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PI-2472: Tweak area weighting regrid: reorder variables #3571

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Dec 2, 2019
Merged

PI-2472: Tweak area weighting regrid: reorder variables #3571

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42 changes: 21 additions & 21 deletions lib/iris/experimental/regrid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -473,6 +473,24 @@ def _regrid_area_weighted_array(
grid.

"""
# Determine which grid bounds are within src extent.
y_within_bounds = _within_bounds(
src_y_bounds, grid_y_bounds, grid_y_decreasing
)
x_within_bounds = _within_bounds(
src_x_bounds, grid_x_bounds, grid_x_decreasing
)

# Cache which src_bounds are within grid bounds
cached_x_bounds = []
cached_x_indices = []
for (x_0, x_1) in grid_x_bounds:
if grid_x_decreasing:
x_0, x_1 = x_1, x_0
x_bounds, x_indices = _cropped_bounds(src_x_bounds, x_0, x_1)
cached_x_bounds.append(x_bounds)
cached_x_indices.append(x_indices)

# Create empty data array to match the new grid.
# Note that dtype is not preserved and that the array is
# masked to allow for regions that do not overlap.
Expand All @@ -497,24 +515,6 @@ def _regrid_area_weighted_array(
# Assign to mask to explode it, allowing indexed assignment.
new_data.mask = False

# Determine which grid bounds are within src extent.
y_within_bounds = _within_bounds(
src_y_bounds, grid_y_bounds, grid_y_decreasing
)
x_within_bounds = _within_bounds(
src_x_bounds, grid_x_bounds, grid_x_decreasing
)

# Cache which src_bounds are within grid bounds
cached_x_bounds = []
cached_x_indices = []
for (x_0, x_1) in grid_x_bounds:
if grid_x_decreasing:
x_0, x_1 = x_1, x_0
x_bounds, x_indices = _cropped_bounds(src_x_bounds, x_0, x_1)
cached_x_bounds.append(x_bounds)
cached_x_indices.append(x_indices)

# Axes of data over which the weighted mean is calculated.
axes = []
if y_dim is not None:
Expand Down Expand Up @@ -565,15 +565,15 @@ def _regrid_area_weighted_array(
raise RuntimeError(
"Cannot handle split bounds " "in both x and y."
)
# Calculate weights based on areas of cropped bounds.
weights = area_func(y_bounds, x_bounds)

if x_dim is not None:
indices[x_dim] = x_indices
if y_dim is not None:
indices[y_dim] = y_indices
data = src_data[tuple(indices)]

# Calculate weights based on areas of cropped bounds.
weights = area_func(y_bounds, x_bounds)

# Transpose weights to match dim ordering in data.
weights_shape_y = weights.shape[0]
weights_shape_x = weights.shape[1]
Expand Down