########## 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,
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("/home/marcelo/FSCT-ECHAM46/FUNCEME_LOGO.png")
foreground = foreground.resize((90, 70), Image.ANTIALIAS)
# Plot map correlacao globo sem oceano
# figname = "correl_echam46_issue_{0}_{1}.{2}_noocean.png".format(fcst_month, target_months, hind_period)
# pm.correl_map_shaded(correl, lons_correl_aux-2.8125/2., lats-2.8125/2., fig_name=figname, fig_title=figtitle, ocean_mask=1)
# Plot map correlacao Brasil
# figname = "correl_echam46_issue_{0}_{1}.{2}_br.png".format(fcst_month, target_months, hind_period)
# pm.correl_map_shaded(correl, lons_correl_aux-2.8125/2., lats-2.8125/2., fig_name=figname, fig_title=figtitle, br_country=1)
# Plot map correlacao Brasil sem oceano
# figname = "correl_echam46_issue_{0}_{1}.{2}_noocean_br.png".format(fcst_month, target_months, hind_period)
figname = outdir + '/' + "bra_precip_persistida_hind%s_%s_null_%s_echam46_1dg_cmap_correlacao.png" % (hind_period, target_months, n_fcst_month)
pm.correl_map_shaded(correl, lons_correl_aux-2.8125/2., lats-2.8125/2., fig_name=figname, fig_title=figtitle, ocean_mask=1, br_country=1)
# Anomalia
anomaly, anomaly_pad = cs.compute_anomaly(fsct, hind)
# Shift na longitude e nos dados do modelo
# Isso é necessário pois a longitude do NetCDF é no formato 0 - 360
# a função aceita valores de longitude no formato -180:+180
# Chefinho quer ver todos os oceanos
# anomaly, fsct_lon = shiftgrid(30., anomaly, fsct_lon, start=False)
anomaly, lons_anom_aux = shiftgrid(180., anomaly, lons, start=False)
# Plot map anomalia globo
# a variavel a ser plotada deve ser 2d
figtitle = "ECHAM4.6 - Anomalia de Prec Acum (mm)\n{0} {1} - {2} {3}".format(fcst_month, fcst_year, target_months, target_year, hind_period)
# figname = "anomalia_echam46_issue_{0}{1}_target_{2}{3}.{4}.png".format(fcst_month, fcst_year, target_months, target_year, hind_period)
# pm.anom_map_shaded(anomaly[0, :, :], lons_anom_aux-2.8125/2., lats-2.8125/2., fig_name=figname, fig_title=figtitle)
# Plot map anomalia globo sem oceano
# pm.plotmap(anom_diff_p, 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: controle menos experimento
for i, y in enumerate(xrange(2011, 2017)):
directory = 'figs_expsolar/diff_anom'
if not os.path.exists(directory):
os.makedirs(directory)
anom, anom_pad = cs.compute_anomaly(pcp[30 + i, :, :], pcpe[0:30, :, :])
anom_exp, anom_pad_exp = cs.compute_anomaly(pcpe[30 + i, :, :],
pcpe[0:30, :, :])
# diff das anomalias
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
# ocean_mask=0, barlevs=lev, barinf='max', barloc='bottom',
# meridians=np.arange(-160., 161., 30.),
# parallels=np.arange(-90., 91., 20.))
if obs_base == 'cmap':
### 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)
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)
anomaly_corrigida, anomaly_pad = \
cs.compute_anomaly(fcst_sig_anom, hind_corrigido)
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.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',
# Carrega arquivo da climatologia
clim_file = Dataset('/Users/Hulk/TestPyFuncemeClimateTools/lib/python2.7/site-packages/PyFuncemeClimateTools/examples/pcp-daily-total-ec4amip_8908JFM.nc')
clim_pcp = clim_file.variables['pcp'][:]
# Carrega arquivo da previsão
fsct_file = Dataset('/Users/Hulk/TestPyFuncemeClimateTools/lib/python2.7/site-packages/PyFuncemeClimateTools/examples/pcp-daily-total-ec4amip_2015JFM.nc')
fsct_pcp = fsct_file.variables['pcp'][:]
# Longitude e latitudes
fsct_lon = fsct_file.variables['longitude'][:]
fsct_lat = fsct_file.variables['latitude'][:]
# Calcula anomalias
# A função abaixo retornar dois parametros
myanom, myanom_pad = cs.compute_anomaly(fsct_pcp, clim_pcp)
# Shift na longitude e nos dados do modelo
# Isso é necessário pois a longitude do NetCDF é no formato 0 - 360
# a função aceita valores de longitude no formato -180 - +180
myanom, fsct_lon = shiftgrid(180., myanom, fsct_lon, start=False)
# Plot map
# a variavel a ser plotada deve ser 2d
pm.anom_map_shaded(myanom[0, :, :], fsct_lon-2.8125/2., fsct_lat-2.8125/2.)
