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)