lat = nc_file.variables["LAT"][:] # Mude aqui
tim = nc_file.variables["TIME"][:] # Mude aqui
nlon = len(lon)
nlat = len(lat)
ntime = 360 # Mude aqui
nmon = ntime / 30
nyear = ntime / 12
tri = ("JFM", "FMA", "MAM", "AMJ", "MJJ", "JJA", "JAS", "ASO", "SON", "OND", "NDJ", "DJF")
ini = ("01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12")
aux = np.zeros((ntime, nlat, nlon)) * np.nan
# SEPARA JFM, FMA, MAM...
for k in range(0, ntime):
aux[k, :, :] = np.sum(var[k : 3 + k, :, :], axis=0)
# a = aux.reshape(nmon,nyear,nlat,nlon) # 1989: JFM, FMA, MAM...
a = aux.reshape(nyear, nmon, nlat, nlon) # 1989: JFM, 1990: JFM...
# ESCREVE NETCDF
for k in range(0, nmon):
fname = output_name[i] + ".{0}.2.5dg.nc".format(tri[k])
create_netcdf(a[:, k, :, :], lat, lon, fileout=fname, ntime=30)
# cdo Versão 3
# cdo genbil,pcp-daily-ec4amip51_2015AMJ.ctl.nc cmap.precip.mon.mean.1981-2010.JFM.2.5dg.nc weights.nc
# cdo remap,pcp-daily-ec4amip51_2015AMJ.ctl.nc,weights.nc cmap.precip.mon.mean.1981-2010.JFM.2.5dg.nc cmap.precip.mon.mean.1981-2010.JFM.T42.nc
# Captura a maskara usada no arquivo de entrada
var = np.where(np.ma.getmask(var[:, :, :]), np.NaN, var)
nlon = len(lon)
nlat = len(lat)
ntime = 240 # Mude aqui - Qtd de passos de tempo do arquivo de saída
nmon = ntime/20 # Mude aqui - Qtd de anos do arquivo de saída
nyear = ntime/12 # Mude aqui - Qtd de meses do arquivo de saída
tri = ('JFM', 'FMA', 'MAM', 'AMJ', 'MJJ', 'JJA', 'JAS', 'ASO', 'SON', 'OND', 'NDJ', 'DJF')
ini = ('01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12')
aux = np.zeros((ntime, nlat, nlon)) * -999
# Separa os trimestres JFM, FMA, MAM...
for k in range(0, ntime):
aux[k, :, :] = np.ma.sum(var[k:3+k, :, :], axis=0)
# Define que onde for NaN substitua por -999
dummy = np.where(np.isnan(aux), -999, aux)
# a = dummy.reshape(nmon,nyear,nlat,nlon) # 1989: JFM, FMA, MAM...
a = dummy.reshape(nyear, nmon, nlat, nlon) # 1989: JFM, 1990: JFM...
# ESCREVE NETCDF
for k in range(0, nmon):
fname = output_name[i]+'.{0}.nc'.format(tri[k])
create_netcdf(a[:, k, :, :], lat, lon, fileout=fname, ntime=20, fillvalue=-999, varname='prec', varunits='mm', varlongname='monthly mean precipitation')
