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Purge iris.experimental.regrid np<1.7 support #3539

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Merged
merged 1 commit into from
Nov 15, 2019
Merged

Purge iris.experimental.regrid np<1.7 support #3539

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79 changes: 16 additions & 63 deletions lib/iris/experimental/regrid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@

"""

from collections import namedtuple
import copy
import functools
import warnings
Expand All @@ -35,12 +34,6 @@
from iris.util import _meshgrid


_Version = namedtuple("Version", ("major", "minor", "micro"))
_NP_VERSION = _Version(
*(int(val) for val in np.version.version.split(".") if val.isdigit())
)


def _get_xy_coords(cube):
"""
Return the x and y coordinates from a cube.
Expand Down Expand Up @@ -582,62 +575,22 @@ def _regrid_area_weighted_array(
# Calculate weights based on areas of cropped bounds.
weights = area_func(y_bounds, x_bounds)

# Numpy 1.7 allows the axis keyword arg to be a tuple.
# If the version of NumPy is less than 1.7 manipulate the axes
# of the data so the x and y dimensions can be flattened.
if _NP_VERSION.minor < 7:
if y_dim is not None and x_dim is not None:
flattened_shape = list(data.shape)
if y_dim > x_dim:
data = np.rollaxis(data, y_dim, data.ndim)
data = np.rollaxis(data, x_dim, data.ndim)
del flattened_shape[y_dim]
del flattened_shape[x_dim]
else:
data = np.rollaxis(data, x_dim, data.ndim)
data = np.rollaxis(data, y_dim, data.ndim)
del flattened_shape[x_dim]
del flattened_shape[y_dim]
weights = weights.T
flattened_shape.append(-1)
data = data.reshape(*flattened_shape)
elif y_dim is not None:
flattened_shape = list(data.shape)
del flattened_shape[y_dim]
flattened_shape.append(-1)
data = data.swapaxes(y_dim, -1).reshape(
*flattened_shape
)
elif x_dim is not None:
flattened_shape = list(data.shape)
del flattened_shape[x_dim]
flattened_shape.append(-1)
data = data.swapaxes(x_dim, -1).reshape(
*flattened_shape
)
weights = weights.ravel()
axis = -1
else:
# Transpose weights to match dim ordering in data.
weights_shape_y = weights.shape[0]
weights_shape_x = weights.shape[1]
if (
x_dim is not None
and y_dim is not None
and x_dim < y_dim
):
weights = weights.T
# Broadcast the weights array to allow numpy's ma.average
# to be called.
weights_padded_shape = [1] * data.ndim
if y_dim is not None:
weights_padded_shape[y_dim] = weights_shape_y
if x_dim is not None:
weights_padded_shape[x_dim] = weights_shape_x
# 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)
# Transpose weights to match dim ordering in data.
weights_shape_y = weights.shape[0]
weights_shape_x = weights.shape[1]
if x_dim is not None and y_dim is not None and x_dim < y_dim:
weights = weights.T
# Broadcast the weights array to allow numpy's ma.average
# to be called.
weights_padded_shape = [1] * data.ndim
if y_dim is not None:
weights_padded_shape[y_dim] = weights_shape_y
if x_dim is not None:
weights_padded_shape[x_dim] = weights_shape_x
# 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)

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