def __update_wind(fname, datapath, source, **kargs):
if source == 'quikscat':
new_wind_info = 'wind from quikscat'
from okean.datasets import quikscat
a = quikscat.WINDData(datapath)
elif source == 'blended':
new_wind_info = 'wind from myocean blended'
from okean.datasets import blended_wind
a = blended_wind.WINDData(datapath)
time = netcdf.nctime(fname, 'time')
date0 = dts.next_date(time[0], -1)
date1 = dts.next_date(time[-1], +2)
data = a.data(date0, date1)
update_wind(fname, data, new_wind_info, **kargs)
def update_wind_blended2(fname, datapaths, **kargs):
'''
In days without blended data will try to use quikscat data
'''
from okean.datasets import quikscat
from okean.datasets import blended_wind
a = blended_wind.WINDData(datapaths[0])
b = quikscat.WINDData(datapaths[1])
time = netcdf.nctime(fname, 'time')
date0 = dts.next_date(time[0], -1)
date1 = dts.next_date(time[-1], +2)
data = a.data(date0, date1)
# limit are... otherwise, quikscat interp will be very slow!
grd = netcdf.fatt(fname, 'grd_file')
import os
if not os.path.isfile(grd): grd = kargs['grd']
cond, inds = rt.grid_vicinity(grd,
data['x'],
data['y'],
margin=5,
rect=True,
retinds=True)
i1, i2, j1, j2 = inds
for d in data.keys():
if d == 'x': data[d] = data[d][i1:i2]
elif d == 'y': data[d] = data[d][j1:j2]
else: data[d] = data[d][j1:j2, i1:i2]
# check for missing days:
time0 = data.keys()
x0 = data['x']
y0 = data['y']
x0, y0 = np.meshgrid(x0, y0)
time0.remove('x')
time0.remove('y')
out = OrderedDict()
out['x'] = x0
out['y'] = y0
info = ''
qs_ij_limits_done = False
for d in dts.drange(date0, date1):
found = 0
for t in time0:
if (t.year, t.month, t.day) == (d.year, d.month, d.day):
print('==> blended : ', t)
out[t] = data[t]
found = 1
if not found: # use quikscat:
print('==> quikscat : ', d.strftime('%Y-%m-%d'))
tmp = b.data(d, dts.next_date(d))
if not tmp.has_key('x'): continue
x, y = tmp['x'], tmp['y']
x, y = np.meshgrid(x, y)
# reduce qs data:
if not qs_ij_limits_done:
i1, i2, j1, j2 = calc.ij_limits(x, y,
[x0.min(), x0.max()],
[y0.min(), y0.max()])
qs_ij_limits_done = True
x = x[j1:j2, i1:i2]
y = y[j1:j2, i1:i2]
tmp[tmp.keys()[0]] = tmp[tmp.keys()[0]][j1:j2, i1:i2]
print(' griddata u')
u = calc.griddata(x, y, tmp[tmp.keys()[0]].real, x0, y0)
print(' griddata v')
v = calc.griddata(x, y, tmp[tmp.keys()[0]].imag, x0, y0)
out[tmp.keys()[0]] = u + 1.j * v
info += '#' + d.strftime('%Y%m%d')
new_wind_info = 'blended+quikscat at days: ' + info
update_wind(fname, out, new_wind_info, **kargs)