def hovmoller(array, lat_array, lon_array, latmin, latmax, lonmin,
lonmax, mean):
"""Calcula a media meridional ou zonal
:param array:
:param lat_array:
:param lon_array:
:param latmin:
:param latmax:
:param lonmin:
:param lonmax:
:param mean:
"""
latmin, lonmin, ilatmin, ilonmin = Dg.gridpoint(lat_array, lon_array,
latmin, lonmin)
latmax, lonmax, ilatmax, ilonmax = Dg.gridpoint(lat_array, lon_array,
latmax, lonmax)
lat = lat_array[ilatmin:ilatmax + 1]
lon = lon_array[ilonmin:ilonmax + 1]
if mean == 'lat':
hov = np.nanmean(array[:, ilatmin:ilatmax + 1, ilonmin:ilonmax + 1],
axis=1)
elif mean == 'lon':
hov = np.nanmean(array[:, ilatmin:ilatmax + 1, ilonmin:ilonmax + 1],
axis=2)
return hov, lat, lon
def cut2region(indata, inlat, inlon, minlat, minlon, maxlat, maxlon):
"""Cut input data to the region of interest
:param indata: 3d data array that will be cut
:type indata: numpy array
:param inlat: 1d lat array that will be latitude
:type inlat: numpy array
:param inlon: 1d lon array that will be longitude
:type inlon: numpy array
:param minlat: value of the minimun latitude
:type minlat: float
:param minlon: value of the minimun longitude
:type minlon: float
:param maxlat: value of the maximum latitude
:type maxlat: float
:param maxlon: value of the maxium longitude
:type maxlon: float
:return: Return data array cut, lon array cut and lat array cut to
the region of interest
"""
min_lat, min_lon, min_lat_index, min_lon_index = Dg.gridpoint(inlat, inlon,
minlat,
minlon)
max_lat, max_lon, max_lat_index, max_lon_index = Dg.gridpoint(inlat, inlon,
maxlat,
maxlon)
cut_data = indata[..., min_lat_index:max_lat_index,
min_lon_index:max_lon_index]
cut_lat = inlat[min_lat_index:max_lat_index]
cut_lon = inlon[min_lon_index:max_lon_index]
return cut_data, cut_lat, cut_lon
esi_st = []
spi_st = []
for year in range(2003, 2015 + 1):
for month in mes_esi:
print year, month
# Declaring variables
name1 = 'esi_4WK_SMN_{0:04d}_SA.nc'.format(year)
data1 = netCDF4.Dataset(str(path_in1) + name1)
lat = data1.variables['latitude'][:] # Declaring latitude
lon = data1.variables['longitude'][:] # Declaring longitude
min_lat, min_lon, min_lat_index, min_lon_index = Dg.gridpoint(
lat, lon, -22., -48.0)
max_lat, max_lon, max_lat_index, max_lon_index = Dg.gridpoint(
lat, lon, -6., -36.)
lats = lat[min_lat_index:max_lat_index]
lons = lon[min_lon_index:max_lon_index]
aux_in1 = data1.variables['esi'][
dic_esi[month], min_lat_index:max_lat_index, min_lon_index:
max_lon_index] # Declaring variable under study to calculate the thiessen aux_in
time_esi = data1.variables['time']
aux_in1 = np.expand_dims(aux_in1, axis=0)
aux_in1 = ma.masked_where(aux_in1 <= -6., aux_in1)
aux_in1 = ma.masked_where(aux_in1 >= 6., aux_in1)
print "esi_sao_francisco"
Dmasked1 = caso_shape(aux_in1[:, :, :], lats, lons,
print season
for ANO in range(2003, 2016 + 1):
print ANO, dic_esi[season]
name = 'esi_4WK_SMN_{0:4d}_SA.nc'.format(ANO)
data = netCDF4.Dataset(str(path_in) + name)
aux_in = data.variables['esi'][dic_esi[season], :, :]
aux_in = np.expand_dims(aux_in, axis=0)
lat = data.variables['latitude'][:] # Declaring latitude
lon = data.variables['longitude'][:] # Declaring longitude
min_lat, min_lon, min_lat_index, min_lon_index = Dg.gridpoint(
lat, lon, -20., -50.0)
max_lat, max_lon, max_lat_index, max_lon_index = Dg.gridpoint(
lat, lon, 0., -34.)
lats = lat[min_lat_index:max_lat_index]
lons = lon[min_lon_index:max_lon_index]
aux_in = data.variables['esi'][dic_esi[season],
min_lat_index:max_lat_index,
min_lon_index:max_lon_index]
aux_in = np.expand_dims(aux_in, axis=0)
aux_in = ma.masked_where(aux_in <= -6., aux_in)
aux_in = ma.masked_where(aux_in >= 6., aux_in)
txtbox(u'Fonte: NOAA/ESSIC and USDA-ARS\nElaboração: FUNCEME', 0.46,
# lats = read_fcst_file.variables['lat'][:]
# 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
