Re: [Numpy-discussion] indexed assignment testcases
I guess I always treated scalars as something special when it comes to broadcasting. Seeing these examples, I can see how my grokking of broadcasting was incomplete. I still think that the assignment of an array of values (as opposed to a scalar) to nothing could potentially mask deeper issues, but now I see that it may be impossible to distinguish from the perfectly normal case. Cheers! Ben Root On Sun, Jul 6, 2014 at 5:48 PM, Nathaniel Smith wrote: > On Sun, Jul 6, 2014 at 9:14 PM, Benjamin Root wrote: > > as for the broadcasting issue, I can see it for the second case, but the > > first case still doesn't sit right with me. My understanding of > broadcasting > > is to effectively *expand* an array to match the shape of another array > (or > > some target shape). In this case, the array is being effectively > > *contracted* in shape. That makes zero sense to me. > > That's how it's always worked though, in all cases of broadcasting; > nothing special about indexing: > > In [8]: a = np.zeros((3, 0)) > > In [9]: a + 1 > Out[9]: array([], shape=(3, 0), dtype=float64) > > In [10]: a + [[1], [2], [3]] > Out[10]: array([], shape=(3, 0), dtype=float64) > > IME it's extremely useful in practice for avoiding special cases when > some axis has a vary size that can be zero. > > -- > Nathaniel J. Smith > Postdoctoral researcher - Informatics - University of Edinburgh > http://vorpus.org > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] indexed assignment testcases
On Sun, Jul 6, 2014 at 9:14 PM, Benjamin Root wrote: > as for the broadcasting issue, I can see it for the second case, but the > first case still doesn't sit right with me. My understanding of broadcasting > is to effectively *expand* an array to match the shape of another array (or > some target shape). In this case, the array is being effectively > *contracted* in shape. That makes zero sense to me. That's how it's always worked though, in all cases of broadcasting; nothing special about indexing: In [8]: a = np.zeros((3, 0)) In [9]: a + 1 Out[9]: array([], shape=(3, 0), dtype=float64) In [10]: a + [[1], [2], [3]] Out[10]: array([], shape=(3, 0), dtype=float64) IME it's extremely useful in practice for avoiding special cases when some axis has a vary size that can be zero. -- Nathaniel J. Smith Postdoctoral researcher - Informatics - University of Edinburgh http://vorpus.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] indexed assignment testcases
On So, 2014-07-06 at 16:14 -0400, Benjamin Root wrote: > re: deprecation warnings... that's what I get when I am working on my > non-dev box because I am at the conference, and have gotten too used > to the setup of my dev box... > > > as for the broadcasting issue, I can see it for the second case, but > the first case still doesn't sit right with me. My understanding of > broadcasting is to effectively *expand* an array to match the shape of > another array (or some target shape). In this case, the array is being > effectively *contracted* in shape. That makes zero sense to me. > Well, from a technical point of view, it is more like changing the shape to whatever fits while setting the stride to 0. I am sure there are a few places where the doc is not clear. From a practical point of view, it makes sense if you consider this: arr[arr < 0] = 0 Where it might be that the array has no elements smaller 0. Though I admit I would write 0 here, and not [0]. - Sebastian > > Ben > > > > On Sun, Jul 6, 2014 at 3:59 PM, Charles R Harris > wrote: > > > > On Sun, Jul 6, 2014 at 1:32 PM, Benjamin Root > wrote: > While trying to wrap my head around the issues with > matplotlib's tri module and the new numpy indexing, I > have made some test cases where I wonder if warnings > should be issued. > > > import numpy as np > > a = np.ones((10,)) > > all_false = np.zeros((10,), dtype=bool) > > a[all_false] = np.array([2.0]) # the shapes don't > match here > > > > It broadcasts because the leading dimension is 1. > > > > > mask_in = np.array([False]*8 + [True, True]) > > a[mask_in] = np.array([])# raises ValueError as > expected > > a[mask_in] = np.array([[]]) # no exception because it > is 2-D, for some reason (on master, but not > release-0.9b1) > > > Now falls back to old behavior and raises a > DeprecationWarning. You don't see that by default. > > > > > a[mask_in] = np.array([2.0]) # This works and repeats > 2.0 twice. I thought this wasn't supposed to happen > anymore? > > > > Broadcasting again. > > > > Chuck > > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion signature.asc Description: This is a digitally signed message part ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] indexed assignment testcases
re: deprecation warnings... that's what I get when I am working on my non-dev box because I am at the conference, and have gotten too used to the setup of my dev box... as for the broadcasting issue, I can see it for the second case, but the first case still doesn't sit right with me. My understanding of broadcasting is to effectively *expand* an array to match the shape of another array (or some target shape). In this case, the array is being effectively *contracted* in shape. That makes zero sense to me. Ben On Sun, Jul 6, 2014 at 3:59 PM, Charles R Harris wrote: > > > > On Sun, Jul 6, 2014 at 1:32 PM, Benjamin Root wrote: > >> While trying to wrap my head around the issues with matplotlib's tri >> module and the new numpy indexing, I have made some test cases where I >> wonder if warnings should be issued. >> >> import numpy as np >> a = np.ones((10,)) >> all_false = np.zeros((10,), dtype=bool) >> a[all_false] = np.array([2.0]) # the shapes don't match here >> > > It broadcasts because the leading dimension is 1. > > >> >> mask_in = np.array([False]*8 + [True, True]) >> a[mask_in] = np.array([])# raises ValueError as expected >> a[mask_in] = np.array([[]]) # no exception because it is 2-D, for some >> reason (on master, but not release-0.9b1) >> > > Now falls back to old behavior and raises a DeprecationWarning. You don't > see that by default. > > >> >> a[mask_in] = np.array([2.0]) # This works and repeats 2.0 twice. I >> thought this wasn't supposed to happen anymore? >> > > Broadcasting again. > > Chuck > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] indexed assignment testcases
On Sun, Jul 6, 2014 at 1:32 PM, Benjamin Root wrote: > While trying to wrap my head around the issues with matplotlib's tri > module and the new numpy indexing, I have made some test cases where I > wonder if warnings should be issued. > > import numpy as np > a = np.ones((10,)) > all_false = np.zeros((10,), dtype=bool) > a[all_false] = np.array([2.0]) # the shapes don't match here > It broadcasts because the leading dimension is 1. > > mask_in = np.array([False]*8 + [True, True]) > a[mask_in] = np.array([])# raises ValueError as expected > a[mask_in] = np.array([[]]) # no exception because it is 2-D, for some > reason (on master, but not release-0.9b1) > Now falls back to old behavior and raises a DeprecationWarning. You don't see that by default. > > a[mask_in] = np.array([2.0]) # This works and repeats 2.0 twice. I thought > this wasn't supposed to happen anymore? > Broadcasting again. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] indexed assignment testcases
On So, 2014-07-06 at 15:32 -0400, Benjamin Root wrote: > While trying to wrap my head around the issues with matplotlib's tri > module and the new numpy indexing, I have made some test cases where I > wonder if warnings should be issued. > > > import numpy as np > > a = np.ones((10,)) > > all_false = np.zeros((10,), dtype=bool) > > a[all_false] = np.array([2.0]) # the shapes don't match here > The shapes match using broadcasting. Values shape of (1,) can be broadcast to indexing result shape of (0,). > > mask_in = np.array([False]*8 + [True, True]) > > a[mask_in] = np.array([])# raises ValueError as expected > > a[mask_in] = np.array([[]]) # no exception because it is 2-D, for > some reason (on master, but not release-0.9b1) > Gives a (maybe not good) deprecation warning in master. But those are typically invisible... > > a[mask_in] = np.array([2.0]) # This works and repeats 2.0 twice. I > thought this wasn't supposed to happen anymore? > Again, broadcasting of values onto out shape. - Sebastian > > Ben Root > > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion signature.asc Description: This is a digitally signed message part ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
