[SciPy-User] masked recarray, recarray with one field of type "ndarray"

Gustavo Goretkin gustavo.goretkin@gmail....
Sun Feb 19 10:52:25 CST 2012

```In short: do recarrays support masking?

On Wed, Feb 1, 2012 at 11:36 AM, Gustavo Goretkin
<gustavo.goretkin@gmail.com> wrote:
> Thanks for the help! Now is there any way to mask elements of a
> recarray? I should explain the application because I think I may be
> I'll be building a tree and each node will have some attributes (for
> example, a matrix). I often have to iterate through every node of the
> tree and do a calculation -- something that I could do in a vectorized
> way with NumPy if all the attributes were stored in an array. So I
> thought I could represent the tree as a recarray (that I'd
> occasionally need to grow).
>
> I'd also need to delete nodes from the tree occasionally. I'd
> accomplish this by masking entries of the recarray. When I needed to
> add a node to the tree, I'd try to populate a masked entry before
> going to the end of the array.
>
> On Tue, Jan 31, 2012 at 9:33 AM, Warren Weckesser
> <warren.weckesser@enthought.com> wrote:
>>
>>
>> On Tue, Jan 31, 2012 at 2:36 AM, Gustavo Goretkin
>> <gustavo.goretkin@gmail.com> wrote:
>>>
>>> Does a recarray support masking?
>>>
>>> Can I have a recarray where one of the fields is an M-by-N ndarray
>>> (not recarray) of some dtype?
>>> ex: a = np.recarray(shape=(10),formats=['i4','f8','3-by-3 ndarray of
>>> dtype=float64'])
>>
>>
>>
>> Here's how it can be done with the dtype argument (in this case, the
>> "sub-arrays" are 3x5 float32):
>>
>> In [21]: dt = np.dtype([('id', int32), ('values', float32, (3,5))])
>>
>> In [22]: a = np.recarray(shape=(3,), dtype=dt)
>>
>> In [23]: a.id
>> Out[23]: array([      7, 2345536, 8585218])
>>
>> In [24]: a[0].id
>> Out[24]: 7
>>
>> In [25]: a[0].values
>> Out[25]:
>> array([[  9.80908925e-45,   2.15997513e-37,   3.16079124e-39,
>>           1.18408375e-38,   2.81552923e-38],
>>        [  2.13004362e-37,  -7.69011974e-02,   9.80908925e-45,
>>           9.80908925e-45,   3.62636667e-21],
>>        [  5.67059093e-24,   5.67095065e-24,   5.64768872e-24,
>>           7.86448908e+11,   0.00000000e+00]], dtype=float32)
>>
>> In [26]: a[0].values.shape
>> Out[26]: (3, 5)
>>
>>
>> Warren
>>
>>
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```