I can see several columns (fields
) at once in a numpy
structured array by indexing with a list of the field names, for example
import n
Building on @HYRY's answer, you could also use ndarray
's method getfield
:
def fields_view(array, fields):
return array.getfield(numpy.dtype(
{name: array.dtype.fields[name] for name in fields}
))
I don't think there is an easy way to achieve what you want. In general, you cannot take an arbitrary view into an array. Try the following:
>>> a
array([(1.5, 2.5, [[1.0, 2.0], [1.0, 2.0]]),
(3.0, 4.0, [[4.0, 5.0], [4.0, 5.0]]),
(1.0, 3.0, [[2.0, 6.0], [2.0, 6.0]])],
dtype=[('x', '<f8'), ('y', '<f8'), ('value', '<f8', (2, 2))])
>>> a.view(float)
array([ 1.5, 2.5, 1. , 2. , 1. , 2. , 3. , 4. , 4. , 5. , 4. ,
5. , 1. , 3. , 2. , 6. , 2. , 6. ])
The float view of your record array shows you how the actual data is stored in memory. A view into this data has to be expressible as a combination of a shape, strides and offset into the above data. So if you wanted, for instance, a view of 'x'
and 'y'
only, you could do the following:
>>> from numpy.lib.stride_tricks import as_strided
>>> b = as_strided(a.view(float), shape=a.shape + (2,),
strides=a.strides + a.view(float).strides)
>>> b
array([[ 1.5, 2.5],
[ 3. , 4. ],
[ 1. , 3. ]])
The as_strided
does the same as the perhaps easier to understand:
>>> bb = a.view(float).reshape(a.shape + (-1,))[:, :2]
>>> bb
array([[ 1.5, 2.5],
[ 3. , 4. ],
[ 1. , 3. ]])
Either of this is a view into a
:
>>> b[0,0] =0
>>> a
array([(0.0, 2.5, [[0.0, 2.0], [1.0, 2.0]]),
(3.0, 4.0, [[4.0, 5.0], [4.0, 5.0]]),
(1.0, 3.0, [[2.0, 6.0], [2.0, 6.0]])],
dtype=[('x', '<f8'), ('y', '<f8'), ('value', '<f8', (2, 2))])
>>> bb[2, 1] = 0
>>> a
array([(0.0, 2.5, [[0.0, 2.0], [1.0, 2.0]]),
(3.0, 4.0, [[4.0, 5.0], [4.0, 5.0]]),
(1.0, 0.0, [[2.0, 6.0], [2.0, 6.0]])],
dtype=[('x', '<f8'), ('y', '<f8'), ('value', '<f8', (2, 2))])
It would be nice if either of this could be converted into a record array, but numpy refuses to do so, the reason not being all that clear to me:
>>> b.view([('x',float), ('y',float)])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: new type not compatible with array.
Of course what works (sort of) for 'x'
and 'y'
would not work, for instance, for 'x'
and 'value'
, so in general the answer is: it cannot be done.
In my case 'several columns' happens to be equal to two columns of the same data type, where I can use the following function to make a view:
def make_view(arr, fields, dtype):
offsets = [arr.dtype.fields[f][1] for f in fields]
offset = min(offsets)
stride = max(offsets)
return np.ndarray((len(arr), 2), buffer=arr, offset=offset, strides=(arr.strides[0], stride-offset), dtype=dtype)
I think this boils down the the same thing @Jamie said, it cannot be done in general, but for two columns of the same dtype it can. The result of this function is not a dict but a good old fashioned numpy array.
You can create a dtype object contains only the fields that you want, and use numpy.ndarray()
to create a view of original array:
import numpy as np
strc = np.zeros(3, dtype=[('x', int), ('y', float), ('z', int), ('t', "i8")])
def fields_view(arr, fields):
dtype2 = np.dtype({name:arr.dtype.fields[name] for name in fields})
return np.ndarray(arr.shape, dtype2, arr, 0, arr.strides)
v1 = fields_view(strc, ["x", "z"])
v1[0] = 10, 100
v2 = fields_view(strc, ["y", "z"])
v2[1:] = [(3.14, 7)]
v3 = fields_view(strc, ["x", "t"])
v3[1:] = [(1000, 2**16)]
print(strc)
here is the output:
[(10, 0.0, 100, 0L) (1000, 3.14, 7, 65536L) (1000, 3.14, 7, 65536L)]
As of Numpy version 1.13, the code you propose will return a view. See 'NumPy 1.12.0 Release Notes->Future Changes->Multiple-field manipulation of structured arrays' on this page:
https://docs.scipy.org/doc/numpy-dev/release.html