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Fix dask aggregate boundary contamination and clean up cumulative bookkeeping #1477

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brendancol merged 1 commit into from
May 4, 2026
+79 −25
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Fix dask aggregate boundary contamination and clean up cumulative bookkeeping #1477

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50 changes: 25 additions & 25 deletions xrspatial/resample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -621,37 +621,33 @@ def _run_dask_numpy(data, scale_y, scale_x, method):
dtype=np.float32, meta=meta)

import math
min_size = max(_min_chunksize_for_scale(scale_y),
_min_chunksize_for_scale(scale_x))
data = _ensure_min_chunksize(data, min_size)

# Aggregate windows can cross chunk boundaries; size chunks to satisfy
# both the scale-driven minimum and the depth-driven minimum in one pass,
# then build the cumulative arrays once.
global_in_h = int(sum(data.chunks[0]))
global_in_w = int(sum(data.chunks[1]))
global_out_h, global_out_w = _output_shape(
global_in_h, global_in_w, scale_y, scale_x)
depth_y = math.ceil(global_in_h / global_out_h)
depth_x = math.ceil(global_in_w / global_out_w)
min_size = max(_min_chunksize_for_scale(scale_y),
_min_chunksize_for_scale(scale_x),
2 * depth_y + 1, 2 * depth_x + 1)
data = _ensure_min_chunksize(data, min_size)

out_y = _output_chunks(data.chunks[0], scale_y)
out_x = _output_chunks(data.chunks[1], scale_x)
cum_in_y = np.cumsum([0] + list(data.chunks[0]))
cum_in_x = np.cumsum([0] + list(data.chunks[1]))
cum_out_y = np.cumsum([0] + list(out_y))
cum_out_x = np.cumsum([0] + list(out_x))

# Aggregate windows can cross chunk boundaries; add overlap.
depth_y = math.ceil(global_in_h / global_out_h)
depth_x = math.ceil(global_in_w / global_out_w)
data = _ensure_min_chunksize(data, max(2 * depth_y + 1, 2 * depth_x + 1))
# Recompute in case rechunk changed layout
if data.chunks[0] != tuple(cum_in_y[1:] - cum_in_y[:-1]):
cum_in_y = np.cumsum([0] + list(data.chunks[0]))
cum_in_x = np.cumsum([0] + list(data.chunks[1]))
out_y = _output_chunks(data.chunks[0], scale_y)
out_x = _output_chunks(data.chunks[1], scale_x)
cum_out_y = np.cumsum([0] + list(out_y))
cum_out_x = np.cumsum([0] + list(out_x))

# boundary=np.nan keeps overlap padding from contaminating the aggregate
# at the global edges. The kernels skip NaN inputs and return NaN for
# empty windows, so the padded region is ignored naturally.
from dask.array.overlap import overlap as _add_overlap
src = _add_overlap(data, depth={0: depth_y, 1: depth_x},
boundary='nearest')
boundary=np.nan)

fn = partial(_agg_block_np, method=method,
global_in_h=global_in_h, global_in_w=global_in_w,
Expand Down Expand Up @@ -704,18 +700,19 @@ def _run_dask_cupy(data, scale_y, scale_x, method):
dtype=cupy.float32, meta=meta)

import math
min_size = max(_min_chunksize_for_scale(scale_y),
_min_chunksize_for_scale(scale_x))
data = _ensure_min_chunksize(data, min_size)

# Aggregate windows can cross chunk boundaries; size chunks to satisfy
# both the scale-driven minimum and the depth-driven minimum in one pass,
# then build the cumulative arrays once.
global_in_h = int(sum(data.chunks[0]))
global_in_w = int(sum(data.chunks[1]))
global_out_h, global_out_w = _output_shape(
global_in_h, global_in_w, scale_y, scale_x)

depth_y = math.ceil(global_in_h / global_out_h)
depth_x = math.ceil(global_in_w / global_out_w)
data = _ensure_min_chunksize(data, max(2 * depth_y + 1, 2 * depth_x + 1))
min_size = max(_min_chunksize_for_scale(scale_y),
_min_chunksize_for_scale(scale_x),
2 * depth_y + 1, 2 * depth_x + 1)
data = _ensure_min_chunksize(data, min_size)

out_y = _output_chunks(data.chunks[0], scale_y)
out_x = _output_chunks(data.chunks[1], scale_x)
Expand All @@ -724,9 +721,12 @@ def _run_dask_cupy(data, scale_y, scale_x, method):
cum_out_y = np.cumsum([0] + list(out_y))
cum_out_x = np.cumsum([0] + list(out_x))

# boundary=np.nan keeps overlap padding from contaminating the aggregate
# at the global edges. The kernels skip NaN inputs and return NaN for
# empty windows, so the padded region is ignored naturally.
from dask.array.overlap import overlap as _add_overlap
src = _add_overlap(data, depth={0: depth_y, 1: depth_x},
boundary='nearest')
boundary=cupy.nan)

fn = partial(_agg_block_cupy, method=method,
global_in_h=global_in_h, global_in_w=global_in_w,
Expand Down
54 changes: 54 additions & 0 deletions xrspatial/tests/test_resample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -345,6 +345,60 @@ def test_aggregate_parity(self, numpy_and_dask_rasters, method):
atol=1e-5, equal_nan=True)


# ---------------------------------------------------------------------------
# Aggregate dask boundary contamination (issue #1469)
# ---------------------------------------------------------------------------

@dask_array_available
class TestAggregateDaskBoundary:
"""Aggregate dask path must not let overlap-padding bias the result."""

@pytest.mark.parametrize('method', ['min', 'max', 'median'])
def test_chunk_spanning_window_bit_identical(self, method):
"""Aggregate windows that cross chunk boundaries must equal eager
numpy bit-exactly (same kernel, no padding contribution)."""
rng = np.random.RandomState(20240504)
data = rng.rand(24, 24).astype(np.float32)
np_agg = create_test_raster(data, backend='numpy',
attrs={'res': (1.0, 1.0)})
# Use a chunk size that does NOT divide the input cleanly so that
# output windows span chunk boundaries.
dk_agg = create_test_raster(data, backend='dask+numpy',
attrs={'res': (1.0, 1.0)},
chunks=(7, 7))
np_out = resample(np_agg, scale_factor=0.5, method=method)
dk_out = resample(dk_agg, scale_factor=0.5, method=method)
# min/max/median over the same set of source cells should be
# bit-identical -- no atol slack.
np.testing.assert_array_equal(dk_out.values, np_out.values)

@pytest.mark.parametrize('method', ['min', 'max', 'median'])
def test_global_edge_extremes_match_eager(self, method):
"""Extreme values on the global array edges must not contaminate
adjacent output windows. The aggregate kernel's indexing keeps the
global-edge pad out of any window, and boundary=np.nan reinforces
that contract: pad cells are NaN and would be skipped even if read.
Result must match the eager (non-overlapped) numpy path exactly."""
# Inner field is in a low range; the rightmost column is an
# extreme value that, if duplicated by 'nearest' padding, would
# show up in adjacent output windows that should not see it.
rng = np.random.RandomState(1469)
data = rng.uniform(0.0, 0.1, size=(20, 20)).astype(np.float32)
data[:, -1] = 999.0 # extreme right column
data[:, 0] = -999.0 # extreme left column
data[-1, :] = 999.0 # extreme bottom row
data[0, :] = -999.0 # extreme top row

np_agg = create_test_raster(data, backend='numpy',
attrs={'res': (1.0, 1.0)})
dk_agg = create_test_raster(data, backend='dask+numpy',
attrs={'res': (1.0, 1.0)},
chunks=(8, 8))
np_out = resample(np_agg, scale_factor=0.5, method=method)
dk_out = resample(dk_agg, scale_factor=0.5, method=method)
np.testing.assert_array_equal(dk_out.values, np_out.values)


# ---------------------------------------------------------------------------
# CuPy parity
# ---------------------------------------------------------------------------
Expand Down
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