def aggregate(dd, state, period, epsilon=1. / 86400.):
dd = state.get('dd', []) + dd
state['dd'] = []
if len(dd) == 0 or dd[0] is None:
return dd
def merge(dd, t1, t2):
if len(ddb) > 0:
dx = ds.merge(ddb, 'time')
dx['time_bnds'][0, 0] = max(t1, dx['time_bnds'][0, 0])
dx['time_bnds'][-1, 1] = min(t2, dx['time_bnds'][-1, 1])
if dx['time_bnds'][-1, 1] > dx['time_bnds'][0, 0]:
return [dx]
return []
ddo = []
ddb = []
t = dd[0]['time_bnds'][0, 0]
r = (t + 0.5) % period
t1 = state.get('t1', t - r)
t2 = state.get('t2', t1 + period)
for d in dd:
if d is None:
ddo += merge(ddb, t1, t2) + [None]
break
i1 = 0
i = 0
while i < len(d['time']):
if not (d['time_bnds'][i, 1] >= t2):
i += 1
continue
ii = np.arange(i1, i + (t2 - d['time_bnds'][i, 0] > epsilon))
if len(ii) > 0:
dx = copy.copy(d)
ds.select(dx, {'time': ii})
ddb += [dx]
ddo += merge(ddb, t1, t2)
i1 = i
t = d['time_bnds'][i, 0]
r = (t + 0.5) % period
t1 = max(t2, t - r)
t2 = t1 + period
ddb = []
ii = np.arange(i1, len(d['time']))
if len(ii) > 0:
dx = copy.copy(d)
ds.select(dx, {'time': ii})
ddb += [dx]
state['dd'] = ddb
state['t1'] = t1
state['t2'] = t2
return ddo
def aggregate(dd, state, period):
dd = state.get('dd', []) + dd
state['dd'] = []
if len(dd) == 0 or dd[0] is None:
return dd
dd0 = []
dd1 = []
t1 = dd[0]['time'][0] - ((dd[0]['time'][0] + 0.5) % 1.0)
t2 = t1 + period
for i, d in enumerate(dd):
if d is None:
d1 = ds.merge(dd1, 'time')
dd0.append(d1)
dd0.append(None)
break
j0 = 0
j = 0
while j < len(d['time']):
t = d['time'][j]
if t >= t2:
idx = np.arange(j0, j)
if len(idx) > 0:
d_copy = copy.copy(d)
ds.select(d_copy, {'time': idx})
dd1.append(d_copy)
if len(dd1) > 0:
d1 = ds.merge(dd1, 'time')
dd0.append(d1)
j0 = j
t2 = t + period
dd1 = []
j += 1
idx = np.arange(j0, len(d['time']))
if len(idx) > 0:
d_copy = copy.copy(d)
ds.select(d_copy, {'time': idx})
dd1.append(d_copy)
state['dd'] = dd1
return dd0
def read(dirname, track, warnings=[], step=6. / 24.):
dd_index = ds.readdir(
dirname,
variables=['time', 'latitude', 'longitude', 'level_height'],
jd=True,
full=True,
warnings=warnings,
)
start_time = track['time'][0]
end_time = track['time'][-1]
d_var = {}
for var in VARIABLES:
dd = []
for d_index in dd_index:
if var not in d_index['.']:
continue
time = d_index['time']
time_half = misc.half(time)
lat = d_index['latitude']
lon = d_index['longitude']
level_height = d_index['level_height']
filename = d_index['filename']
ii = np.nonzero((time_half[1:] >= start_time)
& (time_half[:-1] < end_time))[0]
for i in ii:
t = time[i]
i2 = np.argmin(np.abs(track['time'] - time[i]))
lat0 = track['lat'][i2]
lon0 = track['lon'][i2]
j = np.argmin(np.abs(lat - lat0))
k = np.argmin(np.abs(lon - lon0))
d = ds.read(filename,
variables=[var],
sel={
'time': i,
'latitude': j,
'longitude': k
})
d_new = {
'lat': np.array([lat[j]]),
'lon': np.array([lon[k]]),
'time': np.array([t]),
'level_height': np.array([level_height]),
'.': META,
}
d_new['.']['level_height'] = {'.dims': ['level']}
d_new[TRANS[var]] = d[var].reshape([1] + list(d[var].shape))
if TRANS[var] == 'cl':
d_new[TRANS[var]] *= 100.
dd.append(d_new)
if len(dd) > 0:
d_var[TRANS[var]] = ds.op.merge(dd, 'time')
time_list = [set(d_var[var]['time']) for var in d_var.keys()]
time = time_list[0].intersection(*time_list[1:]) \
if len(time_list) > 0 \
else set()
d = {}
d['.'] = {}
if len(time) == 0:
return None
for var in d_var.keys():
idx = [i for i, t in enumerate(d_var[var]['time']) if t in time]
ds.select(d_var[var], {'time': idx})
d[var] = d_var[var][var]
d['lat'] = d_var[var]['lat']
d['lon'] = d_var[var]['lon']
d['time'] = d_var[var]['time']
d['level_height'] = d_var[var]['level_height']
if 'ps' not in d:
n, m = d['pfull'].shape
d['ps'] = np.full(n, np.nan, dtype=np.float64)
for i in range(n):
d['ps'][i] = 2 * d['pfull'][i, 0] - d['pfull'][i, 1]
if 'zfull' not in d:
n, m = d['pfull'].shape
d['zfull'] = np.full((n, m), np.nan, dtype=np.float64)
for i in range(n):
d['zfull'][i, :] = d['level_height']
del d['level_height']
if 'orog' not in d:
n, m = d['zfull'].shape
d['orog'] = np.full(n, np.nan, dtype=np.float64)
for i in range(n):
d['orog'][i] = 2 * d['zfull'][i, 0] - d['zfull'][i, 1]
if 'time' in d:
d['time_bnds'] = misc.time_bnds(d['time'], step)
d['.'] = META
return d
def read(dirname, track, warnings=[], step=6. / 24.):
d_ll = ds.read(os.path.join(dirname, 'LL125.nc'),
['latitude', 'longitude', 'z'])
lat_ll = d_ll['latitude']
lon_ll = d_ll['longitude']
orog_ll = d_ll['z'][0, :, :] / 9.80665
dd_idx = ds.readdir(
dirname,
variables=['time', 'latitude', 'longitude'],
jd=True,
full=True,
warnings=warnings,
)
start_time = track['time'][0]
end_time = track['time'][-1]
d_out = {}
for var in VARS:
dd = []
var2 = TRANS[var]
for d_idx in dd_idx:
if var not in d_idx['.']:
continue
time = d_idx['time']
lat = d_idx['latitude']
lon = d_idx['longitude']
filename = d_idx['filename']
ii = np.nonzero((time >= start_time - step * 0.5)
& (time < end_time + step * 0.5))[0]
for i in ii:
t = time[i]
i2 = np.argmin(np.abs(track['time'] - time[i]))
lat0 = track['lat'][i2]
lon0 = track['lon'][i2]
j = np.argmin(np.abs(lat - lat0))
k = np.argmin(np.abs(lon - lon0))
j_ll = np.argmin(np.abs(lat_ll - lat0))
k_ll = np.argmin(np.abs(lon_ll - lon0))
d = ds.read(
filename,
VARS_AUX + [var],
sel={
'time': [i],
'latitude': j,
'longitude': k,
},
jd=True,
)
for a, b in TRANS.items():
if a in d.keys():
ds.rename(d, a, b)
d['lat'] = np.array([d['lat']])
d['lon'] = np.array([d['lon']])
d['orog'] = np.array([orog_ll[j_ll, k_ll]])
d['.']['lat']['.dims'] = ['time']
d['.']['lon']['.dims'] = ['time']
d['.']['orog'] = {'.dims': ['time']}
if 'pfull' in ds.get_vars(d):
d['pfull'] = d['pfull'].reshape([1, len(d['pfull'])])
d['.']['pfull']['.dims'] = ['time', 'pfull']
d['pfull'] = d['pfull'][:, ::-1]
d[var2] = d[var2][:, ::-1]
ds.select(d, {'pfull': np.arange(27)})
dd.append(d)
d = ds.op.merge(dd, 'time')
for var_aux in VARS_AUX:
if TRANS[var_aux] in ds.get_vars(d_out) \
and TRANS[var_aux] in ds.get_vars(d) \
and not np.all(d_out[TRANS[var_aux]] == d[TRANS[var_aux]]):
raise ValueError('%s: Field differs between input files' %
TRANS[var_aux])
d_out.update(d)
d_out['pfull'] = d_out['pfull'] * 1e2
if 'time' in d_out:
d_out['time_bnds'] = misc.time_bnds(d_out['time'], step, start_time,
end_time)
d_out['time'] = np.mean(d_out['time_bnds'], axis=1)
d_out['.'] = META
return d_out