def get_area_cells(grid, lon, lat):
"""Calculate the area for each cell in the grid.
lon/lat are coordinates in decimal degrees.
Parameters
----------
grid : 2d-array
A rectangular grid.
x, y : 1d-arrays
The coordinates of the cells or nodes (edges). If x and y are
of the same lengh as grid dimensions, then cell-centered
coordinates are assumed, otherwise edges are assumed.
Returns
-------
out : 2d-array
Same shape as 'grid' with the values of the area on each cell.
Notes
-----
Grid-cell area is in km**2.
"""
ny, nx = grid.shape
area = np.zeros_like(grid)
if len(lat) == ny:
lon, lat = cell2node(lon, lat) # convert cells -> nodes
for i in xrange(ny):
for j in xrange(nx):
a = haversine(lon[j], lat[i], lon[j], lat[i+1])
b = haversine(lon[j], lat[i], lon[j+1], lat[i])
area[i,j] = a * b
return area
f = tb.open_file(FILE_MSK, 'r')
x_msk = f.root.x[:]
y_msk = f.root.y[:]
msk = f.root.mask[:]
f.close()
print 'done'
if 1: # 2d -> 1d, x/y -> lon/lat
x_msk, y_msk = np.meshgrid(x_msk, y_msk) # 1d -> 2d
x_msk, y_msk = x_msk.ravel(), y_msk.ravel() # 2d -> 1d
msk = msk.ravel()
lon_msk, lat_msk = ap.xy2ll(x_msk, y_msk, units='m')
lon_msk = ap.lon_180_360(lon_msk)
del x_msk, y_msk
lon_nodes, lat_nodes = ap.cell2node(lon, lat)
area = np.full(data[0].shape, np.nan)
cells_idx = []
# count number of mask cells falling into each data cell
for i in xrange(len(lat)):
for j in xrange(len(lon)):
i_cells, = np.where((lon_nodes[j] <= lon_msk) & \
(lon_msk <= lon_nodes[j+1]) & \
(lat_nodes[i] <= lat_msk) & \
(lat_msk <= lat_nodes[i+1]) & \
(msk == 4)) # 4 = ice shelf
area[i,j] = len(i_cells) # each mask cell is 1 km**2
cells_idx = np.append(cells_idx, i_cells)
cells_idx = [int(i) for i in cells_idx]
if 1: # print values
f = poly_rate[~np.isnan(poly_rate)].round(2)
print 'poly rate (m/yr): ', f
f = line_rate[~np.isnan(line_rate)].round(2)
print 'line rate (m/yr): ', f
f = dpoly_rate[~np.isnan(dpoly_rate)].round(2)
print 'dpoly acce (m/yr2): ', f
#---------------------------------------------------------------------
# save data
#---------------------------------------------------------------------
# spherical -> cartesian
xx, yy = np.meshgrid(lon, lat)
xed, yed = ap.cell2node(lon, lat)
xed2d, yed2d = np.meshgrid(xed, yed)
xyz = np.column_stack(ap.sph2xyz(xed2d.ravel(), yed2d.ravel()))
if 1:
print('saving data...')
fout = tb.open_file(DIR + FILE_OUT, 'w')
try:
fout.create_array('/', 'time', time)
except:
pass
try:
fout.create_array('/', 'lon', lon)
fout.create_array('/', 'lat', lat)
except:
pass
data = as_frame(data, dt, lat, lon)
data = data.apply(peak_filt, x=time, n_std=3, max_deg=3, iterative=True, plot=False, raw=True)
# (NO) filter ts with std > max_std
if 0:
print 'std filtering...'
data = as_frame(data, dt, lat, lon)
data = data.apply(std_filt, x=time, max_std=2, max_deg=3, plot=False, raw=False)
# (yes) filter out uncomplete time series
if 1:
print 'percent filtering...'
data = data.apply(percent_filt, min_perc=0.7, plot=False, raw=True)
# spherical -> cartesian
xed, yed = ap.cell2node(lon, lat)
xed2d, yed2d = np.meshgrid(xed, yed)
xyz = np.column_stack(ap.sph2xyz(xed2d.ravel(), yed2d.ravel()))
data = as_frame(data, dt, lat, lon)
data = data.apply(ap.referenced, to='mean', raw=True) # to mean !!!
data = as_array(data)
error[np.isnan(data)] = np.nan
#---------------------------------------------------------------------
if PLOT: # plot time series
data = as_array(data)
j, = ap.find_nearest(lon, LON)
i, = ap.find_nearest(lat, LAT)
i, j = i[0], j[0]
