for i, day in enumerate(days):
[ano, mes, dia] = day.split("-", 2)
namedata = "{0}{1}{2}".format(dia, mes, ano)
print 'dia:', namedata, dbtd, 'membro:', m + 51
thiall = []
print 'calculando thiessen...'
for bacia in bacias:
thi = th.thiessen(var[m, i, :, :], lat, lon, bacia, pf=1, sep=';', usenc=True)
thiall.append(float(thi[0,:]))
thiall = map(str, np.round(thiall, 2))
thiall.insert(0, namedata)
thiall.insert(1, str(dbtd))
line.append(thiall)
dbtd = dbtd + 1
line = np.array(line)
from netCDF4 import Dataset
from PyFuncemeClimateTools import Thiessen as th
from mpl_toolkits.basemap import shiftgrid
import numpy as np
myfile = 'plev.2016.DJF.DAILY.PER13.10-19.nc'
d = Dataset(myfile, 'r')
# print(d.variables)
pcp = d.variables['pcp'][:]
lon = d.variables['lon'][:]
lat = d.variables['lat'][:]
d.close()
print(pcp.shape)
data_all = np.full((1, 10), np.nan)
for year in range(1):
for memb in range(10):
thi = th.thiessen(pcp[memb:memb+1, ...], lat, lon, '../piranhas-acu.asc',
pf=-1, sep=' ', usenc=True, figname='piranhas-acu.png')
print(thi)
data_all[year, memb] = thi
np.save('rsm-th-2015', data_all)
from netCDF4 import Dataset
from PyFuncemeClimateTools import Thiessen as th
from mpl_toolkits.basemap import shiftgrid
import numpy as np
myfile = 'prec.cptec.total.1981-2015.DJF.nc'
d = Dataset(myfile, 'r')
# print(d.variables)
pcp = d.variables['prec'][:]
lon = d.variables['lon'][:]
lat = d.variables['lat'][:]
d.close()
print(pcp.shape)
data_all = []
for year in range(35):
print(np.max(pcp[year:year+1, ...]), np.min(pcp[year:year+1, ...]))
thi = th.thiessen(pcp[year:year+1, ...], lat, lon, 'piranhas-acu.asc',
pf=-1, sep=' ', usenc=True, figname='piranhas-acu.png')
print(thi)
data_all.append(thi)
np.save('obs-th-1981-2015', data_all)
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)
my_colors = ('#ff0219', '#ff2e1b', '#ff5f26', '#ff9d37', '#fbe78a',
'#ffffff', '#b0f0f7', '#93d3f6', '#76bbf3', '#4ba7ef', '#3498ed') #11
pm.plotmap(error_bias, 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='both', ocean_mask=1)
# THIESSEN
th_fcst = np.full((20, 30), np.nan)
for memb in range(20):
for year in range(30):
thi = th.thiessen(hind[memb, year:year+1, ...], lat, lon, 'pontos_ce.txt',
pf=-1, sep=' ', usenc=True, figname='ce-th.png')
data_all[memb, year] = thi
# myfiles = ['pcp-rgkf-weeksst-3km-2009020200-2009060100-24h.npy',
# 'pcp-rgkf-weeksst-3km-2009020300-2009060200-48h.npy',
# 'pcp-rgkf-weeksst-3km-2009020400-2009060300-72h.npy',
# 'pcp-rgkf-weeksst-3km-2009020400-2009060300-acc3d.npy']
# flats = 'lats-3km.npy'
# flons = 'lons-3km.npy'
for fdata in myfiles:
mydata = np.load(fdata)
lats = np.load(flats)
lons = np.load(flons)
# 12 num bacias
# 120 num dias
resth = np.full((12, 120), np.nan)
for i, bacia in enumerate(range(1, 13)):
rh = 'reg_hidro/reg{0}.txt'.format(bacia)
print rh, fdata
nfig = 'bacia{0}.png'.format(bacia)
res = th.thiessen(mydata, lats, lons, rh, -1, usenc=True, figname=nfig)
resth[i, ...] = res[0, ...]
# salva todas as bacias
fout = 'thiessen-{0}'.format(fdata)
np.save(fout, resth)
