Area weighted regridder caching #2370
Description
Area weighted averaging is quite expensive. According to the Iris documentation it should be possible to 'cache' the regridder :
However, I could not observe any benefits in doing that. The cost of setting up the regridder is negligible.
An example showing this by regridding a single field is below. The only thing that seems to be cached is an uninitialised chunk of memory.
import numpy as np
import iris
print iris.__version__
from time import time
import matplotlib.pyplot as plt
import iris
import iris.quickplot as qplt
%matplotlib inline
resolution = {
'N24': ( 24 * 3/4. * 2, 2 * 24),
'N44': ( 44 * 3/4. * 2, 2 * 44),
'N96': ( 96 * 3/4. * 2, 2 * 96),
'N160': ( 160 * 3/4. * 2, 2 * 160),
'N216': ( 216 * 3/4. * 2, 2 * 216),
}
def create_cube(lat, long):
"""
Create test cube.
lat, long - cube dimensions
"""
# lat coord
spacing = 180. / lat
lower_bound = -90 + spacing/2.
upper_bound = 90 - spacing/2.
latitude = iris.coords.DimCoord(np.linspace(lower_bound, upper_bound, lat),
standard_name='latitude',
units='degrees')
#long coord
spacing = 360. / long
lower_bound = 0 + spacing/2.
upper_bound = 360 - spacing/2.
longitude = iris.coords.DimCoord(np.linspace(lower_bound, upper_bound, long),
standard_name='longitude',
units='degrees')
# create cube
cube = iris.cube.Cube(np.random.rand(lat, long),
dim_coords_and_dims=[(latitude, 0), (longitude, 1)])
# add bounds
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
# add name
cube.long_name = 'Test cube'
return cube
print "# Test effect of 'caching'"
print '# Create regridder'
cube1 = create_cube(*resolution['N44'])
cube2 = create_cube(*resolution['N96'])
cube1.data
cube2.data
ts = time()
areaweighted_regridder = iris.analysis.AreaWeighted().regridder(cube1, cube2)
print areaweighted_regridder._target_grid_cube_cache
print round(time() - ts, 3), 's'
print '# Regrid with cached regridder'
for cube in [cube1] * 3:
ts = time()
result = areaweighted_regridder(cube)
print round(time() - ts, 3), 's'
plt.imshow(areaweighted_regridder._target_grid_cube_cache.data, interpolation='nearest', cmap='hot')
plt.title('Output of np.empty is cached (uninitialised).')
plt.colorbar()
plt.show()
plt.close()
print '# Regrid with new regridder'
scheme = iris.analysis.AreaWeighted()
for cube1 in [cube1] * 3:
ts = time()
result = cube1.regrid(cube2, scheme)
print round(time() - ts, 3), 's'Output:
# Test effect of 'caching'
# Create regridder
None
0.001 s
# Regrid with cached regridder
5.162 s
<plot>
5.087 s
<plot>
5.094 s
<plot>
# Regrid with new regridder
5.088 s
5.081 s
5.151 s