[Numpy-discussion] Feature requests/Enhancements for upper-level engineering students
Greetings, As the Fall semester is fast approaching (10 days away for us at UConn), we are looking for senior design (also called capstone) projects for the 2020-2021 school year. The COVID situation has strengthened the need for remote work. The process here is that students are assigned to projects by late September. Then, they have 6 main deliverables over the course of 2 semesters: 1. Initial Fall Presentation (~Oct) 2. Final Fall Presentation (~Dec) 3. Mid-year report (~Jan) 4. Initial Spring Presentation (~Mar) 5. Final Spring Presntation (~Apr) 6. Final report (~May) My question to the NumPy community is: Are there any features or enhancements that would be nice to have, but might not have a team dedicated to the idea? I would be happy to advise any projects that people are interested in proposing. I would like to hear what people think would be worthwhile for students to build together. Some background, these students have all used Python and Matlab for mechanical engineering applications like linear regression, modal analyses, ode integration, and root solving. They learn quickly, but may not be interested in UX/UI design problems. -- Sent from: http://numpy-discussion.10968.n7.nabble.com/ ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] What is up with raw boolean indices (like a[False])?
You're right. I was confusing the broadcasting logic for boolean arrays. However, I did find this example >>> np.arange(10).reshape((2, 5))[np.array([[0, 0, 0, 0, 0]], dtype=np.int64), >>> False] Traceback (most recent call last): File "", line 1, in IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (1,5) (0,) That certainly seems to imply there is some broadcasting being done. Aaron Meurer On Wed, Aug 19, 2020 at 6:55 PM Sebastian Berg wrote: > > On Wed, 2020-08-19 at 18:07 -0600, Aaron Meurer wrote: > > > > 3. If you have multiple advanced indexing you get annoying > > > > broadcasting > > > >of all of these. That is *always* confusing for boolean > > > > indices. > > > >0-D should not be too special there... > > > > OK, now that I am learning more about advanced indexing, this > > statement is confusing to me. It seems that scalar boolean indices do > > not broadcast. For example: > > Well, broadcasting means you broadcast the *nonzero result* unless I am > very confused... There is a reason I dismissed it. We could (and > arguably should) just deprecate it. And I have doubts anyone would > even notice. > > > > > > > > np.arange(2)[False, np.array([True, False])] > > array([], dtype=int64) > > > > > np.arange(2)[tuple(np.broadcast_arrays(False, np.array([True, > > > > > False])))] > > Traceback (most recent call last): > > File "", line 1, in > > IndexError: too many indices for array: array is 1-dimensional, but 2 > > were indexed > > > > And indeed, the docs even say, as you noted, "the nonzero equivalence > > for Boolean arrays does not hold for zero dimensional boolean > > arrays," > > which I guess also applies to the broadcasting. > > I actually think that probably also holds. Nonzero just behave weird > for 0D because arrays (because it returns a tuple). > But since broadcasting the nonzero result is so weird, and since 0-D > booleans require some additional logic and don't generalize 100% (code > wise), I won't rule out there are differences. > > > From what I can tell, the logic is that all integer and boolean > > arrays > > Did you try that? Because as I said above, IIRC broadcasting the > boolean array without first calling `nonzero` isn't really whats going > on. And I don't know how it could be whats going on, since adding > dimensions to a boolean index would have much more implications? > > - Sebastian > > > > (and scalar ints) are broadcast together, *except* for boolean > > scalars. Then the first boolean scalar is replaced with and(all > > boolean scalars) and the rest are removed from the index. Then that > > index adds a length 1 axis if it is True and 0 if it is False. > > > > So they don't broadcast, but rather "fake broadcast". I still contend > > that it would be much more useful, if True were a synonym for newaxis > > and False worked like newaxis but instead added a length 0 axis. > > Alternately, True and False scalars should behave exactly like all > > other boolean arrays with no exceptions (i.e., work like > > np.nonzero(), > > broadcast, etc.). This would be less useful, but more consistent. > > > > Aaron Meurer > > ___ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Feature requests/Enhancements for upper-level engineering students
would your team be interested in contributing to my port of Numpy to .NET? https://github.com/Quansight-Labs/numpy.net I have the vast majority of the Numpy core working as a pure .NET library. All of the other libraries that rely on Numpy are not ported. I am sure we could find some good projects for your team to work on. These would be "green field" projects and would likely be great learning opportunities for them. -- Sent from: http://numpy-discussion.10968.n7.nabble.com/ ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] What is up with raw boolean indices (like a[False])?
On Thu, 2020-08-20 at 12:21 -0600, Aaron Meurer wrote: > You're right. I was confusing the broadcasting logic for boolean > arrays. > > However, I did find this example > > > > > np.arange(10).reshape((2, 5))[np.array([[0, 0, 0, 0, 0]], > > > > dtype=np.int64), False] > Traceback (most recent call last): > File "", line 1, in > IndexError: shape mismatch: indexing arrays could not be broadcast > together with shapes (1,5) (0,) > > That certainly seems to imply there is some broadcasting being done. Yes, it broadcasts the array after converting it with `nonzero`, i.e. its much the same as: indices = [[0, 0, 0, 0, 0]], *np.nonzero(False) indices = np.broadcast_arrays(*indices) will give the same result (see also `np.ix_` which converts booleans as well for this reason, to give you outer indexing). I was half way through a mock-up/pseudo code, but thought you likely wasn't sure it was ending up clear. It sounds like things are probably falling into place for you (if they are not, let me know what might help you): 1. Convert all boolean indices into a series of integer indices using `np.nonzero(index)` 2. For True/False scalars, that doesn't work, because `np.nonzero()`. `nonzero` gave us an index array (which is good, we obviously want one), but we need to index into `boolean_index.ndim == 0` dimensions! So that won't work, the approach using `nonzero` cannot generalize here, although boolean indices generalize perfectly. The solution to the dilemma is simple: If we have to index one dimension, but should be indexing zero, then we simply add that dimension to the original array (or at least pretend there was an additional dimension). 3. Do normal indexing with the result *including broadcasting*, we forget it was converted. The other way to solve it would be to always reshape the original array to combine all axes being indexed by a single boolean index into one axis and then index it using `np.flatnonzero`. (But that would get a different result if you try to broadcast!) In any case, I am not sure I would bother with making sense of this, except for sports! Its pretty much nonsense and I think the time understanding it is probably better spend deprecating it. The only reason I did not Deprecate itt before, is that I tried to do be minimal in the changes when I rewrote advanced indexing (and generalized boolean scalars correctly) long ago. That was likely the right start/choice at the time, since there were much bigger fish to catch, but I do not think anything is holding us back now. Cheers, Sebastian > > Aaron Meurer > > On Wed, Aug 19, 2020 at 6:55 PM Sebastian Berg > wrote: > > On Wed, 2020-08-19 at 18:07 -0600, Aaron Meurer wrote: > > > > > 3. If you have multiple advanced indexing you get annoying > > > > > broadcasting > > > > >of all of these. That is *always* confusing for boolean > > > > > indices. > > > > >0-D should not be too special there... > > > > > > OK, now that I am learning more about advanced indexing, this > > > statement is confusing to me. It seems that scalar boolean > > > indices do > > > not broadcast. For example: > > > > Well, broadcasting means you broadcast the *nonzero result* unless > > I am > > very confused... There is a reason I dismissed it. We could (and > > arguably should) just deprecate it. And I have doubts anyone would > > even notice. > > > > > > > > np.arange(2)[False, np.array([True, False])] > > > array([], dtype=int64) > > > > > > np.arange(2)[tuple(np.broadcast_arrays(False, > > > > > > np.array([True, > > > > > > False])))] > > > Traceback (most recent call last): > > > File "", line 1, in > > > IndexError: too many indices for array: array is 1-dimensional, > > > but 2 > > > were indexed > > > > > > And indeed, the docs even say, as you noted, "the nonzero > > > equivalence > > > for Boolean arrays does not hold for zero dimensional boolean > > > arrays," > > > which I guess also applies to the broadcasting. > > > > I actually think that probably also holds. Nonzero just behave > > weird > > for 0D because arrays (because it returns a tuple). > > But since broadcasting the nonzero result is so weird, and since 0- > > D > > booleans require some additional logic and don't generalize 100% > > (code > > wise), I won't rule out there are differences. > > > > > From what I can tell, the logic is that all integer and boolean > > > arrays > > > > Did you try that? Because as I said above, IIRC broadcasting the > > boolean array without first calling `nonzero` isn't really whats > > going > > on. And I don't know how it could be whats going on, since adding > > dimensions to a boolean index would have much more implications? > > > > - Sebastian > > > > > > > (and scalar ints) are broadcast together, *except* for boolean > > > scalars. Then the first boolean scalar is replaced with and(all > > > boolean scalars) and the rest are removed from the index. Then > > > that > > > index a
Re: [Numpy-discussion] What is up with raw boolean indices (like a[False])?
On Thu, 2020-08-20 at 16:50 -0500, Sebastian Berg wrote: > On Thu, 2020-08-20 at 12:21 -0600, Aaron Meurer wrote: > > You're right. I was confusing the broadcasting logic for boolean > > arrays. > > > > However, I did find this example > > > > > > > np.arange(10).reshape((2, 5))[np.array([[0, 0, 0, 0, 0]], > > > > > dtype=np.int64), False] > > Traceback (most recent call last): > > File "", line 1, in > > IndexError: shape mismatch: indexing arrays could not be broadcast > > together with shapes (1,5) (0,) > > > > That certainly seems to imply there is some broadcasting being > > done. > > Yes, it broadcasts the array after converting it with `nonzero`, i.e. > its much the same as: > >indices = [[0, 0, 0, 0, 0]], *np.nonzero(False) >indices = np.broadcast_arrays(*indices) > > will give the same result (see also `np.ix_` which converts booleans > as > well for this reason, to give you outer indexing). > I was half way through a mock-up/pseudo code, but thought you likely > wasn't sure it was ending up clear. It sounds like things are > probably > falling into place for you (if they are not, let me know what might > help you): Sorry editing error up there, in short I hope those steps sense to you, note that the broadcasting is basically part of a later "integer only" indexing step, and the `nonzero` part is pre-processing. > > 1. Convert all boolean indices into a series of integer indices using >`np.nonzero(index)` > > 2. For True/False scalars, that doesn't work, because `np.nonzero()`. > > `nonzero` gave us an index array (which is good, we obviously want > > one), but we need to index into `boolean_index.ndim == 0` >dimensions! >So that won't work, the approach using `nonzero` cannot generalize > > here, although boolean indices generalize perfectly. > >The solution to the dilemma is simple: If we have to index one >dimension, but should be indexing zero, then we simply add that >dimension to the original array (or at least pretend there was >an additional dimension). > > 3. Do normal indexing with the result *including broadcasting*, >we forget it was converted. > > The other way to solve it would be to always reshape the original > array > to combine all axes being indexed by a single boolean index into one > axis and then index it using `np.flatnonzero`. (But that would get a > different result if you try to broadcast!) > > > In any case, I am not sure I would bother with making sense of this, > except for sports! > Its pretty much nonsense and I think the time understanding it is > probably better spend deprecating it. The only reason I did not > Deprecate itt before, is that I tried to do be minimal in the changes > when I rewrote advanced indexing (and generalized boolean scalars > correctly) long ago. That was likely the right start/choice at the > time, since there were much bigger fish to catch, but I do not think > anything is holding us back now. > > Cheers, > > Sebastian > > > > Aaron Meurer > > > > On Wed, Aug 19, 2020 at 6:55 PM Sebastian Berg > > wrote: > > > On Wed, 2020-08-19 at 18:07 -0600, Aaron Meurer wrote: > > > > > > 3. If you have multiple advanced indexing you get annoying > > > > > > broadcasting > > > > > >of all of these. That is *always* confusing for boolean > > > > > > indices. > > > > > >0-D should not be too special there... > > > > > > > > OK, now that I am learning more about advanced indexing, this > > > > statement is confusing to me. It seems that scalar boolean > > > > indices do > > > > not broadcast. For example: > > > > > > Well, broadcasting means you broadcast the *nonzero result* > > > unless > > > I am > > > very confused... There is a reason I dismissed it. We could (and > > > arguably should) just deprecate it. And I have doubts anyone > > > would > > > even notice. > > > > > > > > > > np.arange(2)[False, np.array([True, False])] > > > > array([], dtype=int64) > > > > > > > np.arange(2)[tuple(np.broadcast_arrays(False, > > > > > > > np.array([True, > > > > > > > False])))] > > > > Traceback (most recent call last): > > > > File "", line 1, in > > > > IndexError: too many indices for array: array is 1-dimensional, > > > > but 2 > > > > were indexed > > > > > > > > And indeed, the docs even say, as you noted, "the nonzero > > > > equivalence > > > > for Boolean arrays does not hold for zero dimensional boolean > > > > arrays," > > > > which I guess also applies to the broadcasting. > > > > > > I actually think that probably also holds. Nonzero just behave > > > weird > > > for 0D because arrays (because it returns a tuple). > > > But since broadcasting the nonzero result is so weird, and since > > > 0- > > > D > > > booleans require some additional logic and don't generalize 100% > > > (code > > > wise), I won't rule out there are differences. > > > > > > > From what I can tell, the logic is that all integer and boolean > > > > arrays > > > > > > Did you try that?
Re: [Numpy-discussion] What is up with raw boolean indices (like a[False])?
Just to be clear, what exactly do you think should be deprecated? Boolean scalar indices in general, or just boolean scalars combined with other arrays, or something else? Aaron Meurer On Thu, Aug 20, 2020 at 3:56 PM Sebastian Berg wrote: > > On Thu, 2020-08-20 at 16:50 -0500, Sebastian Berg wrote: > > On Thu, 2020-08-20 at 12:21 -0600, Aaron Meurer wrote: > > > You're right. I was confusing the broadcasting logic for boolean > > > arrays. > > > > > > However, I did find this example > > > > > > > > > np.arange(10).reshape((2, 5))[np.array([[0, 0, 0, 0, 0]], > > > > > > dtype=np.int64), False] > > > Traceback (most recent call last): > > > File "", line 1, in > > > IndexError: shape mismatch: indexing arrays could not be broadcast > > > together with shapes (1,5) (0,) > > > > > > That certainly seems to imply there is some broadcasting being > > > done. > > > > Yes, it broadcasts the array after converting it with `nonzero`, i.e. > > its much the same as: > > > >indices = [[0, 0, 0, 0, 0]], *np.nonzero(False) > >indices = np.broadcast_arrays(*indices) > > > > will give the same result (see also `np.ix_` which converts booleans > > as > > well for this reason, to give you outer indexing). > > I was half way through a mock-up/pseudo code, but thought you likely > > wasn't sure it was ending up clear. It sounds like things are > > probably > > falling into place for you (if they are not, let me know what might > > help you): > > Sorry editing error up there, in short I hope those steps sense to you, > note that the broadcasting is basically part of a later "integer only" > indexing step, and the `nonzero` part is pre-processing. > > > > > 1. Convert all boolean indices into a series of integer indices using > >`np.nonzero(index)` > > > > 2. For True/False scalars, that doesn't work, because `np.nonzero()`. > > > > `nonzero` gave us an index array (which is good, we obviously want > > > > one), but we need to index into `boolean_index.ndim == 0` > >dimensions! > >So that won't work, the approach using `nonzero` cannot generalize > > > > here, although boolean indices generalize perfectly. > > > >The solution to the dilemma is simple: If we have to index one > >dimension, but should be indexing zero, then we simply add that > >dimension to the original array (or at least pretend there was > >an additional dimension). > > > > 3. Do normal indexing with the result *including broadcasting*, > >we forget it was converted. > > > > The other way to solve it would be to always reshape the original > > array > > to combine all axes being indexed by a single boolean index into one > > axis and then index it using `np.flatnonzero`. (But that would get a > > different result if you try to broadcast!) > > > > > > In any case, I am not sure I would bother with making sense of this, > > except for sports! > > Its pretty much nonsense and I think the time understanding it is > > probably better spend deprecating it. The only reason I did not > > Deprecate itt before, is that I tried to do be minimal in the changes > > when I rewrote advanced indexing (and generalized boolean scalars > > correctly) long ago. That was likely the right start/choice at the > > time, since there were much bigger fish to catch, but I do not think > > anything is holding us back now. > > > > Cheers, > > > > Sebastian > > > > > > > Aaron Meurer > > > > > > On Wed, Aug 19, 2020 at 6:55 PM Sebastian Berg > > > wrote: > > > > On Wed, 2020-08-19 at 18:07 -0600, Aaron Meurer wrote: > > > > > > > 3. If you have multiple advanced indexing you get annoying > > > > > > > broadcasting > > > > > > >of all of these. That is *always* confusing for boolean > > > > > > > indices. > > > > > > >0-D should not be too special there... > > > > > > > > > > OK, now that I am learning more about advanced indexing, this > > > > > statement is confusing to me. It seems that scalar boolean > > > > > indices do > > > > > not broadcast. For example: > > > > > > > > Well, broadcasting means you broadcast the *nonzero result* > > > > unless > > > > I am > > > > very confused... There is a reason I dismissed it. We could (and > > > > arguably should) just deprecate it. And I have doubts anyone > > > > would > > > > even notice. > > > > > > > > > > > > np.arange(2)[False, np.array([True, False])] > > > > > array([], dtype=int64) > > > > > > > > np.arange(2)[tuple(np.broadcast_arrays(False, > > > > > > > > np.array([True, > > > > > > > > False])))] > > > > > Traceback (most recent call last): > > > > > File "", line 1, in > > > > > IndexError: too many indices for array: array is 1-dimensional, > > > > > but 2 > > > > > were indexed > > > > > > > > > > And indeed, the docs even say, as you noted, "the nonzero > > > > > equivalence > > > > > for Boolean arrays does not hold for zero dimensional boolean > > > > > arrays," > > > > > which I guess also applies to the broadcasting. > > > > > > > > I actually think that proba
Re: [Numpy-discussion] What is up with raw boolean indices (like a[False])?
On Thu, 2020-08-20 at 16:00 -0600, Aaron Meurer wrote: > Just to be clear, what exactly do you think should be deprecated? > Boolean scalar indices in general, or just boolean scalars combined > with other arrays, or something else? My angle is that we should allow only: * Any number of integer array indices (ideally only explicitly with `arr.vindex[]`, but we do not have that luxury right now.) * A single boolean index (array or scalar is identical) but no mix of the above (including multiple boolean indices). Because I think they are at least one level more confusing than multiple advanced indices. I admit, I forgot that the broadcasting logic is fine in this case: arr = np.zeros((2, 3)) arr[[True], np.array(3)] where the advanced index is also a scalar index. In that case the result is straight forward, since broadcasting does not affect `np.array(3)`. I am happy to be wrong about that assessment, but I think your opinion on it could likely push us towards just doing a Deprecation. The only use case for "multiple boolean indices" that I could think of was this: arr = np.diag([1, 2, 3, 4]) # 2-d square array indx = arr.diagonal() > 2 # mask for each row and column masked_diagonal = arr[indx, indx] print(repr(masked_diagonal)) # array([3, 4]) and my guess is that the reaction to that code is a: "Wait what?!" That code might seem reasonable, but it only works if you have the exact same number of `True` values in the two indices. And if you have the exact same number but two different arrays, then I fail to reason about the result without doing the `nonzero` step, which I think indicates that there just is no logical concept for it. So, I think we may be better of forcing the few power-user who may have found a use for this type of nugget to use `np.nonzero()` or find another solution. - Sebastian > > Aaron Meurer > > On Thu, Aug 20, 2020 at 3:56 PM Sebastian Berg > wrote: > > On Thu, 2020-08-20 at 16:50 -0500, Sebastian Berg wrote: > > > On Thu, 2020-08-20 at 12:21 -0600, Aaron Meurer wrote: > > > > You're right. I was confusing the broadcasting logic for > > > > boolean > > > > arrays. > > > > > > > > However, I did find this example > > > > > > > > > > > np.arange(10).reshape((2, 5))[np.array([[0, 0, 0, 0, 0]], > > > > > > > dtype=np.int64), False] > > > > Traceback (most recent call last): > > > > File "", line 1, in > > > > IndexError: shape mismatch: indexing arrays could not be > > > > broadcast > > > > together with shapes (1,5) (0,) > > > > > > > > That certainly seems to imply there is some broadcasting being > > > > done. > > > > > > Yes, it broadcasts the array after converting it with `nonzero`, > > > i.e. > > > its much the same as: > > > > > >indices = [[0, 0, 0, 0, 0]], *np.nonzero(False) > > >indices = np.broadcast_arrays(*indices) > > > > > > will give the same result (see also `np.ix_` which converts > > > booleans > > > as > > > well for this reason, to give you outer indexing). > > > I was half way through a mock-up/pseudo code, but thought you > > > likely > > > wasn't sure it was ending up clear. It sounds like things are > > > probably > > > falling into place for you (if they are not, let me know what > > > might > > > help you): > > > > Sorry editing error up there, in short I hope those steps sense to > > you, > > note that the broadcasting is basically part of a later "integer > > only" > > indexing step, and the `nonzero` part is pre-processing. > > > > > 1. Convert all boolean indices into a series of integer indices > > > using > > >`np.nonzero(index)` > > > > > > 2. For True/False scalars, that doesn't work, because > > > `np.nonzero()`. > > > > > > `nonzero` gave us an index array (which is good, we obviously > > > want > > > > > > one), but we need to index into `boolean_index.ndim == 0` > > >dimensions! > > >So that won't work, the approach using `nonzero` cannot > > > generalize > > > > > > here, although boolean indices generalize perfectly. > > > > > >The solution to the dilemma is simple: If we have to index one > > >dimension, but should be indexing zero, then we simply add > > > that > > >dimension to the original array (or at least pretend there was > > >an additional dimension). > > > > > > 3. Do normal indexing with the result *including broadcasting*, > > >we forget it was converted. > > > > > > The other way to solve it would be to always reshape the original > > > array > > > to combine all axes being indexed by a single boolean index into > > > one > > > axis and then index it using `np.flatnonzero`. (But that would > > > get a > > > different result if you try to broadcast!) > > > > > > > > > In any case, I am not sure I would bother with making sense of > > > this, > > > except for sports! > > > Its pretty much nonsense and I think the time understanding it is > > > probably better spend deprecating it. The only reason I did not > > > Deprecate
Re: [Numpy-discussion] What is up with raw boolean indices (like a[False])?
On Thu, Aug 20, 2020 at 4:38 PM Sebastian Berg wrote: > > On Thu, 2020-08-20 at 16:00 -0600, Aaron Meurer wrote: > > Just to be clear, what exactly do you think should be deprecated? > > Boolean scalar indices in general, or just boolean scalars combined > > with other arrays, or something else? > > My angle is that we should allow only: > > * Any number of integer array indices (ideally only explicitly > with `arr.vindex[]`, but we do not have that luxury right now.) > > * A single boolean index (array or scalar is identical) > > but no mix of the above (including multiple boolean indices). > > Because I think they are at least one level more confusing than > multiple advanced indices. > > I admit, I forgot that the broadcasting logic is fine in this case: > >arr = np.zeros((2, 3)) >arr[[True], np.array(3)] > > where the advanced index is also a scalar index. In that case the > result is straight forward, since broadcasting does not affect > `np.array(3)`. > > > I am happy to be wrong about that assessment, but I think your opinion > on it could likely push us towards just doing a Deprecation. > The only use case for "multiple boolean indices" that I could think of > was this: > > arr = np.diag([1, 2, 3, 4]) # 2-d square array > indx = arr.diagonal() > 2 # mask for each row and column > masked_diagonal = arr[indx, indx] > print(repr(masked_diagonal)) > # array([3, 4]) > > and my guess is that the reaction to that code is a: "Wait what?!" > > That code might seem reasonable, but it only works if you have the > exact same number of `True` values in the two indices. > And if you have the exact same number but two different arrays, then I > fail to reason about the result without doing the `nonzero` step, which > I think indicates that there just is no logical concept for it. > > > So, I think we may be better of forcing the few power-user who may have > found a use for this type of nugget to use `np.nonzero()` or find > another solution. Well I'm cautious because despite implementing the logic for all this, I'm a bit divorced from most use-cases. So I don't have a great feeling for what is currently being used. For example, is it possible to have a situation where you build a mask out of an expression, like a[x > 0] or whatever, where the mask expression could be any number of dimensions depending on the input values? And if so, does the current logic for scalar booleans do the right thing when the number of dimensions happens to be 0. Mixing nonscalar boolean and integer arrays seems fine, as far as the logic is concerned. I'm not really sure if it makes sense semantically. I'll have to think about it more. The thing that has the most odd corner cases in the indexing logic is boolean scalars. It would be nice if you could treat them uniformly with the same logic as other boolean arrays, but they have special cases everywhere. This is in contrast with integer scalars which perfectly match the logic of integer arrays with the shape == (). Maybe I'm just not looking at it from the right angle. I don't know. In ndindex, I've left the "arrays separated by slices, ellipses, or newaxes" case unimplemented. Travis Oliphant told me he thinks it was a mistake and it would be better to not allow it. I've also left boolean scalars mixed with other arrays unimplemented because I don't want to waste more time trying to figure out what is going on in the example I posted earlier (though what you wrote helps). I have nonscalar boolean arrays mixed with integer arrays working just fine, and the logic isn't really any different than it would be if I only supported them separately. Aaron Meurer > > - Sebastian > > > > > > Aaron Meurer > > > > On Thu, Aug 20, 2020 at 3:56 PM Sebastian Berg > > wrote: > > > On Thu, 2020-08-20 at 16:50 -0500, Sebastian Berg wrote: > > > > On Thu, 2020-08-20 at 12:21 -0600, Aaron Meurer wrote: > > > > > You're right. I was confusing the broadcasting logic for > > > > > boolean > > > > > arrays. > > > > > > > > > > However, I did find this example > > > > > > > > > > > > > np.arange(10).reshape((2, 5))[np.array([[0, 0, 0, 0, 0]], > > > > > > > > dtype=np.int64), False] > > > > > Traceback (most recent call last): > > > > > File "", line 1, in > > > > > IndexError: shape mismatch: indexing arrays could not be > > > > > broadcast > > > > > together with shapes (1,5) (0,) > > > > > > > > > > That certainly seems to imply there is some broadcasting being > > > > > done. > > > > > > > > Yes, it broadcasts the array after converting it with `nonzero`, > > > > i.e. > > > > its much the same as: > > > > > > > >indices = [[0, 0, 0, 0, 0]], *np.nonzero(False) > > > >indices = np.broadcast_arrays(*indices) > > > > > > > > will give the same result (see also `np.ix_` which converts > > > > booleans > > > > as > > > > well for this reason, to give you outer indexing). > > > > I was half way through a mock-up/pseudo code, but thought you > > > > likely > > > > w
Re: [Numpy-discussion] What is up with raw boolean indices (like a[False])?
On Thu, 2020-08-20 at 17:08 -0600, Aaron Meurer wrote: > On Thu, Aug 20, 2020 at 4:38 PM Sebastian Berg > wrote: > > On Thu, 2020-08-20 at 16:00 -0600, Aaron Meurer wrote: > > > Just to be clear, what exactly do you think should be deprecated? > > > Boolean scalar indices in general, or just boolean scalars > > > combined > > > with other arrays, or something else? > > > > My angle is that we should allow only: > > > > * Any number of integer array indices (ideally only explicitly > > with `arr.vindex[]`, but we do not have that luxury right now.) > > > > * A single boolean index (array or scalar is identical) > > > > but no mix of the above (including multiple boolean indices). > > > > Because I think they are at least one level more confusing than > > multiple advanced indices. > > > > I admit, I forgot that the broadcasting logic is fine in this case: > > > >arr = np.zeros((2, 3)) > >arr[[True], np.array(3)] > > > > where the advanced index is also a scalar index. In that case the > > result is straight forward, since broadcasting does not affect > > `np.array(3)`. > > > > > > I am happy to be wrong about that assessment, but I think your > > opinion > > on it could likely push us towards just doing a Deprecation. > > The only use case for "multiple boolean indices" that I could think > > of > > was this: > > > > arr = np.diag([1, 2, 3, 4]) # 2-d square array > > indx = arr.diagonal() > 2 # mask for each row and column > > masked_diagonal = arr[indx, indx] > > print(repr(masked_diagonal)) > > # array([3, 4]) > > > > and my guess is that the reaction to that code is a: "Wait what?!" > > > > That code might seem reasonable, but it only works if you have the > > exact same number of `True` values in the two indices. > > And if you have the exact same number but two different arrays, > > then I > > fail to reason about the result without doing the `nonzero` step, > > which > > I think indicates that there just is no logical concept for it. > > > > > > So, I think we may be better of forcing the few power-user who may > > have > > found a use for this type of nugget to use `np.nonzero()` or find > > another solution. > > Well I'm cautious because despite implementing the logic for all > this, > I'm a bit divorced from most use-cases. So I don't have a great > feeling for what is currently being used. For example, is it possible > to have a situation where you build a mask out of an expression, like > a[x > 0] or whatever, where the mask expression could be any number > of I am not sure anyone does it, but I certainly can think of ways to use this functionality: ``` def good_images(image_or_stack): """Filter dark images image_or_stack : ndarray (..., N, M, 3) Returns --- good_images : ndarray (K, N, M, 3) Returns all good images as a one dimensional stack for further processing, where `K` is the number of good images. """ assert image_or_stack.ndim >= 3 assert image_or_stack.shape[-1] == 3 # 3 colors, fixed. average_brightness = image_or_stack.mean((-3, -2, -1)) return image_or_stack[average_brigthness, ...] ``` Note that the above uses a single True/False if you pass in a single image. > dimensions depending on the input values? And if so, does the current > logic for scalar booleans do the right thing when the number of > dimensions happens to be 0. > > Mixing nonscalar boolean and integer arrays seems fine, as far as the > logic is concerned. I'm not really sure if it makes sense > semantically. I'll have to think about it more. The thing that has > the > most odd corner cases in the indexing logic is boolean scalars. It I think they are perfectly fine semantically, but they definitely do require special handling. Although the reason for that special handling is that we have to implement boolean indices using integer array indices and that is not possible without additional logic. If you browse the NumPy code, you will see there is a `HAS_0D_BOOL` macro (basically enum), to distinguish: internal_indx = np.nonzero(False) and: internal_indx = np.nonzero([False]) because the first effectively inserts a new dimension and then indices it, while the former just indices an existing dimension. > would be nice if you could treat them uniformly with the same logic > as > other boolean arrays, but they have special cases everywhere. This is > in contrast with integer scalars which perfectly match the logic of > integer arrays with the shape == (). Maybe I'm just not looking at it > from the right angle. I don't know. I hope the example above helps you, I think you should always remember the two rules of boolean indexing mentioned somewhere in the docs: * A boolean array indexes into `arr.ndim` dimensions, and effectively removes them. * A boolean array index adds a single input array. I guess, I should have written th
