print ' ... OBS ...', obs.shape, '\n'
outdir = '{0}{1}-{2}'.format(fcst_month, fcst_year, hind_period)
if not os.path.exists(outdir):
os.makedirs(outdir)
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 = -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)
print 'lons:', obs_lons
outdir = '{0}{1}-{2}-rsm97'.format(fcst_month, fcst_year, hind_period)
if not os.path.exists(outdir):
os.makedirs(outdir)
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 = -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)
# '_echam46_1dg_null_precip.png'.format(hind_period_name,
# target_year, target_months, fcst_year, n_fcst_month, outdir)
# pm.plotmap(fcst[0, :, :], newlats, newlons, 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.))
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)
# Observado
pcp_obs_ce = obs_file['pcp'][:, 29:31, 114]
obs_ce = np.asarray(pcp_obs_ce)
# Média sobre o CE
fsct_aux_ce = np.apply_over_axes(np.mean, fsct_ce, [1, 2])
hind_aux_ce = np.apply_over_axes(np.mean, hind_ce, [1, 2])
obs_aux_ce = np.apply_over_axes(np.mean, obs_ce, [1, 2])
outdir = "%s%s_HIND%s" % (fcst_month.upper(), fcst_year, hind_period)
if not os.path.exists(outdir):
os.makedirs(outdir)
# Curva de probabilidade para o CE
figname = outdir + '/' + 'prob_echam46_issue_%s%s_target_%s%s_%s_curve_ce.png' % (fcst_month, fcst_year, target_months, target_year, hind_period)
below_ce, normal_ce, above_ce, f_signal_ce, f_std_ce, o_pad_ce = cs.compute_probability(fsct_aux_ce[:, 0, 0], hind_aux_ce[:, 0, 0], obs_aux_ce[:, 0, 0])
pm.plotnorm(f_signal_ce, f_std_ce, o_pad_ce, fig_name=figname, fig_title='')
### Fim Bloco Curva para o CE ###
# Tercil mais provável para toda a região do globo
file_out = outdir + '/' + "prob_echam46_issue_%s%s_target_%s%s_%s.nc" % (fcst_month, fcst_year, target_months, target_year, hind_period)
below, normal, above, f_signal, f_std, o_pad = cs.compute_probability(fsct, hind, obs)
cn.create_netcdf_probs(below, normal, above, lats, lons, fileout=file_out)
# ferret_command = "/home/marcelo/pyferret-0.0.9/bin/pyferret.sh -nojnl -gif -script plot_tercis_ensemble.jnl {0}".format(file_out)
# os.system(ferret_command)
# Correlacao
correl = cs.corr_pearson(hind, obs)
lats = obs_data.variables['lat'][:]
obs_data.close()
pcp_obs = np.mean(pcp_obs, axis=0)
# pcp_obs_aux, lons_obs_aux = shiftgrid(180., pcp_obs, lons, start=False)
obs_path = "/Users/Hulk/clim-verification/CMAP/CMAP89-08-FMA-T42.nc"
clim_obs_path = "/Users/Hulk/obs_pcp/cmap/cmap.merge.funceme.precip.standard.FMA.season.accum.1dg.nc"
clim_obs_data = pupynere.NetCDFFile(clim_obs_path, 'r')
print clim_obs_data.variables
clim_pcp_obs = clim_obs_data.variables['precip'][:]
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)
# clim_obs_path = "/Users/Hulk/FSCT-ECHAM46/obs_data/cmap/CMAP.glb.89-08-FMA-1.0dg.nc"
clim_obs_path = "/Users/Hulk/obs_pcp/cmap.precip.mean.enhanced.1989-2008.FMA.nc3.nc"
clim_obs_data = pupynere.NetCDFFile(clim_obs_path, 'r')
# clim_pcp_obs = clim_obs_data.variables['pcp'][:]
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.),
obs = obs_file.variables['pcp'][:, :, :] # obs_ce = obs_file.variables['pcp'][:, 53:58, 49:53] # obs_mean_ce = np.apply_over_axes(np.mean, obs_ce, [1, 2]) # obs_ce = obs_file.variables['pcp'][:, 52:54, 51:53] # obs_mean_ce = np.apply_over_axes(np.mean, obs_ce, [1, 2]) obs_ce = obs_file.variables['pcp'][:, 52:53, 53:54] obs_mean_ce = np.apply_over_axes(np.mean, obs_ce, [1, 2]) # Longitude e latitude lons = fsct_file.variables['lon'][:] lats = fsct_file.variables['lat'][:] # Curva de probabilidade below_ce, normal_ce, above_ce, f_signal_ce, f_std_ce, o_pad_ce = cm.compute_probability(fsct[:, 0, 0], hind[:, 0, 0], obs[:, 0, 0]) pm.plotnorm(f_signal_ce, f_std_ce, o_pad_ce, fig_name='prob.echam46+nmme.curve.ce.png', fig_title='') # below, normal, above, f_signal, f_std, o_pad = cm.compute_probability(fsct, hind, obs) # cn.create_netcdf_probs(below, normal, above, lats, lons, fileout="prob_nmme+echam46_issue_jan2015_target_fma2015.nc") # Anomalia # anomaly, anomaly_pad = cm.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)
def compute_reliability(model, obs, lentime):
'''
Salva um arquivo com a verificação dos tercis mais prováveis comparando o modelado com o observado
Os arrays model e obs tem que ter o mesmo shape
:param model: numpy.Array do dado modelado
:param obs: numpy.Array do dado observado
:param path_saida: Caminho/Nome do arquivo txt de saida.
:return: arquivo de verificação
'''
try:
if type(model).__module__ != np.__name__:
raise ValueError('A variável "model" não é do tipo numpy array!!!')
elif type(obs).__module__ != np.__name__:
raise ValueError('A variável "obs" não é do tipo numpy array!!!')
elif model.shape != obs.shape:
raise Exception('Os arrays não possuem o mesmo shape!!!')
except ValueError:
print '===> Atenção na entrada dos np.arrays:'
raise
except Exception:
print '===> Cheque o shape dos arrays'
raise
pcp_masked = np.ma.array(obs) # transforma num masked array para retirar a mascara
mask_obs = pcp_masked.mask
if mask_obs:
pcp_nan = np.where(mask_obs, np.NaN,obs)
else:
pcp_nan = obs
xlow = 1./3.*100
xup = 2./3.*100
pcp_low = np.percentile(pcp_nan, xlow, axis=0)
pcp_up = np.percentile(pcp_nan, xup, axis=0)
#~ path_below = ''.join((path_saida.split(".")[0], "_below.", path_saida.split(".")[-1]))
#~ path_normal = ''.join((path_saida.split(".")[0], "_normal.", path_saida.split(".")[-1]))
#~ path_above = ''.join((path_saida.split(".")[0], "_above.", path_saida.split(".")[-1]))
path_below = "reliability_below.txt"
path_normal = "reliability_normal.txt"
path_above = "reliability_above.txt"
f_verifc_below = open(path_below, 'w')
f_verifc_normal = open(path_normal, 'w')
f_verifc_above = open(path_above, 'w')
for i in range(0, obs.shape[0]):
p_below, p_normal, p_above, fcst_signal, fcst_std, obs_pad, dummy =\
cs.compute_probability(model[i], model, pcp_nan, lentime)
p_below, p_normal, p_above = p_below/100., p_normal/100., p_above/100.
obs_below = (obs[i, :, :] < pcp_low)
obs_normal = ((obs[i, :, :] >= pcp_low) & (obs[i, :, :] <= pcp_up))
obs_above = (obs[i, :, :] > pcp_up)
for j in range(0,obs.shape[1]):
for k in range(0,obs.shape[2]):
if obs_below[j,k] == True:
f_verifc_below.write("{0} {1} \n".format(p_below[j,k], 1))
else:
f_verifc_below.write("{0} {1} \n".format(p_below[j,k], 0))
if obs_normal[j,k] == True:
f_verifc_normal.write("{0} {1} \n".format(p_normal[j,k], 1))
else:
f_verifc_normal.write("{0} {1} \n".format(p_normal[j,k], 0))
if obs_above[j,k] == True:
f_verifc_above.write("{0} {1} \n".format(p_above[j,k], 1))
else:
f_verifc_above.write("{0} {1} \n".format(p_above[j,k], 0))
above = np.loadtxt("echam46_above.txt")
normal = np.loadtxt("echam46_normal.txt")
below = np.loadtxt("echam46_below.txt")
print "\n => Plot diagram...\n"
mytitle = "Diagrama de atributos: {2}/{1} - ({0})".format(hind_period, target_months, fcst_month)
figura, ax = plot_reliability(below[:,0], below[:,1], maintitle=mytitle, cor='blue')
figura, ax = plot_reliability(normal[:,0], normal[:,1], maintitle=mytitle, first=False, fig=figura, cor='y')
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 ..."
from PyFuncemeClimateTools import PlotMaps as pm
from PyFuncemeClimateTools import CreateNetCDF as cn
from read_data import read_data
pcp, pcpe, obs = read_data()
nla = np.linspace(-90., 90., 181)
nlo = np.linspace(-180., 179., 360)
x, y = np.meshgrid(nlo, nla)
y1, y2, x1, x2 = -60., 15., -90., -30.
correl = cs.compute_pearson(pcp[0:30, :, :], obs[0:30, :, :])
figtitle = u'ECHAM4.6 x CMAP - {0}/{1} ({2})\nCorrelação - Precip Acum' \
.format('JAN', 'FMA', '8110')
if not os.path.exists('figs_expsolar/map_correl'):
os.makedirs('figs_expsolar/map_correl')
figname = 'figs_expsolar/map_correl' \
'/bra_precip_persistida_{0}_null-{1}_null_{2}_echam46_1dg_cmap_' \
'correlacao.png' \
.format('8110', 'FMA', 'JAN')
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',
figname = "img/curve_prob_{0}_{1}_{2}_{3}_{4}_{5}_{6}_{7}.png".format(
mylon, mylat, myopt, myhind, myfmon, mytseason, myfyear, mytyear
)
# Se a imagem nao tiver sido gerada anteriomente
if not os.path.exists(figrealname):
print "entrooou... delicia! =)"
fcst, hind, obs = readdata(myhind, myfmon, myfyear, mytyear, mytseason)
if not os.path.exists("img"):
os.makedirs("img")
below, normal, above, fsignal, fstd, opad, fs_anom = cs.compute_probability(
fcst[0, i_lat, i_lon], hind[:, i_lat, i_lon], obs[:, i_lat, i_lon], lenhind
)
fsignal = np.expand_dims(fsignal, axis=0)
pm.plotnorm(fsignal, fstd, opad, fig_name=figname, fig_title="")
shutil.copy2(figname, figrealname)
else:
print "nao entrou =("
# se a imagem ja tiver sido gerada, apenas faz copia para exibir no site
shutil.copy2(figrealname, figname)
print "Erro ao ler arquivo do hindcast"
exit()
# Arquivo observação
try:
obs_file = "cmap.{0}-{1}-1.0dg.fix.nc".format(hind_period, target_months)
read_obs_file = pupynere.NetCDFFile(obs_file, 'r')
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
# 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.)
# lons = read_fcst_file.variables['lon'][:]
# read_fcst_file.close()
# hind_file = "/home/marcelo/FSCT-ECHAM46/APR2015_HIND89-08-1DG/pcp-daily-total-ec4amip_89-08MJJ.1.0dg.fix.nc"
# read_hind_file = pupynere.NetCDFFile(hind_file, 'r')
# hind = read_hind_file.variables['pcp'][:, :, :]
# read_hind_file.close()
#
# obs_file = "/home/marcelo/FSCT-ECHAM46/APR2015_HIND89-08-1DG/cmap.89-08-MJJ-1.0dg.fix.nc"
# read_obs_file = pupynere.NetCDFFile(obs_file, 'r')
# obs = read_obs_file.variables['pcp'][:, :, :]
# read_obs_file.close()
if myopt == "p":
n_lon, n_lat, i_lon, i_lat = dg.gridpoint(lons, lats, mylon, mylat)
myfcst = fsct[0, i_lat, i_lon]
myhind = hind[:, i_lat, i_lon]
myobs = obs[:, i_lat, i_lon]
below, normal, above, fsignal, fstd, opad = cs.compute_probability(myfcst, myhind, myobs)
fsignal = np.expand_dims(fsignal, axis=0)
figname = "curve_prob_%s_%s.png" % (mylon, mylat)
pm.plotnorm(fsignal, fstd, opad, fig_name=figname, fig_title='')
elif myopt == "a":
print 't1'
pass
else:
print 't2'
pass
obs_aux = obs_file.variables['pre'][:]
lons_obs = obs_file.variables['lon'][:]
lats_obs = obs_file.variables['lat'][:]
obs_file.close()
x, y = np.meshgrid(lons_hind, lats_hind)
# Interpola hindcast
obs = np.full((30, 110, 129), np.nan)
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)
def ProbMemb(fcst_month, fcst_year, target_year, target_months,
hind_period, nyears, shapef, nmemb=20):
"""
Esta função calcula a previsão (sinal) para todos
os membros.
:param fcst_month: mês previsão
:type fcst_month: str
:param fcst_year: ano do mês previsão
:type fcst_year: str
:param target_year: ano alvo da previsão
:type target_year: str
:param target_months: trimestre alvo da previsão
:type target_months: str
:param hind_period: período do hindcast
:type hind_period: str
:param nyears: número de anos do hindcast
:type nyears: int
:param shapef: arquivo de txt com pontos da região
:type shapef: arquivo de txt
:param nmemb: número de membros da preevisão/hinscast
:type nmemb: int
"""
#### OBSERVADO ####
print "\n +++ LENDO A CLIMATOLOGIA OBSERVADA +++\n"
url = 'http://opendap2.funceme.br:8001/data/dados-obs/cmap/2.5dg/' \
'cmap.precip.season.accum.standard.2.5dg.{0}.{1}.nc' \
.format("1981-2010", target_months)
#~ print url
obs_data = Dataset(url, 'r')
obs_aux = obs_data.variables['precip'][:]
obs_lons_360 = obs_data.variables['lon'][:]
obs_lats = obs_data.variables['lat'][:]
obs_data.close()
obs_aux, obs_lons = shiftgrid(180., obs_aux, obs_lons_360, start=False)
print " +++ INTERPOLANDO CLIMATOLOGIA OBSERVADA +++ \n"
# Interpolação para 1 grau
# Nova grade de 1 grau
newlats = np.linspace(-90, 90, 181)
newlons = np.linspace(-180, 179, 360)
obs = np.zeros((int(obs_aux.shape[0]), int(len(newlats)), int(len(newlons))))
x, y = np.meshgrid(newlons, newlats)
for i in range(0, int(obs_aux.shape[0])):
obs[i, :, :] = interp(obs_aux[i, :, :], obs_lons, obs_lats, x, y, order=1)
#### PREVISÃO ####
print " +++ LENDO OS MEMBROS PREVISÃO +++ \n"
#~ Monta a url do ano da previsão com todos os membros
url = 'http://opendap2.funceme.br:8001/data/dados-pos-processados-echam46' \
'/forecasts/{0}/{2}/{1}/PCP/TRI/pcp-seasonacc-echam46-hind{0}-en%2.2d-{1}{2}_{3}{4}.ctl' \
.format(hind_period, fcst_month, fcst_year, target_year, target_months)
files = [url % d for d in range(51, 71)]
# Inicializa array que irá receber os membros do ano da previsão
fcst_t42 = np.zeros((20, 64, 128)) * 0
for i, nc in enumerate(files):
#~ print "", nc
fcst_data = Dataset(nc, 'r')
fcst_aux = fcst_data.variables['pcp'][:]
fcst_lons_360 = fcst_data.variables['longitude'][:]
fcst_lats = fcst_data.variables['latitude'][:]
fcst_data.close()
fcst_aux, fcst_lons = shiftgrid(180., fcst_aux, fcst_lons_360, start=False)
fcst_t42[i, :, :] = fcst_aux[0, :, :]
print " +++ INTERPOLANDO OS MEMBROS PREVISÃO +++ \n"
# Interpolação para 1 grau
# Nova grade de 1 grau
fcst = np.zeros((20, int(len(newlats)), int(len(newlons)))) * 0
for i in range(20):
fcst[i, :, :] = interp(fcst_t42[i, :, :], fcst_lons, fcst_lats, x, y, order=1)
#### HINDCAST ####
print " +++ LENDO OS MEMBROS DE CADA ANO DO HINDCAST +++\n"
print " ==> AGUARDE...\n"
# Inicializa array que irá receber todos os anos e todos os membros
hind_t42 = np.zeros((30, 20, 64, 128)) * 0
for i, hind_year in enumerate(range(1981, 2011)):
#~ print " ANO:", hind_year
# Monta a url de cada ano com todos os membros
url = 'http://opendap2.funceme.br:8001/data/dados-pos-processados-echam46' \
'/hindcasts/{0}/{1}/PCP/TRI/pcp-seasonacc-echam46-hind{0}-en%2.2d-{1}{3}_{3}{2}.ctl' \
.format(hind_period, fcst_month, target_months, hind_year)
files = [url % d for d in range(51, 71)]
# Inicializa array que irá receber os membros para cada ano
hindmemb = np.zeros((20, 64, 128)) * 0
for j, nc in enumerate(files):
hind_data = Dataset(nc, 'r')
hind_aux = hind_data.variables['pcp'][:]
hind_lons_360 = hind_data.variables['longitude'][:]
hind_lats = hind_data.variables['latitude'][:]
hind_data.close()
hind_aux, hind_lons = shiftgrid(180., hind_aux, hind_lons_360, start=False)
hindmemb[j, :, :] = hind_aux[0, :, :]
hind_t42[i, :, :, :] = hindmemb[:, :, :]
print " +++ INTERPOLANDO OS ANOS DE CADA MEMBRO DO HINDCAST. +++ \n"
print " ==> AGUARDE... \n"
# Interpolação para 1 grau
hind = np.zeros((30 , 20, int(len(newlats)), int(len(newlons)))) * 0
for i in range(20):
#~ print " MEMBRO:", i + 51
for j in range(30):
hind[j, i, :, :] = interp(hind_t42[j, i, :, :], hind_lons, hind_lats, x, y, order=1)
print " +++ APLICANDO MÁSCARA DA REGIÃO +++ \n"
# Retorna matriz com os pontos sobre o Ceará
pointsgrid, lonlatgrid, mymatriz = dg.pointinside(newlats, newlons, shapefile=shapef)
ce_fcst = np.ma.array(fcst, mask=np.tile(mymatriz, (fcst.shape[0], 1)))
ce_hind = np.ma.array(hind, mask=np.tile(mymatriz, (hind.shape[0], hind.shape[1], 1)))
ce_obs = np.ma.array(obs, mask=np.tile(mymatriz, (obs.shape[0], 1)))
print " +++ MÉDIA DOS PONTOS SOBRE A REGIÃO PARA TODOS OS MEMBROS +++ \n"
ave_ce_fcst = np.zeros((int(nmemb), 1)) * 0
ave_ce_hind = np.zeros((int(nyears), int(nmemb), 1)) * 0
ave_ce_obs = np.zeros((int(nyears), 1)) * 0
for i in range(int(nmemb)):
ave_ce_fcst[i, 0] = ce_fcst[i, :, :].mean()
for j in range(int(nyears)):
ave_ce_hind[j, i, 0] = ce_hind[j, i, :, :].mean()
for i in range(int(nyears)):
ave_ce_obs[i, 0] = ce_obs[i, :, :].mean()
sig_membs_ce = np.zeros((int(nmemb))) * 0
print " +++ CALCULANDO SINAL DA PREVISÃO PARA TODOS OS MEMBROS +++\n"
for i in range(nmemb):
below_ce, normal_ce, above_ce, sig_membs_ce[i], f_std_ce, \
o_pad_ce, fcst_sig_anom = cs.compute_probability(ave_ce_fcst[i, 0],
ave_ce_hind[:, i, 0], ave_ce_obs[:, 0], nyears)
return sig_membs_ce
# 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
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,
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)
exit(1)
try:
# Arquivo observação
obs_file = open_url(obs_url)
pcp_obs = obs_file['precip']
obs = np.asarray(pcp_obs)
except:
print "+++ Erro ao abrir arquivo do observado +++"
print obs_url
exit(1)
myfcst = fsct[0, i_lat, i_lon]
myhind = hind[:, i_lat, i_lon]
myobs = obs[:, i_lat, i_lon]
below, normal, above, fsignal, fstd, opad, fs_anom = cs.compute_probability(myfcst, myhind, myobs, lenhind)
fsignal = np.expand_dims(fsignal, axis=0)
pm.plotnorm(fsignal, fstd, opad, fig_name=figname, fig_title='')
shutil.copy2(figname, figrealname)
else:
# se a imagem ja tiver sido gerada, apenas faz copia para exibir no site
shutil.copy2(figrealname, figname)
elif myopt == "a":
print 't1'
pass
else:
print 't2'
