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Remove deprecated function nearest_neighbour_indices #2800

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Remove deprecated function nearest_neighbour_indices #2800

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11 changes: 0 additions & 11 deletions lib/iris/analysis/_interpolate_backdoor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,17 +65,6 @@ def _warn_deprecated(msg=None):
iris_warn_deprecated(msg)


def nearest_neighbour_indices(cube, sample_points):
msg = (_INTERPOLATE_DEPRECATION_WARNING + '\n' +
'Please replace usage of '
'iris.analysis.interpolate.nearest_neighbour_indices() '
'with iris.coords.Coord.nearest_neighbour_index()).')
_warn_deprecated(msg)
return _interp.nearest_neighbour_indices(cube, sample_points)

nearest_neighbour_indices.__doc__ = _interp.nearest_neighbour_indices.__doc__


def extract_nearest_neighbour(cube, sample_points):
msg = (_INTERPOLATE_DEPRECATION_WARNING + '\n' +
'Please replace usage of '
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99 changes: 0 additions & 99 deletions lib/iris/analysis/_interpolate_private.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,105 +94,6 @@ def _cartesian_sample_points(sample_points, sample_point_coord_names):
return cartesian_points


def nearest_neighbour_indices(cube, sample_points):
"""
Returns the indices to select the data value(s) closest to the given coordinate point values.

The sample_points mapping does not have to include coordinate values corresponding to all data
dimensions. Any dimensions unspecified will default to a full slice.

For example:

>>> cube = iris.load_cube(iris.sample_data_path('ostia_monthly.nc'))
>>> iris.analysis.interpolate.nearest_neighbour_indices(cube, [('latitude', 0), ('longitude', 10)])
(slice(None, None, None), 9, 12)
>>> iris.analysis.interpolate.nearest_neighbour_indices(cube, [('latitude', 0)])
(slice(None, None, None), 9, slice(None, None, None))

Args:

* cube:
An :class:`iris.cube.Cube`.
* sample_points
A list of tuple pairs mapping coordinate instances or unique coordinate names in the cube to point values.

Returns:
The tuple of indices which will select the point in the cube closest to the supplied coordinate values.

.. note::

Nearest neighbour interpolation of multidimensional coordinates is not
yet supported.

.. deprecated:: 1.10

The module :mod:`iris.analysis.interpolate` is deprecated.
Please replace usage of
:func:`iris.analysis.interpolate.nearest_neighbour_indices`
with :meth:`iris.coords.Coord.nearest_neighbour_index`.

"""
if isinstance(sample_points, dict):
msg = ('Providing a dictionary to specify points is deprecated. '
'Please provide a list of (coordinate, values) pairs.')
warn_deprecated(msg)
sample_points = list(sample_points.items())

if sample_points:
try:
coord, values = sample_points[0]
except ValueError:
raise ValueError('Sample points must be a list of (coordinate, value) pairs. Got %r.' % sample_points)

points = []
for coord, values in sample_points:
if isinstance(coord, six.string_types):
coord = cube.coord(coord)
else:
coord = cube.coord(coord)
points.append((coord, values))
sample_points = points

# Build up a list of indices to span the cube.
indices = [slice(None, None)] * cube.ndim

# Build up a dictionary which maps the cube's data dimensions to a list (which will later
# be populated by coordinates in the sample points list)
dim_to_coord_map = {}
for i in range(cube.ndim):
dim_to_coord_map[i] = []

# Iterate over all of the specifications provided by sample_points
for coord, point in sample_points:
data_dim = cube.coord_dims(coord)

# If no data dimension then we don't need to make any modifications to indices.
if not data_dim:
continue
elif len(data_dim) > 1:
raise iris.exceptions.CoordinateMultiDimError("Nearest neighbour interpolation of multidimensional "
"coordinates is not supported.")
data_dim = data_dim[0]

dim_to_coord_map[data_dim].append(coord)

#calculate the nearest neighbour
min_index = coord.nearest_neighbour_index(point)

if getattr(coord, 'circular', False):
warnings.warn("Nearest neighbour on a circular coordinate may not be picking the nearest point.", DeprecationWarning)

# If the dimension has already been interpolated then assert that the index from this coordinate
# agrees with the index already calculated, otherwise we have a contradicting specification
if indices[data_dim] != slice(None, None) and min_index != indices[data_dim]:
raise ValueError('The coordinates provided (%s) over specify dimension %s.' %
(', '.join([coord.name() for coord in dim_to_coord_map[data_dim]]), data_dim))

indices[data_dim] = min_index

return tuple(indices)


def _nearest_neighbour_indices_ndcoords(cube, sample_points, cache=None):
"""
See documentation for :func:`iris.analysis.interpolate.nearest_neighbour_indices`.
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