def download_range(self,date0,date1,quiet=True):
'''
Download analysis data in the interval [date0... date1[
'''
dates=dateu.drange(date0,date1,inclast=False)
for date in dates:
self.download_fast(date,FA='a',del1=True,checkinv=True,quiet=quiet,prevopt=True)
def __files(self,date0,date1=False,FA='a',nforec='auto'):
'''
Used by files_analysis and files_forecast
'''
if FA=='f': date1=False
if nforec=='auto': args={}
else: args={'nforec':nforec}
a=GFSDownload(basefolder=self.basefolder,**args)
if date1 is False: dates=[date0]
else: dates=dateu.drange(date0,date1)
files=[]
time=[]
isbest=[]
# first file, 00h data (last of previous day)
datePrev=dateu.next_date(date0,-1)
file0=a.daily_files(datePrev,FA='a')[1][-1]['name']
for d in dates:
Src,Dest=a.daily_files(d,FA=FA)
for dest in Dest:
files+=[dest['name']]
if files: files=[file0]+files
for f in files:
time+=[get_date(f)]
if os.path.isfile(f): isbest+=[not os.path.islink(f)]
else: isbest+=[None]
return files,time,isbest
def __files(self, date0, date1=False, FA='a', nforec='auto'):
'''
Used by files_analysis and files_forecast
'''
if FA == 'f': date1 = False
if nforec == 'auto': args = {}
else: args = {'nforec': nforec}
a = GFSDownload(basefolder=self.basefolder, **args)
if date1 is False: dates = [date0]
else: dates = dateu.drange(date0, date1)
files = []
time = []
isbest = []
# first file, 00h data (last of previous day)
datePrev = dateu.next_date(date0, -1)
file0 = a.daily_files(datePrev, FA='a')[1][-1]['name']
for d in dates:
Src, Dest = a.daily_files(d, FA=FA)
for dest in Dest:
files += [dest['name']]
if files: files = [file0] + files
for f in files:
time += [get_date(f)]
if os.path.isfile(f): isbest += [not os.path.islink(f)]
else: isbest += [None]
return files, time, isbest
def download_range(self,date0,date1,quiet=True):
'''
Download analysis data in the interval [date0... date1[
'''
dates=dateu.drange(date0,date1,inclast=False)
for date in dates:
self.download_fast(date,FA='a',del1=True,checkinv=True,quiet=quiet,prevopt=True)
def files(self,date0,date1=False):
date0=dateu.parse_date(date0)
if not date1: date1=date0+datetime.timedelta(1)
else: date1=dateu.parse_date(date1)
dates=dateu.drange(date0,date1,True)
files=[]
time=[]
for d in dates:
if d==dates[-1]: hours=[0]
else: hours=range(0,24,narrdt)
for hour in hours:
#fname='narr-a_%d_%s_%02d00_000.grb' % (int(d.strftime('%j'))-1,d.strftime('%Y%m%d'),hour)
fname='narr-a_221_%s_%02d00_000.grb' % (d.strftime('%Y%m%d'),hour)
files+=[os.path.join(baseurl,d.strftime('%Y%m'),d.strftime('%Y%m%d'),fname)]
time+=[d+datetime.timedelta(hour/24.)]
return files, np.array(time)
def decompress_year(self, year):
dates = dateu.drange((year, 1, 1), (year + 1, 1, 1))
for d in dates:
self.decompress_day(d)
def download_year(self, year):
dates = dateu.drange((year, 1, 1), (year + 1, 1, 1))
for d in dates:
self.download_day(d)
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)
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 = cb.odict()
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 + 1.0j * v
info += "#" + d.strftime("%Y%m%d")
new_wind_info = "blended+quikscat at days: " + info
update_wind(fname, out, 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)
def decompress_year(self,year): dates=dateu.drange((year,1,1),(year+1,1,1)) for d in dates: self.decompress_day(d)
def download_year(self,year): dates=dateu.drange((year,1,1),(year+1,1,1)) for d in dates: self.download_day(d)
