# Copyright Iris contributors
#
# This file is part of Iris and is released under the BSD license.
# See LICENSE in the root of the repository for full licensing details.
"""Miscellaneous utility functions."""
from __future__ import annotations
from abc import ABCMeta, abstractmethod
from collections.abc import Hashable, Iterable
import functools
import inspect
import os
import os.path
import sys
import tempfile
from typing import Literal
import cf_units
from dask import array as da
import numpy as np
import numpy.ma as ma
from iris._deprecation import warn_deprecated
from iris._lazy_data import is_lazy_data, is_lazy_masked_data
from iris._shapefiles import create_shapefile_mask
from iris.common import SERVICES
from iris.common.lenient import _lenient_client
import iris.exceptions
[docs]
def broadcast_to_shape(array, shape, dim_map, chunks=None):
"""Broadcast an array to a given shape.
Each dimension of the array must correspond to a dimension in the
given shape. The result is a read-only view (see :func:`numpy.broadcast_to`).
If you need to write to the resulting array, make a copy first.
Parameters
----------
array : :class:`numpy.ndarray`-like
An array to broadcast.
shape : :class:`list`, :class:`tuple` etc
The shape the array should be broadcast to.
dim_map : :class:`list`, :class:`tuple` etc
A mapping of the dimensions of *array* to their corresponding
element in *shape*. *dim_map* must be the same length as the
number of dimensions in *array*. Each element of *dim_map*
corresponds to a dimension of *array* and its value provides
the index in *shape* which the dimension of *array* corresponds
to, so the first element of *dim_map* gives the index of *shape*
that corresponds to the first dimension of *array* etc.
chunks : :class:`tuple`, optional
If the source array is a :class:`dask.array.Array` and a value is
provided, then the result will use these chunks instead of the same
chunks as the source array. Setting chunks explicitly as part of
broadcast_to_shape is more efficient than rechunking afterwards. See
also :func:`dask.array.broadcast_to`. Note that the values provided
here will only be used along dimensions that are new on the result or
have size 1 on the source array.
Examples
--------
Broadcasting an array of shape (2, 3) to the shape (5, 2, 6, 3)
where the first dimension of the array corresponds to the second
element of the desired shape and the second dimension of the array
corresponds to the fourth element of the desired shape::
a = np.array([[1, 2, 3], [4, 5, 6]])
b = broadcast_to_shape(a, (5, 2, 6, 3), (1, 3))
Broadcasting an array of shape (48, 96) to the shape (96, 48, 12)::
# a is an array of shape (48, 96)
result = broadcast_to_shape(a, (96, 48, 12), (1, 0))
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if isinstance(array, da.Array):
if chunks is not None:
chunks = list(chunks)
for src_idx, tgt_idx in enumerate(dim_map):
# Only use the specified chunks along new dimensions or on
# dimensions that have size 1 in the source array.
if array.shape[src_idx] != 1:
chunks[tgt_idx] = array.chunks[src_idx]
broadcast = functools.partial(da.broadcast_to, shape=shape, chunks=chunks)
else:
broadcast = functools.partial(np.broadcast_to, shape=shape)
n_orig_dims = len(array.shape)
n_new_dims = len(shape) - n_orig_dims
array = array.reshape(array.shape + (1,) * n_new_dims)
# Get dims in required order.
array = np.moveaxis(array, range(n_orig_dims), dim_map)
new_array = broadcast(array)
if ma.isMA(array):
# broadcast_to strips masks so we need to handle them explicitly.
mask = ma.getmask(array)
if mask is ma.nomask:
new_mask = ma.nomask
else:
new_mask = broadcast(mask)
new_array = ma.array(new_array, mask=new_mask)
elif is_lazy_masked_data(array):
# broadcast_to strips masks so we need to handle them explicitly.
mask = da.ma.getmaskarray(array)
new_mask = broadcast(mask)
new_array = da.ma.masked_array(new_array, new_mask)
return new_array
[docs]
def delta(ndarray, dimension, circular=False):
"""Calculate the difference between values along a given dimension.
Parameters
----------
ndarray :
The array over which to do the difference.
dimension :
The dimension over which to do the difference on ndarray.
circular : bool, default=False
If not False then return n results in the requested dimension
with the delta between the last and first element included in
the result otherwise the result will be of length n-1 (where n
is the length of ndarray in the given dimension's direction)
If circular is numeric then the value of circular will be added
to the last element of the given dimension if the last element
is negative, otherwise the value of circular will be subtracted
from the last element.
The example below illustrates the process::
original array -180, -90, 0, 90
delta (with circular=360): 90, 90, 90, -270+360
Notes
-----
.. note::
The difference algorithm implemented is forward difference:
>>> import numpy as np
>>> import iris.util
>>> original = np.array([-180, -90, 0, 90])
>>> iris.util.delta(original, 0)
array([90, 90, 90])
>>> iris.util.delta(original, 0, circular=360)
array([90, 90, 90, 90])
.. note::
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if circular is not False:
_delta = np.roll(ndarray, -1, axis=dimension)
last_element = [slice(None, None)] * ndarray.ndim
last_element[dimension] = slice(-1, None)
last_element = tuple(last_element)
if not isinstance(circular, bool):
result = np.where(ndarray[last_element] >= _delta[last_element])[0]
_delta[last_element] -= circular
_delta[last_element][result] += 2 * circular
np.subtract(_delta, ndarray, _delta)
else:
_delta = np.diff(ndarray, axis=dimension)
return _delta
[docs]
def describe_diff(cube_a, cube_b, output_file=None):
"""Print the differences that prevent compatibility between two cubes.
Print the differences that prevent compatibility between two cubes, as
defined by :meth:`iris.cube.Cube.is_compatible()`.
Parameters
----------
cube_a :
An instance of :class:`iris.cube.Cube` or
:class:`iris.cube.CubeMetadata`.
cube_b :
An instance of :class:`iris.cube.Cube` or
:class:`iris.cube.CubeMetadata`.
output_file : optional
A :class:`file` or file-like object to receive output. Defaults to
sys.stdout.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
.. note::
Compatibility does not guarantee that two cubes can be merged.
Instead, this function is designed to provide a verbose description
of the differences in metadata between two cubes. Determining whether
two cubes will merge requires additional logic that is beyond the
scope of this function.
See Also
--------
iris.cube.Cube.is_compatible :
Check if a Cube is compatible with another.
"""
if output_file is None:
output_file = sys.stdout
if cube_a.is_compatible(cube_b):
output_file.write("Cubes are compatible\n")
else:
common_keys = set(cube_a.attributes).intersection(cube_b.attributes)
for key in common_keys:
if np.any(cube_a.attributes[key] != cube_b.attributes[key]):
output_file.write(
'"%s" cube_a attribute value "%s" is not '
"compatible with cube_b "
'attribute value "%s"\n'
% (key, cube_a.attributes[key], cube_b.attributes[key])
)
if cube_a.name() != cube_b.name():
output_file.write(
'cube_a name "%s" is not compatible '
'with cube_b name "%s"\n' % (cube_a.name(), cube_b.name())
)
if cube_a.units != cube_b.units:
output_file.write(
'cube_a units "%s" are not compatible with cube_b units "%s"\n'
% (cube_a.units, cube_b.units)
)
if cube_a.cell_methods != cube_b.cell_methods:
output_file.write(
"Cell methods\n%s\nand\n%s\nare not compatible\n"
% (cube_a.cell_methods, cube_b.cell_methods)
)
Axis = Literal["X", "Y", "Z", "T"]
[docs]
def guess_coord_axis(coord) -> Axis | None:
"""Return a "best guess" axis name of the coordinate.
Heuristic categorisation of the coordinate into either label
'T', 'Z', 'Y', 'X' or None.
Parameters
----------
coord :
The :class:`iris.coords.Coord`.
Returns
-------
{'T', 'Z', 'Y', 'X'} or None.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
The ``guess_coord_axis`` behaviour can be skipped by setting the
:attr:`~iris.coords.Coord.ignore_axis` property on `coord` to ``False``.
"""
axis: Axis | None = None
if hasattr(coord, "ignore_axis") and coord.ignore_axis is True:
return axis
elif coord.standard_name in (
"longitude",
"grid_longitude",
"projection_x_coordinate",
):
axis = "X"
elif coord.standard_name in (
"latitude",
"grid_latitude",
"projection_y_coordinate",
):
axis = "Y"
elif coord.units.is_convertible("hPa") or coord.attributes.get("positive") in (
"up",
"down",
):
axis = "Z"
elif coord.units.is_time_reference():
axis = "T"
return axis
[docs]
def rolling_window(
a: np.ndarray | da.Array,
window: int = 1,
step: int = 1,
axis: int = -1,
) -> np.ndarray | da.Array:
"""Make an ndarray with a rolling window of the last dimension.
Parameters
----------
a : array_like
Array to add rolling window to.
window : int, default=1
Size of rolling window.
step : int, default=1
Size of step between rolling windows.
axis : int, default=-1
Axis to take the rolling window over.
Returns
-------
array
Array that is a view of the original array with an added dimension
of the size of the given window at axis + 1.
Examples
--------
::
>>> x = np.arange(10).reshape((2, 5))
>>> rolling_window(x, 3)
array([[[0, 1, 2], [1, 2, 3], [2, 3, 4]],
[[5, 6, 7], [6, 7, 8], [7, 8, 9]]])
Calculate rolling mean of last dimension::
>>> np.mean(rolling_window(x, 3), -1)
array([[ 1., 2., 3.],
[ 6., 7., 8.]])
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if window < 1:
raise ValueError("`window` must be at least 1.")
if window > a.shape[axis]:
raise ValueError("`window` is too long.")
if step < 1:
raise ValueError("`step` must be at least 1.")
axis = axis % a.ndim
array_module = da if isinstance(a, da.Array) else np
steps = tuple(
slice(None, None, step) if i == axis else slice(None) for i in range(a.ndim)
)
def _rolling_window(array):
return array_module.moveaxis(
array_module.lib.stride_tricks.sliding_window_view(
array,
window_shape=window,
axis=axis,
)[steps],
-1,
axis + 1,
)
rw = _rolling_window(a)
if isinstance(da.utils.meta_from_array(a), np.ma.MaskedArray):
mask = _rolling_window(array_module.ma.getmaskarray(a))
rw = array_module.ma.masked_array(rw, mask)
return rw
[docs]
def array_equal(array1, array2, withnans=False):
"""Return whether two arrays have the same shape and elements.
Parameters
----------
array1, array2 : arraylike
Args to be compared, normalised if necessary with :func:`np.asarray`.
withnans : bool, default=False
When unset (default), the result is False if either input contains NaN
points. This is the normal floating-point arithmetic result.
When set, return True if inputs contain the same value in all elements,
_including_ any NaN values.
Notes
-----
This provides similar functionality to :func:`numpy.array_equal`, but
will compare arrays as unequal when their masks differ and has
additional support for arrays of strings and NaN-tolerant operation.
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if withnans and (array1 is array2):
return True
def normalise_array(array):
if not is_lazy_data(array):
if not ma.isMaskedArray(array):
array = np.asanyarray(array)
return array
array1, array2 = normalise_array(array1), normalise_array(array2)
eq = array1.shape == array2.shape
if eq:
array1_masked = ma.is_masked(array1)
eq = array1_masked == ma.is_masked(array2)
if eq and array1_masked:
eq = np.array_equal(ma.getmaskarray(array1), ma.getmaskarray(array2))
if eq:
eqs = array1 == array2
if withnans and (array1.dtype.kind == "f" or array2.dtype.kind == "f"):
eqs = np.where(np.isnan(array1) & np.isnan(array2), True, eqs)
eq = np.all(eqs)
eq = bool(eq) or eq is ma.masked
return eq
[docs]
def approx_equal(a, b, max_absolute_error=1e-10, max_relative_error=1e-10):
"""Check if two numbers are almost equal.
Returns whether two numbers are almost equal, allowing for the finite
precision of floating point numbers.
Notes
-----
This function does maintain laziness when called; it doesn't realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
.. deprecated:: 3.2.0
Instead use :func:`math.isclose`. For example, rather than calling
``approx_equal(a, b, max_abs, max_rel)`` replace with ``math.isclose(a,
b, max_rel, max_abs)``. Note that :func:`~math.isclose` will return True
if the actual error equals the maximum, whereas :func:`util.approx_equal`
will return False.
"""
wmsg = (
"iris.util.approx_equal has been deprecated and will be removed, "
"please use math.isclose instead."
)
warn_deprecated(wmsg)
# Deal with numbers close to zero
if abs(a - b) < max_absolute_error:
return True
# Ensure we get consistent results if "a" and "b" are supplied in the
# opposite order.
max_ab = max([a, b], key=abs)
relative_error = abs(a - b) / max_ab
return relative_error < max_relative_error
[docs]
def between(lh, rh, lh_inclusive=True, rh_inclusive=True):
"""Provide convenient way of defining a 3 element inequality.
Such as ``a < number < b``.
Parameters
----------
lh :
The left hand element of the inequality.
rh :
The right hand element of the inequality.
lh_inclusive : bool, default=True
Affects the left hand comparison operator to use in the inequality.
True for ``<=`` false for ``<``. Defaults to True.
rh_inclusive : bool, default=True
Same as lh_inclusive but for right hand operator.
Examples
--------
::
between_3_and_6 = between(3, 6)
for i in range(10):
print(i, between_3_and_6(i))
between_3_and_6 = between(3, 6, rh_inclusive=False)
for i in range(10):
print(i, between_3_and_6(i))
Notes
-----
This function does maintain laziness when called; it doesn't realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if lh_inclusive and rh_inclusive:
return lambda c: lh <= c <= rh
elif lh_inclusive and not rh_inclusive:
return lambda c: lh <= c < rh
elif not lh_inclusive and rh_inclusive:
return lambda c: lh < c <= rh
else:
return lambda c: lh < c < rh
[docs]
def reverse(cube_or_array, coords_or_dims):
"""Reverse the cube or array along the given dimensions.
Parameters
----------
cube_or_array : :class:`iris.cube.Cube` or :class:`numpy.ndarray`
The cube or array to reverse.
coords_or_dims : int, str, :class:`iris.coords.Coord` or sequence of these
Identify one or more dimensions to reverse. If cube_or_array is a
numpy array, use int or a sequence of ints, as in the examples below.
If cube_or_array is a Cube, a Coord or coordinate name (or sequence of
these) may be specified instead.
Examples
--------
::
>>> import numpy as np
>>> a = np.arange(24).reshape(2, 3, 4)
>>> print(a)
[[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
<BLANKLINE>
[[12 13 14 15]
[16 17 18 19]
[20 21 22 23]]]
>>> print(reverse(a, 1))
[[[ 8 9 10 11]
[ 4 5 6 7]
[ 0 1 2 3]]
<BLANKLINE>
[[20 21 22 23]
[16 17 18 19]
[12 13 14 15]]]
>>> print(reverse(a, [1, 2]))
[[[11 10 9 8]
[ 7 6 5 4]
[ 3 2 1 0]]
<BLANKLINE>
[[23 22 21 20]
[19 18 17 16]
[15 14 13 12]]]
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
from iris.cube import Cube
index = [slice(None, None)] * cube_or_array.ndim
if isinstance(coords_or_dims, Cube):
raise TypeError(
"coords_or_dims must be int, str, coordinate or "
"sequence of these. Got cube."
)
if isinstance(coords_or_dims, str) or not isinstance(coords_or_dims, Iterable):
coords_or_dims = [coords_or_dims]
axes = set()
for coord_or_dim in coords_or_dims:
if isinstance(coord_or_dim, int):
axes.add(coord_or_dim)
elif isinstance(cube_or_array, np.ndarray):
raise TypeError("To reverse an array, provide an int or sequence of ints.")
else:
try:
axes.update(cube_or_array.coord_dims(coord_or_dim))
except AttributeError:
raise TypeError(
"coords_or_dims must be int, str, coordinate "
"or sequence of these."
)
axes = np.array(list(axes), ndmin=1)
if axes.ndim != 1 or axes.size == 0:
raise ValueError(
"Reverse was expecting a single axis or a 1d array "
"of axes, got %r" % axes
)
if np.min(axes) < 0 or np.max(axes) > cube_or_array.ndim - 1:
raise ValueError(
"An axis value out of range for the number of "
"dimensions from the given array (%s) was received. "
"Got: %r" % (cube_or_array.ndim, axes)
)
for axis in axes:
index[axis] = slice(None, None, -1)
return cube_or_array[tuple(index)]
[docs]
def monotonic(array, strict=False, return_direction=False):
"""Return whether the given 1d array is monotonic.
Note that, the array must not contain missing data.
Parameters
----------
strict : bool, default=False
Flag to enable strict monotonic checking.
return_direction : bool, default=False
Flag to change return behaviour to return
(monotonic_status, direction). Direction will be 1 for positive
or -1 for negative. The direction is meaningless if the array is
not monotonic.
Returns
-------
bool
Whether the array was monotonic. If the return_direction flag was given
then the returned value will be: ``(monotonic_status, direction)``.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if array.ndim != 1 or len(array) <= 1:
raise ValueError(
"The array to check must be 1 dimensional and have more than 1 element."
)
if ma.isMaskedArray(array) and ma.count_masked(array) != 0:
raise ValueError("The array to check contains missing data.")
# Identify the directions of the largest/most-positive and
# smallest/most-negative steps.
d = np.diff(array)
sign_max_d = np.sign(np.max(d))
sign_min_d = np.sign(np.min(d))
if strict:
monotonic = sign_max_d == sign_min_d and sign_max_d != 0
else:
monotonic = (
(sign_min_d < 0 and sign_max_d <= 0)
or (sign_max_d > 0 and sign_min_d >= 0)
or (sign_min_d == sign_max_d == 0)
)
if return_direction:
if sign_max_d == 0:
direction = sign_min_d
else:
direction = sign_max_d
return monotonic, direction
return monotonic
[docs]
def column_slices_generator(full_slice, ndims):
"""Return a dictionary mapping old data dimensions to new.
Given a full slice full of tuples, return a dictionary mapping old
data dimensions to new and a generator which gives the successive
slices needed to index correctly (across columns).
This routine deals with the special functionality for tuple based
indexing e.g. [0, (3, 5), :, (1, 6, 8)] by first providing a slice
which takes the non tuple slices out first i.e. [0, :, :, :] then
subsequently iterates through each of the tuples taking out the
appropriate slices i.e. [(3, 5), :, :] followed by [:, :, (1, 6, 8)]
This method was developed as numpy does not support the direct
approach of [(3, 5), : , (1, 6, 8)] for column based indexing.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
list_of_slices = []
# Map current dimensions to new dimensions, or None
dimension_mapping = {None: None}
_count_current_dim = 0
for i, i_key in enumerate(full_slice):
if isinstance(i_key, (int, np.integer)):
dimension_mapping[i] = None
else:
dimension_mapping[i] = _count_current_dim
_count_current_dim += 1
# Get all of the dimensions for which a tuple of indices were provided
# (numpy.ndarrays are treated in the same way tuples in this case)
def is_tuple_style_index(key):
return isinstance(key, tuple) or (isinstance(key, np.ndarray) and key.ndim == 1)
tuple_indices = [i for i, key in enumerate(full_slice) if is_tuple_style_index(key)]
# stg1: Take a copy of the full_slice specification, turning all tuples
# into a full slice
if tuple_indices != list(range(len(full_slice))):
first_slice = list(full_slice)
for tuple_index in tuple_indices:
first_slice[tuple_index] = slice(None, None)
# turn first_slice back into a tuple ready for indexing
first_slice = tuple(first_slice)
list_of_slices.append(first_slice)
# stg2 iterate over each of the tuples
for tuple_index in tuple_indices:
# Create a list with the indices to span the whole data array that we
# currently have
spanning_slice_with_tuple = [slice(None, None)] * _count_current_dim
# Replace the slice(None, None) with our current tuple
spanning_slice_with_tuple[dimension_mapping[tuple_index]] = full_slice[
tuple_index
]
# if we just have [(0, 1)] turn it into [(0, 1), ...] as this is
# Numpy's syntax.
if len(spanning_slice_with_tuple) == 1:
spanning_slice_with_tuple.append(Ellipsis)
spanning_slice_with_tuple = tuple(spanning_slice_with_tuple)
list_of_slices.append(spanning_slice_with_tuple)
# return the dimension mapping and a generator of slices
return dimension_mapping, iter(list_of_slices)
def _build_full_slice_given_keys(keys, ndim):
"""Build an equivalent tuple of keys which span ndims.
Given the keys passed to a __getitem__ call, build an equivalent
tuple of keys which span ndims.
"""
# Ensure that we always have a tuple of keys
if not isinstance(keys, tuple):
keys = tuple([keys])
# catch the case where an extra Ellipsis has been provided which can be
# discarded iff len(keys)-1 == ndim
if len(keys) - 1 == ndim and Ellipsis in filter(
lambda obj: not isinstance(obj, np.ndarray), keys
):
keys = list(keys)
is_ellipsis = [key is Ellipsis for key in keys]
keys.pop(is_ellipsis.index(True))
keys = tuple(keys)
# for ndim >= 1 appending a ":" to the slice specification is allowable,
# remove this now
if len(keys) > ndim and ndim != 0 and keys[-1] == slice(None, None):
keys = keys[:-1]
if len(keys) > ndim:
raise IndexError(
"More slices requested than dimensions. Requested "
"%r, but there were only %s dimensions." % (keys, ndim)
)
# For each dimension get the slice which has been requested.
# If no slice provided, then default to the whole dimension
full_slice = [slice(None, None)] * ndim
for i, key in enumerate(keys):
if key is Ellipsis:
# replace any subsequent Ellipsis objects in keys with
# slice(None, None) as per Numpy
keys = keys[:i] + tuple(
[slice(None, None) if key is Ellipsis else key for key in keys[i:]]
)
# iterate over the remaining keys in reverse to fill in
# the gaps from the right hand side
for j, key in enumerate(keys[:i:-1]):
full_slice[-j - 1] = key
# we've finished with i now so stop the iteration
break
else:
full_slice[i] = key
# remove any tuples on dimensions, turning them into numpy array's for
# consistent behaviour
full_slice = tuple(
[
np.array(key, ndmin=1) if isinstance(key, tuple) else key
for key in full_slice
]
)
return full_slice
def _slice_data_with_keys(data, keys):
"""Index an array-like object as "data[keys]", with orthogonal indexing.
Parameters
----------
data : array-like
Array to index.
keys : list
List of indexes, as received from a __getitem__ call.
Returns
-------
dim_map : dict
A dimension map, as returned by :func:`column_slices_generator`.
i.e. "dim_map[old_dim_index]" --> "new_dim_index" or None.
data_region : array-like
The sub-array.
Notes
-----
This enforces an orthogonal interpretation of indexing, which means that
both 'real' (numpy) arrays and other array-likes index in the same way,
instead of numpy arrays doing 'fancy indexing'.
.. note::
Avoids copying the data, where possible.
"""
# Combines the use of _build_full_slice_given_keys and
# column_slices_generator.
# By slicing on only one index at a time, this also mostly avoids copying
# the data, except some cases when a key contains a list of indices.
n_dims = len(data.shape)
full_slice = _build_full_slice_given_keys(keys, n_dims)
dims_mapping, slices_iter = column_slices_generator(full_slice, n_dims)
for this_slice in slices_iter:
data = data[this_slice]
if data.ndim > 0 and min(data.shape) < 1:
# Disallow slicings where a dimension has no points, like "[5:5]".
raise IndexError("Cannot index with zero length slice.")
return dims_mapping, data
def _wrap_function_for_method(function, docstring=None):
"""Return a wrapper function modified to be suitable for use as a method.
The wrapper function renames the first argument as "self" and allows
an alternative docstring, thus allowing the built-in help(...)
routine to display appropriate output.
"""
# Generate the Python source for the wrapper function.
# NB. The first argument is replaced with "self".
args, varargs, varkw, defaults = inspect.getargspec(function)
if defaults is None:
basic_args = ["self"] + args[1:]
default_args = []
simple_default_args = []
else:
cutoff = -len(defaults)
basic_args = ["self"] + args[1:cutoff]
default_args = ["%s=%r" % pair for pair in zip(args[cutoff:], defaults)]
simple_default_args = args[cutoff:]
var_arg = [] if varargs is None else ["*" + varargs]
var_kw = [] if varkw is None else ["**" + varkw]
arg_source = ", ".join(basic_args + default_args + var_arg + var_kw)
simple_arg_source = ", ".join(basic_args + simple_default_args + var_arg + var_kw)
source = "def %s(%s):\n return function(%s)" % (
function.__name__,
arg_source,
simple_arg_source,
)
# Compile the wrapper function
# NB. There's an outstanding bug with "exec" where the locals and globals
# dictionaries must be the same if we're to get closure behaviour.
my_locals = {"function": function}
exec(source, my_locals, my_locals)
# Update the docstring if required, and return the modified function
wrapper = my_locals[function.__name__]
if docstring is None:
wrapper.__doc__ = function.__doc__
else:
wrapper.__doc__ = docstring
return wrapper
class _MetaOrderedHashable(ABCMeta):
"""Ensures that non-abstract subclasses are given a default __init__ method.
A metaclass that ensures that non-abstract subclasses of _OrderedHashable
without an explicit __init__ method are given a default __init__ method
with the appropriate method signature.
Also, an _init method is provided to allow subclasses with their own
__init__ constructors to initialise their values via an explicit method
signature.
NB. This metaclass is used to construct the _OrderedHashable class as well
as all its subclasses.
"""
def __new__(cls, name, bases, namespace):
# We only want to modify concrete classes that have defined the
# "_names" property.
if "_names" in namespace and not getattr(
namespace["_names"], "__isabstractmethod__", False
):
args = ", ".join(namespace["_names"])
# Ensure the class has a constructor with explicit arguments.
if "__init__" not in namespace:
# Create a default __init__ method for the class
method_source = (
"def __init__(self, %s):\n "
"self._init_from_tuple((%s,))" % (args, args)
)
exec(method_source, namespace)
# Ensure the class has a "helper constructor" with explicit
# arguments.
if "_init" not in namespace:
# Create a default _init method for the class
method_source = (
"def _init(self, %s):\n "
"self._init_from_tuple((%s,))" % (args, args)
)
exec(method_source, namespace)
return super().__new__(cls, name, bases, namespace)
@functools.total_ordering
class _OrderedHashable(Hashable, metaclass=_MetaOrderedHashable):
"""Convenience class for creating "immutable", hashable, and ordered classes.
Instance identity is defined by the specific list of attribute names
declared in the abstract attribute "_names". Subclasses must declare the
attribute "_names" as an iterable containing the names of all the
attributes relevant to equality/hash-value/ordering.
Initial values should be set by using ::
self._init(self, value1, value2, ..)
.. note::
It's the responsibility of the subclass to ensure that the values of
its attributes are themselves hashable.
"""
@property
@abstractmethod
def _names(self):
"""Override this attribute to declare the names of all the attributes relevant.
Override this attribute to declare the names of all the attributes
relevant to the hash/comparison semantics.
"""
pass
def _init_from_tuple(self, values):
for name, value in zip(self._names, values):
object.__setattr__(self, name, value)
def __repr__(self):
class_name = type(self).__name__
attributes = ", ".join(
"%s=%r" % (name, value)
for (name, value) in zip(self._names, self._as_tuple())
)
return "%s(%s)" % (class_name, attributes)
def _as_tuple(self):
return tuple(getattr(self, name) for name in self._names)
# Prevent attribute updates
def __setattr__(self, name, value):
raise AttributeError("Instances of %s are immutable" % type(self).__name__)
def __delattr__(self, name):
raise AttributeError("Instances of %s are immutable" % type(self).__name__)
# Provide hash semantics
def _identity(self):
return self._as_tuple()
def __hash__(self):
return hash(self._identity())
def __eq__(self, other):
return isinstance(other, type(self)) and self._identity() == other._identity()
def __ne__(self, other):
# Since we've defined __eq__ we should also define __ne__.
return not self == other
# Provide default ordering semantics
def __lt__(self, other):
if isinstance(other, _OrderedHashable):
return self._identity() < other._identity()
else:
return NotImplemented
[docs]
def create_temp_filename(suffix=""):
"""Return a temporary file name.
Parameters
----------
suffix : str, optional, default=""
Filename extension.
"""
temp_file = tempfile.mkstemp(suffix)
os.close(temp_file[0])
return temp_file[1]
[docs]
def clip_string(the_str, clip_length=70, rider="..."):
"""Return clipped version of the string based on the specified clip length.
Return a clipped version of the string based on the specified clip
length and whether or not any graceful clip points can be found.
If the string to be clipped is shorter than the specified clip
length, the original string is returned.
If the string is longer than the clip length, a graceful point (a
space character) after the clip length is searched for. If a
graceful point is found the string is clipped at this point and the
rider is added. If no graceful point can be found, then the string
is clipped exactly where the user requested and the rider is added.
Parameters
----------
the_str : str
The string to be clipped.
clip_length : int, default=70
The length in characters that the input string should be clipped
to. Defaults to a preconfigured value if not specified.
rider : str, default="..."
A series of characters appended at the end of the returned
string to show it has been clipped. Defaults to a preconfigured
value if not specified.
Returns
-------
str
The string clipped to the required length with a rider appended.
If the clip length was greater than the original string, the
original string is returned unaltered.
Notes
-----
This function does maintain laziness when called; it doesn't realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if clip_length >= len(the_str) or clip_length <= 0:
return the_str
else:
if the_str[clip_length].isspace():
return the_str[:clip_length] + rider
else:
first_part = the_str[:clip_length]
remainder = the_str[clip_length:]
# Try to find a graceful point at which to trim i.e. a space
# If no graceful point can be found, then just trim where the user
# specified by adding an empty slice of the remainder ( [:0] )
termination_point = remainder.find(" ")
if termination_point == -1:
termination_point = 0
return first_part + remainder[:termination_point] + rider
[docs]
def new_axis(src_cube, scalar_coord=None, expand_extras=()): # maybe not lazy
"""Create a new axis as the leading dimension of the cube.
Create a new axis as the leading dimension of the cube, promoting a scalar
coordinate if specified.
Parameters
----------
src_cube : :class:`iris.cube.Cube`
Source cube on which to generate a new axis.
scalar_coord : :class:`iris.coord.Coord` or str, optional
Scalar coordinate to promote to a dimension coordinate.
expand_extras : iterable, optional
Auxiliary coordinates, ancillary variables and cell measures which will
be expanded so that they map to the new dimension as well as the
existing dimensions.
Returns
-------
:class:`iris.cube.Cube`
A new :class:`iris.cube.Cube` instance with one extra leading dimension
(length 1). Chosen auxiliary coordinates, cell measures and ancillary
variables will also be given an additional dimension, associated with
the leading dimension of the cube.
Examples
--------
::
>>> cube.shape
(360, 360)
>>> ncube = iris.util.new_axis(cube, 'time')
>>> ncube.shape
(1, 360, 360)
Notes
-----
This function does maintain laziness when called; it doesn't realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
def _reshape_data_array(data_manager):
# Indexing numpy arrays requires loading deferred data here returning a
# copy of the data with a new leading dimension.
# If the data of the source cube (or values of the dimensional metadata
# object) is a Masked Constant, it is changed here to a Masked Array to
# allow the mask to gain an extra dimension with the data.
if data_manager.has_lazy_data():
new_data = data_manager.lazy_data()[None]
else:
if isinstance(data_manager.data, ma.core.MaskedConstant):
new_data = ma.array([np.nan], mask=[True])
else:
new_data = data_manager.data[None]
return new_data
def _handle_dimensional_metadata(cube, dm_item, cube_add_method, expand_extras):
cube_dims = dm_item.cube_dims(cube)
if dm_item in expand_extras:
if cube_dims == ():
new_dm_item, new_dims = dm_item.copy(), 0
else:
new_dims = np.concatenate([(0,), np.array(cube_dims) + 1])
new_values = _reshape_data_array(dm_item._values_dm)
kwargs = dm_item.metadata._asdict()
new_dm_item = dm_item.__class__(new_values, **kwargs)
try:
if dm_item.has_bounds():
new_dm_item.bounds = _reshape_data_array(dm_item._bounds_dm)
except AttributeError:
pass
else:
new_dims = np.array(cube_dims) + 1
new_dm_item = dm_item.copy()
cube_add_method(new_dm_item, new_dims)
if scalar_coord is not None:
scalar_coord = src_cube.coord(scalar_coord)
try:
src_cube.coord(scalar_coord, dim_coords=False)
except iris.exceptions.CoordinateNotFoundError:
emsg = scalar_coord.name() + " is already a dimension coordinate."
raise ValueError(emsg)
if not scalar_coord.shape == (1,):
emsg = scalar_coord.name() + " is not a scalar coordinate."
raise ValueError(emsg)
expand_extras = [src_cube._dimensional_metadata(item) for item in expand_extras]
new_cube = iris.cube.Cube(_reshape_data_array(src_cube._data_manager))
new_cube.metadata = src_cube.metadata
for coord in src_cube.dim_coords:
coord_dims = np.array(src_cube.coord_dims(coord)) + 1
new_cube.add_dim_coord(coord.copy(), coord_dims)
for coord in src_cube.aux_coords:
if scalar_coord and scalar_coord == coord:
dim_coord = iris.coords.DimCoord.from_coord(coord)
new_cube.add_dim_coord(dim_coord, 0)
else:
_handle_dimensional_metadata(
src_cube, coord, new_cube.add_aux_coord, expand_extras
)
for cm in src_cube.cell_measures():
_handle_dimensional_metadata(
src_cube, cm, new_cube.add_cell_measure, expand_extras
)
for av in src_cube.ancillary_variables():
_handle_dimensional_metadata(
src_cube, av, new_cube.add_ancillary_variable, expand_extras
)
nonderived_coords = src_cube.dim_coords + src_cube.aux_coords
coord_mapping = {id(old_co): new_cube.coord(old_co) for old_co in nonderived_coords}
for factory in src_cube.aux_factories:
new_factory = factory.updated(coord_mapping)
new_cube.add_aux_factory(new_factory)
return new_cube
[docs]
def squeeze(cube):
"""Remove any dimension of length one.
Remove any dimension of length one. If it has an associated DimCoord or
AuxCoord, this becomes a scalar coord.
Parameters
----------
cube : :class:`iris.cube.Cube`
Source cube to remove length 1 dimension(s) from.
Returns
-------
:class:`iris.cube.Cube`
A new :class:`iris.cube.Cube` instance without any dimensions of
length 1.
Examples
--------
For example::
>>> cube.shape
(1, 360, 360)
>>> ncube = iris.util.squeeze(cube)
>>> ncube.shape
(360, 360)
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
slices = [0 if cube.shape[dim] == 1 else slice(None) for dim in range(cube.ndim)]
squeezed = cube[tuple(slices)]
return squeezed
[docs]
def file_is_newer_than(result_path, source_paths):
"""Determine if the 'result' file was modified last.
Return whether the 'result' file has a later modification time than all of
the 'source' files.
If a stored result depends entirely on known 'sources', it need only be
re-built when one of them changes. This function can be used to test that
by comparing file timestamps.
Parameters
----------
result_path : str
The filepath of a file containing some derived result data.
source_paths : str or iterable of str
The path(s) to the original datafiles used to make the result. May
include wildcards and '~' expansions (like Iris load paths), but not
URIs.
Returns
-------
bool
True if all the sources are older than the result, else False.
If any of the file paths describes no existing files, an exception will
be raised.
Notes
-----
.. note::
There are obvious caveats to using file timestamps for this, as correct
usage depends on how the sources might change. For example, a file
could be replaced by one of the same name, but an older timestamp.
If wildcards and '~' expansions are used, this introduces even more
uncertainty, as then you cannot even be sure that the resulting list of
file names is the same as the originals. For example, some files may
have been deleted or others added.
.. note::
The result file may often be a :mod:`pickle` file. In that case, it
also depends on the relevant module sources, so extra caution is
required. Ideally, an additional check on iris.__version__ is advised.
"""
# Accept a string as a single source path
if isinstance(source_paths, str):
source_paths = [source_paths]
# Fix our chosen timestamp function
file_date = os.path.getmtime
# Get the 'result file' time
result_timestamp = file_date(result_path)
# Get all source filepaths, with normal Iris.io load helper function
source_file_paths = iris.io.expand_filespecs(source_paths)
# Compare each filetime, for each spec, with the 'result time'
for path in source_file_paths:
source_timestamp = file_date(path)
if source_timestamp >= result_timestamp:
return False
return True
[docs]
def is_regular(coord):
"""Determine if the given coord is regular.
Notes
-----
This function does not maintain laziness when called; it realises data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
try:
regular_step(coord)
except iris.exceptions.CoordinateNotRegularError:
return False
except (TypeError, ValueError):
return False
return True
[docs]
def regular_step(coord):
"""Return the regular step from a coord or fail.
Notes
-----
This function does not maintain laziness when called; it realises data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if coord.ndim != 1:
raise iris.exceptions.CoordinateMultiDimError("Expected 1D coord")
if coord.shape[0] < 2:
raise ValueError("Expected a non-scalar coord")
avdiff, regular = points_step(coord.points)
if not regular:
msg = "Coord %s is not regular" % coord.name()
raise iris.exceptions.CoordinateNotRegularError(msg)
return avdiff.astype(coord.points.dtype)
[docs]
def regular_points(zeroth, step, count):
"""Make an array of regular points.
Create an array of `count` points from `zeroth` + `step`, adding `step` each
time. In float32 if this gives a sufficiently regular array (tested with
points_step) and float64 if not.
Parameters
----------
zeroth : number
The value *prior* to the first point value.
step : number
The numeric difference between successive point values.
count : number
The number of point values.
Notes
-----
This function does maintain laziness when called; it doesn't realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
def make_steps(dtype: np.dtype):
start = np.add(zeroth, step, dtype=dtype)
steps = np.multiply(step, np.arange(count), dtype=dtype)
return np.add(start, steps, dtype=dtype)
points = make_steps(np.float32)
_, regular = iris.util.points_step(points)
if not regular:
points = make_steps(np.float64)
return points
[docs]
def points_step(points):
"""Determine whether `points` has a regular step.
Parameters
----------
points : numeric, array-like
The sequence of values to check for a regular difference.
Returns
-------
numeric, bool
A tuple containing the average difference between values, and whether
the difference is regular.
Notes
-----
This function does not maintain laziness when called; it realises data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
# Calculations only make sense with multiple points
points = np.asanyarray(points)
if points.size >= 2:
diffs = np.diff(points)
avdiff = np.mean(diffs)
# TODO: This value for `rtol` is set for test_analysis to pass...
regular = np.allclose(diffs, avdiff, rtol=0.001)
else:
avdiff = np.nan
regular = True
return avdiff, regular
[docs]
def unify_time_units(cubes):
"""Perform an in-place conversion of the time units.
Perform an in-place conversion of the time units of all time coords in the
cubes in a given iterable. One common epoch is defined for each calendar
found in the cubes to prevent units being defined with inconsistencies
between epoch and calendar. During this process, all time coordinates have
their data type converted to 64-bit floats to allow for smooth concatenation.
Each epoch is defined from the first suitable time coordinate found in the
input cubes.
Parameters
----------
cubes :
An iterable containing :class:`iris.cube.Cube` instances.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
epochs = {}
for cube in cubes:
for time_coord in cube.coords():
if time_coord.units.is_time_reference():
time_coord.points = time_coord.core_points().astype("float64")
if time_coord.bounds is not None:
time_coord.bounds = time_coord.core_bounds().astype("float64")
epoch = epochs.setdefault(
time_coord.units.calendar, time_coord.units.origin
)
new_unit = cf_units.Unit(epoch, time_coord.units.calendar)
time_coord.convert_units(new_unit)
def _is_circular(points, modulus, bounds=None):
"""Determine whether the provided points or bounds are circular.
Determine whether the provided points or bounds are circular in nature
relative to the modulus value.
If the bounds are provided then these are checked for circularity rather
than the points.
Parameters
----------
points : :class:`numpy.ndarray`
:class:`numpy.ndarray` of point values.
modulus :
Circularity modulus value.
bounds : :class:`numpy.ndarray`, optional
:class:`numpy.ndarray` of bound values.
Returns
-------
bool
"""
circular = False
if bounds is not None:
# Set circular to True if the bounds ends are equivalent.
first_bound = last_bound = None
if bounds.ndim == 1 and bounds.shape[-1] == 2:
first_bound = bounds[0] % modulus
last_bound = bounds[1] % modulus
elif bounds.ndim == 2 and bounds.shape[-1] == 2:
first_bound = bounds[0, 0] % modulus
last_bound = bounds[-1, 1] % modulus
if first_bound is not None and last_bound is not None:
circular = np.allclose(first_bound, last_bound, rtol=1.0e-5)
else:
# set circular if points are regular and last+1 ~= first
if len(points) > 1:
diffs = list(set(np.diff(points)))
diff = np.mean(diffs)
abs_tol = np.abs(diff * 1.0e-4)
diff_approx_equal = np.max(np.abs(diffs - diff)) < abs_tol
if diff_approx_equal:
circular_value = (points[-1] + diff) % modulus
try:
np.testing.assert_approx_equal(
points[0], circular_value, significant=4
)
circular = True
except AssertionError:
if points[0] == 0:
try:
np.testing.assert_approx_equal(
modulus, circular_value, significant=4
)
circular = True
except AssertionError:
pass
else:
# XXX - Inherited behaviour from NetCDF PyKE rules.
# We need to decide whether this is valid!
circular = points[0] >= modulus
return circular
[docs]
def demote_dim_coord_to_aux_coord(cube, name_or_coord):
r"""Demote a dimension coordinate on the cube to an auxiliary coordinate.
The DimCoord is demoted to an auxiliary coordinate on the cube.
The dimension of the cube that was associated with the DimCoord becomes
anonymous. The class of the coordinate is left as DimCoord, it is not
recast as an AuxCoord instance.
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`.
name_or_coord :
* \(a) An instance of :class:`iris.coords.DimCoord`
* \(b) the :attr:`standard_name`, :attr:`long_name`, or
:attr:`var_name` of an instance of an instance of
:class:`iris.coords.DimCoord`.
Examples
--------
.. testsetup:: demote
import iris
from iris.coord_categorisation import add_year
from iris.util import demote_dim_coord_to_aux_coord, promote_aux_coord_to_dim_coord
cube = iris.load_cube(iris.sample_data_path("E1_north_america.nc"))
cube.remove_coord("forecast_reference_time")
cube.remove_coord("height")
cube.attributes = {}
cube.cell_methods = ()
add_year(cube, "time")
.. doctest:: demote
>>> print(cube)
air_temperature / (K) (time: 240; latitude: 37; longitude: 49)
Dimension coordinates:
time x - -
latitude - x -
longitude - - x
Auxiliary coordinates:
forecast_period x - -
year x - -
>>> demote_dim_coord_to_aux_coord(cube, "time")
>>> print(cube)
air_temperature / (K) (-- : 240; latitude: 37; longitude: 49)
Dimension coordinates:
latitude - x -
longitude - - x
Auxiliary coordinates:
forecast_period x - -
time x - -
year x - -
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
from iris.coords import Coord
if isinstance(name_or_coord, str):
dim_coord = cube.coord(name_or_coord)
elif isinstance(name_or_coord, Coord):
dim_coord = name_or_coord
else:
# Don't know how to handle this type
msg = (
"Don't know how to handle coordinate of type {}. "
"Ensure all coordinates are of type str or "
"iris.coords.Coord."
)
msg = msg.format(type(name_or_coord))
raise TypeError(msg)
if dim_coord not in cube.dim_coords:
# nothing to do
return
coord_dim = cube.coord_dims(dim_coord)
cube.remove_coord(dim_coord)
cube.add_aux_coord(dim_coord, coord_dim)
@functools.wraps(np.meshgrid)
def _meshgrid(*xi, **kwargs):
"""Ensure consistent meshgrid behaviour across numpy versions.
@numpy v1.13, the dtype of each output n-D coordinate is the same as its
associated input 1D coordinate. This is not the case prior to numpy v1.13,
where the output dtype is cast up to its highest resolution, regardlessly.
Reference: https://github.com/numpy/numpy/pull/5302
"""
mxi = np.meshgrid(*xi, **kwargs)
for i, (mxii, xii) in enumerate(zip(mxi, xi)):
if mxii.dtype != xii.dtype:
mxi[i] = mxii.astype(xii.dtype)
return mxi
[docs]
def find_discontiguities(cube, rel_tol=1e-5, abs_tol=1e-8):
"""Identify spatial discontiguities.
Searches the 'x' and 'y' coord on the cube for discontiguities in the
bounds array, returned as a boolean array (True for all cells which are
discontiguous with the cell immediately above them or to their right).
Parameters
----------
cube : `iris.cube.Cube`
The cube to be checked for discontinuities in its 'x' and 'y'
coordinates. These coordinates must be 2D.
rel_tol : float, default=1e-5
The relative equality tolerance to apply in coordinate bounds
checking.
abs_tol : float, default=1e-8
The absolute value tolerance to apply in coordinate bounds
checking.
Returns
-------
numpy.ndarray
Boolean True/false map of which cells in the cube XY grid have
discontiguities in the coordinate points array.
This can be used as the input array for
:func:`iris.util.mask_cube`.
Examples
--------
::
# Find any unknown discontiguities in your cube's x and y arrays:
discontiguities = iris.util.find_discontiguities(cube)
# Pass the resultant boolean array to `iris.util.mask_cube`
# with a cube slice; this will use the boolean array to mask
# any discontiguous data points before plotting:
masked_cube_slice = iris.util.mask_cube(cube[0], discontiguities)
# Plot the masked cube slice:
iplt.pcolormesh(masked_cube_slice)
Notes
-----
This function does not maintain laziness when called; it realises data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
lats_and_lons = [
"latitude",
"grid_latitude",
"longitude",
"grid_longitude",
]
spatial_coords = [
coord for coord in cube.aux_coords if coord.name() in lats_and_lons
]
dim_err_msg = (
"Discontiguity searches are currently only supported for "
"2-dimensional coordinates."
)
if len(spatial_coords) != 2:
raise NotImplementedError(dim_err_msg)
# Check which dimensions are spanned by each coordinate.
for coord in spatial_coords:
if coord.ndim != 2:
raise NotImplementedError(dim_err_msg)
else:
span = set(cube.coord_dims(coord))
if not span:
msg = "The coordinate {!r} doesn't span a data dimension."
raise ValueError(msg.format(coord.name()))
# Check that the 2d coordinate arrays are the same shape as each other
if len(spatial_coords) == 2:
assert spatial_coords[0].points.shape == spatial_coords[1].points.shape
# Set up unmasked boolean array the same size as the coord points array:
bad_points_boolean = np.zeros(spatial_coords[0].points.shape, dtype=bool)
for coord in spatial_coords:
_, (diffs_x, diffs_y) = coord._discontiguity_in_bounds(
rtol=rel_tol, atol=abs_tol
)
bad_points_boolean[:, :-1] = np.logical_or(bad_points_boolean[:, :-1], diffs_x)
# apply mask for y-direction discontiguities:
bad_points_boolean[:-1, :] = np.logical_or(bad_points_boolean[:-1, :], diffs_y)
return bad_points_boolean
def _mask_array(array, points_to_mask, in_place=False):
"""Apply masking to array where points_to_mask is True/non-zero.
Designed to work with iris.analysis.maths._binary_op_common so array
and points_to_mask will be broadcastable to each other.
array and points_to_mask may be numpy or dask types (or one of each).
If array is lazy then in_place is ignored: _math_op_common will use the
returned value regardless of in_place, so we do not need to implement it
here. If in_place is True then array must be a
:class:`numpy.ma.MaskedArray` or :class:`dask.array.Array`
(must be a dask array if points_to_mask is lazy).
"""
# Decide which array library to use.
if is_lazy_data(points_to_mask) or is_lazy_data(array):
al = da
if not is_lazy_data(array) and in_place:
# Non-lazy array and lazy mask should not come up for in_place
# case, due to _binary_op_common handling added at #3790.
raise TypeError("Cannot apply lazy mask in-place to a non-lazy array.")
in_place = False
elif in_place and not isinstance(array, ma.MaskedArray):
raise TypeError("Cannot apply a mask in-place to a plain numpy array.")
else:
al = np
points_to_mask = points_to_mask.astype(bool)
# Treat any masked points on our mask as False.
points_to_mask = al.ma.filled(points_to_mask, False)
# Get broadcasted views of the arrays. Note that broadcast_arrays does not
# preserve masks, so we need to explicitly handle any existing mask on array.
array_mask = al.ma.getmaskarray(array)
array_data, array_mask, points_to_mask = al.broadcast_arrays(
array, array_mask, points_to_mask
)
new_mask = al.logical_or(array_mask, points_to_mask)
if in_place:
array.mask = new_mask
result = array # Resolve uses returned value even if working in place.
else:
# Return a new, independent array.
result = al.ma.masked_array(array_data.copy(), mask=new_mask)
return result
[docs]
@_lenient_client(services=SERVICES)
def mask_cube(cube, points_to_mask, in_place=False, dim=None):
"""Mask any cells in the cube's data array.
Masks any cells in the cube's data array which correspond to cells marked
``True`` (or non zero) in ``points_to_mask``. ``points_to_mask`` may be
specified as a :class:`numpy.ndarray`, :class:`dask.array.Array`,
:class:`iris.coords.Coord` or :class:`iris.cube.Cube`, following the same
broadcasting approach as cube arithmetic (see :ref:`cube maths`).
Parameters
----------
cube : iris.cube.Cube
Cube containing data that requires masking.
points_to_mask : numpy.ndarray, dask.array.Array, iris.coords.Coord or iris.cube.Cube
Specifies booleans (or ones and zeros) indicating which points will
be masked.
in_place : bool, default=False
If `True`, masking is applied to the input cube. Otherwise a copy is
masked and returned.
dim : int, optional
If `points_to_mask` is a coord which does not exist on the cube,
specify the dimension to which it should be mapped.
Returns
-------
iris.cube.Cube
A cube whose data array is masked at points specified by ``points_to_mask``.
Notes
-----
If either ``cube`` or ``points_to_mask`` is lazy, the result will be lazy.
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if in_place and not cube.has_lazy_data():
# Ensure cube data is masked type so we can work on it in-place.
cube.data = ma.asanyarray(cube.data)
mask_function = functools.partial(_mask_array, in_place=True)
else:
mask_function = _mask_array
input_metadata = cube.metadata
result = iris.analysis.maths._binary_op_common(
mask_function,
"mask",
cube,
points_to_mask,
cube.units,
in_place=in_place,
dim=dim,
sanitise_metadata=False,
)
# Resolve combines the metadata from the two operands, but we want to
# preserve the metadata from the (first) input cube.
result.metadata = input_metadata
if not in_place:
return result
[docs]
def equalise_attributes(cubes):
"""Delete cube attributes that are not identical over all cubes in a group.
This function deletes any attributes which are not the same for all the
given cubes. The cubes will then have identical attributes, and the
removed attributes are returned. The given cubes are modified in-place.
Parameters
----------
cubes : iterable of :class:`iris.cube.Cube`
A collection of cubes to compare and adjust.
Returns
-------
list
A list of dicts holding the removed attributes.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
# deferred import to avoid circularity problem
from iris.common._split_attribute_dicts import (
_convert_splitattrs_to_pairedkeys_dict,
)
cube_attrs = [cube.attributes for cube in cubes]
# Convert all the input dictionaries to ones with 'paired' keys, so each key
# becomes a pair, ('local'/'global', attribute-name), making them specific to each
# "type", i.e. global or local.
# This is needed to ensure that afterwards all cubes will have identical
# attributes, E.G. it treats an attribute which is global on one cube and local
# on another as *not* the same. This is essential to its use in making merges work.
#
# This approach does also still function with "ordinary" dictionaries, or
# :class:`iris.common.mixin.LimitedAttributeDict`, though somewhat inefficiently,
# so the routine works on *other* objects bearing attributes, i.e. not just Cubes.
# That is also important since the original code allows that (though the docstring
# does not admit it).
cube_attrs = [_convert_splitattrs_to_pairedkeys_dict(dic) for dic in cube_attrs]
# Work out which attributes are identical across all the cubes.
common_keys = list(cube_attrs[0].keys())
keys_to_remove = set(common_keys)
for attrs in cube_attrs[1:]:
cube_keys = list(attrs.keys())
keys_to_remove.update(cube_keys)
common_keys = [
key
for key in common_keys
if (key in cube_keys and np.all(attrs[key] == cube_attrs[0][key]))
]
keys_to_remove.difference_update(common_keys)
# Convert back from the resulting 'paired' keys set, extracting just the
# attribute-name parts, as a set of names to be discarded.
# Note: we don't care any more what type (global/local) these were : we will
# simply remove *all* attributes with those names.
keys_to_remove = set(key_pair[1] for key_pair in keys_to_remove)
# Remove all the non-matching attributes.
removed = []
for cube in cubes:
deleted_attributes = {
key: cube.attributes.pop(key)
for key in keys_to_remove
if key in cube.attributes
}
removed.append(deleted_attributes)
return removed
[docs]
def is_masked(array):
"""Equivalent to :func:`numpy.ma.is_masked`, but works for both lazy AND realised arrays.
Parameters
----------
array : :class:`numpy.Array` or :class:`dask.array.Array`
The array to be checked for masks.
Returns
-------
bool
Whether or not the array has any masks.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if is_lazy_data(array):
result = da.ma.getmaskarray(array).any().compute()
else:
result = ma.is_masked(array)
return result
def _strip_metadata_from_dims(cube, dims):
"""Remove ancillary variables and cell measures that map to specific dimensions.
Returns a cube copy with (possibly) some cell-measures and ancillary
variables removed.
To be used by operations that modify or remove dimensions.
Note: does nothing to (aux)-coordinates. Those would be handled explicitly
by the calling operation.
"""
reduced_cube = cube.copy()
# Remove any ancillary variables that span the dimension(s) being collapsed
for ancil in reduced_cube.ancillary_variables():
ancil_dims = reduced_cube.ancillary_variable_dims(ancil)
if set(dims).intersection(ancil_dims):
reduced_cube.remove_ancillary_variable(ancil)
# Remove any cell measures that span the dimension(s) being collapsed
for cm in reduced_cube.cell_measures():
cm_dims = reduced_cube.cell_measure_dims(cm)
if set(dims).intersection(cm_dims):
reduced_cube.remove_cell_measure(cm)
return reduced_cube
[docs]
def mask_cube_from_shapefile(cube, shape, minimum_weight=0.0, in_place=False):
"""Take a shape object and masks all points not touching it in a cube.
Finds the overlap between the `shape` and the `cube` in 2D xy space and
masks out any cells with less % overlap with shape than set.
Default behaviour is to count any overlap between shape and cell as valid
Parameters
----------
cube : :class:`~iris.cube.Cube` object
The `Cube` object to masked. Must be singular, rather than a `CubeList`.
shape : Shapely.Geometry object
A single `shape` of the area to remain unmasked on the `cube`.
If it a line object of some kind then minimum_weight will be ignored,
because you cannot compare the area of a 1D line and 2D Cell.
minimum_weight : float , default=0.0
A number between 0-1 describing what % of a cube cell area must
the shape overlap to include it.
in_place : bool, default=False
Whether to mask the `cube` in-place or return a newly masked `cube`.
Defaults to False.
Returns
-------
iris.Cube
A masked version of the input cube, if in_place is False.
See Also
--------
:func:`~iris.util.mask_cube`
Mask any cells in the cube’s data array.
Notes
-----
This function allows masking a cube with any cartopy projection by a shape object,
most commonly from Natural Earth Shapefiles via cartopy.
To mask a cube from a shapefile, both must first be on the same coordinate system.
Shapefiles are mostly on a lat/lon grid with a projection very similar to GeogCS
The shapefile is projected to the coord system of the cube using cartopy, then each cell
is compared to the shapefile to determine overlap and populate a true/false array
This array is then used to mask the cube using the `iris.util.mask_cube` function
This uses numpy arithmetic logic for broadcasting, so you may encounter unexpected
results if your cube has other dimensions the same length as the x/y dimensions
Examples
--------
>>> import shapely
>>> from iris.util import mask_cube_from_shapefile
>>> cube = iris.load_cube(iris.sample_data_path("E1_north_america.nc"))
>>> shape = shapely.geometry.box(-100,30, -80,40) # box between 30N-40N 100W-80W
>>> masked_cube = mask_cube_from_shapefile(cube, shape)
"""
shapefile_mask = create_shapefile_mask(shape, cube, minimum_weight)
masked_cube = mask_cube(cube, shapefile_mask, in_place=in_place)
if not in_place:
return masked_cube
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