def where(space, w_arr, w_x=None, w_y=None):
"""where(condition, [x, y])
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x` and `y` need to have the same
shape as `condition`.
Returns
-------
out : ndarray or tuple of ndarrays
If both `x` and `y` are specified, the output array contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
NOTE: support for not passing x and y is unsupported
"""
if space.is_none(w_y):
if space.is_none(w_x):
raise OperationError(
space.w_NotImplementedError,
space.wrap("1-arg where unsupported right now"))
raise OperationError(
space.w_ValueError,
space.wrap("Where should be called with either 1 or 3 arguments"))
if space.is_none(w_x):
raise OperationError(
space.w_ValueError,
space.wrap("Where should be called with either 1 or 3 arguments"))
arr = convert_to_array(space, w_arr)
x = convert_to_array(space, w_x)
y = convert_to_array(space, w_y)
if x.is_scalar() and y.is_scalar() and arr.is_scalar():
if arr.get_dtype().itemtype.bool(arr.get_scalar_value()):
return x
return y
dtype = ufuncs.find_binop_result_dtype(space, x.get_dtype(), y.get_dtype())
shape = shape_agreement(space, arr.get_shape(), x)
shape = shape_agreement(space, shape, y)
out = W_NDimArray.from_shape(space, shape, dtype)
return loop.where(space, out, shape, arr, x, y, dtype)
def where(space, w_arr, w_x=None, w_y=None):
"""where(condition, [x, y])
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x` and `y` need to have the same
shape as `condition`.
Returns
-------
out : ndarray or tuple of ndarrays
If both `x` and `y` are specified, the output array contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
NOTE: support for not passing x and y is unsupported
"""
if space.is_none(w_y):
if space.is_none(w_x):
raise OperationError(space.w_NotImplementedError, space.wrap("1-arg where unsupported right now"))
raise OperationError(space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments"))
if space.is_none(w_x):
raise OperationError(space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments"))
arr = convert_to_array(space, w_arr)
x = convert_to_array(space, w_x)
y = convert_to_array(space, w_y)
if x.is_scalar() and y.is_scalar() and arr.is_scalar():
if arr.get_dtype().itemtype.bool(arr.get_scalar_value()):
return x
return y
dtype = interp_ufuncs.find_binop_result_dtype(space, x.get_dtype(), y.get_dtype())
shape = shape_agreement(space, arr.get_shape(), x)
shape = shape_agreement(space, shape, y)
out = W_NDimArray.from_shape(space, shape, dtype)
return loop.where(out, shape, arr, x, y, dtype)