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POArgument types differ in normal and reflected operator overload (__sub__ / __rsub__)
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copied!<p>How do I get access to the properties of an numpy array after passing it through an righthand operator like <code>__rsub__</code>?</p> <p>I wrote a very simple class in python that defines the two functions:</p> <pre><code>class test(object): def __sub__(self, other): return other def __rsub__(self, other): return other </code></pre> <p>Basically they should do the same. The left-hand operator <code>__sub__</code> works as expected, but it seems that the numpy array is stripped off its properties on the right-hand operator</p> <pre><code>from skimage import data from skimage.color import rgb2gray lena = data.lena() grayLena = rgb2gray(lena) t = test() ## overloaded - operator left_hand = t - grayLena print left_hand # Output: #array([[ 0.60802863, 0.60802863, 0.60779059, ..., 0.64137412, # 0.57998235, 0.46985725], # [ 0.60802863, 0.60802863, 0.60779059, ..., 0.64137412, # 0.57998235, 0.46985725], # [ 0.60802863, 0.60802863, 0.60779059, ..., 0.64137412, # 0.57998235, 0.46985725], # ..., # [ 0.13746353, 0.13746353, 0.16881412, ..., 0.37271804, # 0.35559529, 0.34377725], # [ 0.14617059, 0.14617059, 0.18730588, ..., 0.36788784, # 0.37292549, 0.38467529], # [ 0.14617059, 0.14617059, 0.18730588, ..., 0.36788784, # 0.37292549, 0.38467529]]) right_hand = grayLena - t print right_hand # Output: # array([[0.6080286274509803, 0.6080286274509803, 0.6077905882352941, ..., # 0.6413741176470589, 0.5799823529411765, 0.4698572549019608], # [0.6080286274509803, 0.6080286274509803, 0.6077905882352941, ..., # 0.6413741176470589, 0.5799823529411765, 0.4698572549019608], # [0.6080286274509803, 0.6080286274509803, 0.6077905882352941, ..., # 0.6413741176470589, 0.5799823529411765, 0.4698572549019608], # ..., # [0.1374635294117647, 0.1374635294117647, 0.1688141176470588, ..., # 0.3727180392156863, 0.35559529411764706, 0.34377725490196076], # [0.1461705882352941, 0.1461705882352941, 0.18730588235294118, ..., # 0.3678878431372549, 0.37292549019607846, 0.3846752941176471], # [0.1461705882352941, 0.1461705882352941, 0.18730588235294118, ..., # 0.3678878431372549, 0.37292549019607846, 0.3846752941176471]], dtype=object) </code></pre> <p>So the difference between both operations is that <code>__rsub__</code> receives an array of dtype=object. If I would just set the dtype of this array, everything would work fine.</p> <p>However, it works only with the return value, outside of <code>__rsub__</code>. Inside my <code>__rsub__</code> I get only rubbish, that I cannot convert back, namely if I do</p> <pre><code>npArray = np.array(other, dtype=type(other)) </code></pre> <p>I get a 1D array of the type (floats in my case). But for some reason, the shape information is lost. Has anyone done this or an idea how I can access the original properties of the array (shape and type)?</p>

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