On 2/12/16 6:06 PM, Gutenkunst, Ryan N - (rgutenk) wrote:
Hello all,
In 2009 I developed an application that uses a subclass of masked arrays as a
central data object. My subclass Spectrum possesses additional attributes along
with many custom methods. It was very convenient to be able to use standard
numpy functions for doing arithmetic on these objects. However, my code broke
with numpy 1.10. I've finally had a chance to track down the problem, and I am
hoping someone can suggest a workaround.
See below for an example, which is as minimal as I could concoct. In this case, I have a
Spectrum object that I'd like to take the logarithm of using numpy.ma.log, while
preserving the value of the "folded" attribute. Up to numpy 1.9, this worked as
expected, but in numpy 1.10 and 1.11 the attribute is not preserved.
The change in behavior appears to be driven by a commit made on Jun 16th, 2015
by Marten van Kerkwijk. In particular, the commit changed
_MaskedUnaryOperation.__call__ so that the result array's update_from method is
no longer called with the input array as the argument, but rather the result of
the numpy UnaryOperation (old line 889, new line 885). Because that
UnaryOperation doesn't carry my new attribute, it's not present for update_from
to access. I notice that similar changes were made to MaskedBinaryOperation,
although I haven't tested those. It's not clear to me from the commit message
why this particular change was made, so I don't know whether this new behavior
is intentional.
I know that subclassing arrays isn't widely encouraged, but it has been very
convenient in my code. Is it still possible to subclass masked_array in such a
way that functions like numpy.ma.log preserve additional attributes? If so, can
someone point me in the right direction?
Thanks!
Ryan
*** Begin example
import numpy
print 'Working with numpy {0}'.format(numpy.__version__)
class Spectrum(numpy.ma.masked_array):
def __new__(cls, data, mask=numpy.ma.nomask, data_folded=None):
subarr = numpy.ma.masked_array(data, mask=mask, keep_mask=True,
shrink=True)
subarr = subarr.view(cls)
subarr.folded = data_folded
return subarr
def __array_finalize__(self, obj):
if obj is None:
return
numpy.ma.masked_array.__array_finalize__(self, obj)
self.folded = getattr(obj, 'folded', 'unspecified')
def _update_from(self, obj):
print('Input to update_from: {0}'.format(repr(obj)))
numpy.ma.masked_array._update_from(self, obj)
self.folded = getattr(obj, 'folded', 'unspecified')
def __repr__(self):
return 'Spectrum(%s, folded=%s)'\
% (str(self), str(self.folded))
fs1 = Spectrum([2,3,4.], data_folded=True)
fs2 = numpy.ma.log(fs1)
print('fs2.folded status: {0}'.format(fs2.folded))
print('Expectation is True, achieved with numpy 1.9')
*** End example
--
Ryan Gutenkunst
Assistant Professor
Molecular and Cellular Biology
University of Arizona
phone: (520) 626-0569, office LSS 325
http://gutengroup.mcb.arizona.edu
Latest paper: "Computationally efficient composite likelihood statistics for
demographic inference"
Molecular Biology and Evolution; http://dx.doi.org/10.1093/molbev/msv255
Ryan,
I'm not sure if you will be able to get this to work as in NumPy 1.9,
but the __array_wrap__ method is intended to be the mechanism for
subclasses to set their return type, adjust metadata, etc [1].
Unfortunately, the numpy.ma.log function does not seem to make a call
to __array_wrap__ (at least in NumPy 1.10.2) although numpy.log does:
from __future__ import print_function
import numpy
print('Working with numpy {0}'.format(numpy.__version__))
class Spectrum(numpy.ma.masked_array):
def __new__(cls, data, mask=numpy.ma.nomask, data_folded=None):
subarr = numpy.ma.masked_array(data, mask=mask, keep_mask=True,
shrink=True)
subarr = subarr.view(cls)
subarr.folded = data_folded
return subarr
def __array_finalize__(self, obj):
if obj is None:
return
numpy.ma.masked_array.__array_finalize__(self, obj)
self.folded = getattr(obj, 'folded', 'unspecified')
def __array_wrap__(self, out_arr, context=None):
print('__array_wrap__ called')
return numpy.ndarray.__array_wrap__(self, out_arr, context)
def __repr__(self):
return 'Spectrum(%s, folded=%s)'\
% (str(self), str(self.folded))
fs1 = Spectrum([2,3,4.], data_folded=True)
print('numpy.ma.log:')
fs2 = numpy.ma.log(fs1)
print('fs2 type:', type(fs2))
print('fs2.folded status: {0}'.format(fs2.folded))
print('numpy.log:')
fs3 = numpy.log(fs1)
print('fs3 type:', type(fs3))
print('fs3.folded status: {0}'.format(fs3.folded))
----
$ python example.py
Working with numpy 1.10.2
numpy.ma.log:
fs2 type: <class '__main__.Spectrum'>
fs2.folded status: unspecified
numpy.log:
__array_wrap__ called
fs3 type: <class '__main__.Spectrum'>
fs3.folded status: True
The change mentioned in the original message was made in pull request
3907 [2] in case anyone wants to have a look.
Cheers,
- Jonathan Helmus
[1]
http://docs.scipy.org/doc/numpy-1.10.1/user/basics.subclassing.html#array-wrap-for-ufuncs
[2] https://github.com/numpy/numpy/pull/3907
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