lon = fin.root.lon[:]
lat = fin.root.lat[:]
d = fin.root.dh_mean_mixed_const_xcal[:]
#d = fin.root.dg_mean_xcal[:]
#e = fin.root.dh_error_xcal[:]
#d = fin.root.n_ad_xcal[:]
nz, ny, nx = d.shape
dt = ap.year2date(time)
if 1: # load area info
df_temp = pd.read_csv('ice_shelf_area4.csv')
df_area = df_temp['sampled_km2']
df_area.index = df_temp['ice_shelf']
if 1:
time, d = ap.time_filt(time, d, from_time=1994, to_time=2013)
#time, e = time_filt(time, e, from_time=1992, to_time=20013)
# area-average
#---------------------------------------------------------------------
df = pd.DataFrame(index=time)
df2 = pd.DataFrame(index=time)
for k, s in zip(names, shelves):
shelf, x, y = ap.get_subset(s, d, lon, lat)
#error, x, y = ap.get_subset(s, e, lon, lat)
A = ap.get_area_cells(shelf[10], x, y)
ts, _ = ap.area_weighted_mean(shelf, A)
#ts2, _ = ap.area_weighted_mean(error, A)
df[k] = ts
#df2[k] = ts2
lon = fin.root.lon[:]
lat = fin.root.lat[:]
d = fin.root.dh_mean_mixed_const_xcal[:]
#d = fin.root.dg_mean_xcal[:]
#e = fin.root.dh_error_xcal[:]
#d = fin.root.n_ad_xcal[:]
nz, ny, nx = d.shape
dt = ap.year2date(time)
if 1: # load area info
df_temp = pd.read_csv('ice_shelf_area4.csv')
df_area = df_temp['sampled_km2']
df_area.index = df_temp['ice_shelf']
if 1:
time, d = ap.time_filt(time, d, from_time=1994, to_time=2013)
#time, e = time_filt(time, e, from_time=1992, to_time=20013)
# area-average
#---------------------------------------------------------------------
df = pd.DataFrame(index=time)
df2 = pd.DataFrame(index=time)
for k, s in zip(names, shelves):
shelf, x, y = ap.get_subset(s, d, lon, lat)
#error, x, y = ap.get_subset(s, e, lon, lat)
A = ap.get_area_cells(shelf[10], x, y)
ts, _ = ap.area_weighted_mean(shelf, A)
#ts2, _ = ap.area_weighted_mean(error, A)
df[k] = ts
#df2[k] = ts2
print('done')
if 0: # (for testing only) subset
print lon.shape, lat.shape, data.shape
i, j = ap.where_isnan('Totten', lon, lat)
data[:,i,j] = np.nan
'''
plt.imshow(data[10], origin='lower', interpolation='nearest',
extent=(lon.min(), lon.max(), lat.min(), lat.max()),
aspect='auto')
plt.show()
exit()
'''
if 1: # (yes) filter time
_, data = ap.time_filt(time, data, from_time=1994, to_time=2013)
time, error = ap.time_filt(time, error, from_time=1994, to_time=2013)
dt = ap.year2date(time)
# remove bad grid cells (visual inspection)
if 1:
ii, jj = ap.find_nearest2(xx, yy, lonlat)
k = 0
for i, j in zip(ii, jj):
print k, lonlat[k]
'''
plt.plot(time, data[:,i,j])
plt.show()
'''
data[:,i,j] = np.nan