def _broadcast_to(array, shape, subok, readonly):
shape = tuple(shape) if np.iterable(shape) else (shape,)
array = np.array(array, copy=False, subok=subok)
if not shape and array.shape:
raise ValueError('cannot broadcast a non-scalar to a scalar array')
if any(size < 0 for size in shape):
raise ValueError('all elements of broadcast shape must be non-'
'negative')
needs_writeable = not readonly and array.flags.writeable
extras = ['reduce_ok'] if needs_writeable else []
op_flag = 'readwrite' if needs_writeable else 'readonly'
broadcast = np.nditer(
(array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
op_flags=[op_flag], itershape=shape, order='C').itviews[0]
result = _maybe_view_as_subclass(array, broadcast)
if needs_writeable and not result.flags.writeable:
result.flags.writeable = True
return result
python类broadcast()的实例源码
def _broadcast_shape(*args):
"""Returns the shape of the ararys that would result from broadcasting the
supplied arrays against each other.
"""
if not args:
raise ValueError('must provide at least one argument')
# use the old-iterator because np.nditer does not handle size 0 arrays
# consistently
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
# ironically, np.broadcast does not properly handle np.broadcast
# objects (it treats them as scalars)
# use broadcasting to avoid allocating the full array
b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
def _broadcast_to(array, shape, subok, readonly):
shape = tuple(shape) if np.iterable(shape) else (shape,)
array = np.array(array, copy=False, subok=subok)
if not shape and array.shape:
raise ValueError('cannot broadcast a non-scalar to a scalar array')
if any(size < 0 for size in shape):
raise ValueError('all elements of broadcast shape must be non-'
'negative')
needs_writeable = not readonly and array.flags.writeable
extras = ['reduce_ok'] if needs_writeable else []
op_flag = 'readwrite' if needs_writeable else 'readonly'
broadcast = np.nditer(
(array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
op_flags=[op_flag], itershape=shape, order='C').itviews[0]
result = _maybe_view_as_subclass(array, broadcast)
if needs_writeable and not result.flags.writeable:
result.flags.writeable = True
return result
def _broadcast_shape(*args):
"""Returns the shape of the ararys that would result from broadcasting the
supplied arrays against each other.
"""
if not args:
raise ValueError('must provide at least one argument')
# use the old-iterator because np.nditer does not handle size 0 arrays
# consistently
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
# ironically, np.broadcast does not properly handle np.broadcast
# objects (it treats them as scalars)
# use broadcasting to avoid allocating the full array
b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
def _broadcast_shape(*args):
"""Returns the shape of the ararys that would result from broadcasting the
supplied arrays against each other.
"""
if not args:
raise ValueError('must provide at least one argument')
if len(args) == 1:
# a single argument does not work with np.broadcast
return np.asarray(args[0]).shape
# use the old-iterator because np.nditer does not handle size 0 arrays
# consistently
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
# ironically, np.broadcast does not properly handle np.broadcast
# objects (it treats them as scalars)
# use broadcasting to avoid allocating the full array
b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
def broadcast_shape(shp1, shp2):
"""Broadcast the shape of those arrays
Parameters
----------
shp1 : tuple
shape of array 1
shp2 : tuple
shape of array 2
Returns
-------
tuple
shape resulting from broadcasting two arrays using numpy rules
Raises
------
ValueError
Arrays cannot be broadcasted
"""
try:
return np.broadcast(np.empty(shp1), np.empty(shp2)).shape
except ValueError:
raise ValueError("Arrays cannot be broadcasted - %s and %s " % (str(shp1), str(shp2)))
def _broadcast_to(array, shape, subok, readonly):
shape = tuple(shape) if np.iterable(shape) else (shape,)
array = np.array(array, copy=False, subok=subok)
if not shape and array.shape:
raise ValueError('cannot broadcast a non-scalar to a scalar array')
if any(size < 0 for size in shape):
raise ValueError('all elements of broadcast shape must be non-'
'negative')
needs_writeable = not readonly and array.flags.writeable
extras = ['reduce_ok'] if needs_writeable else []
op_flag = 'readwrite' if needs_writeable else 'readonly'
broadcast = np.nditer(
(array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
op_flags=[op_flag], itershape=shape, order='C').itviews[0]
result = _maybe_view_as_subclass(array, broadcast)
if needs_writeable and not result.flags.writeable:
result.flags.writeable = True
return result
def _broadcast_shape(*args):
"""Returns the shape of the ararys that would result from broadcasting the
supplied arrays against each other.
"""
if not args:
raise ValueError('must provide at least one argument')
# use the old-iterator because np.nditer does not handle size 0 arrays
# consistently
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
# ironically, np.broadcast does not properly handle np.broadcast
# objects (it treats them as scalars)
# use broadcasting to avoid allocating the full array
b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
def _broadcast_to(array, shape, subok, readonly):
shape = tuple(shape) if np.iterable(shape) else (shape,)
array = np.array(array, copy=False, subok=subok)
if not shape and array.shape:
raise ValueError('cannot broadcast a non-scalar to a scalar array')
if any(size < 0 for size in shape):
raise ValueError('all elements of broadcast shape must be non-'
'negative')
needs_writeable = not readonly and array.flags.writeable
extras = ['reduce_ok'] if needs_writeable else []
op_flag = 'readwrite' if needs_writeable else 'readonly'
broadcast = np.nditer(
(array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
op_flags=[op_flag], itershape=shape, order='C').itviews[0]
result = _maybe_view_as_subclass(array, broadcast)
if needs_writeable and not result.flags.writeable:
result.flags.writeable = True
return result
def _broadcast_shape(*args):
"""Returns the shape of the arrays that would result from broadcasting the
supplied arrays against each other.
"""
if not args:
raise ValueError('must provide at least one argument')
# use the old-iterator because np.nditer does not handle size 0 arrays
# consistently
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
# ironically, np.broadcast does not properly handle np.broadcast
# objects (it treats them as scalars)
# use broadcasting to avoid allocating the full array
b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
def _broadcast_to(array, shape, subok, readonly):
shape = tuple(shape) if np.iterable(shape) else (shape,)
array = np.array(array, copy=False, subok=subok)
if not shape and array.shape:
raise ValueError('cannot broadcast a non-scalar to a scalar array')
if any(size < 0 for size in shape):
raise ValueError('all elements of broadcast shape must be non-'
'negative')
needs_writeable = not readonly and array.flags.writeable
extras = ['reduce_ok'] if needs_writeable else []
op_flag = 'readwrite' if needs_writeable else 'readonly'
broadcast = np.nditer(
(array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
op_flags=[op_flag], itershape=shape, order='C').itviews[0]
result = _maybe_view_as_subclass(array, broadcast)
if needs_writeable and not result.flags.writeable:
result.flags.writeable = True
return result
def _broadcast_shape(*args):
"""Returns the shape of the ararys that would result from broadcasting the
supplied arrays against each other.
"""
if not args:
raise ValueError('must provide at least one argument')
# use the old-iterator because np.nditer does not handle size 0 arrays
# consistently
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
# ironically, np.broadcast does not properly handle np.broadcast
# objects (it treats them as scalars)
# use broadcasting to avoid allocating the full array
b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
def test_broadcast_in_args(self):
# gh-5881
arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
np.empty((5, 1, 7))]
mits = [np.broadcast(*arrs),
np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
for mit in mits:
assert_equal(mit.shape, (5, 6, 7))
assert_equal(mit.nd, 3)
assert_equal(mit.numiter, 4)
for a, ia in zip(arrs, mit.iters):
assert_(a is ia.base)
def test_broadcast_single_arg(self):
# gh-6899
arrs = [np.empty((5, 6, 7))]
mit = np.broadcast(*arrs)
assert_equal(mit.shape, (5, 6, 7))
assert_equal(mit.nd, 3)
assert_equal(mit.numiter, 1)
assert_(arrs[0] is mit.iters[0].base)
def test_number_of_arguments(self):
arr = np.empty((5,))
for j in range(35):
arrs = [arr] * j
if j < 1 or j > 32:
assert_raises(ValueError, np.broadcast, *arrs)
else:
mit = np.broadcast(*arrs)
assert_equal(mit.numiter, j)
def test_boolean_assignment_value_mismatch(self):
# A boolean assignment should fail when the shape of the values
# cannot be broadcast to the subscription. (see also gh-3458)
a = np.arange(4)
def f(a, v):
a[a > -1] = v
assert_raises(ValueError, f, a, [])
assert_raises(ValueError, f, a, [1, 2, 3])
assert_raises(ValueError, f, a[:1], [1, 2, 3])
def setUp(self):
self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
self.b = np.empty((3, 0, 5, 6))
self.complex_indices = ['skip', Ellipsis,
0,
# Boolean indices, up to 3-d for some special cases of eating up
# dimensions, also need to test all False
np.array(False),
np.array([True, False, False]),
np.array([[True, False], [False, True]]),
np.array([[[False, False], [False, False]]]),
# Some slices:
slice(-5, 5, 2),
slice(1, 1, 100),
slice(4, -1, -2),
slice(None, None, -3),
# Some Fancy indexes:
np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast
np.array([0, 1, -2]),
np.array([[2], [0], [1]]),
np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
np.array([2, -1], dtype=np.int8),
np.zeros([1]*31, dtype=int), # trigger too large array.
np.array([0., 1.])] # invalid datatype
# Some simpler indices that still cover a bit more
self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip']
# Very simple ones to fill the rest:
self.fill_indices = [slice(None, None), 0]
def broadcast_to(array, shape, subok=False):
"""Broadcast an array to a new shape.
Parameters
----------
array : array_like
The array to broadcast.
shape : tuple
The shape of the desired array.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise
the returned array will be forced to be a base-class array (default).
Returns
-------
broadcast : array
A readonly view on the original array with the given shape. It is
typically not contiguous. Furthermore, more than one element of a
broadcasted array may refer to a single memory location.
Raises
------
ValueError
If the array is not compatible with the new shape according to NumPy's
broadcasting rules.
Notes
-----
.. versionadded:: 1.10.0
Examples
--------
>>> x = np.array([1, 2, 3])
>>> np.broadcast_to(x, (3, 3))
array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]])
"""
return _broadcast_to(array, shape, subok=subok, readonly=True)
def test_broadcast_in_args(self):
# gh-5881
arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
np.empty((5, 1, 7))]
mits = [np.broadcast(*arrs),
np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
for mit in mits:
assert_equal(mit.shape, (5, 6, 7))
assert_equal(mit.nd, 3)
assert_equal(mit.numiter, 4)
for a, ia in zip(arrs, mit.iters):
assert_(a is ia.base)
def test_broadcast_single_arg(self):
# gh-6899
arrs = [np.empty((5, 6, 7))]
mit = np.broadcast(*arrs)
assert_equal(mit.shape, (5, 6, 7))
assert_equal(mit.nd, 3)
assert_equal(mit.numiter, 1)
assert_(arrs[0] is mit.iters[0].base)
def test_number_of_arguments(self):
arr = np.empty((5,))
for j in range(35):
arrs = [arr] * j
if j < 1 or j > 32:
assert_raises(ValueError, np.broadcast, *arrs)
else:
mit = np.broadcast(*arrs)
assert_equal(mit.numiter, j)
def test_boolean_assignment_value_mismatch(self):
# A boolean assignment should fail when the shape of the values
# cannot be broadcast to the subscription. (see also gh-3458)
a = np.arange(4)
def f(a, v):
a[a > -1] = v
assert_raises(ValueError, f, a, [])
assert_raises(ValueError, f, a, [1, 2, 3])
assert_raises(ValueError, f, a[:1], [1, 2, 3])
def setUp(self):
self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
self.b = np.empty((3, 0, 5, 6))
self.complex_indices = ['skip', Ellipsis,
0,
# Boolean indices, up to 3-d for some special cases of eating up
# dimensions, also need to test all False
np.array(False),
np.array([True, False, False]),
np.array([[True, False], [False, True]]),
np.array([[[False, False], [False, False]]]),
# Some slices:
slice(-5, 5, 2),
slice(1, 1, 100),
slice(4, -1, -2),
slice(None, None, -3),
# Some Fancy indexes:
np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast
np.array([0, 1, -2]),
np.array([[2], [0], [1]]),
np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
np.array([2, -1], dtype=np.int8),
np.zeros([1]*31, dtype=int), # trigger too large array.
np.array([0., 1.])] # invalid datatype
# Some simpler indices that still cover a bit more
self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip']
# Very simple ones to fill the rest:
self.fill_indices = [slice(None, None), 0]
def broadcast_to(array, shape, subok=False):
"""Broadcast an array to a new shape.
Parameters
----------
array : array_like
The array to broadcast.
shape : tuple
The shape of the desired array.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise
the returned array will be forced to be a base-class array (default).
Returns
-------
broadcast : array
A readonly view on the original array with the given shape. It is
typically not contiguous. Furthermore, more than one element of a
broadcasted array may refer to a single memory location.
Raises
------
ValueError
If the array is not compatible with the new shape according to NumPy's
broadcasting rules.
Notes
-----
.. versionadded:: 1.10.0
Examples
--------
>>> x = np.array([1, 2, 3])
>>> np.broadcast_to(x, (3, 3))
array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]])
"""
return _broadcast_to(array, shape, subok=subok, readonly=True)
def iterkeys(self,index):
#import pdb; pdb.set_trace()
if not isinstance(index,tuple) and self.shape[0] == 1:
index = (1,index)
if isinstance(index, int):
key = np.unravel_index(index-1, self.shape, order='F')
yield tuple(k+1 for k in key)
elif isinstance(index,slice):
index = range((index.start or 1)-1,
index.stop or np.prod(self.shape),
index.step or 1)
for key in np.transpose(np.unravel_index(index, self.shape, order='F')): # 0-based
yield tuple(k+1 for k in key)
elif isinstance(index,(list,np.ndarray)):
index = np.asarray(index)-1
for key in np.transpose(np.unravel_index(index, self.shape, order='F')):
yield tuple(k+1 for k in key)
else:
assert isinstance(index,tuple),index.__class__
indices = [] # 1-based
for i,ix in enumerate(index):
if isinstance(ix,slice):
indices.append(np.arange((ix.start or 1),
(ix.stop or self.shape[i]) + 1,
ix.step or 1,
dtype=int))
else:
indices.append(np.asarray(ix))
assert len(index) == 2
indices[0].shape = (-1,1)
for key in np.broadcast(*indices):
yield tuple(map(int,key))
test_numeric.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def test_broadcast_in_args(self):
# gh-5881
arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
np.empty((5, 1, 7))]
mits = [np.broadcast(*arrs),
np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
for mit in mits:
assert_equal(mit.shape, (5, 6, 7))
assert_equal(mit.nd, 3)
assert_equal(mit.numiter, 4)
for a, ia in zip(arrs, mit.iters):
assert_(a is ia.base)
test_numeric.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
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def test_number_of_arguments(self):
arr = np.empty((5,))
for j in range(35):
arrs = [arr] * j
if j < 2 or j > 32:
assert_raises(ValueError, np.broadcast, *arrs)
else:
mit = np.broadcast(*arrs)
assert_equal(mit.numiter, j)
test_indexing.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def test_boolean_assignment_value_mismatch(self):
# A boolean assignment should fail when the shape of the values
# cannot be broadcast to the subscription. (see also gh-3458)
a = np.arange(4)
def f(a, v):
a[a > -1] = v
assert_raises(ValueError, f, a, [])
assert_raises(ValueError, f, a, [1, 2, 3])
assert_raises(ValueError, f, a[:1], [1, 2, 3])
test_indexing.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
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def setUp(self):
self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
self.b = np.empty((3, 0, 5, 6))
self.complex_indices = ['skip', Ellipsis,
0,
# Boolean indices, up to 3-d for some special cases of eating up
# dimensions, also need to test all False
np.array(False),
np.array([True, False, False]),
np.array([[True, False], [False, True]]),
np.array([[[False, False], [False, False]]]),
# Some slices:
slice(-5, 5, 2),
slice(1, 1, 100),
slice(4, -1, -2),
slice(None, None, -3),
# Some Fancy indexes:
np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast
np.array([0, 1, -2]),
np.array([[2], [0], [1]]),
np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
np.array([2, -1], dtype=np.int8),
np.zeros([1]*31, dtype=int), # trigger too large array.
np.array([0., 1.])] # invalid datatype
# Some simpler indices that still cover a bit more
self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip']
# Very simple ones to fill the rest:
self.fill_indices = [slice(None, None), 0]
test_indexing.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
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def _compare_index_result(self, arr, index, mimic_get, no_copy):
"""Compare mimicked result to indexing result.
"""
arr = arr.copy()
indexed_arr = arr[index]
assert_array_equal(indexed_arr, mimic_get)
# Check if we got a view, unless its a 0-sized or 0-d array.
# (then its not a view, and that does not matter)
if indexed_arr.size != 0 and indexed_arr.ndim != 0:
assert_(np.may_share_memory(indexed_arr, arr) == no_copy)
# Check reference count of the original array
if no_copy:
# refcount increases by one:
assert_equal(sys.getrefcount(arr), 3)
else:
assert_equal(sys.getrefcount(arr), 2)
# Test non-broadcast setitem:
b = arr.copy()
b[index] = mimic_get + 1000
if b.size == 0:
return # nothing to compare here...
if no_copy and indexed_arr.ndim != 0:
# change indexed_arr in-place to manipulate original:
indexed_arr += 1000
assert_array_equal(arr, b)
return
# Use the fact that the array is originally an arange:
arr.flat[indexed_arr.ravel()] += 1000
assert_array_equal(arr, b)
