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PI-2472: _regrid_area_weighted_array: Axes creation before loop #3519

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Nov 15, 2019
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PI-2472: _regrid_area_weighted_array: Axes creation before loop #3519

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13 changes: 8 additions & 5 deletions lib/iris/experimental/regrid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -493,6 +493,14 @@ def _regrid_area_weighted_array(src_data, x_dim, y_dim,
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:
axes.append(y_dim)
if x_dim is not None:
axes.append(x_dim)
axis = tuple(axes)

# Simple for loop approach.
for j, (y_0, y_1) in enumerate(grid_y_bounds):
# Reverse lower and upper if dest grid is decreasing.
Expand Down Expand Up @@ -581,19 +589,14 @@ def _regrid_area_weighted_array(src_data, x_dim, y_dim,
# Broadcast the weights array to allow numpy's ma.average
# to be called.
weights_padded_shape = [1] * data.ndim
axes = []
if y_dim is not None:
weights_padded_shape[y_dim] = weights_shape_y
axes.append(y_dim)
if x_dim is not None:
weights_padded_shape[x_dim] = weights_shape_x
axes.append(x_dim)
# Assign new shape to raise error on copy.
weights.shape = weights_padded_shape
# Broadcast weights to match shape of data.
_, weights = np.broadcast_arrays(data, weights)
# Axes of data over which the weighted mean is calculated.
axis = tuple(axes)

# Calculate weighted mean taking into account missing data.
new_data_pt = _weighted_mean_with_mdtol(
Expand Down