CODEWITHSUNDEEP
pythonnumpyattributessubclassrecarray
mher
mher

Reputation: 419

subclassed empty numpy recarray losing its type and added attributes in numpy 1.8

I am trying to implement a subclass of a numpy recarray (recsub) and assign instances of it to an ndarray of dtype 'object' (ndarr). It works well, but i have a problem when the subclassed recarray is instantiated with an empty array. This is the code for the subclassed recarry:

class recsub(numpy.recarray):
"""subclassed recarray"""

def __new__(cls, *args, **kwargs):

    obj = numpy.recarray.__new__(cls, *args, **kwargs)

    return obj

def __init__(self, *arg, **kwargs):

    self.x = -1

def new_method(self):
    print 'new_method() : fooooooooooooo'

I create the ndarray as :

ndarr = numpy.ndarray(5, 'object')

now if i create two instances of recsub :

ndarr[0] = recsub(2, [('a','f8')])
ndarr[1] = recsub((), [('a','f8')])

Now here is the weird stuff that is happening. The output of :

print type(ndarr[0])
print type(ndarr[1])

is:

>>> <class '__main__.recsub'>
>>> <class 'numpy.core.records.record'>

so i can not access ndarr[1].x

This used to work in numpy 1.7, but not anymore in numpy 1.8! So it seems something is missing upon instantiating the recarray with a shape () as opposed to (n)

any suggestion is welcome,

tnx in advance,

Upvotes: 2

Views: 270

Answers (1)

hpaulj
hpaulj

Reputation: 231615

I get similar behavior in dev 1.9 with simpler arrays

ndarr = np.ndarray(2,dtype=np.object)
x = np.array([1,2])
ndarr[0] = x
y = np.array(3)
ndarr[1] = y
type(ndarr[0])
# numpy.ndarray
type(ndarr[1])
# numpy.int32
ndarr
# array([array([1, 2]), 3], dtype=object)

So the array with shape () gets inserted into ndarr as a scalar.

I don't know whether this is a bug, feature, or intended consequence of some change between 1.7 and 1.8. I guess the first place to look is the release notes for 1.8.

This issue may relevant: https://github.com/numpy/numpy/issues/1679

array([array([]), array(0, object)])
array([array([], dtype=float64), 0], dtype=object)

with the bug fix, https://github.com/numpy/numpy/pull/4109, the items that were stored as array are returned in the same way (instead of as scalars).

type(ndarr[1])
# <type 'numpy.ndarray'>
ndarr
# [array([1, 2]) array(3)]
# [array([], dtype=float64) array(0, dtype=object)]
# [array([], dtype=float64) 0]

And the OP example runs as expected.

Upvotes: 1

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