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ENH: use __skip_array_function__ internally in NumPy #13585

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35 changes: 21 additions & 14 deletions numpy/core/numeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -156,10 +156,11 @@ def zeros_like(a, dtype=None, order='K', subok=True, shape=None):
array([0., 0., 0.])

"""
res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
res = empty_like.__skip_array_function__(
a, dtype=dtype, order=order, subok=subok, shape=shape)
# needed instead of a 0 to get same result as zeros for for string dtypes
z = zeros(1, dtype=res.dtype)
multiarray.copyto(res, z, casting='unsafe')
multiarray.copyto.__skip_array_function__(res, z, casting='unsafe')
return res


Expand Down Expand Up @@ -212,7 +213,7 @@ def ones(shape, dtype=None, order='C'):

"""
a = empty(shape, dtype, order)
multiarray.copyto(a, 1, casting='unsafe')
multiarray.copyto.__skip_array_function__(a, 1, casting='unsafe')
return a


Expand Down Expand Up @@ -282,8 +283,9 @@ def ones_like(a, dtype=None, order='K', subok=True, shape=None):
array([1., 1., 1.])

"""
res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
multiarray.copyto(res, 1, casting='unsafe')
res = empty_like.__skip_array_function__(
a, dtype=dtype, order=order, subok=subok, shape=shape)
multiarray.copyto.__skip_array_function__(res, 1, casting='unsafe')
return res


Expand Down Expand Up @@ -330,7 +332,7 @@ def full(shape, fill_value, dtype=None, order='C'):
if dtype is None:
dtype = array(fill_value).dtype
a = empty(shape, dtype, order)
multiarray.copyto(a, fill_value, casting='unsafe')
multiarray.copyto.__skip_array_function__(a, fill_value, casting='unsafe')
return a


Expand Down Expand Up @@ -397,8 +399,10 @@ def full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None):
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])

"""
res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
multiarray.copyto(res, fill_value, casting='unsafe')
res = empty_like.__skip_array_function__(
a, dtype=dtype, order=order, subok=subok, shape=shape)
multiarray.copyto.__skip_array_function__(
res, fill_value, casting='unsafe')
return res


Expand Down Expand Up @@ -575,7 +579,8 @@ def argwhere(a):
[1, 2]])

"""
return transpose(nonzero(a))
return transpose.__skip_array_function__(
nonzero.__skip_array_function__(a))


def _flatnonzero_dispatcher(a):
Expand Down Expand Up @@ -620,7 +625,8 @@ def flatnonzero(a):
array([-2, -1, 1, 2])

"""
return np.nonzero(np.ravel(a))[0]
return np.nonzero.__skip_array_function__(
np.ravel.__skip_array_function__(a))[0]


_mode_from_name_dict = {'v': 0,
Expand Down Expand Up @@ -2127,7 +2133,8 @@ def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
True

"""
res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))
res = all(isclose.__skip_array_function__(
a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))
return bool(res)


Expand Down Expand Up @@ -2231,12 +2238,12 @@ def within_tol(x, y, atol, rtol):
return within_tol(x, y, atol, rtol)
else:
finite = xfin & yfin
cond = zeros_like(finite, subok=True)
cond = zeros_like.__skip_array_function__(finite, subok=True)
# Because we're using boolean indexing, x & y must be the same shape.
# Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in
# lib.stride_tricks, though, so we can't import it here.
x = x * ones_like(cond)
y = y * ones_like(cond)
x = x * ones_like.__skip_array_function__(cond)
y = y * ones_like.__skip_array_function__(cond)
# Avoid subtraction with infinite/nan values...
cond[finite] = within_tol(x[finite], y[finite], atol, rtol)
# Check for equality of infinite values...
Expand Down
21 changes: 12 additions & 9 deletions numpy/core/shape_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -273,7 +273,8 @@ def vstack(tup):
[4]])

"""
return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)
return _nx.concatenate.__skip_array_function__(
[atleast_2d.__skip_array_function__(_m) for _m in tup], 0)


@array_function_dispatch(_vhstack_dispatcher)
Expand Down Expand Up @@ -324,12 +325,12 @@ def hstack(tup):
[3, 4]])

"""
arrs = [atleast_1d(_m) for _m in tup]
arrs = [atleast_1d.__skip_array_function__(_m) for _m in tup]
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrs and arrs[0].ndim == 1:
return _nx.concatenate(arrs, 0)
return _nx.concatenate.__skip_array_function__(arrs, 0)
else:
return _nx.concatenate(arrs, 1)
return _nx.concatenate.__skip_array_function__(arrs, 1)


def _stack_dispatcher(arrays, axis=None, out=None):
Expand Down Expand Up @@ -413,7 +414,8 @@ def stack(arrays, axis=0, out=None):

sl = (slice(None),) * axis + (_nx.newaxis,)
expanded_arrays = [arr[sl] for arr in arrays]
return _nx.concatenate(expanded_arrays, axis=axis, out=out)
return _nx.concatenate.__skip_array_function__(
expanded_arrays, axis=axis, out=out)


def _block_format_index(index):
Expand Down Expand Up @@ -496,8 +498,8 @@ def _block_check_depths_match(arrays, parent_index=[]):
return parent_index + [None], 0, 0
else:
# We've 'bottomed out' - arrays is either a scalar or an array
size = _nx.size(arrays)
return parent_index, _nx.ndim(arrays), size
size = _nx.size.__skip_array_function__(arrays)
return parent_index, _nx.ndim.__skip_array_function__(arrays), size


def _atleast_nd(a, ndim):
Expand Down Expand Up @@ -640,7 +642,8 @@ def _block(arrays, max_depth, result_ndim, depth=0):
if depth < max_depth:
arrs = [_block(arr, max_depth, result_ndim, depth+1)
for arr in arrays]
return _nx.concatenate(arrs, axis=-(max_depth-depth))
return _nx.concatenate.__skip_array_function__(
arrs, axis=-(max_depth-depth))
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
Expand Down Expand Up @@ -858,7 +861,7 @@ def _block_slicing(arrays, list_ndim, result_ndim):

# Test preferring F only in the case that all input arrays are F
F_order = all(arr.flags['F_CONTIGUOUS'] for arr in arrays)
C_order = all(arr.flags['C_CONTIGUOUS'] for arr in arrays)
C_order = all(arr.flags['C_CONTIGUOUS'] for arr in arrays)
order = 'F' if F_order and not C_order else 'C'
result = _nx.empty(shape=shape, dtype=dtype, order=order)
# Note: In a c implementation, the function
Expand Down
2 changes: 1 addition & 1 deletion numpy/lib/arraypad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -804,7 +804,7 @@ def pad(array, pad_width, mode='constant', **kwargs):
# function operates inplace on the padded array.

# view with the iteration axis at the end
view = np.moveaxis(padded, axis, -1)
view = np.moveaxis.__skip_array_function__(padded, axis, -1)

# compute indices for the iteration axes, and append a trailing
# ellipsis to prevent 0d arrays decaying to scalars (gh-8642)
Expand Down
47 changes: 29 additions & 18 deletions numpy/lib/function_base.py
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Expand Up @@ -134,26 +134,29 @@ def rot90(m, k=1, axes=(0,1)):
raise ValueError("Axes must be different.")

if (axes[0] >= m.ndim or axes[0] < -m.ndim
or axes[1] >= m.ndim or axes[1] < -m.ndim):
or axes[1] >= m.ndim or axes[1] < -m.ndim):
raise ValueError("Axes={} out of range for array of ndim={}."
.format(axes, m.ndim))
.format(axes, m.ndim))

k %= 4

if k == 0:
return m[:]
if k == 2:
return flip(flip(m, axes[0]), axes[1])
return flip.__skip_array_function__(
flip.__skip_array_function__(m, axes[0]), axes[1])

axes_list = arange(0, m.ndim)
(axes_list[axes[0]], axes_list[axes[1]]) = (axes_list[axes[1]],
axes_list[axes[0]])

if k == 1:
return transpose(flip(m,axes[1]), axes_list)
return transpose.__skip_array_function__(
flip.__skip_array_function__(m, axes[1]), axes_list)
else:
# k == 3
return flip(transpose(m, axes_list), axes[1])
return flip.__skip_array_function__(
transpose.__skip_array_function__(m, axes_list), axes[1])


def _flip_dispatcher(m, axis=None):
Expand Down Expand Up @@ -393,9 +396,11 @@ def average(a, axis=None, weights=None, returned=False):
wgt = np.asanyarray(weights)

if issubclass(a.dtype.type, (np.integer, np.bool_)):
result_dtype = np.result_type(a.dtype, wgt.dtype, 'f8')
result_dtype = np.result_type.__skip_array_function__(
a.dtype, wgt.dtype, 'f8')
else:
result_dtype = np.result_type(a.dtype, wgt.dtype)
result_dtype = np.result_type.__skip_array_function__(
a.dtype, wgt.dtype)

# Sanity checks
if a.shape != wgt.shape:
Expand Down Expand Up @@ -606,7 +611,8 @@ def piecewise(x, condlist, funclist, *args, **kw):

if n == n2 - 1: # compute the "otherwise" condition.
condelse = ~np.any(condlist, axis=0, keepdims=True)
condlist = np.concatenate([condlist, condelse], axis=0)
condlist = np.concatenate.__skip_array_function__(
[condlist, condelse], axis=0)
n += 1
elif n != n2:
raise ValueError(
Expand Down Expand Up @@ -690,13 +696,13 @@ def select(condlist, choicelist, default=0):

# need to get the result type before broadcasting for correct scalar
# behaviour
dtype = np.result_type(*choicelist)
dtype = np.result_type.__skip_array_function__(*choicelist)

# Convert conditions to arrays and broadcast conditions and choices
# as the shape is needed for the result. Doing it separately optimizes
# for example when all choices are scalars.
condlist = np.broadcast_arrays(*condlist)
choicelist = np.broadcast_arrays(*choicelist)
condlist = np.broadcast_arrays.__skip_array_function__(*condlist)
choicelist = np.broadcast_arrays.__skip_array_function__(*choicelist)

# If cond array is not an ndarray in boolean format or scalar bool, abort.
deprecated_ints = False
Expand Down Expand Up @@ -733,7 +739,7 @@ def select(condlist, choicelist, default=0):
choicelist = choicelist[-2::-1]
condlist = condlist[::-1]
for choice, cond in zip(choicelist, condlist):
np.copyto(result, choice, where=cond)
np.copyto.__skip_array_function__(result, choice, where=cond)

return result

Expand Down Expand Up @@ -1512,14 +1518,16 @@ def unwrap(p, discont=pi, axis=-1):
"""
p = asarray(p)
nd = p.ndim
dd = diff(p, axis=axis)
dd = diff.__skip_array_function__(p, axis=axis)
slice1 = [slice(None, None)]*nd # full slices
slice1[axis] = slice(1, None)
slice1 = tuple(slice1)
ddmod = mod(dd + pi, 2*pi) - pi
_nx.copyto(ddmod, pi, where=(ddmod == -pi) & (dd > 0))
_nx.copyto.__skip_array_function__(
ddmod, pi, where=(ddmod == -pi) & (dd > 0))
ph_correct = ddmod - dd
_nx.copyto(ph_correct, 0, where=abs(dd) < discont)
_nx.copyto.__skip_array_function__(
ph_correct, 0, where=abs(dd) < discont)
up = array(p, copy=True, dtype='d')
up[slice1] = p[slice1] + ph_correct.cumsum(axis)
return up
Expand Down Expand Up @@ -1674,7 +1682,10 @@ def extract(condition, arr):
array([0, 3, 6, 9])

"""
return _nx.take(ravel(arr), nonzero(ravel(condition))[0])
return _nx.take.__skip_array_function__(
ravel.__skip_array_function__(arr),
nonzero.__skip_array_function__(
ravel.__skip_array_function__(condition))[0])


def _place_dispatcher(arr, mask, vals):
Expand Down Expand Up @@ -4697,9 +4708,9 @@ def append(arr, values, axis=None):
if axis is None:
if arr.ndim != 1:
arr = arr.ravel()
values = ravel(values)
values = ravel.__skip_array_function__(values)
axis = arr.ndim-1
return concatenate((arr, values), axis=axis)
return concatenate.__skip_array_function__((arr, values), axis=axis)


def _digitize_dispatcher(x, bins, right=None):
Expand Down
21 changes: 12 additions & 9 deletions numpy/lib/histograms.py
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Expand Up @@ -448,7 +448,7 @@ def _search_sorted_inclusive(a, v):

In the context of a histogram, this makes the last bin edge inclusive
"""
return np.concatenate((
return np.concatenate.__skip_array_function__((
a.searchsorted(v[:-1], 'left'),
a.searchsorted(v[-1:], 'right')
))
Expand Down Expand Up @@ -865,17 +865,18 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
cum_n = np.zeros(bin_edges.shape, ntype)
if weights is None:
for i in _range(0, len(a), BLOCK):
sa = np.sort(a[i:i+BLOCK])
sa = np.sort.__skip_array_function__(a[i:i+BLOCK])
cum_n += _search_sorted_inclusive(sa, bin_edges)
else:
zero = np.zeros(1, dtype=ntype)
for i in _range(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
tmp_w = weights[i:i+BLOCK]
sorting_index = np.argsort(tmp_a)
sorting_index = np.argsort.__skip_array_function__(tmp_a)
sa = tmp_a[sorting_index]
sw = tmp_w[sorting_index]
cw = np.concatenate((zero, sw.cumsum()))
cw = np.concatenate.__skip_array_function__(
(zero, sw.cumsum()))
bin_index = _search_sorted_inclusive(sa, bin_edges)
cum_n += cw[bin_index]

Expand Down Expand Up @@ -1000,7 +1001,7 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
N, D = sample.shape
except (AttributeError, ValueError):
# Sample is a sequence of 1D arrays.
sample = np.atleast_2d(sample).T
sample = np.atleast_2d.__skip_array_function__(sample).T
N, D = sample.shape

nbin = np.empty(D, int)
Expand All @@ -1027,12 +1028,13 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None,

# Create edge arrays
for i in _range(D):
if np.ndim(bins[i]) == 0:
if np.ndim.__skip_array_function__(bins[i]) == 0:
if bins[i] < 1:
raise ValueError(
'`bins[{}]` must be positive, when an integer'.format(i))
smin, smax = _get_outer_edges(sample[:,i], range[i])
edges[i] = np.linspace(smin, smax, bins[i] + 1)
edges[i] = np.linspace.__skip_array_function__(
smin, smax, bins[i] + 1)
elif np.ndim(bins[i]) == 1:
edges[i] = np.asarray(bins[i])
if np.any(edges[i][:-1] > edges[i][1:]):
Expand All @@ -1044,12 +1046,13 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
'`bins[{}]` must be a scalar or 1d array'.format(i))

nbin[i] = len(edges[i]) + 1 # includes an outlier on each end
dedges[i] = np.diff(edges[i])
dedges[i] = np.diff.__skip_array_function__(edges[i])

# Compute the bin number each sample falls into.
Ncount = tuple(
# avoid np.digitize to work around gh-11022
np.searchsorted(edges[i], sample[:, i], side='right')
np.searchsorted.__skip_array_function__(
edges[i], sample[:, i], side='right')
for i in _range(D)
)

Expand Down
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