假设我有以下 numpy 结构化数组：
In [250]: x
Out[250]:
array([(22, 2, -1000000000, 2000), (22, 2, 400, 2000),
(22, 2, 804846, 2000), (44, 2, 800, 4000), (55, 5, 900, 5000),
(55, 5, 1000, 5000), (55, 5, 8900, 5000), (55, 5, 11400, 5000),
(33, 3, 14500, 3000), (33, 3, 40550, 3000), (33, 3, 40990, 3000),
(33, 3, 44400, 3000)],
dtype=[('f1', '<i4'), ('f2', '<f4'), ('f3', '<f4'), ('f4', '<i4')])
我正在尝试将上述数组的子集修改为常规的 numpy 数组。对于我的应用程序来说，不创建任何副本（仅视图）是必不可少的。
使用以下函数从上述结构化数组中检索字段：
def fields_view(array, fields):
return array.getfield(numpy.dtype(
{name: array.dtype.fields[name] for name in fields}
))
如果我对“f2”和“f3”字段感兴趣，我会执行以下操作：
In [251]: y=fields_view(x,['f2','f3'])
In [252]: y
Out [252]:
array([(2.0, -1000000000.0), (2.0, 400.0), (2.0, 804846.0), (2.0, 800.0),
(5.0, 900.0), (5.0, 1000.0), (5.0, 8900.0), (5.0, 11400.0),
(3.0, 14500.0), (3.0, 40550.0), (3.0, 40990.0), (3.0, 44400.0)],
dtype={'names':['f2','f3'], 'formats':['<f4','<f4'], 'offsets':[4,8], 'itemsize':12})
有一种方法可以直接从原始结构化数组的“f2”和“f3”字段中获取 ndarray。但是，对于我的应用程序，有必要构建这个中间结构化数组，因为这个数据子集是一个类的属性。
如果不进行复制，我无法将中间结构化数组转换为常规的 numpy 数组。
In [253]: y.view(('<f4', len(y.dtype.names)))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-54-f8fc3a40fd1b> in <module>()
----> 1 y.view(('<f4', len(y.dtype.names)))
ValueError: new type not compatible with array.
此函数还可用于将记录数组转换为 ndarray：
def recarr_to_ndarr(x,typ):
fields = x.dtype.names
shape = x.shape + (len(fields),)
offsets = [x.dtype.fields[name][1] for name in fields]
assert not any(np.diff(offsets, n=2))
strides = x.strides + (offsets[1] - offsets[0],)
y = np.ndarray(shape=shape, dtype=typ, buffer=x,
offset=offsets[0], strides=strides)
return y
但是，我收到以下错误：
In [254]: recarr_to_ndarr(y,'<f4')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-65-2ebda2a39e9f> in <module>()
----> 1 recarr_to_ndarr(y,'<f4')
<ipython-input-62-8a9eea8e7512> in recarr_to_ndarr(x, typ)
8 strides = x.strides + (offsets[1] - offsets[0],)
9 y = np.ndarray(shape=shape, dtype=typ, buffer=x,
---> 10 offset=offsets[0], strides=strides)
11 return y
12
TypeError: expected a single-segment buffer object
如果我创建一个副本，该功能可以正常工作：
In [255]: recarr_to_ndarr(np.array(y),'<f4')
Out[255]:
array([[ 2.00000000e+00, -1.00000000e+09],
[ 2.00000000e+00, 4.00000000e+02],
[ 2.00000000e+00, 8.04846000e+05],
[ 2.00000000e+00, 8.00000000e+02],
[ 5.00000000e+00, 9.00000000e+02],
[ 5.00000000e+00, 1.00000000e+03],
[ 5.00000000e+00, 8.90000000e+03],
[ 5.00000000e+00, 1.14000000e+04],
[ 3.00000000e+00, 1.45000000e+04],
[ 3.00000000e+00, 4.05500000e+04],
[ 3.00000000e+00, 4.09900000e+04],
[ 3.00000000e+00, 4.44000000e+04]], dtype=float32)
两个数组之间似乎没有区别：
In [66]: y
Out[66]:
array([(2.0, -1000000000.0), (2.0, 400.0), (2.0, 804846.0), (2.0, 800.0),
(5.0, 900.0), (5.0, 1000.0), (5.0, 8900.0), (5.0, 11400.0),
(3.0, 14500.0), (3.0, 40550.0), (3.0, 40990.0), (3.0, 44400.0)],
dtype={'names':['f2','f3'], 'formats':['<f4','<f4'], 'offsets':[4,8], 'itemsize':12})
In [67]: np.array(y)
Out[67]:
array([(2.0, -1000000000.0), (2.0, 400.0), (2.0, 804846.0), (2.0, 800.0),
(5.0, 900.0), (5.0, 1000.0), (5.0, 8900.0), (5.0, 11400.0),
(3.0, 14500.0), (3.0, 40550.0), (3.0, 40990.0), (3.0, 44400.0)],
dtype={'names':['f2','f3'], 'formats':['<f4','<f4'], 'offsets':[4,8], 'itemsize':12})