from functools import reduce
import operator
import warnings
from collections.abc import Iterable
import numpy as np
import scipy.sparse
import numba
from .._sparse_array import SparseArray
from .._utils import (
isscalar,
is_unsigned_dtype,
normalize_axis,
check_zero_fill_value,
check_consistent_fill_value,
can_store,
)
def asCOO(x, name="asCOO", check=True):
"""
Convert the input to :obj:`COO`. Passes through :obj:`COO` objects as-is.
Parameters
----------
x : Union[SparseArray, scipy.sparse.spmatrix, numpy.ndarray]
The input array to convert.
name : str, optional
The name of the operation to use in the exception.
check : bool, optional
Whether to check for a dense input.
Returns
-------
COO
The converted :obj:`COO` array.
Raises
------
ValueError
If ``check`` is true and a dense input is supplied.
"""
from .core import COO
if check and not isinstance(x, (SparseArray, scipy.sparse.spmatrix)):
raise ValueError(
"Performing this operation would produce a dense result: %s" % name
)
if not isinstance(x, COO):
x = COO(x)
return x
def linear_loc(coords, shape):
if shape == () and len(coords) == 0:
# `np.ravel_multi_index` is not aware of arrays, so cannot produce a
# sensible result here (https://github.com/numpy/numpy/issues/15690).
# Since `coords` is an array and not a sequence, we know the correct
# dimensions.
return np.zeros(coords.shape[1:], dtype=np.intp)
else:
return np.ravel_multi_index(coords, shape)
[docs]def kron(a, b):
"""Kronecker product of 2 sparse arrays.
Parameters
----------
a, b : SparseArray, scipy.sparse.spmatrix, or np.ndarray
The arrays over which to compute the Kronecker product.
Returns
-------
res : COO
The kronecker product
Raises
------
ValueError
If all arguments are dense or arguments have nonzero fill-values.
Examples
--------
>>> from sparse import eye
>>> a = eye(3, dtype='i8')
>>> b = np.array([1, 2, 3], dtype='i8')
>>> res = kron(a, b)
>>> res.todense() # doctest: +SKIP
array([[1, 2, 3, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 2, 3, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 2, 3]], dtype=int64)
"""
from .core import COO
from .._umath import _cartesian_product
check_zero_fill_value(a, b)
a_sparse = isinstance(a, (SparseArray, scipy.sparse.spmatrix))
b_sparse = isinstance(b, (SparseArray, scipy.sparse.spmatrix))
a_ndim = np.ndim(a)
b_ndim = np.ndim(b)
if not (a_sparse or b_sparse):
raise ValueError(
"Performing this operation would produce a dense " "result: kron"
)
if a_ndim == 0 or b_ndim == 0:
return a * b
a = asCOO(a, check=False)
b = asCOO(b, check=False)
# Match dimensions
max_dim = max(a.ndim, b.ndim)
a = a.reshape((1,) * (max_dim - a.ndim) + a.shape)
b = b.reshape((1,) * (max_dim - b.ndim) + b.shape)
a_idx, b_idx = _cartesian_product(np.arange(a.nnz), np.arange(b.nnz))
a_expanded_coords = a.coords[:, a_idx]
b_expanded_coords = b.coords[:, b_idx]
o_coords = a_expanded_coords * np.asarray(b.shape)[:, None] + b_expanded_coords
o_data = a.data[a_idx] * b.data[b_idx]
o_shape = tuple(i * j for i, j in zip(a.shape, b.shape))
return COO(o_coords, o_data, shape=o_shape, has_duplicates=False)
def concatenate(arrays, axis=0):
"""
Concatenate the input arrays along the given dimension.
Parameters
----------
arrays : Iterable[SparseArray]
The input arrays to concatenate.
axis : int, optional
The axis along which to concatenate the input arrays. The default is zero.
Returns
-------
COO
The output concatenated array.
Raises
------
ValueError
If all elements of :code:`arrays` don't have the same fill-value.
See Also
--------
numpy.concatenate : NumPy equivalent function
"""
from .core import COO
check_consistent_fill_value(arrays)
arrays = [x if isinstance(x, COO) else COO(x) for x in arrays]
axis = normalize_axis(axis, arrays[0].ndim)
assert all(
x.shape[ax] == arrays[0].shape[ax]
for x in arrays
for ax in set(range(arrays[0].ndim)) - {axis}
)
nnz = 0
dim = sum(x.shape[axis] for x in arrays)
shape = list(arrays[0].shape)
shape[axis] = dim
data = np.concatenate([x.data for x in arrays])
coords = np.concatenate([x.coords for x in arrays], axis=1)
if not can_store(coords.dtype, max(shape)):
coords = coords.astype(np.min_scalar_type(max(shape)))
dim = 0
for x in arrays:
if dim:
coords[axis, nnz : x.nnz + nnz] += dim
dim += x.shape[axis]
nnz += x.nnz
return COO(
coords,
data,
shape=shape,
has_duplicates=False,
sorted=(axis == 0),
fill_value=arrays[0].fill_value,
)
def stack(arrays, axis=0):
"""
Stack the input arrays along the given dimension.
Parameters
----------
arrays : Iterable[SparseArray]
The input arrays to stack.
axis : int, optional
The axis along which to stack the input arrays.
Returns
-------
COO
The output stacked array.
Raises
------
ValueError
If all elements of :code:`arrays` don't have the same fill-value.
See Also
--------
numpy.stack : NumPy equivalent function
"""
from .core import COO
check_consistent_fill_value(arrays)
assert len({x.shape for x in arrays}) == 1
arrays = [x if isinstance(x, COO) else COO(x) for x in arrays]
axis = normalize_axis(axis, arrays[0].ndim + 1)
data = np.concatenate([x.data for x in arrays])
coords = np.concatenate([x.coords for x in arrays], axis=1)
shape = list(arrays[0].shape)
shape.insert(axis, len(arrays))
nnz = 0
dim = 0
new = np.empty(shape=(coords.shape[1],), dtype=np.intp)
for x in arrays:
new[nnz : x.nnz + nnz] = dim
dim += 1
nnz += x.nnz
coords = [coords[i] for i in range(coords.shape[0])]
coords.insert(axis, new)
coords = np.stack(coords, axis=0)
return COO(
coords,
data,
shape=shape,
has_duplicates=False,
sorted=(axis == 0),
fill_value=arrays[0].fill_value,
)
[docs]def triu(x, k=0):
"""
Returns an array with all elements below the k-th diagonal set to zero.
Parameters
----------
x : COO
The input array.
k : int, optional
The diagonal below which elements are set to zero. The default is
zero, which corresponds to the main diagonal.
Returns
-------
COO
The output upper-triangular matrix.
Raises
------
ValueError
If :code:`x` doesn't have zero fill-values.
See Also
--------
numpy.triu : NumPy equivalent function
"""
from .core import COO
check_zero_fill_value(x)
if not x.ndim >= 2:
raise NotImplementedError(
"sparse.triu is not implemented for scalars or 1-D arrays."
)
mask = x.coords[-2] + k <= x.coords[-1]
coords = x.coords[:, mask]
data = x.data[mask]
return COO(coords, data, shape=x.shape, has_duplicates=False, sorted=True)
[docs]def tril(x, k=0):
"""
Returns an array with all elements above the k-th diagonal set to zero.
Parameters
----------
x : COO
The input array.
k : int, optional
The diagonal above which elements are set to zero. The default is
zero, which corresponds to the main diagonal.
Returns
-------
COO
The output lower-triangular matrix.
Raises
------
ValueError
If :code:`x` doesn't have zero fill-values.
See Also
--------
numpy.tril : NumPy equivalent function
"""
from .core import COO
check_zero_fill_value(x)
if not x.ndim >= 2:
raise NotImplementedError(
"sparse.tril is not implemented for scalars or 1-D arrays."
)
mask = x.coords[-2] + k >= x.coords[-1]
coords = x.coords[:, mask]
data = x.data[mask]
return COO(coords, data, shape=x.shape, has_duplicates=False, sorted=True)
[docs]def nansum(x, axis=None, keepdims=False, dtype=None, out=None):
"""
Performs a ``NaN`` skipping sum operation along the given axes. Uses all axes by default.
Parameters
----------
x : SparseArray
The array to perform the reduction on.
axis : Union[int, Iterable[int]], optional
The axes along which to sum. Uses all axes by default.
keepdims : bool, optional
Whether or not to keep the dimensions of the original array.
dtype : numpy.dtype
The data type of the output array.
Returns
-------
COO
The reduced output sparse array.
See Also
--------
:obj:`COO.sum` : Function without ``NaN`` skipping.
numpy.nansum : Equivalent Numpy function.
"""
assert out is None
x = asCOO(x, name="nansum")
return nanreduce(x, np.add, axis=axis, keepdims=keepdims, dtype=dtype)
[docs]def nanmean(x, axis=None, keepdims=False, dtype=None, out=None):
"""
Performs a ``NaN`` skipping mean operation along the given axes. Uses all axes by default.
Parameters
----------
x : SparseArray
The array to perform the reduction on.
axis : Union[int, Iterable[int]], optional
The axes along which to compute the mean. Uses all axes by default.
keepdims : bool, optional
Whether or not to keep the dimensions of the original array.
dtype : numpy.dtype
The data type of the output array.
Returns
-------
COO
The reduced output sparse array.
See Also
--------
:obj:`COO.mean` : Function without ``NaN`` skipping.
numpy.nanmean : Equivalent Numpy function.
"""
assert out is None
x = asCOO(x, name="nanmean")
if not np.issubdtype(x.dtype, np.floating):
return x.mean(axis=axis, keepdims=keepdims, dtype=dtype)
mask = np.isnan(x)
x2 = where(mask, 0, x)
# Count the number non-nan elements along axis
nancount = mask.sum(axis=axis, dtype="i8", keepdims=keepdims)
if axis is None:
axis = tuple(range(x.ndim))
elif not isinstance(axis, tuple):
axis = (axis,)
den = reduce(operator.mul, (x.shape[i] for i in axis), 1)
den -= nancount
if (den == 0).any():
warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2)
num = np.sum(x2, axis=axis, dtype=dtype, keepdims=keepdims)
with np.errstate(invalid="ignore", divide="ignore"):
if num.ndim:
return np.true_divide(num, den, casting="unsafe")
return (num / den).astype(dtype)
[docs]def nanmax(x, axis=None, keepdims=False, dtype=None, out=None):
"""
Maximize along the given axes, skipping ``NaN`` values. Uses all axes by default.
Parameters
----------
x : SparseArray
The array to perform the reduction on.
axis : Union[int, Iterable[int]], optional
The axes along which to maximize. Uses all axes by default.
keepdims : bool, optional
Whether or not to keep the dimensions of the original array.
dtype : numpy.dtype
The data type of the output array.
Returns
-------
COO
The reduced output sparse array.
See Also
--------
:obj:`COO.max` : Function without ``NaN`` skipping.
numpy.nanmax : Equivalent Numpy function.
"""
assert out is None
x = asCOO(x, name="nanmax")
ar = x.reduce(np.fmax, axis=axis, keepdims=keepdims, dtype=dtype)
if (isscalar(ar) and np.isnan(ar)) or np.isnan(ar.data).any():
warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=2)
return ar
[docs]def nanmin(x, axis=None, keepdims=False, dtype=None, out=None):
"""
Minimize along the given axes, skipping ``NaN`` values. Uses all axes by default.
Parameters
----------
x : SparseArray
The array to perform the reduction on.
axis : Union[int, Iterable[int]], optional
The axes along which to minimize. Uses all axes by default.
keepdims : bool, optional
Whether or not to keep the dimensions of the original array.
dtype : numpy.dtype
The data type of the output array.
Returns
-------
COO
The reduced output sparse array.
See Also
--------
:obj:`COO.min` : Function without ``NaN`` skipping.
numpy.nanmin : Equivalent Numpy function.
"""
assert out is None
x = asCOO(x, name="nanmin")
ar = x.reduce(np.fmin, axis=axis, keepdims=keepdims, dtype=dtype)
if (isscalar(ar) and np.isnan(ar)) or np.isnan(ar.data).any():
warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=2)
return ar
[docs]def nanprod(x, axis=None, keepdims=False, dtype=None, out=None):
"""
Performs a product operation along the given axes, skipping ``NaN`` values.
Uses all axes by default.
Parameters
----------
x : SparseArray
The array to perform the reduction on.
axis : Union[int, Iterable[int]], optional
The axes along which to multiply. Uses all axes by default.
keepdims : bool, optional
Whether or not to keep the dimensions of the original array.
dtype : numpy.dtype
The data type of the output array.
Returns
-------
COO
The reduced output sparse array.
See Also
--------
:obj:`COO.prod` : Function without ``NaN`` skipping.
numpy.nanprod : Equivalent Numpy function.
"""
assert out is None
x = asCOO(x)
return nanreduce(x, np.multiply, axis=axis, keepdims=keepdims, dtype=dtype)
[docs]def where(condition, x=None, y=None):
"""
Select values from either ``x`` or ``y`` depending on ``condition``.
If ``x`` and ``y`` are not given, returns indices where ``condition``
is nonzero.
Performs the equivalent of :obj:`numpy.where`.
Parameters
----------
condition : SparseArray
The condition based on which to select values from
either ``x`` or ``y``.
x : SparseArray, optional
The array to select values from if ``condition`` is nonzero.
y : SparseArray, optional
The array to select values from if ``condition`` is zero.
Returns
-------
COO
The output array with selected values if ``x`` and ``y`` are given;
else where the array is nonzero.
Raises
------
ValueError
If the operation would produce a dense result; or exactly one of
``x`` and ``y`` are given.
See Also
--------
numpy.where : Equivalent Numpy function.
"""
from .._umath import elemwise
x_given = x is not None
y_given = y is not None
if not (x_given or y_given):
condition = asCOO(condition, name=str(np.where))
return tuple(condition.coords)
if x_given != y_given:
raise ValueError("either both or neither of x and y should be given")
return elemwise(np.where, condition, x, y)
[docs]def argwhere(a):
"""
Find the indices of array elements that are non-zero, grouped by element.
Parameters
----------
a : array_like
Input data.
Returns
-------
index_array: numpy.ndarray
See Also
--------
:obj:`where`, :obj:`COO.nonzero`
Examples
--------
>>> import sparse
>>> x = sparse.COO(np.arange(6).reshape((2, 3)))
>>> sparse.argwhere(x > 1)
array([[0, 2],
[1, 0],
[1, 1],
[1, 2]])
"""
return np.transpose(a.nonzero())
def _replace_nan(array, value):
"""
Replaces ``NaN``s in ``array`` with ``value``.
Parameters
----------
array : COO
The input array.
value : numpy.number
The values to replace ``NaN`` with.
Returns
-------
COO
A copy of ``array`` with the ``NaN``s replaced.
"""
if not np.issubdtype(array.dtype, np.floating):
return array
return where(np.isnan(array), value, array)
[docs]def nanreduce(x, method, identity=None, axis=None, keepdims=False, **kwargs):
"""
Performs an ``NaN`` skipping reduction on this array. See the documentation
on :obj:`COO.reduce` for examples.
Parameters
----------
x : COO
The array to reduce.
method : numpy.ufunc
The method to use for performing the reduction.
identity : numpy.number
The identity value for this reduction. Inferred from ``method`` if not given.
Note that some ``ufunc`` objects don't have this, so it may be necessary to give it.
axis : Union[int, Iterable[int]], optional
The axes along which to perform the reduction. Uses all axes by default.
keepdims : bool, optional
Whether or not to keep the dimensions of the original array.
**kwargs : dict
Any extra arguments to pass to the reduction operation.
Returns
-------
COO
The result of the reduction operation.
Raises
------
ValueError
If reducing an all-zero axis would produce a nonzero result.
See Also
--------
COO.reduce : Similar method without ``NaN`` skipping functionality.
"""
arr = _replace_nan(x, method.identity if identity is None else identity)
return arr.reduce(method, axis, keepdims, **kwargs)
[docs]def roll(a, shift, axis=None):
"""
Shifts elements of an array along specified axis. Elements that roll beyond
the last position are circulated and re-introduced at the first.
Parameters
----------
a : COO
Input array
shift : int or tuple of ints
Number of index positions that elements are shifted. If a tuple is
provided, then axis must be a tuple of the same size, and each of the
given axes is shifted by the corresponding number. If an int while axis
is a tuple of ints, then broadcasting is used so the same shift is
applied to all axes.
axis : int or tuple of ints, optional
Axis or tuple specifying multiple axes. By default, the
array is flattened before shifting, after which the original shape is
restored.
Returns
-------
res : ndarray
Output array, with the same shape as a.
"""
from .core import COO, as_coo
from numpy.core._exceptions import UFuncTypeError
a = as_coo(a)
# roll flattened array
if axis is None:
return roll(a.reshape((-1,)), shift, 0).reshape(a.shape)
# roll across specified axis
else:
# parse axis input, wrap in tuple
axis = normalize_axis(axis, a.ndim)
if not isinstance(axis, tuple):
axis = (axis,)
# make shift iterable
if not isinstance(shift, Iterable):
shift = (shift,)
elif np.ndim(shift) > 1:
raise ValueError("'shift' and 'axis' must be integers or 1D sequences.")
# handle broadcasting
if len(shift) == 1:
shift = np.full(len(axis), shift)
# check if dimensions are consistent
if len(axis) != len(shift):
raise ValueError(
"If 'shift' is a 1D sequence, " "'axis' must have equal length."
)
if not can_store(a.coords.dtype, max(a.shape + shift)):
raise ValueError(
"cannot roll with coords.dtype {} and shift {}. Try casting coords to a larger dtype.".format(
a.coords.dtype,
shift,
)
)
# shift elements
coords, data = np.copy(a.coords), np.copy(a.data)
try:
for sh, ax in zip(shift, axis):
coords[ax] += sh
coords[ax] %= a.shape[ax]
except UFuncTypeError:
if is_unsigned_dtype(coords.dtype):
raise ValueError(
"rolling with coords.dtype as {} is not safe. Try using a signed dtype.".format(
coords.dtype
)
)
return COO(
coords,
data=data,
shape=a.shape,
has_duplicates=False,
fill_value=a.fill_value,
)
[docs]def diagonal(a, offset=0, axis1=0, axis2=1):
"""
Extract diagonal from a COO array. The equivalent of :obj:`numpy.diagonal`.
Parameters
----------
a : COO
The array to perform the operation on.
offset : int, optional
Offset of the diagonal from the main diagonal. Defaults to main diagonal (0).
axis1 : int, optional
First axis from which the diagonals should be taken.
Defaults to first axis (0).
axis2 : int, optional
Second axis from which the diagonals should be taken.
Defaults to second axis (1).
Examples
--------
>>> import sparse
>>> x = sparse.as_coo(np.arange(9).reshape(3,3))
>>> sparse.diagonal(x).todense()
array([0, 4, 8])
>>> sparse.diagonal(x,offset=1).todense()
array([1, 5])
>>> x = sparse.as_coo(np.arange(12).reshape((2,3,2)))
>>> x_diag = sparse.diagonal(x, axis1=0, axis2=2)
>>> x_diag.shape
(3, 2)
>>> x_diag.todense()
array([[ 0, 7],
[ 2, 9],
[ 4, 11]])
Returns
-------
out: COO
The result of the operation.
Raises
------
ValueError
If a.shape[axis1] != a.shape[axis2]
See Also
--------
:obj:`numpy.diagonal` : NumPy equivalent function
"""
from .core import COO
if a.shape[axis1] != a.shape[axis2]:
raise ValueError("a.shape[axis1] != a.shape[axis2]")
diag_axes = [
axis for axis in range(len(a.shape)) if axis != axis1 and axis != axis2
] + [axis1]
diag_shape = [a.shape[axis] for axis in diag_axes]
diag_shape[-1] -= abs(offset)
diag_idx = _diagonal_idx(a.coords, axis1, axis2, offset)
diag_coords = [a.coords[axis][diag_idx] for axis in diag_axes]
diag_data = a.data[diag_idx]
return COO(diag_coords, diag_data, diag_shape)
[docs]def diagonalize(a, axis=0):
"""
Diagonalize a COO array. The new dimension is appended at the end.
.. WARNING:: :obj:`diagonalize` is not :obj:`numpy` compatible as there is no direct :obj:`numpy` equivalent. The API may change in the future.
Parameters
----------
a : Union[COO, np.ndarray, scipy.sparse.spmatrix]
The array to diagonalize.
axis : int, optional
The axis to diagonalize. Defaults to first axis (0).
Examples
--------
>>> import sparse
>>> x = sparse.as_coo(np.arange(1,4))
>>> sparse.diagonalize(x).todense()
array([[1, 0, 0],
[0, 2, 0],
[0, 0, 3]])
>>> x = sparse.as_coo(np.arange(24).reshape((2,3,4)))
>>> x_diag = sparse.diagonalize(x, axis=1)
>>> x_diag.shape
(2, 3, 4, 3)
:obj:`diagonalize` is the inverse of :obj:`diagonal`
>>> a = sparse.random((3,3,3,3,3), density=0.3)
>>> a_diag = sparse.diagonalize(a, axis=2)
>>> (sparse.diagonal(a_diag, axis1=2, axis2=5) == a.transpose([0,1,3,4,2])).all()
True
Returns
-------
out: COO
The result of the operation.
See Also
--------
:obj:`numpy.diag` : NumPy equivalent for 1D array
"""
from .core import COO, as_coo
a = as_coo(a)
diag_shape = a.shape + (a.shape[axis],)
diag_coords = np.vstack([a.coords, a.coords[axis]])
return COO(diag_coords, a.data, diag_shape)
[docs]def isposinf(x, out=None):
"""
Test element-wise for positive infinity, return result as sparse ``bool`` array.
Parameters
----------
x
Input
out, optional
Output array
Examples
--------
>>> import sparse
>>> x = sparse.as_coo(np.array([np.inf]))
>>> sparse.isposinf(x).todense()
array([ True])
See Also
--------
numpy.isposinf : The NumPy equivalent
"""
from .core import elemwise
return elemwise(lambda x, out=None, dtype=None: np.isposinf(x, out=out), x, out=out)
[docs]def isneginf(x, out=None):
"""
Test element-wise for negative infinity, return result as sparse ``bool`` array.
Parameters
----------
x
Input
out, optional
Output array
Examples
--------
>>> import sparse
>>> x = sparse.as_coo(np.array([-np.inf]))
>>> sparse.isneginf(x).todense()
array([ True])
See Also
--------
numpy.isneginf : The NumPy equivalent
"""
from .core import elemwise
return elemwise(lambda x, out=None, dtype=None: np.isneginf(x, out=out), x, out=out)
[docs]def result_type(*arrays_and_dtypes):
"""Returns the type that results from applying the NumPy type promotion rules to the
arguments.
See Also
--------
numpy.result_type : The NumPy equivalent
"""
return np.result_type(*(_as_result_type_arg(x) for x in arrays_and_dtypes))
def _as_result_type_arg(x):
if not isinstance(x, SparseArray):
return x
if x.ndim > 0:
return x.dtype
# 0-dimensional arrays give different result_type outputs than their dtypes
return x.todense()
@numba.jit(nopython=True, nogil=True)
def _diagonal_idx(coordlist, axis1, axis2, offset):
"""
Utility function that returns all indices that correspond to a diagonal element.
Parameters
----------
coordlist : list of lists
Coordinate indices.
axis1, axis2 : int
The axes of the diagonal.
offset : int
Offset of the diagonal from the main diagonal. Defaults to main diagonal (0).
"""
return np.array(
[
i
for i in range(len(coordlist[axis1]))
if coordlist[axis1][i] + offset == coordlist[axis2][i]
]
)
[docs]def clip(a, a_min=None, a_max=None, out=None):
"""
Clip (limit) the values in the array.
Return an array whose values are limited to ``[min, max]``. One of min
or max must be given.
Parameters
----------
a
a_min : scalar or `SparseArray` or `None`
Minimum value. If `None`, clipping is not performed on lower
interval edge.
a_max : scalar or `SparseArray` or `None`
Maximum value. If `None`, clipping is not performed on upper
interval edge.
out : SparseArray, optional
If provided, the results will be placed in this array. It may be
the input array for in-place clipping. `out` must be of the right
shape to hold the output. Its type is preserved.
Returns
-------
clipped_array : SparseArray
An array with the elements of `self`, but where values < `min` are
replaced with `min`, and those > `max` with `max`.
Examples
--------
>>> import sparse
>>> x = sparse.COO.from_numpy([0, 0, 0, 1, 2, 3])
>>> sparse.clip(x, a_min=1).todense() # doctest: +NORMALIZE_WHITESPACE
array([1, 1, 1, 1, 2, 3])
>>> sparse.clip(x, a_max=1).todense() # doctest: +NORMALIZE_WHITESPACE
array([0, 0, 0, 1, 1, 1])
>>> sparse.clip(x, a_min=1, a_max=2).todense() # doctest: +NORMALIZE_WHITESPACE
array([1, 1, 1, 1, 2, 2])
See Also
--------
numpy.clip : Equivalent NumPy function
"""
a = asCOO(a, name="clip")
return a.clip(a_min, a_max)