lat=D.variables['lat'][:]
lon=D.variables['lon'][:]
Y= lat.size
X= lon.size
vars= ['PRECT']
data_filt={}
data_map_filt={}
self_corr= {}
self_corr_mask= {}
for vv in vars:
#== trop mean data
tmp= data[vv]
tmp= tmp.reshape(tmp.size)
tmp= panta(tmp)
tmp_smooth= running_mean(tmp,6)
tmp_f = tmp - tmp_smooth; data_filt[vv]= tmp_f
#== map data
tmp= data_map[vv]
tmp= tmp.reshape(tmp.shape[0]*tmp.shape[1], tmp.shape[2], tmp.shape[3])
tmp= panta(tmp)
tmp_smooth= running_mean(tmp,6)
tmp_map_f= tmp - tmp_smooth; data_map_filt[vv]= tmp_map_f
#== correlation
Y,X= tmp_map_f.shape[1:3]
cor= np.zeros((Y,X))
cor_mask= np.zeros((Y,X))
for yy in range(Y):
for xx in range(X):
tmp_map_f_loc= tmp_map_f[:, yy, xx]
prec_mask= (tmp_map_f_loc > 10e-1)
with open(data_file, 'rb') as f:
data_map = pickle.load(f)
data_file = data_path_P
with open(data_file, 'rb') as f:
pEOF = pickle.load(f)
plat = pEOF['lat']
plon = pEOF['lon']
data_file = '/data/cloud/Goldtimes5/data/GCM_SPCAM/CPL64/CRM_region_mean/PRECT.pk'
with open(data_file, 'rb') as f:
CRMP = pickle.load(f)
CRMP = CRMP.reshape((CRMP.size))
CRMP = panta(CRMP)
CRMP_f = running_mean(CRMP, 6)
CRMP = CRMP_f
#Slow
CRMP = CRMP * 0.2583 # CRM region area / total tropical area
vars = [
'RCE', 'PRECT', 'Rad_cool', 'SHFLX', 'FLNS', 'FSNS', 'FLNT', 'FSNTOA',
'FSDTOA', 'FSUTOA'
]
data_filt = {}
data_map_filt = {}
self_corr = {}
for vv in vars:
#== trop mean data
tmp = data[vv]
tmp = tmp.reshape(tmp.size)