[Numpy-discussion] Python objects in Numpy: compatibility issues with methods and functions
Felix Richter
felix@physik3.uni-rostock...
Wed Sep 17 05:44:51 CDT 2008
Hi all,
for my calculations, I need multi-precision arithmetics. For this, I use the
wonderful Python-only mpmath (http://code.google.com/p/mpmath/) and store and
handle my data in Numpy arrays. I thus have Numpy arrays with dtype=object
holding mpmath objects.
Now, some of the array operations that make Numpy such an invaluable tool work
(like adding to arrays or even the dot product!), other silently fail
(like .imag or .conj()). I added an example session below.
What happens if .imag is evaluated on a Numpy array? How does Numpy determine
the imaginary part of an object dtype? Is this propagated to the elements and
are there some hooks?
(The case of conj is quite confusing, as m2.conj() silently fails but
np.conj(m2) works perfectly.)
In other words: What interface must a Python class provide to ensure that
Numpy array operations work? I guess this is somewhat close to what T.J.
Alumbaugh asked some days ago.
Thanks for any hints,
Felix
In [1]:import numpy as np
In [2]:import mpmath
In [3]:m2 = np.array([mpmath.mpc(1+2j),mpmath.mpc(2+3j)])
In [4]:m2
Out[4]:array([(1.0 + 2.0j), (2.0 + 3.0j)], dtype=object)
In [5]:m2.imag
Out[5]:array([0, 0], dtype=object)
In [6]:m2[0].imag
Out[6]:mpf('2.0')
In [7]:m2.conj()
Out[7]:array([(1.0 + 2.0j), (2.0 + 3.0j)], dtype=object)
In [8]:m2[0].conj()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
AttributeError: 'mpc' object has no attribute 'conj'
In [10]:np.conj(m2[0])
Out[10]:mpc(real='1.0', imag='-2.0')
In [11]:np.conj(m2)
Out[11]:array([(1.0 - 2.0j), (2.0 - 3.0j)], dtype=object)
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