Re: [Numpy-discussion] Unexpected MaskedArray behavior
Ryan May wrote: Pierre GM wrote: On Dec 16, 2008, at 1:57 PM, Ryan May wrote: I just noticed the following and I was kind of surprised: a = ma.MaskedArray([1,2,3,4,5], mask=[False,True,True,False,False]) b = a*5 b masked_array(data = [5 -- -- 20 25], mask = [False True True False False], fill_value=99) b.data array([ 5, 10, 15, 20, 25]) I was expecting that the underlying data wouldn't get modified while masked. Is this actual behavior expected? Meh. Masked data shouldn't be trusted anyway, so I guess it doesn't really matter one way or the other. But I tend to agree, it'd make more sense leave masked data untouched (or at least, reset them to their original value after the operation), which would mimic the behavior of gimp/photoshop. Looks like there's a relatively easy fix. I need time to check whether it doesn't break anything elsewhere, nor that it slows things down too much. I won't have time to test all that before next week, though. In any case, that would be for 1.3.x, not for 1.2.x. In the meantime, if you need the functionality, use something like ma.where(a.mask,a,a*5) I agree that masked values probably shouldn't be trusted, I was just surprised to see the behavior. I just assumed that no operations were taking place on masked values. Just to clarify what I was doing here: I had a masked array of data, where the mask was set by a variety of different masked values. Later on in the code, after doing some unit conversions, I went back to look at the raw data to find points that had one particular masked value set. Instead, I was surprised to see all of the masked values had changed and I could no longer find any of the special values in the data. Ryan Sorry for being dense about this, but I really do not understand why masked values should not be trusted. If I apply a procedure to an array with elements designated as untouchable, I would expect that contract to be honored. What am I missing here? Thanks for your patience! -- jv ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Unexpected MaskedArray behavior
On Dec 17, 2008, at 12:13 PM, Jim Vickroy wrote: Sorry for being dense about this, but I really do not understand why masked values should not be trusted. If I apply a procedure to an array with elements designated as untouchable, I would expect that contract to be honored. What am I missing here? Thanks for your patience! -- jv Everything depends on your interpretation of masked data. Traditionally, masked data indicate invalid data, whatever the cause of the invalidity. Operations involving invalid data yield invalid data, hence the presence of a mask on the result. However, the value underneath the mask is still invalid, hence the statement don't trust masked values. Interpreting a mask as a way to prevent some elements of an array to be processed (designating them as untouchable) is a bit of a stretch. Nevertheless, I agree that this behavior is not intuitive, so I'll check what I can do. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Unexpected MaskedArray behavior
Pierre GM wrote: On Dec 16, 2008, at 1:57 PM, Ryan May wrote: I just noticed the following and I was kind of surprised: a = ma.MaskedArray([1,2,3,4,5], mask=[False,True,True,False,False]) b = a*5 b masked_array(data = [5 -- -- 20 25], mask = [False True True False False], fill_value=99) b.data array([ 5, 10, 15, 20, 25]) I was expecting that the underlying data wouldn't get modified while masked. Is this actual behavior expected? Meh. Masked data shouldn't be trusted anyway, so I guess it doesn't really matter one way or the other. But I tend to agree, it'd make more sense leave masked data untouched (or at least, reset them to their original value after the operation), which would mimic the behavior of gimp/photoshop. Looks like there's a relatively easy fix. I need time to check whether it doesn't break anything elsewhere, nor that it slows things down too much. I won't have time to test all that before next week, though. In any case, that would be for 1.3.x, not for 1.2.x. In the meantime, if you need the functionality, use something like ma.where(a.mask,a,a*5) I agree that masked values probably shouldn't be trusted, I was just surprised to see the behavior. I just assumed that no operations were taking place on masked values. Just to clarify what I was doing here: I had a masked array of data, where the mask was set by a variety of different masked values. Later on in the code, after doing some unit conversions, I went back to look at the raw data to find points that had one particular masked value set. Instead, I was surprised to see all of the masked values had changed and I could no longer find any of the special values in the data. 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
[Numpy-discussion] Unexpected MaskedArray behavior
Hi, I just noticed the following and I was kind of surprised: a = ma.MaskedArray([1,2,3,4,5], mask=[False,True,True,False,False]) b = a*5 b masked_array(data = [5 -- -- 20 25], mask = [False True True False False], fill_value=99) b.data array([ 5, 10, 15, 20, 25]) I was expecting that the underlying data wouldn't get modified while masked. Is this actual behavior expected? 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] Unexpected MaskedArray behavior
On Dec 16, 2008, at 1:57 PM, Ryan May wrote: I just noticed the following and I was kind of surprised: a = ma.MaskedArray([1,2,3,4,5], mask=[False,True,True,False,False]) b = a*5 b masked_array(data = [5 -- -- 20 25], mask = [False True True False False], fill_value=99) b.data array([ 5, 10, 15, 20, 25]) I was expecting that the underlying data wouldn't get modified while masked. Is this actual behavior expected? Meh. Masked data shouldn't be trusted anyway, so I guess it doesn't really matter one way or the other. But I tend to agree, it'd make more sense leave masked data untouched (or at least, reset them to their original value after the operation), which would mimic the behavior of gimp/photoshop. Looks like there's a relatively easy fix. I need time to check whether it doesn't break anything elsewhere, nor that it slows things down too much. I won't have time to test all that before next week, though. In any case, that would be for 1.3.x, not for 1.2.x. In the meantime, if you need the functionality, use something like ma.where(a.mask,a,a*5) ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
