def post_process_detailed_3di(full_path):
"""
Make detailed images using a 0.5m height map.
"""
print 'post processing %s...' % full_path
data = Data(full_path) # NetCDF data
# TODO: Find out which AHN tiles
for timestep in range(data.num_timesteps):
print('Working on timestep %d...' % timestep)
ma_3di = data.to_masked_array(data.depth, timestep)
ds_3di = to_dataset(ma_3di, data.geotransform)
# testing
ahn_indices = models.AhnIndex.get_ahn_indices(ds_3di)
print ahn_indices
for ahn_index in ahn_indices:
print 'reading ahn data... %s' % ahn_index
ahn_index.get_ds()
filename_base = '_step%d' % timestep
cdict = {
'red': ((0.0, 51./256, 51./256),
(0.5, 237./256, 237./256),
(1.0, 83./256, 83./256)),
'green': ((0.0, 114./256, 114./256),
(0.5, 245./256, 245./256),
(1.0, 83./256, 83./256)),
'blue': ((0.0, 54./256, 54./256),
(0.5, 170./256, 170./256),
(1.0, 83./256, 83./256)),
}
colormap = mpl.colors.LinearSegmentedColormap('something', cdict, N=1024)
min_value, max_value = 0.0, 4.0
normalize = mpl.colors.Normalize(vmin=min_value, vmax=max_value)
rgba = colormap(normalize(ma_3di), bytes=True)
#rgba[:,:,3] = np.where(rgba[:,:,0], 153 , 0)
Image.fromarray(rgba).save(filename_base + '.png', 'PNG')
def post_process_3di(full_path):
"""
Simple version: do not use AHN tiles to do the calculation
This method is quite fast, but the result has squares.
"""
print 'post processing %s...' % full_path
data = Data(full_path) # NetCDF data
#process_3di_nc(full_path)
# TODO: Find out which AHN tiles
for timestep in range(data.num_timesteps):
print('Working on timestep %d...' % timestep)
ma_3di = data.to_masked_array(data.depth, timestep)
ds_3di = to_dataset(ma_3di, data.geotransform)
# testing
#print ', '.join([i.bladnr for i in get_ahn_indices(ds_3di)])
filename_base = '_step%d' % timestep
cdict = {
'red': ((0.0, 51./256, 51./256),
(0.5, 237./256, 237./256),
(1.0, 83./256, 83./256)),
'green': ((0.0, 114./256, 114./256),
(0.5, 245./256, 245./256),
(1.0, 83./256, 83./256)),
'blue': ((0.0, 54./256, 54./256),
(0.5, 170./256, 170./256),
(1.0, 83./256, 83./256)),
}
colormap = mpl.colors.LinearSegmentedColormap('something', cdict, N=1024)
min_value, max_value = 0.0, 4.0
normalize = mpl.colors.Normalize(vmin=min_value, vmax=max_value)
rgba = colormap(normalize(ma_3di), bytes=True)
#rgba[:,:,3] = np.where(rgba[:,:,0], 153 , 0)
Image.fromarray(rgba).save(filename_base + '.png', 'PNG')
