Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
Thanks Warren, I think these are sensible additions. I would argue to treat the None-False condition as an error. Indeed I agree one might argue the correcr behavior is to 'shuffle' the singleton block of data, which does nothing; but its more likely to come up as an unintended error than as a natural outcome of parametrized behavior. On Sun, Oct 12, 2014 at 3:31 AM, John Zwinck jzwi...@gmail.com wrote: On Sun, Oct 12, 2014 at 6:51 AM, Warren Weckesser warren.weckes...@gmail.com wrote: I created an issue on github for an enhancement to numpy.random.shuffle: https://github.com/numpy/numpy/issues/5173 I like this idea. I was a bit surprised there wasn't something like this already. A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. Let's think about it from the other direction: if a user wants to shuffle all the elements as if it were 1-d, as you point out they could do this: shuffle(a, axis=None, independent=True) But that's a lot of typing. Maybe we should just let this do the same thing: shuffle(a, axis=None) That seems to be in keeping with the other APIs taking axis as you mentioned. To me, independent has no relevance when the array is 1-d, it can simply be ignored. John Zwinck ___ 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] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 3:51 PM, Eelco Hoogendoorn hoogendoorn.ee...@gmail.com wrote: I would argue to treat the None-False condition as an error. Indeed I agree one might argue the correcr behavior is to 'shuffle' the singleton block of data, which does nothing; but its more likely to come up as an unintended error than as a natural outcome of parametrized behavior. I'm interested to know why you think axis=None should raise an error if independent=False when independent=False is the default. What I mean is, if someone uses this function and wants axis=None (which seems not totally unusual), why force them to always type in the boilerplate independent=True to make it work? John Zwinck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
Hi Warren On 2014-10-12 00:51:56, Warren Weckesser warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. I like the suggested changes. Since independent loses its meaning when axis is None, I would expect this to have the same effect as `shuffle(a, independent=True, axis=None)`. I think a shuffle function that doesn't shuffle will confuse a lot of people! Stéfan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
yeah, a shuffle function that does not shuffle indeed seems like a major source of bugs to me. Indeed one could argue that setting axis=None should suffice to give a clear enough declaration of intent; though I wouldn't mind typing the extra bit to ensure consistent semantics. On Sun, Oct 12, 2014 at 10:56 AM, Stefan van der Walt ste...@sun.ac.za wrote: Hi Warren On 2014-10-12 00:51:56, Warren Weckesser warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. I like the suggested changes. Since independent loses its meaning when axis is None, I would expect this to have the same effect as `shuffle(a, independent=True, axis=None)`. I think a shuffle function that doesn't shuffle will confuse a lot of people! Stéfan ___ 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] Request for enhancement to numpy.random.shuffle
On Sat, Oct 11, 2014 at 11:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? It seems to me a perfectly good reason to have two methods instead of one. I can't imagine when I wouldn't be using a literal True or False for this, so it really should be two different methods. That said, I would just make the axis=None behavior the same for both methods. axis=None does *not* mean treat this like a single monolithic blob in any of the axis=-having methods; it means flatten the array and do the operation on the single flattened axis. I think the latter behavior is a reasonable interpretation of axis=None for both methods. -- Robert Kern ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 7:57 AM, Robert Kern robert.k...@gmail.com wrote: On Sat, Oct 11, 2014 at 11:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? It seems to me a perfectly good reason to have two methods instead of one. I can't imagine when I wouldn't be using a literal True or False for this, so it really should be two different methods. I agree, and my first inclination was to propose a different method (and I had the bikeshedding conversation with myself about the name: disarrange, scramble, disorder, randomize, ashuffle, some other variation of the word shuffle, ...), but I figured the first thing folks would say is Why not just add options to shuffle? So, choose your battles and all that. What do other folks think of making a separate method? That said, I would just make the axis=None behavior the same for both methods. axis=None does *not* mean treat this like a single monolithic blob in any of the axis=-having methods; it means flatten the array and do the operation on the single flattened axis. I think the latter behavior is a reasonable interpretation of axis=None for both methods. Sounds good to me. Warren -- Robert Kern ___ 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] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 10:54 AM, Warren Weckesser warren.weckes...@gmail.com wrote: On Sun, Oct 12, 2014 at 7:57 AM, Robert Kern robert.k...@gmail.com wrote: On Sat, Oct 11, 2014 at 11:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? It seems to me a perfectly good reason to have two methods instead of one. I can't imagine when I wouldn't be using a literal True or False for this, so it really should be two different methods. I agree, and my first inclination was to propose a different method (and I had the bikeshedding conversation with myself about the name: disarrange, scramble, disorder, randomize, ashuffle, some other variation of the word shuffle, ...), but I figured the first thing folks would say is Why not just add options to shuffle? So, choose your battles and all that. What do other folks think of making a separate method? I'm not a fan of many similar functions. What's the difference between permute, shuffle and scramble? And how do I find or remember which is which? That said, I would just make the axis=None behavior the same for both methods. axis=None does *not* mean treat this like a single monolithic blob in any of the axis=-having methods; it means flatten the array and do the operation on the single flattened axis. I think the latter behavior is a reasonable interpretation of axis=None for both methods. Sounds good to me. +1 (since all the arguments have been already given Josef - Why does sort treat columns independently instead of sorting rows? - because there is lexsort - Oh, lexsort, I haven thought about it in 5 years. It's not even next to sort in the pop up code completion Warren -- Robert Kern ___ 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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 11:20 AM, josef.p...@gmail.com wrote: On Sun, Oct 12, 2014 at 10:54 AM, Warren Weckesser warren.weckes...@gmail.com wrote: On Sun, Oct 12, 2014 at 7:57 AM, Robert Kern robert.k...@gmail.com wrote: On Sat, Oct 11, 2014 at 11:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? It seems to me a perfectly good reason to have two methods instead of one. I can't imagine when I wouldn't be using a literal True or False for this, so it really should be two different methods. I agree, and my first inclination was to propose a different method (and I had the bikeshedding conversation with myself about the name: disarrange, scramble, disorder, randomize, ashuffle, some other variation of the word shuffle, ...), but I figured the first thing folks would say is Why not just add options to shuffle? So, choose your battles and all that. What do other folks think of making a separate method? I'm not a fan of many similar functions. What's the difference between permute, shuffle and scramble? The difference between `shuffle` and the new method being proposed is explained in the first email in this thread. `np.random.permutation` with an array argument returns a shuffled copy of the array; it does not modify its argument. (It should also get an `axis` argument when `shuffle` gets an `axis` argument.) And how do I find or remember which is which? You could start with `doc(np.random)` (or `np.random?` in ipython). Warren That said, I would just make the axis=None behavior the same for both methods. axis=None does *not* mean treat this like a single monolithic blob in any of the axis=-having methods; it means flatten the array and do the operation on the single flattened axis. I think the latter behavior is a reasonable interpretation of axis=None for both methods. Sounds good to me. +1 (since all the arguments have been already given Josef - Why does sort treat columns independently instead of sorting rows? - because there is lexsort - Oh, lexsort, I haven thought about it in 5 years. It's not even next to sort in the pop up code completion Warren -- Robert Kern ___ 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 ___ 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] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 11:33 AM, Warren Weckesser warren.weckes...@gmail.com wrote: On Sun, Oct 12, 2014 at 11:20 AM, josef.p...@gmail.com wrote: On Sun, Oct 12, 2014 at 10:54 AM, Warren Weckesser warren.weckes...@gmail.com wrote: On Sun, Oct 12, 2014 at 7:57 AM, Robert Kern robert.k...@gmail.com wrote: On Sat, Oct 11, 2014 at 11:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? It seems to me a perfectly good reason to have two methods instead of one. I can't imagine when I wouldn't be using a literal True or False for this, so it really should be two different methods. I agree, and my first inclination was to propose a different method (and I had the bikeshedding conversation with myself about the name: disarrange, scramble, disorder, randomize, ashuffle, some other variation of the word shuffle, ...), but I figured the first thing folks would say is Why not just add options to shuffle? So, choose your battles and all that. What do other folks think of making a separate method? I'm not a fan of many similar functions. What's the difference between permute, shuffle and scramble? The difference between `shuffle` and the new method being proposed is explained in the first email in this thread. `np.random.permutation` with an array argument returns a shuffled copy of the array; it does not modify its argument. (It should also get an `axis` argument when `shuffle` gets an `axis` argument.) And how do I find or remember which is which? You could start with `doc(np.random)` (or `np.random?` in ipython). If you have to check the docstring each time, then there is something wrong. In my opinion all docstrings should be read only once. It's like a Windows program where the GUI menus are not **self-explanatory**. What did Save-As do ? Josef Warren That said, I would just make the axis=None behavior the same for both methods. axis=None does *not* mean treat this like a single monolithic blob in any of the axis=-having methods; it means flatten the array and do the operation on the single flattened axis. I think the latter behavior is a reasonable interpretation of axis=None for both methods. Sounds good to me. +1 (since all the arguments have been already given Josef - Why does sort treat columns independently instead of sorting rows? - because there is lexsort - Oh, lexsort, I haven thought about it in 5 years. It's not even next to sort in the pop up code completion Warren -- Robert Kern ___ 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 ___ 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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
On Sat, Oct 11, 2014 at 6:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: I created an issue on github for an enhancement to numpy.random.shuffle: https://github.com/numpy/numpy/issues/5173 I'd like to get some feedback on the idea. Currently, `shuffle` shuffles the first dimension of an array in-place. For example, shuffling a 2D array shuffles the rows: In [227]: a Out[227]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) In [228]: np.random.shuffle(a) In [229]: a Out[229]: array([[ 0, 1, 2], [ 9, 10, 11], [ 3, 4, 5], [ 6, 7, 8]]) To add an axis keyword, we could (in effect) apply `shuffle` to `a.swapaxes(axis, 0)`. For a 2-D array, `axis=1` would shuffles the columns: In [232]: a = np.arange(15).reshape(3,5) In [233]: a Out[233]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) In [234]: axis = 1 In [235]: np.random.shuffle(a.swapaxes(axis, 0)) In [236]: a Out[236]: array([[ 3, 2, 4, 0, 1], [ 8, 7, 9, 5, 6], [13, 12, 14, 10, 11]]) So that's the first part--adding an `axis` keyword. The other part of the enhancement request is to add a shuffle behavior that shuffles the 1-d slices *independently*. That is, for a 2-d array, shuffling with `axis=0` would apply a different shuffle to each column. In the github issue, I defined a function called `disarrange` that implements this behavior: In [240]: a Out[240]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]]) In [241]: disarrange(a, axis=0) In [242]: a Out[242]: array([[ 6, 1, 2], [ 3, 13, 14], [ 9, 10, 5], [12, 7, 8], [ 0, 4, 11]]) Note that each column has been shuffled independently. This behavior is analogous to how `sort` handles the `axis` keyword. `sort` sorts the 1-d slices along the given axis independently. In the github issue, I suggested the following signature for `shuffle` (but I'm not too fond of the name `independent`): def shuffle(a, independent=False, axis=0) If `independent` is False, the current behavior of `shuffle` is used. If `independent` is True, each 1-d slice is shuffled independently (in the same way that `sort` sorts each 1-d slice). Like most functions that take an `axis` argument, `axis=None` means to shuffle the flattened array. With `independent=True`, it would act like `np.random.shuffle(a.flat)`, e.g. In [247]: a Out[247]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) In [248]: np.random.shuffle(a.flat) In [249]: a Out[249]: array([[ 0, 14, 9, 1, 13], [ 2, 8, 5, 3, 4], [ 6, 10, 7, 12, 11]]) A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? Warren It is clear from the comments so far that, when `axis` is None, the result should be a shuffle of all the elements in the array, for both methods of shuffling (whether implemented as a new method or with a boolean argument to `shuffle`). Forget I ever suggested doing nothing or raising an error. :) Josef's comment reminded me that `numpy.random.permutation` returns a shuffled copy of the array (when its argument is an array). This function should also get an `axis` argument. `permutation` shuffles the same way `shuffle` does--it simply makes a copy and then calls `shuffle` on the copy. If a new method is added for the new shuffling style, then it would be consistent to also add a new method that uses the new shuffling style and returns a copy of the shuffled array. Then we would then have four methods: In-placeCopy Current shuffle style shuffle permutation New shuffle style (name TBD) (name TBD) (All of them will have an `axis` argument.) I suspect this will make some folks prefer the approach of adding a boolean argument to `shuffle` and `permutation`. Warren ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
On 2014-10-12 16:54, Warren Weckesser wrote: On Sun, Oct 12, 2014 at 7:57 AM, Robert Kern robert.k...@gmail.com mailto:robert.k...@gmail.com wrote: On Sat, Oct 11, 2014 at 11:51 PM, Warren Weckesser warren.weckes...@gmail.com mailto:warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? It seems to me a perfectly good reason to have two methods instead of one. I can't imagine when I wouldn't be using a literal True or False for this, so it really should be two different methods. I agree, and my first inclination was to propose a different method (and I had the bikeshedding conversation with myself about the name: disarrange, scramble, disorder, randomize, ashuffle, some other variation of the word shuffle, ...), but I figured the first thing folks would say is Why not just add options to shuffle? So, choose your battles and all that. What do other folks think of making a separate method I'm not a fan of more methods with similar functionality in Numpy. It's already hard to overlook the existing functions and all their possible applications and variants. The axis=None proposal for shuffling all items is very intuitive. I think we don't want to take the path of matlab: a huge amount of powerful functions, but few people know of their powerful possibilities. regards, Sebastian ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 12:14 PM, Warren Weckesser warren.weckes...@gmail.com wrote: On Sat, Oct 11, 2014 at 6:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: I created an issue on github for an enhancement to numpy.random.shuffle: https://github.com/numpy/numpy/issues/5173 I'd like to get some feedback on the idea. Currently, `shuffle` shuffles the first dimension of an array in-place. For example, shuffling a 2D array shuffles the rows: In [227]: a Out[227]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) In [228]: np.random.shuffle(a) In [229]: a Out[229]: array([[ 0, 1, 2], [ 9, 10, 11], [ 3, 4, 5], [ 6, 7, 8]]) To add an axis keyword, we could (in effect) apply `shuffle` to `a.swapaxes(axis, 0)`. For a 2-D array, `axis=1` would shuffles the columns: In [232]: a = np.arange(15).reshape(3,5) In [233]: a Out[233]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) In [234]: axis = 1 In [235]: np.random.shuffle(a.swapaxes(axis, 0)) In [236]: a Out[236]: array([[ 3, 2, 4, 0, 1], [ 8, 7, 9, 5, 6], [13, 12, 14, 10, 11]]) So that's the first part--adding an `axis` keyword. The other part of the enhancement request is to add a shuffle behavior that shuffles the 1-d slices *independently*. That is, for a 2-d array, shuffling with `axis=0` would apply a different shuffle to each column. In the github issue, I defined a function called `disarrange` that implements this behavior: In [240]: a Out[240]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]]) In [241]: disarrange(a, axis=0) In [242]: a Out[242]: array([[ 6, 1, 2], [ 3, 13, 14], [ 9, 10, 5], [12, 7, 8], [ 0, 4, 11]]) Note that each column has been shuffled independently. This behavior is analogous to how `sort` handles the `axis` keyword. `sort` sorts the 1-d slices along the given axis independently. In the github issue, I suggested the following signature for `shuffle` (but I'm not too fond of the name `independent`): def shuffle(a, independent=False, axis=0) If `independent` is False, the current behavior of `shuffle` is used. If `independent` is True, each 1-d slice is shuffled independently (in the same way that `sort` sorts each 1-d slice). Like most functions that take an `axis` argument, `axis=None` means to shuffle the flattened array. With `independent=True`, it would act like `np.random.shuffle(a.flat)`, e.g. In [247]: a Out[247]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) In [248]: np.random.shuffle(a.flat) In [249]: a Out[249]: array([[ 0, 14, 9, 1, 13], [ 2, 8, 5, 3, 4], [ 6, 10, 7, 12, 11]]) A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? Warren It is clear from the comments so far that, when `axis` is None, the result should be a shuffle of all the elements in the array, for both methods of shuffling (whether implemented as a new method or with a boolean argument to `shuffle`). Forget I ever suggested doing nothing or raising an error. :) Josef's comment reminded me that `numpy.random.permutation` which kind of proofs my point I sometimes have problems finding `shuffle` because I want a function that does permutation. Josef returns a shuffled copy of the array (when its argument is an array). This function should also get an `axis` argument. `permutation` shuffles the same way `shuffle` does--it simply makes a copy and then calls `shuffle` on the copy. If a new method is added for the new shuffling style, then it would be consistent to also add a new method that uses the new shuffling style and returns a copy of the shuffled array. Then we would then have four methods: In-placeCopy Current shuffle style shuffle permutation New shuffle style (name TBD) (name TBD) (All of them will have an `axis` argument.) I suspect this will make some folks prefer the approach of adding a boolean argument to `shuffle` and `permutation`. Warren ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 12:14 PM, Warren Weckesser warren.weckes...@gmail.com wrote: On Sat, Oct 11, 2014 at 6:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: I created an issue on github for an enhancement to numpy.random.shuffle: https://github.com/numpy/numpy/issues/5173 I'd like to get some feedback on the idea. Currently, `shuffle` shuffles the first dimension of an array in-place. For example, shuffling a 2D array shuffles the rows: In [227]: a Out[227]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) In [228]: np.random.shuffle(a) In [229]: a Out[229]: array([[ 0, 1, 2], [ 9, 10, 11], [ 3, 4, 5], [ 6, 7, 8]]) To add an axis keyword, we could (in effect) apply `shuffle` to `a.swapaxes(axis, 0)`. For a 2-D array, `axis=1` would shuffles the columns: In [232]: a = np.arange(15).reshape(3,5) In [233]: a Out[233]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) In [234]: axis = 1 In [235]: np.random.shuffle(a.swapaxes(axis, 0)) In [236]: a Out[236]: array([[ 3, 2, 4, 0, 1], [ 8, 7, 9, 5, 6], [13, 12, 14, 10, 11]]) So that's the first part--adding an `axis` keyword. The other part of the enhancement request is to add a shuffle behavior that shuffles the 1-d slices *independently*. That is, for a 2-d array, shuffling with `axis=0` would apply a different shuffle to each column. In the github issue, I defined a function called `disarrange` that implements this behavior: In [240]: a Out[240]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]]) In [241]: disarrange(a, axis=0) In [242]: a Out[242]: array([[ 6, 1, 2], [ 3, 13, 14], [ 9, 10, 5], [12, 7, 8], [ 0, 4, 11]]) Note that each column has been shuffled independently. This behavior is analogous to how `sort` handles the `axis` keyword. `sort` sorts the 1-d slices along the given axis independently. In the github issue, I suggested the following signature for `shuffle` (but I'm not too fond of the name `independent`): def shuffle(a, independent=False, axis=0) If `independent` is False, the current behavior of `shuffle` is used. If `independent` is True, each 1-d slice is shuffled independently (in the same way that `sort` sorts each 1-d slice). Like most functions that take an `axis` argument, `axis=None` means to shuffle the flattened array. With `independent=True`, it would act like `np.random.shuffle(a.flat)`, e.g. In [247]: a Out[247]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) In [248]: np.random.shuffle(a.flat) In [249]: a Out[249]: array([[ 0, 14, 9, 1, 13], [ 2, 8, 5, 3, 4], [ 6, 10, 7, 12, 11]]) A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? Warren It is clear from the comments so far that, when `axis` is None, the result should be a shuffle of all the elements in the array, for both methods of shuffling (whether implemented as a new method or with a boolean argument to `shuffle`). Forget I ever suggested doing nothing or raising an error. :) Josef's comment reminded me that `numpy.random.permutation` returns a shuffled copy of the array (when its argument is an array). This function should also get an `axis` argument. `permutation` shuffles the same way `shuffle` does--it simply makes a copy and then calls `shuffle` on the copy. If a new method is added for the new shuffling style, then it would be consistent to also add a new method that uses the new shuffling style and returns a copy of the shuffled array. Then we would then have four methods: In-placeCopy Current shuffle style shuffle permutation New shuffle style (name TBD) (name TBD) (All of them will have an `axis` argument.) That table makes me think that, *if* we go with new methods, the names should be `shuffleXXX` and `permutationXXX`, where `XXX` is a common suffix that is to be determined. That will ensure that the names appear together in alphabetical lists, and should show up together as options in tab-completion or code-completion. Warren I suspect this will make some folks prefer the approach of adding a boolean argument to `shuffle` and `permutation`. Warren ___ NumPy-Discussion mailing list
[Numpy-discussion] [ANN] bcolz 0.7.2
== Announcing bcolz 0.7.2 == What's new == This is a maintenance release that fixes various bits and pieces. Importantly, compatibility with Numpy 1.9 and Cython 0.21 has been fixed and the test suit no longer segfaults on 32 bit UNIX. Feature-wise a new ``carray.view()`` method has been introduced which allows carrays to share the same raw data. ``bcolz`` is a renaming of the ``carray`` project. The new goals for the project are to create simple, yet flexible compressed containers, that can live either on-disk or in-memory, and with some high-performance iterators (like `iter()`, `where()`) for querying them. Together, bcolz and the Blosc compressor, are finally fulfilling the promise of accelerating memory I/O, at least for some real scenarios: http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/querying-ep14.ipynb#Plots For more detailed info, see the release notes in: https://github.com/Blosc/bcolz/wiki/Release-Notes What it is == bcolz provides columnar and compressed data containers. Column storage allows for efficiently querying tables with a large number of columns. It also allows for cheap addition and removal of column. In addition, bcolz objects are compressed by default for reducing memory/disk I/O needs. The compression process is carried out internally by Blosc, a high-performance compressor that is optimized for binary data. bcolz can use numexpr internally so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). numexpr optimizes the memory usage and use several cores for doing the computations, so it is blazing fast. Moreover, the carray/ctable containers can be disk-based, and it is possible to use them for seamlessly performing out-of-memory computations. bcolz has minimal dependencies (NumPy), comes with an exhaustive test suite and fully supports both 32-bit and 64-bit platforms. Also, it is typically tested on both UNIX and Windows operating systems. Installing == bcolz is in the PyPI repository, so installing it is easy:: $ pip install -U bcolz Resources = Visit the main bcolz site repository at: http://github.com/Blosc/bcolz Manual: http://bcolz.blosc.org Home of Blosc compressor: http://blosc.org User's mail list: bc...@googlegroups.com http://groups.google.com/group/bcolz License is the new BSD: https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt **Enjoy data!** ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Detect if array has been transposed
Hi,
In part of my C++ code, I often do
void foo(PyObject * matrix) {
do stuff
}
where matrix is a numpy mxn matrix created on the Python side, where
foo() eg. is invoked as
a = numpy.array([[1,2],[3,5]])
foo(a)
However, if you call transpose() on a, some care should be taken, since
numpy's internal matrix data first gets transposed on demand. In that
case I must do
a = numpy.array([[1,2],[3,5]])
a.transpose()
foo(a.copy())
to make sure the correct data of the array gets transferred to the C++
side.
Is there any way for me to detect (on the Python side) that transpose()
has been invoked on the matrix, and thereby only do the copy operation
when it really is needed? For example
if a_has_transposed_data:
foo(a.copy())
else:
foo(a)
Best regards,
Mads
--
+-+
| Mads Ipsen |
+--+--+
| Gåsebæksvej 7, 4. tv | phone: +45-29716388 |
| DK-2500 Valby| email: mads.ip...@gmail.com |
| Denmark | map : www.tinyurl.com/ns52fpa |
+--+--+
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Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 9:29 AM, Warren Weckesser warren.weckes...@gmail.com wrote: On Sun, Oct 12, 2014 at 12:14 PM, Warren Weckesser warren.weckes...@gmail.com wrote: On Sat, Oct 11, 2014 at 6:51 PM, Warren Weckesser warren.weckes...@gmail.com wrote: I created an issue on github for an enhancement to numpy.random.shuffle: https://github.com/numpy/numpy/issues/5173 I'd like to get some feedback on the idea. Currently, `shuffle` shuffles the first dimension of an array in-place. For example, shuffling a 2D array shuffles the rows: In [227]: a Out[227]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) In [228]: np.random.shuffle(a) In [229]: a Out[229]: array([[ 0, 1, 2], [ 9, 10, 11], [ 3, 4, 5], [ 6, 7, 8]]) To add an axis keyword, we could (in effect) apply `shuffle` to `a.swapaxes(axis, 0)`. For a 2-D array, `axis=1` would shuffles the columns: In [232]: a = np.arange(15).reshape(3,5) In [233]: a Out[233]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) In [234]: axis = 1 In [235]: np.random.shuffle(a.swapaxes(axis, 0)) In [236]: a Out[236]: array([[ 3, 2, 4, 0, 1], [ 8, 7, 9, 5, 6], [13, 12, 14, 10, 11]]) So that's the first part--adding an `axis` keyword. The other part of the enhancement request is to add a shuffle behavior that shuffles the 1-d slices *independently*. That is, for a 2-d array, shuffling with `axis=0` would apply a different shuffle to each column. In the github issue, I defined a function called `disarrange` that implements this behavior: In [240]: a Out[240]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]]) In [241]: disarrange(a, axis=0) In [242]: a Out[242]: array([[ 6, 1, 2], [ 3, 13, 14], [ 9, 10, 5], [12, 7, 8], [ 0, 4, 11]]) Note that each column has been shuffled independently. This behavior is analogous to how `sort` handles the `axis` keyword. `sort` sorts the 1-d slices along the given axis independently. In the github issue, I suggested the following signature for `shuffle` (but I'm not too fond of the name `independent`): def shuffle(a, independent=False, axis=0) If `independent` is False, the current behavior of `shuffle` is used. If `independent` is True, each 1-d slice is shuffled independently (in the same way that `sort` sorts each 1-d slice). Like most functions that take an `axis` argument, `axis=None` means to shuffle the flattened array. With `independent=True`, it would act like `np.random.shuffle(a.flat)`, e.g. In [247]: a Out[247]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) In [248]: np.random.shuffle(a.flat) In [249]: a Out[249]: array([[ 0, 14, 9, 1, 13], [ 2, 8, 5, 3, 4], [ 6, 10, 7, 12, 11]]) A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? Warren It is clear from the comments so far that, when `axis` is None, the result should be a shuffle of all the elements in the array, for both methods of shuffling (whether implemented as a new method or with a boolean argument to `shuffle`). Forget I ever suggested doing nothing or raising an error. :) Josef's comment reminded me that `numpy.random.permutation` returns a shuffled copy of the array (when its argument is an array). This function should also get an `axis` argument. `permutation` shuffles the same way `shuffle` does--it simply makes a copy and then calls `shuffle` on the copy. If a new method is added for the new shuffling style, then it would be consistent to also add a new method that uses the new shuffling style and returns a copy of the shuffled array. Then we would then have four methods: In-placeCopy Current shuffle style shuffle permutation New shuffle style (name TBD) (name TBD) (All of them will have an `axis` argument.) That table makes me think that, *if* we go with new methods, the names should be `shuffleXXX` and `permutationXXX`, where `XXX` is a common suffix that is to be determined. That will ensure that the names appear together in alphabetical lists, and should show up together as options in tab-completion or code-completion. Just to add some noise to a productive conversation: if you add a 'copy' flag to shuffle, then all the functionality is in one
Re: [Numpy-discussion] Detect if array has been transposed
12.10.2014, 20:19, Mads Ipsen kirjoitti: Is there any way for me to detect (on the Python side) that transpose() has been invoked on the matrix, and thereby only do the copy operation when it really is needed? The correct way to do this is to, either: In your C code check PyArray_IS_C_CONTIGUOUS(obj) and raise an error if it is not. In addition, on the Python side, check for `a.flags.c_contiguous` and make a copy if it is not. OR In your C code, get an handle to the array using PyArray_FromANY (or PyArray_FromOTF) with NPY_ARRAY_C_CONTIGUOUS requirement set so that it makes a copy when necessary. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Detect if array has been transposed
On 2014/10/12, 8:29 AM, Pauli Virtanen wrote: 12.10.2014, 20:19, Mads Ipsen kirjoitti: Is there any way for me to detect (on the Python side) that transpose() has been invoked on the matrix, and thereby only do the copy operation when it really is needed? The correct way to do this is to, either: In your C code check PyArray_IS_C_CONTIGUOUS(obj) and raise an error if it is not. In addition, on the Python side, check for `a.flags.c_contiguous` and make a copy if it is not. OR In your C code, get an handle to the array using PyArray_FromANY (or PyArray_FromOTF) with NPY_ARRAY_C_CONTIGUOUS requirement set so that it makes a copy when necessary. or let numpy handle it on the python side: foo(numpy.ascontiguousarray(a)) ___ 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] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 10:56 AM, Jaime Fernández del Río jaime.f...@gmail.com wrote: Just to add some noise to a productive conversation: if you add a 'copy' flag to shuffle, then all the functionality is in one place, and 'permutation' can either be deprecated, or trivially implemented in terms of the new 'shuffle'. +1 Unfortunately, shuffle has the better name, but permutation has the better default behavior. (also, I think inplace might be a less ambiguous name for the argument than copy) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Detect if array has been transposed
12.10.2014, 22:16, Eric Firing kirjoitti: On 2014/10/12, 8:29 AM, Pauli Virtanen wrote: 12.10.2014, 20:19, Mads Ipsen kirjoitti: Is there any way for me to detect (on the Python side) that transpose() has been invoked on the matrix, and thereby only do the copy operation when it really is needed? The correct way to do this is to, either: In your C code check PyArray_IS_C_CONTIGUOUS(obj) and raise an error if it is not. In addition, on the Python side, check for `a.flags.c_contiguous` and make a copy if it is not. OR In your C code, get an handle to the array using PyArray_FromANY (or PyArray_FromOTF) with NPY_ARRAY_C_CONTIGUOUS requirement set so that it makes a copy when necessary. or let numpy handle it on the python side: foo(numpy.ascontiguousarray(a)) Yes, but the C code really should check that the input array is C-contiguous, if it only works for C-contiguous inputs. -- Pauli Virtanen ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Detect if array has been transposed
On Mon, Oct 13, 2014 at 12:07 AM, Pauli Virtanen p...@iki.fi wrote: 12.10.2014, 22:16, Eric Firing kirjoitti: On 2014/10/12, 8:29 AM, Pauli Virtanen wrote: 12.10.2014, 20:19, Mads Ipsen kirjoitti: Is there any way for me to detect (on the Python side) that transpose() has been invoked on the matrix, and thereby only do the copy operation when it really is needed? The correct way to do this is to, either: In your C code check PyArray_IS_C_CONTIGUOUS(obj) and raise an error if it is not. In addition, on the Python side, check for `a.flags.c_contiguous` and make a copy if it is not. OR In your C code, get an handle to the array using PyArray_FromANY (or PyArray_FromOTF) with NPY_ARRAY_C_CONTIGUOUS requirement set so that it makes a copy when necessary. or let numpy handle it on the python side: foo(numpy.ascontiguousarray(a)) Yes, but the C code really should check that the input array is C-contiguous, if it only works for C-contiguous inputs. I.e. your original instructions were correct, but instead of checking a.flags.c_contiguous by hand etc. the OP should just call ascontiguousarray which takes care of that part. -- 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] Instaling numpy without root access
Guys, any advice is highly appreciated. I am a little new to building in Linux. Thanks, Lahiru On Sat, Oct 11, 2014 at 9:43 AM, Lahiru Samarakoon lahir...@gmail.com wrote: I switched to numpy-1.8.2. . Now getting following error. I am using LAPACK that comes with atlast installation. Can this be a problem? Traceback (most recent call last): File stdin, line 1, in module File /home/svu/a0095654/.local/lib/python2.7/site-packages/numpy/__init__.py, line 170, in module from . import add_newdocs File /home/svu/a0095654/.local/lib/python2.7/site-packages/numpy/add_newdocs.py, line 13, in module from numpy.lib import add_newdoc File /home/svu/a0095654/.local/lib/python2.7/site-packages/numpy/lib/__init__.py, line 18, in module from .polynomial import * File /home/svu/a0095654/.local/lib/python2.7/site-packages/numpy/lib/polynomial.py, line 19, in module from numpy.linalg import eigvals, lstsq, inv File /home/svu/a0095654/.local/lib/python2.7/site-packages/numpy/linalg/__init__.py, line 51, in module from .linalg import * File /home/svu/a0095654/.local/lib/python2.7/site-packages/numpy/linalg/linalg.py, line 29, in module from numpy.linalg import lapack_lite, _umath_linalg ImportError: /home/svu/a0095654/.local/lib/python2.7/site-packages/numpy/linalg/lapack_lite.so: undefined symbol: zgesdd_ On Sat, Oct 11, 2014 at 1:30 AM, Julian Taylor jtaylor.deb...@googlemail.com wrote: On 10.10.2014 19:26, Lahiru Samarakoon wrote: Red Hat Enterprise Linux release 5.8 gcc (GCC) 4.1.2 I am also trying to install numpy 1.9. that is the broken platform, please try the master branch or the maintenance/1.9.x branch, those should work now. Are there volunteers to report this to redhat? On Sat, Oct 11, 2014 at 12:59 AM, Julian Taylor jtaylor.deb...@googlemail.com mailto:jtaylor.deb...@googlemail.com wrote: On 10.10.2014 18:51, Lahiru Samarakoon wrote: Dear all, I am trying to install numpy without root access. So I am building from the source. I have installed atlas which also has lapack with it. I changed the site.cfg file as given below [DEFAULT] library_dirs = /home/svu/a0095654/ATLAS/build/lib include_dirs = /home/svu/a0095654/ATLAS/build/include However, I am getting a segmentation fault when importing numpy. Please advise. I also put the build log file at the end of the email if necessary. Which platform are you working on? Which compiler version? We just solved a segfault on import on red hat 5 gcc 4.1.2. Very likely caused by a compiler bug. See https://github.com/numpy/numpy/issues/5163 The build log is complaining about your atlas being to small, possibly the installation is broken? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for enhancement to numpy.random.shuffle
On Sun, Oct 12, 2014 at 5:14 PM, Sebastian se...@sebix.at wrote: On 2014-10-12 16:54, Warren Weckesser wrote: On Sun, Oct 12, 2014 at 7:57 AM, Robert Kern robert.k...@gmail.com mailto:robert.k...@gmail.com wrote: On Sat, Oct 11, 2014 at 11:51 PM, Warren Weckesser warren.weckes...@gmail.com mailto:warren.weckes...@gmail.com wrote: A small wart in this API is the meaning of shuffle(a, independent=False, axis=None) It could be argued that the correct behavior is to leave the array unchanged. (The current behavior can be interpreted as shuffling a 1-d sequence of monolithic blobs; the axis argument specifies which axis of the array corresponds to the sequence index. Then `axis=None` means the argument is a single monolithic blob, so there is nothing to shuffle.) Or an error could be raised. What do you think? It seems to me a perfectly good reason to have two methods instead of one. I can't imagine when I wouldn't be using a literal True or False for this, so it really should be two different methods. I agree, and my first inclination was to propose a different method (and I had the bikeshedding conversation with myself about the name: disarrange, scramble, disorder, randomize, ashuffle, some other variation of the word shuffle, ...), but I figured the first thing folks would say is Why not just add options to shuffle? So, choose your battles and all that. What do other folks think of making a separate method I'm not a fan of more methods with similar functionality in Numpy. It's already hard to overlook the existing functions and all their possible applications and variants. The axis=None proposal for shuffling all items is very intuitive. I think we don't want to take the path of matlab: a huge amount of powerful functions, but few people know of their powerful possibilities. I totally agree with this principle, but I think this is an exception to the rule, b/c unfortunately in this case the function that we *do* have is weird and inconsistent with how most other functions in numpy work. It doesn't vectorize! Cf. 'sort' or how a 'shuffle' gufunc (k,)-(k,) would work. Also, it's easy to implement the current 'shuffle' in terms of any 1d shuffle function, with no explicit loops, Warren's disarrange requires an explicit loop. So, we really implemented the wrong one, oops. What this means going forward, though, is that our only options are either to implement both behaviours with two functions, or else to give up on have the more natural behaviour altogether. I think the former is the lesser of two evils. Regarding names: shuffle/permutation is a terrible naming convention IMHO and shouldn't be propagated further. We already have a good naming convention for inplace-vs-sorted: sort vs. sorted, reverse vs. reversed, etc. So, how about: scramble + scrambled shuffle individual entries within each row/column/..., as in Warren's suggestion. shuffle + shuffled to do what shuffle, permutation do now (mnemonic: these break a 2d array into a bunch of 1d cards, and then shuffle those cards). permuted remains indefinitely, with the docstring: Deprecated alias for 'shuffled'. -n -- 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
