Dataset(filein)
lat = ncfile.variables['latitude'][:]
lon = ncfile.variables['longitude'][:]
above = ncfile.variables['above'][:]
normal = ncfile.variables['normal'][:]
below = ncfile.variables['below'][:]
belowp = np.where(((below > normal) & (below > above)), below, 0)
normalp = np.where(((normal > above) & (normal > below)), normal+100, 0)
abovep = np.where(((above > normal) & (above > below)), above+1000, 0)
finalp = (belowp + normalp + abovep)
deltalat = np.nanmean(np.diff(lat))
deltalon = np.nanmean(np.diff(lon))
y1, y2, x1, x2 = -21.397, 8., -55.637, -34.
figname = "BELOW.png"
my_colors = ('#ffed4c', '#f9bb34', '#f75026', '#dd2b28')
xlabel = 'ABAIXO'
levs = (40., 50., 60, 70.)
myplot(belowp[0, :, :], lat-deltalat/2., lon-deltalon/2., my_colors,
levs, xlabel, figname, y1, y2, x1, x2)
img1 = Image.open(figname)
img11 = img1.crop((110, 455, 550, 575))
# img11.show()
img11.save("img11.png", quality=23)
os.remove(figname)
figname = "NORMAL.png"
my_colors = ('#ccf2cc', '#abf2aa', '#57f255', '#16f200')
xlabel = 'NORMAL'
levs = (40., 50., 60, 70.)
myplot(normalp[0, :, :]-100, lat-deltalat/2., lon-deltalon/2.,
my_colors, levs, xlabel, figname, y1, y2, x1, x2)
img2 = Image.open(figname)
img22 = img2.crop((200, 455, 610, 575))
# img22.show()
img22.save("img22.png", quality=23)
os.remove(figname)
figname = "ABOVE.png"
my_colors = ('#b2b2f2', '#8484f2', '#4143f2', '#0006f2')
xlabel = 'ACIMA'
levs = (40., 50., 60, 70.)
myplot(abovep[0, :, :]-1000, lat-deltalat/2., lon-deltalon/2.,
my_colors, levs, xlabel, figname, y1, y2, x1, x2)
img3 = Image.open(figname)
img33 = img3.crop((250, 455, 700, 575))
# img33.show()
img33.save("img33.png", quality=23)
os.remove(figname)
levs = ( 0., 40., 50., 60, 70.,
100., 140., 150., 160., 170.,
1000., 1040., 1050., 1060., 1070., 1100 )
my_colors = ('#ffffff', '#ffed4c', '#f9bb34', '#f75026', '#dd2b28',
'#ffffff', '#ccf2cc', '#abf2aa', '#57f255', '#16f200',
'#ffffff', '#b2b2f2', '#8484f2', '#4143f2', '#0006f2')
pm.plotmap(finalp[0, :, :], lat-deltalat/2., lon-deltalon/2.,
latsouthpoint=y1, latnorthpoint=y2, lonwestpoint=x1,
loneastpoint=x2, fig_name=figname, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=figtitle,
barinf='neither', ocean_mask=maskocean,
meridians=np.arange(-160., 161., 5.),
parallels=np.arange(-90., 91., 5.))
concat("img11.png", "img22.png", "img33.png", figname, figout)
figname_anom = '{5}/bra_precip_persistida_{0}_{1}-{2}_{3}_{4}_' \
'echam46_1dg_null_anom.png'.format(hind_period_name,
target_year, target_months, fcst_year,
n_fcst_month, outdir)
levs = (-700., -500., -300., -100., -50., -30.,
30., 50., 100., 300., 500., 700.) #12
my_colors = ('#340003', '#ae000c', '#ff2e1b', '#ff5f26', '#ff9d37',
'#fbe78a', '#ffffff', '#b0f0f7', '#93d3f6', '#76bbf3',
'#4ba7ef', '#3498ed', '#2372c9') #13
pm.plotmap(anomaly_corrigida[0, :, :], newlats, newlons,
latsouthpoint=y1, latnorthpoint=y2, lonwestpoint=x1,
loneastpoint=x2, fig_name=figname_anom, barloc='right',
barcolor=my_colors, barlevs=levs, fig_title=figtitle,
barinf='both', ocean_mask=1)
background = Image.open(figname_anom)
foreground = Image.open("FUNCEME_LOGO.png")
foreground = foreground.resize((90, 70), Image.ANTIALIAS)
background.paste(foreground, (334, 461), foreground)
background.save(figname_anom, optimize=True, quality=95)
### Tercil mais provável para toda a região do globo ###
file_out = "{5}/prob_echam46_issue_{0}{1}_target_{2}{3}_{4}.nc" \
.format(fcst_month, fcst_year, target_months, target_year,
hind_period, outdir)
# below, normal, above, f_signal, f_std, o_pad, fcst_sig_anom = \
print " === OUTPUT DIR: {0} ===\n".format(outdir)
# Limites do mapa para fazer o plot
# y1, y2, x1, x2 = -40., 40., -150., 70. # Globo
# y1, y2, x1, x2 = -89., 89., -178., 178. # Globo
y1, y2, x1, x2 = -60., 15., -90., -30. # América do sul
########## Apenas um plot ##########
lev = [0., 50., 100., 200., 300., 500., 700., 900., 1000.]
my_colors = ('#ffffff', '#E5E5E5', '#CBCBCB', '#B2B2B2',
'#989898', '#7F7F7F', '#666666', '#4E4E4E') #8
figtitle = u'ECHAM4.6 - Precip Acum (mm)'
figname = "{5}/bra_precip_persistida_{0}_{1}-{2}_{3}_{4}_echam46_1dg_null_precip_median.png" \
.format(hind_period_name, target_year, target_months, fcst_year, n_fcst_month, outdir)
pm.plotmap(fcst[0, :, :], newlats-1./2., newlons-1./2., latsouthpoint=-88., latnorthpoint=88.,
lonwestpoint=-178., loneastpoint=178., fig_name=figname, fig_title=figtitle,
barcolor=my_colors, ocean_mask=0, barlevs=lev, barinf='max',
barloc='bottom', meridians=np.arange(-160., 161., 30.), parallels=np.arange(-90., 91., 20.))
########## Anomalia corrigida metódo do Caio (regressão linear) ##########
below, normal, above, f_signal, f_std, o_pad, fcst_sig_anom = cs.compute_probability(fcst, hind, obs, n_years)
hind_corrigido = cs.compute_setrighthind(hind, obs, n_years)
anomaly_corrigida, anomaly_pad = cs.compute_anomaly(fcst_sig_anom, hind_corrigido)
#~ figtitle = u'ECHAM4.6 x CMAP - OCT/{4} - {1}/{4}\nAnomaly (mm) - Precip Accum ({2})'\
figtitle = u'ECHAM4.6 x CMAP - {0}/{3} - {1}/{4}\nAnomalia (mm) - Precip Acum ({2})'\
.format(fcst_month_name, target_months, hind_period_name, fcst_year, target_year)
figname_anom = "{5}/bra_precip_persistida_{0}_{1}-{2}_{3}_{4}_echam46_1dg_null_anom_median.png" \
.format(hind_period_name, target_year, target_months, fcst_year, n_fcst_month, outdir)
levs = (-700., -500., -300., -100., -50., -30., 30., 50., 100., 300., 500., 700.) #12
my_colors = ('#340003', '#ae000c', '#ff2e1b', '#ff5f26', '#ff9d37', '#fbe78a',
'#ffffff', '#b0f0f7', '#93d3f6', '#76bbf3', '#4ba7ef', '#3498ed', '#2372c9') #13
pm.plotmap(anomaly_corrigida[0, :, :], newlats-1./2., newlons-1./2., latsouthpoint=y1, latnorthpoint=y2,
figtitle = "RSM97 - Precip Acum (mm)"
figname = "{5}/neb_precip_persistida_{0}_{1}-{2}_{3}_{4}_" "rsm97_1dg_null_precip.png".format(
hind_period_name, target_year, target_months, fcst_year, fcst_months[fcst_month], outdir
)
pm.plotmap(
fcst[0, :, :],
fcst_lats,
fcst_lons,
latsouthpoint=y1,
latnorthpoint=y2,
lonwestpoint=x1,
loneastpoint=x2,
fig_name=figname,
fig_title=figtitle,
barcolor=my_colors,
ocean_mask=0,
barlevs=lev,
barinf="max",
barloc="bottom",
meridians=np.arange(-160.0, 161.0, 2.0),
parallels=np.arange(-90.0, 91.0, 2.0),
)
if obs_base == "inmet" or obs_base == "chirps" or obs_base == "cru":
### Anomalia corrigida: metódo do Caio (regressão linear) ###
below, normal, above, f_signal, f_std, o_pad, fcst_sig_anom = cs.compute_probability(fcst, hind, obs)
hind_corrigido = cs.compute_setrighthind(hind, obs)