How to safely subclass ndarray and get behavior consistent with ndarray - odd nanmin/max results?

Question

I'm trying to subclass an ndarray so that I can add some additional fields. When I do this however, I get odd behavior in a variety of numpy functions. For example nanmin returns now return an object of the type of my new array classs, whereas previously I'd get a float64. Why? Is this a bug with nanmin or my class?

import numpy as np

class NDArrayWithColumns(np.ndarray):
    def __new__(cls, obj, columns=None):
        obj = obj.view(cls)
        obj.columns = tuple(columns)
        return obj

    def __array_finalize__(self, obj):
        if obj is None: return
        self.columns = getattr(obj, 'columns', None)

NAN = float("nan")
r = np.array([1.,0.,1.,0.,1.,0.,1.,0.,NAN, 1., 1.])
print "MIN", np.nanmin(r), type(np.nanmin(r))

gives:

MIN 0.0 <type 'numpy.float64'>

but

>>> r = NDArrayWithColumns(r, ["a"])
>>> print "MIN", np.nanmin(r), type(np.nanmin(r))
MIN 0.0 <class '__main__.NDArrayWithColumns'>
>>> print r.shape 
(11,)

Note the change in type, and also that str(np.nanmin(r)) shows 1 field, not 11.

In case you're interested, I'm subclassing because I'd like to track columns names is matrices of a single type but structure arrays and record type arrays allow for varying type).

Steve Kern · Accepted Answer · 2016-11-14 22:22:39Z

1

You need to implement the __array_wrap__ method that gets called at the end of ufuncs, per the docs:

    def __array_wrap__(self, out_arr, context=None):
        print('In __array_wrap__:')
        print('   self is %s' % repr(self))
        print('   arr is %s' % repr(out_arr))
        # then just call the parent
        return np.ndarray.__array_wrap__(self, out_arr, context)

answered Nov 14, 2016 at 22:22

Steve Kern

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1 Comment

user48956 Over a year ago

I'm sure not it helps. Your code doesn't change the final behavior and it not clear what decision logic might go in here to fix it.

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