Python numpy.core.numeric.dtype() Examples
The following are 30
code examples of numpy.core.numeric.dtype().
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Example #1
| Source File: _iotools.py From lambda-packs with MIT License | 6 votes |
|
def _strict_call(self, value):
try:
# We check if we can convert the value using the current function
new_value = self.func(value)
# In addition to having to check whether func can convert the
# value, we also have to make sure that we don't get overflow
# errors for integers.
if self.func is int:
try:
np.array(value, dtype=self.type)
except OverflowError:
raise ValueError
# We're still here so we can now return the new value
return new_value
except ValueError:
if value.strip() in self.missing_values:
if not self._status:
self._checked = False
return self.default
raise ValueError("Cannot convert string '%s'" % value)
#
Example #2
| Source File: _iotools.py From recruit with Apache License 2.0 | 6 votes |
|
def has_nested_fields(ndtype):
"""
Returns whether one or several fields of a dtype are nested.
Parameters
----------
ndtype : dtype
Data-type of a structured array.
Raises
------
AttributeError
If `ndtype` does not have a `names` attribute.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
>>> np.lib._iotools.has_nested_fields(dt)
False
"""
for name in ndtype.names or ():
if ndtype[name].names:
return True
return False
Example #3
| Source File: _iotools.py From auto-alt-text-lambda-api with MIT License | 6 votes |
|
def has_nested_fields(ndtype):
"""
Returns whether one or several fields of a dtype are nested.
Parameters
----------
ndtype : dtype
Data-type of a structured array.
Raises
------
AttributeError
If `ndtype` does not have a `names` attribute.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
>>> np.lib._iotools.has_nested_fields(dt)
False
"""
for name in ndtype.names or ():
if ndtype[name].names:
return True
return False
Example #4
| Source File: _iotools.py From recruit with Apache License 2.0 | 6 votes |
|
def _strict_call(self, value):
try:
# We check if we can convert the value using the current function
new_value = self.func(value)
# In addition to having to check whether func can convert the
# value, we also have to make sure that we don't get overflow
# errors for integers.
if self.func is int:
try:
np.array(value, dtype=self.type)
except OverflowError:
raise ValueError
# We're still here so we can now return the new value
return new_value
except ValueError:
if value.strip() in self.missing_values:
if not self._status:
self._checked = False
return self.default
raise ValueError("Cannot convert string '%s'" % value)
#
Example #5
| Source File: test__iotools.py From lambda-packs with MIT License | 6 votes |
|
def test_flatten_dtype(self):
"Testing flatten_dtype"
# Standard dtype
dt = np.dtype([("a", "f8"), ("b", "f8")])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, float])
# Recursive dtype
dt = np.dtype([("a", [("aa", '|S1'), ("ab", '|S2')]), ("b", int)])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [np.dtype('|S1'), np.dtype('|S2'), int])
# dtype with shaped fields
dt = np.dtype([("a", (float, 2)), ("b", (int, 3))])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, int])
dt_flat = flatten_dtype(dt, True)
assert_equal(dt_flat, [float] * 2 + [int] * 3)
# dtype w/ titles
dt = np.dtype([(("a", "A"), "f8"), (("b", "B"), "f8")])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, float])
Example #6
| Source File: test__iotools.py From recruit with Apache License 2.0 | 6 votes |
|
def test_flatten_dtype(self):
"Testing flatten_dtype"
# Standard dtype
dt = np.dtype([("a", "f8"), ("b", "f8")])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, float])
# Recursive dtype
dt = np.dtype([("a", [("aa", '|S1'), ("ab", '|S2')]), ("b", int)])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [np.dtype('|S1'), np.dtype('|S2'), int])
# dtype with shaped fields
dt = np.dtype([("a", (float, 2)), ("b", (int, 3))])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, int])
dt_flat = flatten_dtype(dt, True)
assert_equal(dt_flat, [float] * 2 + [int] * 3)
# dtype w/ titles
dt = np.dtype([(("a", "A"), "f8"), (("b", "B"), "f8")])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, float])
Example #7
| Source File: _iotools.py From lambda-packs with MIT License | 6 votes |
|
def _strict_call(self, value):
try:
# We check if we can convert the value using the current function
new_value = self.func(value)
# In addition to having to check whether func can convert the
# value, we also have to make sure that we don't get overflow
# errors for integers.
if self.func is int:
try:
np.array(value, dtype=self.type)
except OverflowError:
raise ValueError
# We're still here so we can now return the new value
return new_value
except ValueError:
if value.strip() in self.missing_values:
if not self._status:
self._checked = False
return self.default
raise ValueError("Cannot convert string '%s'" % value)
#
Example #8
| Source File: function_base.py From recruit with Apache License 2.0 | 6 votes |
|
def _vectorize_call(self, func, args):
"""Vectorized call to `func` over positional `args`."""
if self.signature is not None:
res = self._vectorize_call_with_signature(func, args)
elif not args:
res = func()
else:
ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
# Convert args to object arrays first
inputs = [array(a, copy=False, subok=True, dtype=object)
for a in args]
outputs = ufunc(*inputs)
if ufunc.nout == 1:
res = array(outputs, copy=False, subok=True, dtype=otypes[0])
else:
res = tuple([array(x, copy=False, subok=True, dtype=t)
for x, t in zip(outputs, otypes)])
return res
Example #9
| Source File: _iotools.py From lambda-packs with MIT License | 6 votes |
|
def has_nested_fields(ndtype):
"""
Returns whether one or several fields of a dtype are nested.
Parameters
----------
ndtype : dtype
Data-type of a structured array.
Raises
------
AttributeError
If `ndtype` does not have a `names` attribute.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
>>> np.lib._iotools.has_nested_fields(dt)
False
"""
for name in ndtype.names or ():
if ndtype[name].names:
return True
return False
Example #10
| Source File: _iotools.py From lambda-packs with MIT License | 6 votes |
|
def has_nested_fields(ndtype):
"""
Returns whether one or several fields of a dtype are nested.
Parameters
----------
ndtype : dtype
Data-type of a structured array.
Raises
------
AttributeError
If `ndtype` does not have a `names` attribute.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
>>> np.lib._iotools.has_nested_fields(dt)
False
"""
for name in ndtype.names or ():
if ndtype[name].names:
return True
return False
Example #11
| Source File: function_base.py From lambda-packs with MIT License | 6 votes |
|
def _vectorize_call(self, func, args):
"""Vectorized call to `func` over positional `args`."""
if self.signature is not None:
res = self._vectorize_call_with_signature(func, args)
elif not args:
res = func()
else:
ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
# Convert args to object arrays first
inputs = [array(a, copy=False, subok=True, dtype=object)
for a in args]
outputs = ufunc(*inputs)
if ufunc.nout == 1:
res = array(outputs, copy=False, subok=True, dtype=otypes[0])
else:
res = tuple([array(x, copy=False, subok=True, dtype=t)
for x, t in zip(outputs, otypes)])
return res
Example #12
| Source File: defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def sum(self, axis=None, dtype=None, out=None):
"""
Returns the sum of the matrix elements, along the given axis.
Refer to `numpy.sum` for full documentation.
See Also
--------
numpy.sum
Notes
-----
This is the same as `ndarray.sum`, except that where an `ndarray` would
be returned, a `matrix` object is returned instead.
Examples
--------
>>> x = np.matrix([[1, 2], [4, 3]])
>>> x.sum()
10
>>> x.sum(axis=1)
matrix([[3],
[7]])
>>> x.sum(axis=1, dtype='float')
matrix([[ 3.],
[ 7.]])
>>> out = np.zeros((1, 2), dtype='float')
>>> x.sum(axis=1, dtype='float', out=out)
matrix([[ 3.],
[ 7.]])
"""
return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)
# To update docstring from array to matrix...
Example #13
| Source File: test__iotools.py From lambda-packs with MIT License | 5 votes |
|
def test_has_nested_dtype(self):
"Test has_nested_dtype"
ndtype = np.dtype(np.float)
assert_equal(has_nested_fields(ndtype), False)
ndtype = np.dtype([('A', '|S3'), ('B', float)])
assert_equal(has_nested_fields(ndtype), False)
ndtype = np.dtype([('A', int), ('B', [('BA', float), ('BB', '|S1')])])
assert_equal(has_nested_fields(ndtype), True)
Example #14
| Source File: test__iotools.py From lambda-packs with MIT License | 5 votes |
|
def test_keep_default(self):
"Make sure we don't lose an explicit default"
converter = StringConverter(None, missing_values=asbytes(''),
default=-999)
converter.upgrade(asbytes('3.14159265'))
assert_equal(converter.default, -999)
assert_equal(converter.type, np.dtype(float))
#
converter = StringConverter(
None, missing_values=asbytes(''), default=0)
converter.upgrade(asbytes('3.14159265'))
assert_equal(converter.default, 0)
assert_equal(converter.type, np.dtype(float))
Example #15
| Source File: function_base.py From lambda-packs with MIT License | 5 votes |
|
def sort_complex(a):
"""
Sort a complex array using the real part first, then the imaginary part.
Parameters
----------
a : array_like
Input array
Returns
-------
out : complex ndarray
Always returns a sorted complex array.
Examples
--------
>>> np.sort_complex([5, 3, 6, 2, 1])
array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a, copy=True)
b.sort()
if not issubclass(b.dtype.type, _nx.complexfloating):
if b.dtype.char in 'bhBH':
return b.astype('F')
elif b.dtype.char == 'g':
return b.astype('G')
else:
return b.astype('D')
else:
return b
Example #16
| Source File: defmatrix.py From lambda-packs with MIT License | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #17
| Source File: defmatrix.py From lambda-packs with MIT License | 5 votes |
|
def all(self, axis=None, out=None):
"""
Test whether all matrix elements along a given axis evaluate to True.
Parameters
----------
See `numpy.all` for complete descriptions
See Also
--------
numpy.all
Notes
-----
This is the same as `ndarray.all`, but it returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> y = x[0]; y
matrix([[0, 1, 2, 3]])
>>> (x == y)
matrix([[ True, True, True, True],
[False, False, False, False],
[False, False, False, False]], dtype=bool)
>>> (x == y).all()
False
>>> (x == y).all(0)
matrix([[False, False, False, False]], dtype=bool)
>>> (x == y).all(1)
matrix([[ True],
[False],
[False]], dtype=bool)
"""
return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis)
Example #18
| Source File: defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def mean(self, axis=None, dtype=None, out=None):
"""
Returns the average of the matrix elements along the given axis.
Refer to `numpy.mean` for full documentation.
See Also
--------
numpy.mean
Notes
-----
Same as `ndarray.mean` except that, where that returns an `ndarray`,
this returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.mean()
5.5
>>> x.mean(0)
matrix([[ 4., 5., 6., 7.]])
>>> x.mean(1)
matrix([[ 1.5],
[ 5.5],
[ 9.5]])
"""
return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)
Example #19
| Source File: defmatrix.py From lambda-packs with MIT License | 5 votes |
|
def prod(self, axis=None, dtype=None, out=None):
"""
Return the product of the array elements over the given axis.
Refer to `prod` for full documentation.
See Also
--------
prod, ndarray.prod
Notes
-----
Same as `ndarray.prod`, except, where that returns an `ndarray`, this
returns a `matrix` object instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[ 0, 45, 120, 231]])
>>> x.prod(1)
matrix([[ 0],
[ 840],
[7920]])
"""
return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis)
Example #20
| Source File: defmatrix.py From lambda-packs with MIT License | 5 votes |
|
def std(self, axis=None, dtype=None, out=None, ddof=0):
"""
Return the standard deviation of the array elements along the given axis.
Refer to `numpy.std` for full documentation.
See Also
--------
numpy.std
Notes
-----
This is the same as `ndarray.std`, except that where an `ndarray` would
be returned, a `matrix` object is returned instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.std()
3.4520525295346629
>>> x.std(0)
matrix([[ 3.26598632, 3.26598632, 3.26598632, 3.26598632]])
>>> x.std(1)
matrix([[ 1.11803399],
[ 1.11803399],
[ 1.11803399]])
"""
return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis)
Example #21
| Source File: defmatrix.py From lambda-packs with MIT License | 5 votes |
|
def mean(self, axis=None, dtype=None, out=None):
"""
Returns the average of the matrix elements along the given axis.
Refer to `numpy.mean` for full documentation.
See Also
--------
numpy.mean
Notes
-----
Same as `ndarray.mean` except that, where that returns an `ndarray`,
this returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.mean()
5.5
>>> x.mean(0)
matrix([[ 4., 5., 6., 7.]])
>>> x.mean(1)
matrix([[ 1.5],
[ 5.5],
[ 9.5]])
"""
return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)
Example #22
| Source File: defmatrix.py From lambda-packs with MIT License | 5 votes |
|
def sum(self, axis=None, dtype=None, out=None):
"""
Returns the sum of the matrix elements, along the given axis.
Refer to `numpy.sum` for full documentation.
See Also
--------
numpy.sum
Notes
-----
This is the same as `ndarray.sum`, except that where an `ndarray` would
be returned, a `matrix` object is returned instead.
Examples
--------
>>> x = np.matrix([[1, 2], [4, 3]])
>>> x.sum()
10
>>> x.sum(axis=1)
matrix([[3],
[7]])
>>> x.sum(axis=1, dtype='float')
matrix([[ 3.],
[ 7.]])
>>> out = np.zeros((1, 2), dtype='float')
>>> x.sum(axis=1, dtype='float', out=out)
matrix([[ 3.],
[ 7.]])
"""
return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)
# To update docstring from array to matrix...
Example #23
| Source File: defmatrix.py From lambda-packs with MIT License | 5 votes |
|
def asmatrix(data, dtype=None):
"""
Interpret the input as a matrix.
Unlike `matrix`, `asmatrix` does not make a copy if the input is already
a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``.
Parameters
----------
data : array_like
Input data.
dtype : data-type
Data-type of the output matrix.
Returns
-------
mat : matrix
`data` interpreted as a matrix.
Examples
--------
>>> x = np.array([[1, 2], [3, 4]])
>>> m = np.asmatrix(x)
>>> x[0,0] = 5
>>> m
matrix([[5, 2],
[3, 4]])
"""
return matrix(data, dtype=dtype, copy=False)
Example #24
| Source File: defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def all(self, axis=None, out=None):
"""
Test whether all matrix elements along a given axis evaluate to True.
Parameters
----------
See `numpy.all` for complete descriptions
See Also
--------
numpy.all
Notes
-----
This is the same as `ndarray.all`, but it returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> y = x[0]; y
matrix([[0, 1, 2, 3]])
>>> (x == y)
matrix([[ True, True, True, True],
[False, False, False, False],
[False, False, False, False]], dtype=bool)
>>> (x == y).all()
False
>>> (x == y).all(0)
matrix([[False, False, False, False]], dtype=bool)
>>> (x == y).all(1)
matrix([[ True],
[False],
[False]], dtype=bool)
"""
return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis)
Example #25
| Source File: defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
Example #26
| Source File: function_base.py From lambda-packs with MIT License | 5 votes |
|
def __init__(self, pyfunc, otypes=None, doc=None, excluded=None,
cache=False, signature=None):
self.pyfunc = pyfunc
self.cache = cache
self.signature = signature
self._ufunc = None # Caching to improve default performance
if doc is None:
self.__doc__ = pyfunc.__doc__
else:
self.__doc__ = doc
if isinstance(otypes, str):
for char in otypes:
if char not in typecodes['All']:
raise ValueError("Invalid otype specified: %s" % (char,))
elif iterable(otypes):
otypes = ''.join([_nx.dtype(x).char for x in otypes])
elif otypes is not None:
raise ValueError("Invalid otype specification")
self.otypes = otypes
# Excluded variable support
if excluded is None:
excluded = set()
self.excluded = set(excluded)
if signature is not None:
self._in_and_out_core_dims = _parse_gufunc_signature(signature)
else:
self._in_and_out_core_dims = None
Example #27
| Source File: function_base.py From lambda-packs with MIT License | 5 votes |
|
def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes):
"""Helper for creating output arrays in vectorize."""
shapes = _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims)
arrays = tuple(np.empty(shape, dtype=dtype)
for shape, dtype in zip(shapes, dtypes))
return arrays
Example #28
| Source File: defmatrix.py From recruit with Apache License 2.0 | 5 votes |
|
def asmatrix(data, dtype=None):
"""
Interpret the input as a matrix.
Unlike `matrix`, `asmatrix` does not make a copy if the input is already
a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``.
Parameters
----------
data : array_like
Input data.
dtype : data-type
Data-type of the output matrix.
Returns
-------
mat : matrix
`data` interpreted as a matrix.
Examples
--------
>>> x = np.array([[1, 2], [3, 4]])
>>> m = np.asmatrix(x)
>>> x[0,0] = 5
>>> m
matrix([[5, 2],
[3, 4]])
"""
return matrix(data, dtype=dtype, copy=False)
Example #29
| Source File: function_base.py From lambda-packs with MIT License | 5 votes |
|
def sort_complex(a):
"""
Sort a complex array using the real part first, then the imaginary part.
Parameters
----------
a : array_like
Input array
Returns
-------
out : complex ndarray
Always returns a sorted complex array.
Examples
--------
>>> np.sort_complex([5, 3, 6, 2, 1])
array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a, copy=True)
b.sort()
if not issubclass(b.dtype.type, _nx.complexfloating):
if b.dtype.char in 'bhBH':
return b.astype('F')
elif b.dtype.char == 'g':
return b.astype('G')
else:
return b.astype('D')
else:
return b
Example #30
| Source File: defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def prod(self, axis=None, dtype=None, out=None):
"""
Return the product of the array elements over the given axis.
Refer to `prod` for full documentation.
See Also
--------
prod, ndarray.prod
Notes
-----
Same as `ndarray.prod`, except, where that returns an `ndarray`, this
returns a `matrix` object instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[ 0, 45, 120, 231]])
>>> x.prod(1)
matrix([[ 0],
[ 840],
[7920]])
"""
return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis)
