Re: [Numpy-discussion] Operations on masked items
Ryan May wrote: Pierre, I know you did some preliminary work on helping to make sure that doing operations on masked arrays doesn't change the underlying data. I ran into the following today. import numpy as np a = np.ma.array([1,2,3], mask=[False, True, False]) b = a * 10 c = 10 * a print b.data # Prints [10 2 30] Good! print c.data # Prints [10 10 30] Oops. I tracked it down to __call__ on the _MaskedBinaryOperation class. If there's a mask on the data, you use: result = np.where(m, da, self.f(da, db, *args, **kwargs)) You can see that if a (and hence da) is a scalar, your masked values end up with the value of the scalar. If this is getting too hairy to handle not touching data, I understand. I just thought I should point out the inconsistency here. Well, I guess I hit send too soon. Here's one easy solution (consistent with what you did for __radd__), change the code for __rmul__ to do: return multiply(self, other) instead of: return multiply(other, self) That fixes it for me, and I don't see how it would break anything. Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Operations on masked items
On Feb 3, 2009, at 4:00 PM, Ryan May wrote: Well, I guess I hit send too soon. Here's one easy solution (consistent with what you did for __radd__), change the code for __rmul__ to do: return multiply(self, other) instead of: return multiply(other, self) That fixes it for me, and I don't see how it would break anything. Good call, but once again: Thou shalt not put trust in ye masked values [1]. a = np.ma.array([1,2,3],mask=[0,1,0]) b = np.ma.array([10, 20, 30], mask=[0,1,0]) (a*b).data array([10, 2, 90]) (b*a).data array([10, 20, 90]) So yes, __mul__ is not commutative when you deal w/ masked arrays (at least, when you try to access the data under a mask). Nothing I can do. Remember that preventing the underlying data to be modified is NEVER guaranteed... [1] Epistle of Paul (Dubois). ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Operations on masked items
Pierre, I know you did some preliminary work on helping to make sure that doing operations on masked arrays doesn't change the underlying data. I ran into the following today. import numpy as np a = np.ma.array([1,2,3], mask=[False, True, False]) b = a * 10 c = 10 * a print b.data # Prints [10 2 30] Good! print c.data # Prints [10 10 30] Oops. I tracked it down to __call__ on the _MaskedBinaryOperation class. If there's a mask on the data, you use: result = np.where(m, da, self.f(da, db, *args, **kwargs)) You can see that if a (and hence da) is a scalar, your masked values end up with the value of the scalar. If this is getting too hairy to handle not touching data, I understand. I just thought I should point out the inconsistency here. Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Operations on masked items
Pierre GM wrote: On Feb 3, 2009, at 4:00 PM, Ryan May wrote: Well, I guess I hit send too soon. Here's one easy solution (consistent with what you did for __radd__), change the code for __rmul__ to do: return multiply(self, other) instead of: return multiply(other, self) That fixes it for me, and I don't see how it would break anything. Good call, but once again: Thou shalt not put trust in ye masked values [1]. a = np.ma.array([1,2,3],mask=[0,1,0]) b = np.ma.array([10, 20, 30], mask=[0,1,0]) (a*b).data array([10, 2, 90]) (b*a).data array([10, 20, 90]) So yes, __mul__ is not commutative when you deal w/ masked arrays (at least, when you try to access the data under a mask). Nothing I can do. Remember that preventing the underlying data to be modified is NEVER guaranteed... Fair enough. Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
