# print hind_url
hind_file = open_url(hind_url)
pcp_hind = hind_file['pcp']
hind = np.asarray(pcp_hind)
# Arquivo observação
obs_url = "http://opendap.funceme.br:8001/data/OBSERVATION/CMAP/1.0DG/GLB/cmap.{0}-{1}-1.0dg.fix.nc".format(myhind, mytseason.upper())
# print obs_url
obs_file = open_url(obs_url)
pcp_obs = obs_file['pcp']
obs = np.asarray(pcp_obs)
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)
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'
def compute_reliability(fcst, clim):
'''
fcst e clim devem ter as mesmas dims
fcst - Ex: previsões do modelo
clim - Ex: climatologia do modelo ou observado
:param: fcst - Array com 3 dimensões: (tempo, lat, lon)
:type param: numpy array 3d
:param: clim - Array com 3 dimensões: (tempo, lat, lon)
:type param: numpy array 3d
:return: arquivo de verificação
'''
# transforma num masked array para retirar a mascara
# pcp_masked = np.ma.array(obs)
# mask_obs = pcp_masked.mask
# if mask_obs:
# pcp_nan = np.where(mask_obs, np.NaN,obs)
# else:
# pcp_nan = obs
# Verifica os limites dos tercis
# Não é do mesmo modo que é calculado na previsão!
# Na previsão usa-se uma normal.
xlow = 1. / 3. * 100
xup = 2. / 3. * 100
pcp_low = np.percentile(clim, xlow, axis=0)
pcp_up = np.percentile(clim, xup, axis=0)
# 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')
f_verifc_below = []
f_verifc_normal = []
f_verifc_above = []
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]):
# Por que True?
if obs_below[j, k] == True:
# f_verifc_below.write("{0} {1} \n".format(p_below[j,k], 1))
f_verifc_below.append(p_below[j, k], 1)
else:
# f_verifc_below.write("{0} {1} \n".format(p_below[j,k], 0))
f_verifc_below.append(p_below[j, k], 0)
if obs_normal[j, k] == True:
# f_verifc_normal.write("{0} {1} \n".format(p_normal[j, k], 1))
f_verifc_normal.append(p_normal[j, k], 1))
else:
# f_verifc_normal.write("{0} {1} \n".format(p_normal[j,k], 0))
f_verifc_normal.append(p_normal[j, k], 0))
if obs_above[j, k] == True:
# f_verifc_above.write("{0} {1} \n".format(p_above[j, k], 1))
f_verifc_above.append(p_above[j, k], 1)
else:
# f_verifc_above.write("{0} {1} \n".format(p_above[j,k], 0))
f_verifc_above.append(p_above[j, k], 0)
return f_verifc_below, f_verifc_normal, f_verifc_above
