# 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.
"""Basic mathematical and statistical operations."""
from functools import lru_cache
import inspect
import math
import operator
import warnings
import cf_units
import dask.array as da
import numpy as np
from numpy import ma
from iris._deprecation import warn_deprecated
import iris.analysis
from iris.common import SERVICES, Resolve
from iris.common.lenient import _lenient_client
from iris.config import get_logger
import iris.coords
import iris.exceptions
import iris.util
import iris.warnings
# Configure the logger.
logger = get_logger(__name__)
@lru_cache(maxsize=128, typed=True)
def _output_dtype(op, first_dtype, second_dtype=None, in_place=False):
"""Get the numpy dtype.
Get the numpy dtype corresponding to the result of applying a unary or
binary operation to arguments of specified dtype.
Parameters
----------
op :
A unary or binary operator which can be applied to array-like objects.
first_dtype :
The dtype of the first or only argument to the operator.
second_dtype : optional
The dtype of the second argument to the operator.
in_place : bool, default=False
Whether the operation is to be performed in place.
Returns
-------
:class:`numpy.dtype`
Notes
-----
.. note::
The function always returns the dtype which would result if the
operation were successful, even if the operation could fail due to
casting restrictions for in place operations.
"""
if in_place:
# Always return the first dtype, even if the operation would fail due
# to failure to cast the result.
result = first_dtype
else:
operand_dtypes = (
(first_dtype, second_dtype) if second_dtype is not None else (first_dtype,)
)
arrays = [np.array([1], dtype=dtype) for dtype in operand_dtypes]
result = op(*arrays).dtype
return result
def _get_dtype(operand):
"""Get the numpy dtype corresponding to the numeric data in the object provided.
Parameters
----------
operand :
An instance of :class:`iris.cube.Cube` or :class:`iris.coords.Coord`,
or a number or :class:`numpy.ndarray`.
Returns
-------
:class:`numpy.dtype`
"""
return np.min_scalar_type(operand) if np.isscalar(operand) else operand.dtype
[docs]
def abs(cube, in_place=False):
"""Calculate the absolute values of the data in the Cube provided.
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`.
in_place : bool, default=False
Whether to create a new Cube, or alter the given "cube".
Returns
-------
:class:`iris.cube.Cube`.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(np.abs, cube.dtype, in_place=in_place)
op = da.absolute if cube.has_lazy_data() else np.abs
return _math_op_common(cube, op, cube.units, new_dtype=new_dtype, in_place=in_place)
[docs]
def intersection_of_cubes(cube, other_cube):
"""Return the two Cubes of intersection given two Cubes.
.. note:: The intersection of cubes function will ignore all single valued
coordinates in checking the intersection.
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`.
other_cube :
An instance of :class:`iris.cube.Cube`.
Returns
-------
A paired tuple of :class:`iris.cube.Cube`
A pair of :class:`iris.cube.Cube` instances in a tuple corresponding to
the original cubes restricted to their intersection.
Notes
-----
.. deprecated:: 3.2.0
Instead use :meth:`iris.cube.CubeList.extract_overlapping`. For example,
rather than calling
.. code::
cube1, cube2 = intersection_of_cubes(cube1, cube2)
replace with
.. code::
cubes = CubeList([cube1, cube2])
coords = ["latitude", "longitude"] # Replace with relevant coords
intersections = cubes.extract_overlapping(coords)
cube1, cube2 = (intersections[0], intersections[1])
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
wmsg = (
"iris.analysis.maths.intersection_of_cubes has been deprecated and will "
"be removed, please use iris.cube.CubeList.extract_overlapping "
"instead. See intersection_of_cubes docstring for more information."
)
warn_deprecated(wmsg)
# Take references of the original cubes (which will be copied when
# slicing later).
new_cube_self = cube
new_cube_other = other_cube
# This routine has not been written to cope with multi-dimensional
# coordinates.
for coord in cube.coords() + other_cube.coords():
if coord.ndim != 1:
raise iris.exceptions.CoordinateMultiDimError(coord)
coord_comp = iris.analysis._dimensional_metadata_comparison(cube, other_cube)
if coord_comp["ungroupable_and_dimensioned"]:
raise ValueError(
"Cubes do not share all coordinates in common, cannot intersect."
)
# cubes must have matching coordinates
for coord in cube.coords():
other_coord = other_cube.coord(coord)
# Only intersect coordinates which are different, single values
# coordinates may differ.
if coord.shape[0] > 1 and coord != other_coord:
intersected_coord = coord.intersect(other_coord)
new_cube_self = new_cube_self.subset(intersected_coord)
new_cube_other = new_cube_other.subset(intersected_coord)
return new_cube_self, new_cube_other
def _assert_is_cube(cube):
from iris.cube import Cube
if not isinstance(cube, Cube):
raise TypeError('The "cube" argument must be an instance of ' "iris.cube.Cube.")
[docs]
@_lenient_client(services=SERVICES)
def add(cube, other, dim=None, in_place=False):
"""Calculate the sum.
Calculate the sum of two cubes, or the sum of a cube and a coordinate or
array or scalar value.
When summing two cubes, they must both have the same coordinate systems and
data resolution.
When adding a coordinate to a cube, they must both share the same number of
elements along a shared axis.
Parameters
----------
cube : iris.cube.Cube
First operand to add.
other : iris.cube.Cube, iris.coords.Coord, number, numpy.ndarray or dask.array.Array
Second operand to add.
dim : int, optional
If `other` is a coord which does not exist on the cube, specify the
dimension to which it should be mapped.
in_place : bool, default=False
If `True`, alters the input cube. Otherwise a new cube is created.
Returns
-------
iris.cube.Cube
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(
operator.add,
cube.dtype,
second_dtype=_get_dtype(other),
in_place=in_place,
)
if in_place:
_inplace_common_checks(cube, other, "addition")
op = operator.iadd
else:
op = operator.add
return _add_subtract_common(
op, "add", cube, other, new_dtype, dim=dim, in_place=in_place
)
[docs]
@_lenient_client(services=SERVICES)
def subtract(cube, other, dim=None, in_place=False):
"""Calculate the difference.
Calculate the difference between two cubes, or the difference between
a cube and a coordinate or array or scalar value.
When differencing two cubes, they must both have the same coordinate systems
and data resolution.
When subtracting a coordinate from a cube, they must both share the same
number of elements along a shared axis.
Parameters
----------
cube : iris.cube.Cube
Cube from which to subtract.
other : iris.cube.Cube, iris.coords.Coord, number, numpy.ndarray or dask.array.Array
Object to subtract from the cube.
dim : int, optional
If `other` is a coord which does not exist on the cube, specify the
dimension to which it should be mapped.
in_place : bool, default=False
If `True`, alters the input cube. Otherwise a new cube is created.
Returns
-------
iris.cube.Cube
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(
operator.sub,
cube.dtype,
second_dtype=_get_dtype(other),
in_place=in_place,
)
if in_place:
_inplace_common_checks(cube, other, "subtraction")
op = operator.isub
else:
op = operator.sub
return _add_subtract_common(
op, "subtract", cube, other, new_dtype, dim=dim, in_place=in_place
)
def _add_subtract_common(
operation_function,
operation_name,
cube,
other,
new_dtype,
dim=None,
in_place=False,
):
"""Share common code between addition and subtraction of cubes.
Parameters
----------
operation_function :
Function which does the operation (e.g. numpy.subtract).
operation_name :
The public name of the operation (e.g. 'divide').
cube :
The cube whose data is used as the first argument to `operation_function`.
other :
The cube, coord, ndarray, dask array or number whose
data is used as the second argument.
new_dtype :
The expected dtype of the output. Used in the case of scalar
masked arrays.
dim : optional
Dimension along which to apply `other` if it's a coordinate that is not
found in `cube`.
in_place : bool, default=False
Whether or not to apply the operation in place to `cube` and `cube.data`.
"""
_assert_is_cube(cube)
if cube.units != getattr(other, "units", cube.units):
emsg = (
f"Cannot use {operation_name!r} with differing units "
f"({cube.units} & {other.units})"
)
raise iris.exceptions.NotYetImplementedError(emsg)
result = _binary_op_common(
operation_function,
operation_name,
cube,
other,
cube.units,
new_dtype=new_dtype,
dim=dim,
in_place=in_place,
)
return result
[docs]
@_lenient_client(services=SERVICES)
def multiply(cube, other, dim=None, in_place=False):
"""Calculate the product.
Calculate the product of two cubes, or the product of a cube and a coordinate
or array or scalar value.
When multiplying two cubes, they must both have the same coordinate systems
and data resolution.
When mulplying a cube by a coordinate, they must both share the same number
of elements along a shared axis.
Parameters
----------
cube : iris.cube.Cube
First operand to multiply.
other : iris.cube.Cube, iris.coords.Coord, number, numpy.ndarray or dask.array.Array
Second operand to multiply.
dim : int, optional
If `other` is a coord which does not exist on the cube, specify the
dimension to which it should be mapped.
in_place : bool, default=False
If `True`, alters the input cube. Otherwise a new cube is created.
Returns
-------
iris.cube.Cube
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(
operator.mul,
cube.dtype,
second_dtype=_get_dtype(other),
in_place=in_place,
)
other_unit = getattr(other, "units", "1")
new_unit = cube.units * other_unit
if in_place:
_inplace_common_checks(cube, other, "multiplication")
op = operator.imul
else:
op = operator.mul
result = _binary_op_common(
op,
"multiply",
cube,
other,
new_unit,
new_dtype=new_dtype,
dim=dim,
in_place=in_place,
)
return result
def _inplace_common_checks(cube, other, math_op):
"""Check if an inplace math operation can take place.
Check whether an inplace math operation can take place between `cube` and
`other`. It cannot if `cube` has integer data and `other` has float data
as the operation will always produce float data that cannot be 'safely'
cast back to the integer data of `cube`.
"""
other_dtype = _get_dtype(other)
if not np.can_cast(other_dtype, cube.dtype, "same_kind"):
aemsg = (
"Cannot perform inplace {} between {!r} "
"with {} data and {!r} with {} data."
)
raise ArithmeticError(
aemsg.format(math_op, cube, cube.dtype, other, other_dtype)
)
[docs]
@_lenient_client(services=SERVICES)
def divide(cube, other, dim=None, in_place=False):
"""Calculate the ratio.
Calculate the ratio of two cubes, or the ratio of a cube and a coordinate
or array or scalar value.
When dividing a cube by another cube, they must both have the same coordinate
systems and data resolution.
When dividing a cube by a coordinate, they must both share the same number
of elements along a shared axis.
Parameters
----------
cube : iris.cube.Cube
Numerator.
other : iris.cube.Cube, iris.coords.Coord, number, numpy.ndarray or dask.array.Array
Denominator.
dim : int, optional
If `other` is a coord which does not exist on the cube, specify the
dimension to which it should be mapped.
in_place : bool, default=False
If `True`, alters the input cube. Otherwise a new cube is created.
Returns
-------
iris.cube.Cube
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(
operator.truediv,
cube.dtype,
second_dtype=_get_dtype(other),
in_place=in_place,
)
other_unit = getattr(other, "units", "1")
new_unit = cube.units / other_unit
if in_place:
if cube.dtype.kind in "iu":
# Cannot coerce float result from inplace division back to int.
emsg = (
f"Cannot perform inplace division of cube {cube.name()!r} "
"with integer data."
)
raise ArithmeticError(emsg)
op = operator.itruediv
else:
op = operator.truediv
result = _binary_op_common(
op,
"divide",
cube,
other,
new_unit,
new_dtype=new_dtype,
dim=dim,
in_place=in_place,
)
return result
[docs]
def exponentiate(cube, exponent, in_place=False):
"""Return the result of the given cube to the power of a scalar.
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`.
exponent :
The integer or floating point exponent.
.. note:: When applied to the cube's unit, the exponent must
result in a unit that can be described using only integer
powers of the basic units.
e.g. Unit('meter^-2 kilogram second^-1')
in_place : bool, default=False
Whether to create a new Cube, or alter the given "cube".
Returns
-------
:class:`iris.cube.Cube`.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(
operator.pow,
cube.dtype,
second_dtype=_get_dtype(exponent),
in_place=in_place,
)
if cube.has_lazy_data():
def power(data):
return operator.pow(data, exponent)
else:
def power(data, out=None):
return np.power(data, exponent, out)
return _math_op_common(
cube,
power,
cube.units**exponent,
new_dtype=new_dtype,
in_place=in_place,
)
[docs]
def exp(cube, in_place=False):
"""Calculate the exponential (exp(x)) of the cube.
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`.
in_place : bool, default=False
Whether to create a new Cube, or alter the given "cube".
Returns
-------
:class:`iris.cube.Cube`.
Notes
-----
Taking an exponential will return a cube with dimensionless units.
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(np.exp, cube.dtype, in_place=in_place)
op = da.exp if cube.has_lazy_data() else np.exp
return _math_op_common(
cube, op, cf_units.Unit("1"), new_dtype=new_dtype, in_place=in_place
)
[docs]
def log(cube, in_place=False):
"""Calculate the natural logarithm (base-e logarithm) of the cube.
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`.
in_place : bool, default=False
Whether to create a new Cube, or alter the given "cube".
Returns
-------
:class:`iris.cube.Cube`
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(np.log, cube.dtype, in_place=in_place)
op = da.log if cube.has_lazy_data() else np.log
return _math_op_common(
cube,
op,
cube.units.log(math.e),
new_dtype=new_dtype,
in_place=in_place,
)
[docs]
def log2(cube, in_place=False):
"""Calculate the base-2 logarithm of the cube.
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`.
in_place : bool, default=False
Whether to create a new Cube, or alter the given "cube".
Returns
-------
:class:`iris.cube.Cube`
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(np.log2, cube.dtype, in_place=in_place)
op = da.log2 if cube.has_lazy_data() else np.log2
return _math_op_common(
cube, op, cube.units.log(2), new_dtype=new_dtype, in_place=in_place
)
[docs]
def log10(cube, in_place=False):
"""Calculate the base-10 logarithm of the cube.
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`.
in_place : bool, default=False
Whether to create a new Cube, or alter the given "cube".
Returns
-------
:class:`iris.cube.Cube`.
Notes
-----
This function maintains laziness when called; it does not realise data.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
_assert_is_cube(cube)
new_dtype = _output_dtype(np.log10, cube.dtype, in_place=in_place)
op = da.log10 if cube.has_lazy_data() else np.log10
return _math_op_common(
cube, op, cube.units.log(10), new_dtype=new_dtype, in_place=in_place
)
[docs]
def apply_ufunc(ufunc, cube, other=None, new_unit=None, new_name=None, in_place=False):
"""Apply a `numpy universal function <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_ to a cube.
Apply a `numpy universal function
<https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_ to a cube
or pair of cubes.
.. note:: Many of the numpy.ufunc have been implemented explicitly in Iris
e.g. :func:`numpy.abs`, :func:`numpy.add` are implemented in
:func:`iris.analysis.maths.abs`, :func:`iris.analysis.maths.add`.
It is usually preferable to use these functions rather than
:func:`iris.analysis.maths.apply_ufunc` where possible.
Parameters
----------
ufunc :
An instance of :func:`numpy.ufunc` e.g. :func:`numpy.sin`,
:func:`numpy.mod`.
cube :
An instance of :class:`iris.cube.Cube`.
other : :class:`iris.cube.Cube`, optional
An instance of :class:`iris.cube.Cube` to be given as the second
argument to :func:`numpy.ufunc`.
new_unit : optional
Unit for the resulting Cube.
new_name : optional
Name for the resulting Cube.
in_place : bool, default=False
Whether to create a new Cube, or alter the given "cube".
Returns
-------
:class:`iris.cube.Cube`.
Examples
--------
::
cube = apply_ufunc(numpy.sin, cube, in_place=True)
.. note::
This function maintains laziness when called; it does not realise data. This is dependent on `ufunc` argument
being a numpy operation that is compatible with lazy operation.
See more at :doc:`/userguide/real_and_lazy_data`.
"""
if not isinstance(ufunc, np.ufunc):
ufunc_name = getattr(ufunc, "__name__", "function passed to apply_ufunc")
emsg = f"{ufunc_name} is not recognised, it is not an instance of numpy.ufunc"
raise TypeError(emsg)
ufunc_name = ufunc.__name__
if ufunc.nout != 1:
emsg = (
f"{ufunc_name} returns {ufunc.nout} objects, apply_ufunc currently "
"only supports numpy.ufunc functions returning a single object."
)
raise ValueError(emsg)
if ufunc.nin == 1:
if other is not None:
dmsg = (
"ignoring surplus 'other' argument to apply_ufunc, "
f"provided ufunc {ufunc_name!r} only requires 1 input"
)
logger.debug(dmsg)
new_dtype = _output_dtype(ufunc, cube.dtype, in_place=in_place)
new_cube = _math_op_common(
cube, ufunc, new_unit, new_dtype=new_dtype, in_place=in_place
)
elif ufunc.nin == 2:
if other is None:
emsg = (
f"{ufunc_name} requires two arguments, another cube "
"must also be passed to apply_ufunc."
)
raise ValueError(emsg)
_assert_is_cube(other)
new_dtype = _output_dtype(
ufunc, cube.dtype, second_dtype=other.dtype, in_place=in_place
)
new_cube = _binary_op_common(
ufunc,
ufunc_name,
cube,
other,
new_unit,
new_dtype=new_dtype,
in_place=in_place,
)
else:
emsg = f"Provided ufunc '{ufunc_name}.nin' must be 1 or 2."
raise ValueError(emsg)
new_cube.rename(new_name)
return new_cube
def _binary_op_common(
operation_function,
operation_name,
cube,
other,
new_unit,
new_dtype=None,
dim=None,
in_place=False,
sanitise_metadata=True,
):
"""Share common code between binary operations.
Parameters
----------
operation_function :
Function which does the operation (e.g. numpy.divide).
operation_name :
The public name of the operation (e.g. 'divide').
cube :
The cube whose data is used as the first argument to `operation_function`.
other :
The cube, coord, ndarray, dask array or number whose data is used
as the second argument.
new_unit : optional
Unit for the resulting quantity.
new_dtype :
The expected dtype of the output. Used in the case of scalar masked arrays.
dim : optional
Dimension along which to apply `other` if it's a coordinate that is
not found in `cube`.
in_place : bool, default=False
Whether or not to apply the operation in place to `cube` and `cube.data`.
sanitise_metadata : bool, default=True
Whether or not to remove metadata using _sanitise_metadata function.
"""
from iris.cube import Cube
_assert_is_cube(cube)
# Flag to notify the _math_op_common function to simply wrap the resultant
# data of the maths operation in a cube with no metadata.
skeleton_cube = False
if isinstance(other, iris.coords.Coord):
# The rhs must be an array.
rhs = _broadcast_cube_coord_data(cube, other, operation_name, dim=dim)
elif isinstance(other, Cube):
# Prepare to resolve the cube operands and associated coordinate
# metadata into the resultant cube.
resolver = Resolve(cube, other)
# Get the broadcast, auto-transposed safe versions of the cube operands.
cube = resolver.lhs_cube_resolved
other = resolver.rhs_cube_resolved
# Flag that it's safe to wrap the resultant data of the math operation
# in a cube with no metadata, as all of the metadata of the resultant
# cube is being managed by the resolver.
skeleton_cube = True
# The rhs must be an array.
rhs = other.core_data()
else:
# The rhs must be an array.
if iris._lazy_data.is_lazy_data(other):
rhs = other
else:
rhs = np.asanyarray(other)
def unary_func(lhs):
data = operation_function(lhs, rhs)
if data is NotImplemented:
# Explicitly raise the TypeError, so it gets raised even if, for
# example, `iris.analysis.maths.multiply(cube, other)` is called
# directly instead of `cube * other`.
emsg = (
f"Cannot {operation_function.__name__} {type(lhs).__name__!r} "
f"and {type(rhs).__name__} objects."
)
raise TypeError(emsg)
return data
if in_place and not cube.has_lazy_data():
# In-place arithmetic doesn't work if array type of LHS is less complex
# than RHS.
if iris._lazy_data.is_lazy_data(rhs):
cube.data = cube.lazy_data()
elif ma.is_masked(rhs) and not isinstance(cube.data, ma.MaskedArray):
cube.data = ma.array(cube.data)
elif isinstance(cube.core_data(), ma.MaskedArray) and iris._lazy_data.is_lazy_data(
rhs
):
# Workaround for #2987. numpy#15200 discusses the general problem.
cube = cube.copy(cube.lazy_data())
result = _math_op_common(
cube,
unary_func,
new_unit,
new_dtype=new_dtype,
in_place=in_place,
skeleton_cube=skeleton_cube,
sanitise_metadata=sanitise_metadata,
)
if isinstance(other, Cube):
# Insert the resultant data from the maths operation
# within the resolved cube.
result = resolver.cube(result.core_data(), in_place=in_place)
if sanitise_metadata:
_sanitise_metadata(result, new_unit)
return result
def _broadcast_cube_coord_data(cube, other, operation_name, dim=None):
# What dimension are we processing?
data_dimension = None
if dim is not None:
# Ensure the given dim matches the coord
if other in cube.coords() and cube.coord_dims(other) != [dim]:
raise ValueError("dim provided does not match dim found for coord")
data_dimension = dim
else:
# Try and get a coord dim
if other.shape != (1,):
try:
coord_dims = cube.coord_dims(other)
data_dimension = coord_dims[0] if coord_dims else None
except iris.exceptions.CoordinateNotFoundError:
raise ValueError(
"Could not determine dimension for %s. "
"Use %s(cube, coord, dim=dim)" % (operation_name, operation_name)
)
if other.ndim != 1:
raise iris.exceptions.CoordinateMultiDimError(other)
if other.has_bounds():
warnings.warn(
"Using {!r} with a bounded coordinate is not well "
"defined; ignoring bounds.".format(operation_name),
category=iris.warnings.IrisIgnoringBoundsWarning,
)
points = other.points
# If the `data_dimension` is defined then shape the provided points for
# proper array broadcasting
if data_dimension is not None:
points_shape = [1] * cube.ndim
points_shape[data_dimension] = -1
points = points.reshape(points_shape)
return points
def _sanitise_metadata(cube, unit):
"""Clear appropriate metadata from the resultant cube.
As part of the maths metadata contract, clear the necessary or
unsupported metadata from the resultant cube of the maths operation.
"""
# Clear the cube names.
cube.rename(None)
# Clear the cube cell methods.
cube.cell_methods = None
# Clear the cell measures.
for cm in cube.cell_measures():
cube.remove_cell_measure(cm)
# Clear the ancillary variables.
for av in cube.ancillary_variables():
cube.remove_ancillary_variable(av)
# Clear the STASH attribute, if present.
if "STASH" in cube.attributes:
del cube.attributes["STASH"]
# Set the cube units.
cube.units = unit
def _math_op_common(
cube,
operation_function,
new_unit,
new_dtype=None,
in_place=False,
skeleton_cube=False,
sanitise_metadata=True,
):
from iris.cube import Cube
_assert_is_cube(cube)
if in_place and not skeleton_cube:
if cube.has_lazy_data():
cube.data = operation_function(cube.lazy_data())
else:
try:
operation_function(cube.data, out=cube.data)
except TypeError:
# Non-ufunc function
operation_function(cube.data)
new_cube = cube
else:
data = operation_function(cube.core_data())
if skeleton_cube:
# Simply wrap the resultant data in a cube, as no
# cube metadata is required by the caller.
new_cube = Cube(data)
else:
new_cube = cube.copy(data)
# If the result of the operation is scalar and masked, we need to fix-up the dtype.
if (
new_dtype is not None
and not new_cube.has_lazy_data()
and new_cube.data.shape == ()
and ma.is_masked(new_cube.data)
):
new_cube.data = ma.masked_array(0, 1, dtype=new_dtype)
if sanitise_metadata:
_sanitise_metadata(new_cube, new_unit)
return new_cube
[docs]
class IFunc:
""":class:`IFunc` class for functions that can be applied to an iris cube."""
def __init__(self, data_func, units_func):
"""Create an ifunc from a data function and units function.
Parameters
----------
data_func :
Function to be applied to one or two data arrays, which
are given as positional arguments. Should return another
data array, with the same shape as the first array.
May also have keyword arguments.
units_func :
Function to calculate the units of the resulting cube.
Should take the cube/s as input and return
an instance of :class:`cf_units.Unit`.
Returns
-------
ifunc
Examples
--------
**Example usage 1** Using an existing numpy ufunc, such as numpy.sin
for the data function and a simple lambda function for the units
function::
sine_ifunc = iris.analysis.maths.IFunc(
numpy.sin, lambda cube: cf_units.Unit('1'))
sine_cube = sine_ifunc(cube)
**Example usage 2** Define a function for the data arrays of two cubes
and define a units function that checks the units of the cubes
for consistency, before giving the resulting cube the same units
as the first cube::
def ws_data_func(u_data, v_data):
return numpy.sqrt( u_data**2 + v_data**2 )
def ws_units_func(u_cube, v_cube):
if u_cube.units != getattr(v_cube, 'units', u_cube.units):
raise ValueError("units do not match")
return u_cube.units
ws_ifunc = iris.analysis.maths.IFunc(ws_data_func, ws_units_func)
ws_cube = ws_ifunc(u_cube, v_cube, new_name='wind speed')
**Example usage 3** Using a data function that allows a keyword
argument::
cs_ifunc = iris.analysis.maths.IFunc(numpy.cumsum,
lambda a: a.units)
cs_cube = cs_ifunc(cube, axis=1)
"""
self._data_func_name = getattr(
data_func, "__name__", "data_func argument passed to IFunc"
)
if not callable(data_func):
emsg = f"{self._data_func_name} is not callable."
raise TypeError(emsg)
self._unit_func_name = getattr(
units_func, "__name__", "units_func argument passed to IFunc"
)
if not callable(units_func):
emsg = f"{self._unit_func_name} is not callable."
raise TypeError(emsg)
if hasattr(data_func, "nin"):
self.nin = data_func.nin
else:
sig = inspect.signature(data_func)
args = [
param
for param in sig.parameters.values()
if (param.kind != param.KEYWORD_ONLY and param.default is param.empty)
]
self.nin = len(args)
if self.nin not in [1, 2]:
emsg = (
f"{self._data_func_name} requires {self.nin} input data "
"arrays, the IFunc class currently only supports functions "
"requiring 1 or 2 data arrays as input."
)
raise ValueError(emsg)
if hasattr(data_func, "nout"):
if data_func.nout != 1:
emsg = (
f"{self._data_func_name} returns {data_func.nout} objects, "
"the IFunc class currently only supports functions "
"returning a single object."
)
raise ValueError(emsg)
self.data_func = data_func
self.units_func = units_func
def __repr__(self):
result = (
f"iris.analysis.maths.IFunc({self._data_func_name}, "
f"{self._unit_func_name})"
)
return result
def __str__(self):
result = (
f"IFunc constructed from the data function {self._data_func_name} "
f"and the units function {self._unit_func_name}"
)
return result
[docs]
def __call__(
self,
cube,
other=None,
dim=None,
in_place=False,
new_name=None,
**kwargs_data_func,
):
"""Apply the ifunc to the cube(s).
Parameters
----------
cube :
An instance of :class:`iris.cube.Cube`, whose data is used
as the first argument to the data function.
other : optional
A cube, coord, ndarray, dask array or number whose data is used as the
second argument to the data function.
dim : optional
Dimension along which to apply `other` if it's a coordinate that is
not found in `cube`.
in_place : bool, default=False
Whether to create a new Cube, or alter the given "cube".
new_name : optional
Name for the resulting Cube.
**kwargs_data_func :
Keyword arguments that get passed on to the data_func.
Returns
-------
:class:`iris.cube.Cube`
"""
_assert_is_cube(cube)
def wrap_data_func(*args, **kwargs):
kwargs_combined = dict(kwargs_data_func, **kwargs)
return self.data_func(*args, **kwargs_combined)
if self.nin == 1:
if other is not None:
dmsg = (
"ignoring surplus 'other' argument to IFunc.__call__, "
f"provided data_func {self._data_func_name!r} only requires "
"1 input"
)
logger.debug(dmsg)
new_unit = self.units_func(cube)
new_cube = _math_op_common(
cube, wrap_data_func, new_unit, in_place=in_place
)
else:
if other is None:
emsg = (
f"{self._data_func_name} requires two arguments, another "
"cube must also be passed to IFunc.__call__."
)
raise ValueError(emsg)
new_unit = self.units_func(cube, other)
new_cube = _binary_op_common(
wrap_data_func,
self.data_func.__name__,
cube,
other,
new_unit,
dim=dim,
in_place=in_place,
)
if new_name is not None:
new_cube.rename(new_name)
return new_cube
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