def test_simple_missing_data(self):
"""
Check for missing data handling.
Should mask cells that either ..
(a) go partly outside the source grid
(b) partially overlap masked source data
"""
c1, c2 = self.stock_c1_c2
# regrid from c2 to c1 -- should mask all the edges...
c2_to_c1 = regrid_conservative_via_esmpy(c2, c1)
self.assertArrayEqual(
c2_to_c1.data.mask,
[[True, True, True, True, True], [True, False, False, False, True],
[True, False, False, False, True],
[True, False, False, False, True], [True, True, True, True, True]
])
# do same with a particular point masked
c2m = c2.copy()
c2m.data = np.ma.array(c2m.data)
c2m.data[1, 1] = np.ma.masked
c2m_to_c1 = regrid_conservative_via_esmpy(c2m, c1)
self.assertArrayEqual(
c2m_to_c1.data.mask,
[[True, True, True, True, True], [True, True, True, False, True],
[True, True, True, False, True], [
True, False, False, False, True
], [True, True, True, True, True]])
def test_simple_missing_data(self):
"""
Check for missing data handling.
Should mask cells that either ..
(a) go partly outside the source grid
(b) partially overlap masked source data
"""
c1, c2 = self.stock_c1_c2
# regrid from c2 to c1 -- should mask all the edges...
c2_to_c1 = regrid_conservative_via_esmpy(c2, c1)
self.assertArrayEqual(c2_to_c1.data.mask,
[[True, True, True, True, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, True, True, True, True]])
# do same with a particular point masked
c2m = c2.copy()
c2m.data = np.ma.array(c2m.data)
c2m.data[1, 1] = np.ma.masked
c2m_to_c1 = regrid_conservative_via_esmpy(c2m, c1)
self.assertArrayEqual(c2m_to_c1.data.mask,
[[True, True, True, True, True],
[True, True, True, False, True],
[True, True, True, False, True],
[True, False, False, False, True],
[True, True, True, True, True]])
def test_polar_areas(self):
"""
Test area-conserving regrid between different grids.
Grids have overlapping areas in the same (lat-lon) coordinate system.
Cells are highly non-square (near the pole).
"""
# Like test_basic_area, but not symmetrical + bigger overall errors.
shape1 = (5, 5)
xlims1, ylims1 = ((-2, 2), (84, 88))
c1 = _make_test_cube(shape1, xlims1, ylims1)
c1.data[:] = 0.0
c1.data[2, 2] = 1.0
c1_areasum = _cube_area_sum(c1)
shape2 = (4, 4)
xlims2, ylims2 = ((-1.5, 1.5), (84.5, 87.5))
c2 = _make_test_cube(shape2, xlims2, ylims2)
c2.data[:] = 0.0
c1to2 = regrid_conservative_via_esmpy(c1, c2)
# check for expected pattern
d_expect = np.array(
[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.23614, 0.23614, 0.0],
[0.0, 0.26784, 0.26784, 0.0],
[0.0, 0.0, 0.0, 0.0],
]
)
self.assertArrayAllClose(c1to2.data, d_expect, rtol=5.0e-5)
# check sums
c1to2_areasum = _cube_area_sum(c1to2)
self.assertArrayAllClose(c1to2_areasum, c1_areasum)
#
# transform back again ...
#
c1to2to1 = regrid_conservative_via_esmpy(c1to2, c1)
# check values
d_expect = np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.056091, 0.112181, 0.056091, 0.0],
[0.0, 0.125499, 0.250998, 0.125499, 0.0],
[0.0, 0.072534, 0.145067, 0.072534, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
]
)
self.assertArrayAllClose(c1to2to1.data, d_expect, atol=0.0005)
# check sums
c1to2to1_areasum = _cube_area_sum(c1to2to1)
self.assertArrayAllClose(c1to2to1_areasum, c1_areasum)
def test_multidimensional(self):
"""
Check valid operation on a multidimensional cube.
Calculation should repeat across multiple dimensions.
Any attached orography is interpolated.
NOTE: in future, extra dimensions may be passed through to ESMF: At
present, it repeats the calculation on 2d slices. So we check that
at least the results are equivalent (as it's quite easy to do).
"""
# Get some higher-dimensional test data
c1 = istk.realistic_4d()
# Chop down to small size, and mask some data
c1 = c1[:3, :4, :16, :12]
c1.data[:, 2, :, :] = np.ma.masked
c1.data[1, 1, 3:9, 4:7] = np.ma.masked
# Give it a slightly more challenging indexing order: tzyx --> xzty
c1.transpose((3, 1, 0, 2))
# Construct a (coarser) target grid of about the same extent
c1_cs = c1.coord(axis="x").coord_system
xlims = _minmax(c1.coord(axis="x").contiguous_bounds())
ylims = _minmax(c1.coord(axis="y").contiguous_bounds())
# Reduce the dimensions slightly to avoid NaNs in regridded orography
delta = 0.05
# || NOTE: this is *not* a small amount. Think there is a bug.
# || NOTE: See https://github.com/SciTools/iris/issues/458
xlims = np.interp([delta, 1.0 - delta], [0, 1], xlims)
ylims = np.interp([delta, 1.0 - delta], [0, 1], ylims)
pole_latlon = (
c1_cs.grid_north_pole_latitude,
c1_cs.grid_north_pole_longitude,
)
c2 = _make_test_cube((7, 8), xlims, ylims, pole_latlon=pole_latlon)
# regrid onto new grid
c1_to_c2 = regrid_conservative_via_esmpy(c1, c2)
# check that all the original coords exist in the new cube
# NOTE: this also effectively confirms we haven't lost the orography
def list_coord_names(cube):
return sorted([coord.name() for coord in cube.coords()])
self.assertEqual(list_coord_names(c1_to_c2), list_coord_names(c1))
# check that each xy 'slice' has same values as if done on its own.
for i_p, i_t in np.ndindex(c1.shape[1:3]):
c1_slice = c1[:, i_p, i_t]
c2_slice = regrid_conservative_via_esmpy(c1_slice, c2)
subcube = c1_to_c2[:, i_p, i_t]
self.assertEqual(subcube, c2_slice)
# check all other metadata
self.assertEqual(c1_to_c2.metadata, c1.metadata)
def test_longitude_wraps(self):
"""Check results are independent of where the grid 'seams' are."""
# First repeat global regrid calculation from 'test_global'.
shape1 = (8, 6)
xlim1 = 180.0 * (shape1[0] - 1) / shape1[0]
ylim1 = 90.0 * (shape1[1] - 1) / shape1[1]
xlims1 = (-xlim1, xlim1)
ylims1 = (-ylim1, ylim1)
c1 = _make_test_cube(shape1, xlims1, ylims1)
# Create a small, plausible global array (see test_global).
basedata = np.array([
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 4, 4, 4, 2, 2, 1],
[2, 1, 4, 4, 4, 2, 2, 2],
[2, 5, 5, 1, 1, 1, 5, 5],
[5, 5, 5, 1, 1, 1, 5, 5],
[5, 5, 5, 5, 5, 5, 5, 5],
])
c1.data[:] = basedata
shape2 = (14, 11)
xlim2 = 180.0 * (shape2[0] - 1) / shape2[0]
ylim2 = 90.0 * (shape2[1] - 1) / shape2[1]
xlims_2 = (-xlim2, xlim2)
ylims_2 = (-ylim2, ylim2)
c2 = _make_test_cube(shape2,
xlims_2,
ylims_2,
pole_latlon=(47.4, 25.7))
# Perform regridding
c1toc2 = regrid_conservative_via_esmpy(c1, c2)
# Now redo with dst longitudes rotated, so 'seam' is somewhere else.
x2_shift_steps = shape2[0] // 3
xlims2_shifted = np.array(xlims_2) + 360.0 * x2_shift_steps / shape2[0]
c2_shifted = _make_test_cube(shape2,
xlims2_shifted,
ylims_2,
pole_latlon=(47.4, 25.7))
c1toc2_shifted = regrid_conservative_via_esmpy(c1, c2_shifted)
# Show that results are the same, when output rolled by same amount
rolled_data = np.roll(c1toc2_shifted.data, x2_shift_steps, axis=1)
self.assertArrayAllClose(rolled_data, c1toc2.data)
# Repeat with rolled *source* data : result should be identical
x1_shift_steps = shape1[0] // 3
x_shift_degrees = 360.0 * x1_shift_steps / shape1[0]
xlims1_shifted = [x - x_shift_degrees for x in xlims1]
c1_shifted = _make_test_cube(shape1, xlims1_shifted, ylims1)
c1_shifted.data[:] = np.roll(basedata, x1_shift_steps, axis=1)
c1shifted_toc2 = regrid_conservative_via_esmpy(c1_shifted, c2)
self.assertEqual(c1shifted_toc2, c1toc2)
def test_multidimensional(self):
"""
Check valid operation on a multidimensional cube.
Calculation should repeat across multiple dimensions.
Any attached orography is interpolated.
NOTE: in future, extra dimensions may be passed through to ESMF: At
present, it repeats the calculation on 2d slices. So we check that
at least the results are equivalent (as it's quite easy to do).
"""
# Get some higher-dimensional test data
c1 = istk.realistic_4d()
# Chop down to small size, and mask some data
c1 = c1[:3, :4, :16, :12]
c1.data[:, 2, :, :] = np.ma.masked
c1.data[1, 1, 3:9, 4:7] = np.ma.masked
# Give it a slightly more challenging indexing order: tzyx --> xzty
c1.transpose((3, 1, 0, 2))
# Construct a (coarser) target grid of about the same extent
c1_cs = c1.coord(axis='x').coord_system
xlims = _minmax(c1.coord(axis='x').contiguous_bounds())
ylims = _minmax(c1.coord(axis='y').contiguous_bounds())
# Reduce the dimensions slightly to avoid NaNs in regridded orography
delta = 0.05
# NOTE: this is *not* a small amount. Think there is a bug.
# NOTE: See https://github.com/SciTools/iris/issues/458
xlims = np.interp([delta, 1.0 - delta], [0, 1], xlims)
ylims = np.interp([delta, 1.0 - delta], [0, 1], ylims)
pole_latlon = (c1_cs.grid_north_pole_latitude,
c1_cs.grid_north_pole_longitude)
c2 = _make_test_cube((7, 8), xlims, ylims, pole_latlon=pole_latlon)
# regrid onto new grid
c1_to_c2 = regrid_conservative_via_esmpy(c1, c2)
# check that all the original coords exist in the new cube
# NOTE: this also effectively confirms we haven't lost the orography
def list_coord_names(cube):
return sorted([coord.name() for coord in cube.coords()])
self.assertEqual(list_coord_names(c1_to_c2), list_coord_names(c1))
# check that each xy 'slice' has same values as if done on its own.
for i_p, i_t in np.ndindex(c1.shape[1:3]):
c1_slice = c1[:, i_p, i_t]
c2_slice = regrid_conservative_via_esmpy(c1_slice, c2)
subcube = c1_to_c2[:, i_p, i_t]
self.assertEqual(subcube, c2_slice)
# check all other metadata
self.assertEqual(c1_to_c2.metadata, c1.metadata)
def test_simple_areas(self):
"""
Test area-conserving regrid between simple "near-square" grids.
Grids have overlapping areas in the same (lat-lon) coordinate system.
Grids are "nearly flat" lat-lon spaces (small ranges near the equator).
"""
c1, c2 = self.stock_c1_c2
c1_areasum = self.stock_c1_areasum
# main regrid
c1to2 = regrid_conservative_via_esmpy(c1, c2)
c1to2_areasum = _cube_area_sum(c1to2)
# Check expected result (Cartesian equivalent, so not exact).
d_expect = np.array(
[
[0.00, 0.00, 0.00, 0.00],
[0.00, 0.25, 0.25, 0.00],
[0.00, 0.25, 0.25, 0.00],
[0.00, 0.00, 0.00, 0.00],
]
)
# Numbers are slightly off (~0.25000952). This is expected.
self.assertArrayAllClose(c1to2.data, d_expect, rtol=5.0e-5)
# check that the area sums are equivalent, simple total is a bit off
self.assertArrayAllClose(c1to2_areasum, c1_areasum)
#
# regrid back onto original grid again ...
#
c1to2to1 = regrid_conservative_via_esmpy(c1to2, c1)
c1to2to1_areasum = _cube_area_sum(c1to2to1)
# Check expected result (Cartesian/exact difference now greater)
d_expect = np.array(
[
[0.0, 0.0000, 0.0000, 0.0000, 0.0],
[0.0, 0.0625, 0.1250, 0.0625, 0.0],
[0.0, 0.1250, 0.2500, 0.1250, 0.0],
[0.0, 0.0625, 0.1250, 0.0625, 0.0],
[0.0, 0.0000, 0.0000, 0.0000, 0.0],
]
)
self.assertArrayAllClose(c1to2to1.data, d_expect, atol=0.00002)
# check area sums again
self.assertArrayAllClose(c1to2to1_areasum, c1_areasum)
def test_polar_areas(self):
"""
Test area-conserving regrid between different grids.
Grids have overlapping areas in the same (lat-lon) coordinate system.
Cells are highly non-square (near the pole).
"""
# Like test_basic_area, but not symmetrical + bigger overall errors.
shape1 = (5, 5)
xlims1, ylims1 = ((-2, 2), (84, 88))
c1 = _make_test_cube(shape1, xlims1, ylims1)
c1.data[:] = 0.0
c1.data[2, 2] = 1.0
c1_areasum = _cube_area_sum(c1)
shape2 = (4, 4)
xlims2, ylims2 = ((-1.5, 1.5), (84.5, 87.5))
c2 = _make_test_cube(shape2, xlims2, ylims2)
c2.data[:] = 0.0
c1to2 = regrid_conservative_via_esmpy(c1, c2)
# check for expected pattern
d_expect = np.array([[0.0, 0.0, 0.0, 0.0],
[0.0, 0.23614, 0.23614, 0.0],
[0.0, 0.26784, 0.26784, 0.0],
[0.0, 0.0, 0.0, 0.0]])
self.assertArrayAllClose(c1to2.data, d_expect, rtol=5.0e-5)
# check sums
c1to2_areasum = _cube_area_sum(c1to2)
self.assertArrayAllClose(c1to2_areasum, c1_areasum)
#
# transform back again ...
#
c1to2to1 = regrid_conservative_via_esmpy(c1to2, c1)
# check values
d_expect = np.array([[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.056091, 0.112181, 0.056091, 0.0],
[0.0, 0.125499, 0.250998, 0.125499, 0.0],
[0.0, 0.072534, 0.145067, 0.072534, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]])
self.assertArrayAllClose(c1to2to1.data, d_expect, atol=0.0005)
# check sums
c1to2to1_areasum = _cube_area_sum(c1to2to1)
self.assertArrayAllClose(c1to2to1_areasum, c1_areasum)
def test_longitude_wraps(self):
"""Check results are independent of where the grid 'seams' are."""
# First repeat global regrid calculation from 'test_global'.
shape1 = (8, 6)
xlim1 = 180.0 * (shape1[0] - 1) / shape1[0]
ylim1 = 90.0 * (shape1[1] - 1) / shape1[1]
xlims1 = (-xlim1, xlim1)
ylims1 = (-ylim1, ylim1)
c1 = _make_test_cube(shape1, xlims1, ylims1)
# Create a small, plausible global array (see test_global).
basedata = np.array(
[[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 4, 4, 4, 2, 2, 1],
[2, 1, 4, 4, 4, 2, 2, 2],
[2, 5, 5, 1, 1, 1, 5, 5],
[5, 5, 5, 1, 1, 1, 5, 5],
[5, 5, 5, 5, 5, 5, 5, 5]])
c1.data[:] = basedata
shape2 = (14, 11)
xlim2 = 180.0 * (shape2[0] - 1) / shape2[0]
ylim2 = 90.0 * (shape2[1] - 1) / shape2[1]
xlims_2 = (-xlim2, xlim2)
ylims_2 = (-ylim2, ylim2)
c2 = _make_test_cube(shape2, xlims_2, ylims_2,
pole_latlon=(47.4, 25.7))
# Perform regridding
c1toc2 = regrid_conservative_via_esmpy(c1, c2)
# Now redo with dst longitudes rotated, so 'seam' is somewhere else.
x2_shift_steps = int(shape2[0] / 3)
xlims2_shifted = np.array(xlims_2) + 360.0 * x2_shift_steps / shape2[0]
c2_shifted = _make_test_cube(shape2, xlims2_shifted, ylims_2,
pole_latlon=(47.4, 25.7))
c1toc2_shifted = regrid_conservative_via_esmpy(c1, c2_shifted)
# Show that results are the same, when output rolled by same amount
rolled_data = np.roll(c1toc2_shifted.data, x2_shift_steps, axis=1)
self.assertArrayAllClose(rolled_data, c1toc2.data)
# Repeat with rolled *source* data : result should be identical
x1_shift_steps = int(shape1[0] / 3)
x_shift_degrees = 360.0 * x1_shift_steps / shape1[0]
xlims1_shifted = [x - x_shift_degrees for x in xlims1]
c1_shifted = _make_test_cube(shape1, xlims1_shifted, ylims1)
c1_shifted.data[:] = np.roll(basedata, x1_shift_steps, axis=1)
c1shifted_toc2 = regrid_conservative_via_esmpy(c1_shifted, c2)
self.assertEqual(c1shifted_toc2, c1toc2)
def regrid(hires_cube, regridding, rotate=False):
lowres_cube = None
startdt = datetime.datetime.now()
if regridding == 'iris.regrid.linear':
lowres_cube = hires_cube.regrid(lowres_grid, iris.analysis.Linear())
elif regridding == 'iris.regrid.linear.nan':
lowres_cube = hires_cube.regrid(lowres_grid,
iris.analysis.Linear('nan'))
elif regridding == 'iris.regrid.AreaWeighted':
if rotate:
print 'Doesnt work with changing coordinate systems'
else:
lowres_cube = hires_cube.regrid(lowres_grid,
iris.analysis.AreaWeighted())
elif regridding == 'iris.experimental.area_weighted':
if rotate:
print 'Doesnt work with changing coordinate systems'
else:
lowres_cube = iris.experimental.regrid.regrid_area_weighted_rectilinear_src_and_grid(
hires_cube, lowres_grid)
elif regridding == 'iris.esmpy':
lowres_cube = regrid_conservative_via_esmpy(hires_cube, lowres_grid)
elif regridding == 'tidl.pp_regrid':
lowres_cube = get_cube_from_file('lowres_ppregrid.pp')
elif regridding == 'tidl.pp_regrid_avg':
lowres_cube = get_cube_from_file('lowres_ppregridavg.pp')
enddt = datetime.datetime.now()
deltadt = (enddt - startdt).total_seconds()
return lowres_cube, deltadt
def test_global_collapse(self):
# Test regridding global data to a single cell.
# Fetch 'standard' testcube data
c1, _ = self.stock_c1_c2
c1_areasum = self.stock_c1_areasum
# Condense entire globe onto a single cell
x_coord_2 = iris.coords.DimCoord([0.0],
bounds=[-180.0, 180.0],
standard_name='longitude',
units='degrees',
coord_system=_PLAIN_GEODETIC_CS)
y_coord_2 = iris.coords.DimCoord([0.0],
bounds=[-90.0, 90.0],
standard_name='latitude',
units='degrees',
coord_system=_PLAIN_GEODETIC_CS)
c2 = iris.cube.Cube([[0.0]])
c2.add_dim_coord(y_coord_2, 0)
c2.add_dim_coord(x_coord_2, 1)
# NOTE: at present, this causes an error inside ESMF ...
context = self.assertRaises(NameError)
global_cell_supported = False
if global_cell_supported:
context = _donothing_context_manager()
with context:
c1_to_global = regrid_conservative_via_esmpy(c1, c2)
# Check the total area sum is still the same
self.assertArrayAllClose(c1_to_global.data[0, 0], c1_areasum)
def test_fail_no_cs(self):
# Test error when one coordinate has no coord_system.
shape1 = (5, 5)
xlims1, ylims1 = ((-2, 2), (-2, 2))
c1 = _make_test_cube(shape1, xlims1, ylims1)
c1.data[:] = 0.0
c1.data[2, 2] = 1.0
shape2 = (4, 4)
xlims2, ylims2 = ((-1.5, 1.5), (-1.5, 1.5))
c2 = _make_test_cube(shape2, xlims2, ylims2)
c2.data[:] = 0.0
c2.coord('latitude').coord_system = None
with self.assertRaises(ValueError):
regrid_conservative_via_esmpy(c1, c2)
def test_global(self):
# Test global regridding.
# Compute basic test data cubes.
shape1 = (8, 6)
xlim1 = 180.0 * (shape1[0] - 1) / shape1[0]
ylim1 = 90.0 * (shape1[1] - 1) / shape1[1]
c1 = _make_test_cube(shape1, (-xlim1, xlim1), (-ylim1, ylim1))
# Create a small, plausible global array:
# - top + bottom rows all the same
# - left + right columns "mostly close" for checking across the seam
basedata = np.array([[1, 1, 1, 1, 1, 1, 1,
1], [1, 1, 4, 4, 4, 2, 2, 1],
[2, 1, 4, 4, 4, 2, 2,
2], [2, 5, 5, 1, 1, 1, 5, 5],
[5, 5, 5, 1, 1, 1, 5, 5],
[5, 5, 5, 5, 5, 5, 5, 5]])
c1.data[:] = basedata
# Create a rotated grid to regrid this onto.
shape2 = (14, 11)
xlim2 = 180.0 * (shape2[0] - 1) / shape2[0]
ylim2 = 90.0 * (shape2[1] - 1) / shape2[1]
c2 = _make_test_cube(shape2, (-xlim2, xlim2), (-ylim2, ylim2),
pole_latlon=(47.4, 25.7))
# Perform regridding
c1toc2 = regrid_conservative_via_esmpy(c1, c2)
# Check that before+after area-sums match fairly well
c1_areasum = _cube_area_sum(c1)
c1toc2_areasum = _cube_area_sum(c1toc2)
self.assertArrayAllClose(c1toc2_areasum, c1_areasum, rtol=0.006)
def test_global_collapse(self):
# Test regridding global data to a single cell.
# Fetch 'standard' testcube data
c1, _ = self.stock_c1_c2
c1_areasum = self.stock_c1_areasum
# Condense entire globe onto a single cell
x_coord_2 = iris.coords.DimCoord([0.0], bounds=[-180.0, 180.0],
standard_name='longitude',
units='degrees',
coord_system=_PLAIN_GEODETIC_CS)
y_coord_2 = iris.coords.DimCoord([0.0], bounds=[-90.0, 90.0],
standard_name='latitude',
units='degrees',
coord_system=_PLAIN_GEODETIC_CS)
c2 = iris.cube.Cube([[0.0]])
c2.add_dim_coord(y_coord_2, 0)
c2.add_dim_coord(x_coord_2, 1)
# NOTE: at present, this causes an error inside ESMF ...
context = self.assertRaises(NameError)
global_cell_supported = False
if global_cell_supported:
context = _donothing_context_manager()
with context:
c1_to_global = regrid_conservative_via_esmpy(c1, c2)
# Check the total area sum is still the same
self.assertArrayAllClose(c1_to_global.data[0, 0], c1_areasum)
def test_global(self):
# Test global regridding.
# Compute basic test data cubes.
shape1 = (8, 6)
xlim1 = 180.0 * (shape1[0] - 1) / shape1[0]
ylim1 = 90.0 * (shape1[1] - 1) / shape1[1]
c1 = _make_test_cube(shape1, (-xlim1, xlim1), (-ylim1, ylim1))
# Create a small, plausible global array:
# - top + bottom rows all the same
# - left + right columns "mostly close" for checking across the seam
basedata = np.array(
[[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 4, 4, 4, 2, 2, 1],
[2, 1, 4, 4, 4, 2, 2, 2],
[2, 5, 5, 1, 1, 1, 5, 5],
[5, 5, 5, 1, 1, 1, 5, 5],
[5, 5, 5, 5, 5, 5, 5, 5]])
c1.data[:] = basedata
# Create a rotated grid to regrid this onto.
shape2 = (14, 11)
xlim2 = 180.0 * (shape2[0] - 1) / shape2[0]
ylim2 = 90.0 * (shape2[1] - 1) / shape2[1]
c2 = _make_test_cube(shape2, (-xlim2, xlim2), (-ylim2, ylim2),
pole_latlon=(47.4, 25.7))
# Perform regridding
c1toc2 = regrid_conservative_via_esmpy(c1, c2)
# Check that before+after area-sums match fairly well
c1_areasum = _cube_area_sum(c1)
c1toc2_areasum = _cube_area_sum(c1toc2)
self.assertArrayAllClose(c1toc2_areasum, c1_areasum, rtol=0.006)
def test_fail_no_cs(self):
# Test error when one coordinate has no coord_system.
shape1 = (5, 5)
xlims1, ylims1 = ((-2, 2), (-2, 2))
c1 = _make_test_cube(shape1, xlims1, ylims1)
c1.data[:] = 0.0
c1.data[2, 2] = 1.0
shape2 = (4, 4)
xlims2, ylims2 = ((-1.5, 1.5), (-1.5, 1.5))
c2 = _make_test_cube(shape2, xlims2, ylims2)
c2.data[:] = 0.0
c2.coord('latitude').coord_system = None
with self.assertRaises(ValueError):
regrid_conservative_via_esmpy(c1, c2)
def test_xy_transposed(self):
# Test effects of transposing X and Y in src/dst data.
c1, c2 = self.stock_c1_c2
testcube_xy = self.stock_regrid_c1toc2
# Check that transposed data produces transposed results
# - i.e. regrid(data^T)^T == regrid(data)
c1_yx = c1.copy()
c1_yx.transpose()
testcube_yx = regrid_conservative_via_esmpy(c1_yx, c2)
testcube_yx.transpose()
self.assertEqual(testcube_yx, testcube_xy)
# Check that transposing destination does nothing
c2_yx = c2.copy()
c2_yx.transpose()
testcube_dst_transpose = regrid_conservative_via_esmpy(c1, c2_yx)
self.assertEqual(testcube_dst_transpose, testcube_xy)
def test_xy_transposed(self):
# Test effects of transposing X and Y in src/dst data.
c1, c2 = self.stock_c1_c2
testcube_xy = self.stock_regrid_c1toc2
# Check that transposed data produces transposed results
# - i.e. regrid(data^T)^T == regrid(data)
c1_yx = c1.copy()
c1_yx.transpose()
testcube_yx = regrid_conservative_via_esmpy(c1_yx, c2)
testcube_yx.transpose()
self.assertEqual(testcube_yx, testcube_xy)
# Check that transposing destination does nothing
c2_yx = c2.copy()
c2_yx.transpose()
testcube_dst_transpose = regrid_conservative_via_esmpy(c1, c2_yx)
self.assertEqual(testcube_dst_transpose, testcube_xy)
def test_simple_areas(self):
"""
Test area-conserving regrid between simple "near-square" grids.
Grids have overlapping areas in the same (lat-lon) coordinate system.
Grids are "nearly flat" lat-lon spaces (small ranges near the equator).
"""
c1, c2 = self.stock_c1_c2
c1_areasum = self.stock_c1_areasum
# main regrid
c1to2 = regrid_conservative_via_esmpy(c1, c2)
c1to2_areasum = _cube_area_sum(c1to2)
# Check expected result (Cartesian equivalent, so not exact).
d_expect = np.array([[0.00, 0.00, 0.00, 0.00],
[0.00, 0.25, 0.25, 0.00],
[0.00, 0.25, 0.25, 0.00],
[0.00, 0.00, 0.00, 0.00]])
# Numbers are slightly off (~0.25000952). This is expected.
self.assertArrayAllClose(c1to2.data, d_expect, rtol=5.0e-5)
# check that the area sums are equivalent, simple total is a bit off
self.assertArrayAllClose(c1to2_areasum, c1_areasum)
#
# regrid back onto original grid again ...
#
c1to2to1 = regrid_conservative_via_esmpy(c1to2, c1)
c1to2to1_areasum = _cube_area_sum(c1to2to1)
# Check expected result (Cartesian/exact difference now greater)
d_expect = np.array([[0.0, 0.0000, 0.0000, 0.0000, 0.0],
[0.0, 0.0625, 0.1250, 0.0625, 0.0],
[0.0, 0.1250, 0.2500, 0.1250, 0.0],
[0.0, 0.0625, 0.1250, 0.0625, 0.0],
[0.0, 0.0000, 0.0000, 0.0000, 0.0]])
self.assertArrayAllClose(c1to2to1.data, d_expect, atol=0.00002)
# check area sums again
self.assertArrayAllClose(c1to2to1_areasum, c1_areasum)
def test_fail_different_cs(self):
# Test error when either src or dst coords have different
# coord_systems.
shape1 = (5, 5)
xlims1, ylims1 = ((-2, 2), (-2, 2))
shape2 = (4, 4)
xlims2, ylims2 = ((-1.5, 1.5), (-1.5, 1.5))
# Check basic regrid between these is ok.
c1 = _make_test_cube(shape1, xlims1, ylims1,
pole_latlon=(45.0, 35.0))
c2 = _make_test_cube(shape2, xlims2, ylims2)
regrid_conservative_via_esmpy(c1, c2)
# Replace the coord_system one of the source coords + check this fails.
c1.coord('grid_longitude').coord_system = \
c2.coord('longitude').coord_system
with self.assertRaises(ValueError):
regrid_conservative_via_esmpy(c1, c2)
# Repeat with target coordinate fiddled.
c1 = _make_test_cube(shape1, xlims1, ylims1,
pole_latlon=(45.0, 35.0))
c2 = _make_test_cube(shape2, xlims2, ylims2)
c2.coord('latitude').coord_system = \
c1.coord('grid_latitude').coord_system
with self.assertRaises(ValueError):
regrid_conservative_via_esmpy(c1, c2)
def test_fail_different_cs(self):
# Test error when either src or dst coords have different
# coord_systems.
shape1 = (5, 5)
xlims1, ylims1 = ((-2, 2), (-2, 2))
shape2 = (4, 4)
xlims2, ylims2 = ((-1.5, 1.5), (-1.5, 1.5))
# Check basic regrid between these is ok.
c1 = _make_test_cube(shape1, xlims1, ylims1, pole_latlon=(45.0, 35.0))
c2 = _make_test_cube(shape2, xlims2, ylims2)
regrid_conservative_via_esmpy(c1, c2)
# Replace the coord_system one of the source coords + check this fails.
c1.coord('grid_longitude').coord_system = \
c2.coord('longitude').coord_system
with self.assertRaises(ValueError):
regrid_conservative_via_esmpy(c1, c2)
# Repeat with target coordinate fiddled.
c1 = _make_test_cube(shape1, xlims1, ylims1, pole_latlon=(45.0, 35.0))
c2 = _make_test_cube(shape2, xlims2, ylims2)
c2.coord('latitude').coord_system = \
c1.coord('grid_latitude').coord_system
with self.assertRaises(ValueError):
regrid_conservative_via_esmpy(c1, c2)
def setUp(self):
# Compute basic test data cubes.
shape1 = (5, 5)
xlims1, ylims1 = ((-2, 2), (-2, 2))
c1 = _make_test_cube(shape1, xlims1, ylims1)
c1.data[:] = 0.0
c1.data[2, 2] = 1.0
shape2 = (4, 4)
xlims2, ylims2 = ((-1.5, 1.5), (-1.5, 1.5))
c2 = _make_test_cube(shape2, xlims2, ylims2)
c2.data[:] = 0.0
# Save timesaving pre-computed bits
self.stock_c1_c2 = (c1, c2)
self.stock_regrid_c1toc2 = regrid_conservative_via_esmpy(c1, c2)
self.stock_c1_areasum = _cube_area_sum(c1)
def setUp(self):
# Compute basic test data cubes.
shape1 = (5, 5)
xlims1, ylims1 = ((-2, 2), (-2, 2))
c1 = _make_test_cube(shape1, xlims1, ylims1)
c1.data[:] = 0.0
c1.data[2, 2] = 1.0
shape2 = (4, 4)
xlims2, ylims2 = ((-1.5, 1.5), (-1.5, 1.5))
c2 = _make_test_cube(shape2, xlims2, ylims2)
c2.data[:] = 0.0
# Save timesaving pre-computed bits
self.stock_c1_c2 = (c1, c2)
self.stock_regrid_c1toc2 = regrid_conservative_via_esmpy(c1, c2)
self.stock_c1_areasum = _cube_area_sum(c1)
def test_rotated(self):
"""
Test area-weighted regrid on more complex area.
Use two mutually rotated grids, of similar area + same dims.
Only a small central region in each is non-zero, which maps entirely
inside the other region.
So the area-sum totals should match exactly.
"""
# create source test cube on rotated form
pole_lat = 53.4
pole_lon = -173.2
deg_swing = 35.3
pole_lon += deg_swing
c1_nx = 9 + 6
c1_ny = 7 + 6
c1_xlims = -60.0, 60.0
c1_ylims = -45.0, 20.0
c1_xlims = [x - deg_swing for x in c1_xlims]
c1 = _make_test_cube((c1_nx, c1_ny), c1_xlims, c1_ylims,
pole_latlon=(pole_lat, pole_lon))
c1.data[3:-3, 3:-3] = np.array([
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 199, 199, 199, 199, 100, 100, 100],
[100, 100, 100, 100, 199, 199, 100, 100, 100],
[100, 100, 100, 100, 199, 199, 199, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100]],
dtype=np.float)
c1_areasum = _cube_area_sum(c1)
# construct target cube to receive
nx2 = 9 + 6
ny2 = 7 + 6
c2_xlims = -100.0, 120.0
c2_ylims = -20.0, 50.0
c2 = _make_test_cube((nx2, ny2), c2_xlims, c2_ylims)
c2.data = np.ma.array(c2.data, mask=True)
# perform regrid
c1to2 = regrid_conservative_via_esmpy(c1, c2)
# check we have zeros (or nearly) all around the edge..
c1toc2_zeros = np.ma.array(c1to2.data)
c1toc2_zeros[c1toc2_zeros.mask] = 0.0
c1toc2_zeros = np.abs(c1toc2_zeros.mask) < 1.0e-6
self.assertArrayEqual(c1toc2_zeros[0, :], True)
self.assertArrayEqual(c1toc2_zeros[-1, :], True)
self.assertArrayEqual(c1toc2_zeros[:, 0], True)
self.assertArrayEqual(c1toc2_zeros[:, -1], True)
# check the area-sum operation
c1to2_areasum = _cube_area_sum(c1to2)
self.assertArrayAllClose(c1to2_areasum, c1_areasum, rtol=0.004)
#
# Now repeat, transforming backwards ...
#
c1.data = np.ma.array(c1.data, mask=True)
c2.data[:] = 0.0
c2.data[5:-5, 5:-5] = np.array([
[199, 199, 199, 199, 100],
[100, 100, 199, 199, 100],
[100, 100, 199, 199, 199]],
dtype=np.float)
c2_areasum = _cube_area_sum(c2)
c2toc1 = regrid_conservative_via_esmpy(c2, c1)
# check we have zeros (or nearly) all around the edge..
c2toc1_zeros = np.ma.array(c2toc1.data)
c2toc1_zeros[c2toc1_zeros.mask] = 0.0
c2toc1_zeros = np.abs(c2toc1_zeros.mask) < 1.0e-6
self.assertArrayEqual(c2toc1_zeros[0, :], True)
self.assertArrayEqual(c2toc1_zeros[-1, :], True)
self.assertArrayEqual(c2toc1_zeros[:, 0], True)
self.assertArrayEqual(c2toc1_zeros[:, -1], True)
# check the area-sum operation
c2toc1_areasum = _cube_area_sum(c2toc1)
self.assertArrayAllClose(c2toc1_areasum, c2_areasum, rtol=0.004)
def test_same_grid(self):
# Test regridding onto the identical grid.
# Use regrid with self as target.
c1, _ = self.stock_c1_c2
testcube = regrid_conservative_via_esmpy(c1, c1)
self.assertEqual(testcube, c1)
def test_single_cells(self):
# Test handling of single-cell grids.
# Fetch 'standard' testcube data
c1, c2 = self.stock_c1_c2
c1_areasum = self.stock_c1_areasum
#
# At present NxN -> 1x1 "in-place" doesn't seem to work properly
# - result cell has missing-data ?
#
# Condense entire region into a single cell in the c1 grid
xlims1 = _minmax(c1.coord(axis='x').bounds)
ylims1 = _minmax(c1.coord(axis='y').bounds)
x_c1x1 = iris.coords.DimCoord(xlims1[0],
bounds=xlims1,
standard_name='longitude',
units='degrees',
coord_system=_PLAIN_GEODETIC_CS)
y_c1x1 = iris.coords.DimCoord(ylims1[0],
bounds=ylims1,
standard_name='latitude',
units='degrees',
coord_system=_PLAIN_GEODETIC_CS)
c1x1_gridcube = iris.cube.Cube([[0.0]])
c1x1_gridcube.add_dim_coord(y_c1x1, 0)
c1x1_gridcube.add_dim_coord(x_c1x1, 1)
c1x1 = regrid_conservative_via_esmpy(c1, c1x1_gridcube)
c1x1_areasum = _cube_area_sum(c1x1)
# Check the total area sum is still the same
condense_to_1x1_supported = False
# NOTE: currently disabled (ESMF gets this wrong)
# NOTE ALSO: call hits numpy 1.7 bug in testing.assert_array_compare.
if condense_to_1x1_supported:
self.assertArrayAllClose(c1x1_areasum, c1_areasum)
# Condense entire region onto a single cell covering the area of 'c2'
xlims2 = _minmax(c2.coord(axis='x').bounds)
ylims2 = _minmax(c2.coord(axis='y').bounds)
x_c2x1 = iris.coords.DimCoord(xlims2[0],
bounds=xlims2,
standard_name='longitude',
units=iris.unit.Unit('degrees'),
coord_system=_PLAIN_GEODETIC_CS)
y_c2x1 = iris.coords.DimCoord(ylims2[0],
bounds=ylims2,
standard_name='latitude',
units=iris.unit.Unit('degrees'),
coord_system=_PLAIN_GEODETIC_CS)
c2x1_gridcube = iris.cube.Cube([[0.0]])
c2x1_gridcube.add_dim_coord(y_c2x1, 0)
c2x1_gridcube.add_dim_coord(x_c2x1, 1)
c1_to_c2x1 = regrid_conservative_via_esmpy(c1, c2x1_gridcube)
# Check the total area sum is still the same
c1_to_c2x1_areasum = _cube_area_sum(c1_to_c2x1)
self.assertArrayAllClose(c1_to_c2x1_areasum, c1_areasum, 0.0004)
# 1x1 -> NxN : regrid single cell to NxN grid
# construct a single-cell approximation to 'c1' with the same area sum.
# NOTE: can't use _make_cube (see docstring)
c1x1 = c1.copy()[0:1, 0:1]
xlims1 = _minmax(c1.coord(axis='x').bounds)
ylims1 = _minmax(c1.coord(axis='y').bounds)
c1x1.coord(axis='x').bounds = xlims1
c1x1.coord(axis='y').bounds = ylims1
# Assign data mean as single cell value : Maybe not exact, but "close"
c1x1.data[0, 0] = np.mean(c1.data)
# Regrid this back onto the original NxN grid
c1x1_to_c1 = regrid_conservative_via_esmpy(c1x1, c1)
c1x1_to_c1_areasum = _cube_area_sum(c1x1_to_c1)
# Check that area sum is ~unchanged, as expected
self.assertArrayAllClose(c1x1_to_c1_areasum, c1_areasum, 0.0004)
# Check 1x1 -> 1x1
# NOTE: can *only* get any result with a fully overlapping cell, so
# just regrid onto self
c1x1toself = regrid_conservative_via_esmpy(c1x1, c1x1)
c1x1toself_areasum = _cube_area_sum(c1x1toself)
self.assertArrayAllClose(c1x1toself_areasum, c1_areasum, 0.0004)
def test_single_cells(self):
# Test handling of single-cell grids.
# Fetch 'standard' testcube data
c1, c2 = self.stock_c1_c2
c1_areasum = self.stock_c1_areasum
#
# At present NxN -> 1x1 "in-place" doesn't seem to work properly
# - result cell has missing-data ?
#
# Condense entire region into a single cell in the c1 grid
xlims1 = _minmax(c1.coord(axis='x').bounds)
ylims1 = _minmax(c1.coord(axis='y').bounds)
x_c1x1 = iris.coords.DimCoord(xlims1[0], bounds=xlims1,
standard_name='longitude',
units='degrees',
coord_system=_PLAIN_GEODETIC_CS)
y_c1x1 = iris.coords.DimCoord(ylims1[0], bounds=ylims1,
standard_name='latitude',
units='degrees',
coord_system=_PLAIN_GEODETIC_CS)
c1x1_gridcube = iris.cube.Cube([[0.0]])
c1x1_gridcube.add_dim_coord(y_c1x1, 0)
c1x1_gridcube.add_dim_coord(x_c1x1, 1)
c1x1 = regrid_conservative_via_esmpy(c1, c1x1_gridcube)
c1x1_areasum = _cube_area_sum(c1x1)
# Check the total area sum is still the same
# NOTE: at present, this causes an error inside ESMF ...
context = self.assertRaises(AssertionError)
condense_to_1x1_supported = False
if condense_to_1x1_supported:
context = _donothing_context_manager()
with context:
self.assertArrayAllClose(c1x1_areasum, c1_areasum)
# Condense entire region onto a single cell covering the area of 'c2'
xlims2 = _minmax(c2.coord(axis='x').bounds)
ylims2 = _minmax(c2.coord(axis='y').bounds)
x_c2x1 = iris.coords.DimCoord(xlims2[0], bounds=xlims2,
standard_name='longitude',
units=iris.unit.Unit('degrees'),
coord_system=_PLAIN_GEODETIC_CS)
y_c2x1 = iris.coords.DimCoord(ylims2[0], bounds=ylims2,
standard_name='latitude',
units=iris.unit.Unit('degrees'),
coord_system=_PLAIN_GEODETIC_CS)
c2x1_gridcube = iris.cube.Cube([[0.0]])
c2x1_gridcube.add_dim_coord(y_c2x1, 0)
c2x1_gridcube.add_dim_coord(x_c2x1, 1)
c1_to_c2x1 = regrid_conservative_via_esmpy(c1, c2x1_gridcube)
# Check the total area sum is still the same
c1_to_c2x1_areasum = _cube_area_sum(c1_to_c2x1)
self.assertArrayAllClose(c1_to_c2x1_areasum, c1_areasum, 0.0004)
# 1x1 -> NxN : regrid single cell to NxN grid
# construct a single-cell approximation to 'c1' with the same area sum.
# NOTE: can't use _make_cube (see docstring)
c1x1 = c1.copy()[0:1, 0:1]
xlims1 = _minmax(c1.coord(axis='x').bounds)
ylims1 = _minmax(c1.coord(axis='y').bounds)
c1x1.coord(axis='x').bounds = xlims1
c1x1.coord(axis='y').bounds = ylims1
# Assign data mean as single cell value : Maybe not exact, but "close"
c1x1.data[0, 0] = np.mean(c1.data)
# Regrid this back onto the original NxN grid
c1x1_to_c1 = regrid_conservative_via_esmpy(c1x1, c1)
c1x1_to_c1_areasum = _cube_area_sum(c1x1_to_c1)
# Check that area sum is ~unchanged, as expected
self.assertArrayAllClose(c1x1_to_c1_areasum, c1_areasum, 0.0004)
# Check 1x1 -> 1x1
# NOTE: can *only* get any result with a fully overlapping cell, so
# just regrid onto self
c1x1toself = regrid_conservative_via_esmpy(c1x1, c1x1)
c1x1toself_areasum = _cube_area_sum(c1x1toself)
self.assertArrayAllClose(c1x1toself_areasum, c1_areasum, 0.0004)
def test_same_grid(self):
# Test regridding onto the identical grid.
# Use regrid with self as target.
c1, _ = self.stock_c1_c2
testcube = regrid_conservative_via_esmpy(c1, c1)
self.assertEqual(testcube, c1)
import iris
from iris.experimental.regrid_conservative import regrid_conservative_via_esmpy
import matplotlib.pyplot as plt
import numpy as np
if __name__ == "__main__":
source = iris.load_cube("pp_unrot.pp")
target = iris.load_cube("pp_rotgrid.pp")
regridded = regrid_conservative_via_esmpy(source, target)
regridded_crs = regridded.coord(axis='x').coord_system.as_cartopy_crs()
source_crs = source.coord(axis='x').coord_system.as_cartopy_crs()
source_proj = source.coord(axis='x').coord_system.as_cartopy_projection()
# Which data got masked?
masked = []
for ndi in np.ndindex(*regridded.data.shape):
if regridded.data.mask[ndi]:
masked.append(ndi)
print "regridded.shape", regridded.shape
print "masked items at", masked
# Plot the source
ax = plt.axes(projection=source_proj)
xx, yy = np.meshgrid(
source.coord(axis='x').points,
source.coord(axis='y').points)
plt.scatter(xx.flat, yy.flat, c='g')
# Plot the masked point in the source projection
import iris
from iris.experimental.regrid_conservative import regrid_conservative_via_esmpy
import matplotlib.pyplot as plt
import numpy as np
if __name__ == "__main__":
source = iris.load_cube("pp_unrot.pp")
target = iris.load_cube("pp_rotgrid.pp")
regridded = regrid_conservative_via_esmpy(source, target)
regridded_crs = regridded.coord(axis='x').coord_system.as_cartopy_crs()
source_crs = source.coord(axis='x').coord_system.as_cartopy_crs()
source_proj = source.coord(axis='x').coord_system.as_cartopy_projection()
# Which data got masked?
masked = []
for ndi in np.ndindex(*regridded.data.shape):
if regridded.data.mask[ndi]:
masked.append(ndi)
print "regridded.shape", regridded.shape
print "masked items at", masked
# Plot the source
ax = plt.axes(projection=source_proj)
xx, yy = np.meshgrid(source.coord(axis='x').points, source.coord(axis='y').points)
plt.scatter(xx.flat, yy.flat, c='g')
# Plot the masked point in the source projection
for ndi in masked:
def test_rotated(self):
"""
Test area-weighted regrid on more complex area.
Use two mutually rotated grids, of similar area + same dims.
Only a small central region in each is non-zero, which maps entirely
inside the other region.
So the area-sum totals should match exactly.
"""
# create source test cube on rotated form
pole_lat = 53.4
pole_lon = -173.2
deg_swing = 35.3
pole_lon += deg_swing
c1_nx = 9 + 6
c1_ny = 7 + 6
c1_xlims = -60.0, 60.0
c1_ylims = -45.0, 20.0
c1_xlims = [x - deg_swing for x in c1_xlims]
c1 = _make_test_cube((c1_nx, c1_ny),
c1_xlims,
c1_ylims,
pole_latlon=(pole_lat, pole_lon))
c1.data[3:-3, 3:-3] = np.array(
[[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 199, 199, 199, 199, 100, 100, 100],
[100, 100, 100, 100, 199, 199, 100, 100, 100],
[100, 100, 100, 100, 199, 199, 199, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100]],
dtype=np.float)
c1_areasum = _cube_area_sum(c1)
# construct target cube to receive
nx2 = 9 + 6
ny2 = 7 + 6
c2_xlims = -100.0, 120.0
c2_ylims = -20.0, 50.0
c2 = _make_test_cube((nx2, ny2), c2_xlims, c2_ylims)
c2.data = np.ma.array(c2.data, mask=True)
# perform regrid
c1to2 = regrid_conservative_via_esmpy(c1, c2)
# check we have zeros (or nearly) all around the edge..
c1toc2_zeros = np.ma.array(c1to2.data)
c1toc2_zeros[c1toc2_zeros.mask] = 0.0
c1toc2_zeros = np.abs(c1toc2_zeros.mask) < 1.0e-6
self.assertArrayEqual(c1toc2_zeros[0, :], True)
self.assertArrayEqual(c1toc2_zeros[-1, :], True)
self.assertArrayEqual(c1toc2_zeros[:, 0], True)
self.assertArrayEqual(c1toc2_zeros[:, -1], True)
# check the area-sum operation
c1to2_areasum = _cube_area_sum(c1to2)
self.assertArrayAllClose(c1to2_areasum, c1_areasum, rtol=0.004)
#
# Now repeat, transforming backwards ...
#
c1.data = np.ma.array(c1.data, mask=True)
c2.data[:] = 0.0
c2.data[5:-5, 5:-5] = np.array(
[[199, 199, 199, 199, 100], [100, 100, 199, 199, 100],
[100, 100, 199, 199, 199]],
dtype=np.float)
c2_areasum = _cube_area_sum(c2)
c2toc1 = regrid_conservative_via_esmpy(c2, c1)
# check we have zeros (or nearly) all around the edge..
c2toc1_zeros = np.ma.array(c2toc1.data)
c2toc1_zeros[c2toc1_zeros.mask] = 0.0
c2toc1_zeros = np.abs(c2toc1_zeros.mask) < 1.0e-6
self.assertArrayEqual(c2toc1_zeros[0, :], True)
self.assertArrayEqual(c2toc1_zeros[-1, :], True)
self.assertArrayEqual(c2toc1_zeros[:, 0], True)
self.assertArrayEqual(c2toc1_zeros[:, -1], True)
# check the area-sum operation
c2toc1_areasum = _cube_area_sum(c2toc1)
self.assertArrayAllClose(c2toc1_areasum, c2_areasum, rtol=0.004)
def test_missing_data_rotated(self):
"""
Check missing-data handling between different coordinate systems.
Regrid between mutually rotated lat/lon systems, and check results for
missing data due to grid edge overlap, and source-data masking.
"""
for do_add_missing in (False, True):
# create source test cube on rotated form
pole_lat = 53.4
pole_lon = -173.2
deg_swing = 35.3
pole_lon += deg_swing
c1_nx = 9 + 6
c1_ny = 7 + 6
c1_xlims = -60.0, 60.0
c1_ylims = -45.0, 20.0
c1_xlims = [x - deg_swing for x in c1_xlims]
c1 = _make_test_cube((c1_nx, c1_ny),
c1_xlims,
c1_ylims,
pole_latlon=(pole_lat, pole_lon))
c1.data = np.ma.array(c1.data, mask=False)
c1.data[3:-3, 3:-3] = np.ma.array(
[[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 199, 199, 199, 199, 100, 100, 100],
[100, 100, 100, 100, 199, 199, 100, 100, 100],
[100, 100, 100, 100, 199, 199, 199, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100]],
dtype=np.float)
if do_add_missing:
c1.data = np.ma.array(c1.data)
c1.data[7, 7] = np.ma.masked
c1.data[3:5, 10:12] = np.ma.masked
# construct target cube to receive
nx2 = 9 + 6
ny2 = 7 + 6
c2_xlims = -80.0, 80.0
c2_ylims = -20.0, 50.0
c2 = _make_test_cube((nx2, ny2), c2_xlims, c2_ylims)
c2.data = np.ma.array(c2.data, mask=True)
# perform regrid + snapshot test results
c1toc2 = regrid_conservative_via_esmpy(c1, c2)
# check masking of result is as expected
# (generated by inspecting plot of how src+dst grids overlap)
expected_mask_valuemap = np.array(
# KEY: 0=masked, 7=present, 5=masked with masked datapoints
[[0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 5, 5, 7, 0, 0],
[0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5, 7, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 5, 5, 7, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 5, 5, 7, 7, 7, 7, 0, 0],
[0, 0, 0, 0, 7, 7, 7, 5, 5, 7, 7, 7, 7, 0, 0],
[0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0],
[0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0],
[0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0]])
if do_add_missing:
expected_mask = expected_mask_valuemap < 7
else:
expected_mask = expected_mask_valuemap == 0
actual_mask = c1toc2.data.mask
self.assertArrayEqual(actual_mask, expected_mask)
if not do_add_missing:
# check preservation of area-sums
# NOTE: does *not* work with missing data, even theoretically,
# as the 'missing areas' are not the same.
c1_areasum = _cube_area_sum(c1)
c1to2_areasum = _cube_area_sum(c1toc2)
self.assertArrayAllClose(c1_areasum, c1to2_areasum, rtol=0.003)
def test_missing_data_rotated(self):
"""
Check missing-data handling between different coordinate systems.
Regrid between mutually rotated lat/lon systems, and check results for
missing data due to grid edge overlap, and source-data masking.
"""
for do_add_missing in (False, True):
# create source test cube on rotated form
pole_lat = 53.4
pole_lon = -173.2
deg_swing = 35.3
pole_lon += deg_swing
c1_nx = 9 + 6
c1_ny = 7 + 6
c1_xlims = -60.0, 60.0
c1_ylims = -45.0, 20.0
c1_xlims = [x - deg_swing for x in c1_xlims]
c1 = _make_test_cube((c1_nx, c1_ny), c1_xlims, c1_ylims,
pole_latlon=(pole_lat, pole_lon))
c1.data = np.ma.array(c1.data, mask=False)
c1.data[3:-3, 3:-3] = np.ma.array([
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 199, 199, 199, 199, 100, 100, 100],
[100, 100, 100, 100, 199, 199, 100, 100, 100],
[100, 100, 100, 100, 199, 199, 199, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100, 100, 100]],
dtype=np.float)
if do_add_missing:
c1.data = np.ma.array(c1.data)
c1.data[7, 7] = np.ma.masked
c1.data[3:5, 10:12] = np.ma.masked
# construct target cube to receive
nx2 = 9 + 6
ny2 = 7 + 6
c2_xlims = -80.0, 80.0
c2_ylims = -20.0, 50.0
c2 = _make_test_cube((nx2, ny2), c2_xlims, c2_ylims)
c2.data = np.ma.array(c2.data, mask=True)
# perform regrid + snapshot test results
c1toc2 = regrid_conservative_via_esmpy(c1, c2)
# check masking of result is as expected
# (generated by inspecting plot of how src+dst grids overlap)
expected_mask_valuemap = np.array(
# KEY: 0=masked, 7=present, 5=masked with masked datapoints
[[0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 5, 5, 7, 0, 0],
[0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5, 7, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 5, 5, 7, 0, 0],
[0, 0, 0, 7, 7, 7, 7, 5, 5, 7, 7, 7, 7, 0, 0],
[0, 0, 0, 0, 7, 7, 7, 5, 5, 7, 7, 7, 7, 0, 0],
[0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0],
[0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0],
[0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0]])
if do_add_missing:
expected_mask = expected_mask_valuemap < 7
else:
expected_mask = expected_mask_valuemap == 0
actual_mask = c1toc2.data.mask
self.assertArrayEqual(actual_mask, expected_mask)
if not do_add_missing:
# check preservation of area-sums
# NOTE: does *not* work with missing data, even theoretically,
# as the 'missing areas' are not the same.
c1_areasum = _cube_area_sum(c1)
c1to2_areasum = _cube_area_sum(c1toc2)
self.assertArrayAllClose(c1_areasum, c1to2_areasum, rtol=0.003)
