I've come across a strange behavior for classes subclassed from ndarray.
Here's a minimal example that illustrates the problem:
import numpy as np
class TestArray(np.ndarray):
def __new__(cls, data, info=None, dtype=None, copy=False):
subarr = np.array(data, dtype=dtype, copy=copy)
subarr = subarr.view(cls)
return subarr
def sort(self,*args,**kwargs):
print type(self)
print type(self.base)
Now consider this:
In [1]: tst = TestArray(np.random.rand(2,3))
In [2]: tst.sort()
<class '__main__.TestArray'>
<type 'numpy.ndarray'>
In [3]: np.sort(tst)
<class '__main__.TestArray'>
<type 'NoneType'>
Out[3]:
TestArray([[ 0.90489484, 0.950291 , 0.80753772],
[ 0.49020689, 0.84582283, 0.61532922]])
Why whould tst.sort() show the correct base class and np.sort show
NoneType as base class for tst?
I'd appreciate any insights ...
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