Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 13:26, Neal Becker ndbeck...@gmail.com wrote: I was just bitten by this unexpected behavior: In [24]: all ([i 0 for i in xrange (10)]) Out[24]: False In [25]: all (i 0 for i in xrange (10)) Out[25]: True Turns out: In [31]: all is numpy.all Out[31]: True So numpy.all doesn't seem to do what I would expect when given a generator. Bug? Expected behavior. numpy.all(), like nearly all numpy functions, converts the input to an array using numpy.asarray(). numpy.asarray() knows nothing special about generators and other iterables that are not sequences, so it thinks it's a single scalar object. This scalar object happens to have a __nonzero__() method that returns True like most Python objects that don't override this. In order to use generic iterators that are not sequences, you need to explicitly use numpy.fromiter() to convert them to ndarrays. asarray() and array() can't do it in general because they need to autodiscover the shape and dtype all at the same time. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On 01/31/2012 03:07 PM, Robert Kern wrote: On Tue, Jan 31, 2012 at 13:26, Neal Beckerndbeck...@gmail.com wrote: I was just bitten by this unexpected behavior: In [24]: all ([i0 for i in xrange (10)]) Out[24]: False In [25]: all (i0 for i in xrange (10)) Out[25]: True Turns out: In [31]: all is numpy.all Out[31]: True So numpy.all doesn't seem to do what I would expect when given a generator. Bug? Expected behavior. numpy.all(), like nearly all numpy functions, converts the input to an array using numpy.asarray(). numpy.asarray() knows nothing special about generators and other iterables that are not sequences, so it thinks it's a single scalar object. This scalar object happens to have a __nonzero__() method that returns True like most Python objects that don't override this. In order to use generic iterators that are not sequences, you need to explicitly use numpy.fromiter() to convert them to ndarrays. asarray() and array() can't do it in general because they need to autodiscover the shape and dtype all at the same time. Perhaps np.asarray could specifically check for a generator argument and raise an exception? I imagine that would save people some time when running into this... If you really want In [7]: x = np.asarray(None) In [8]: x[()] = (i for i in range(10)) In [9]: x Out[9]: array(generator object genexpr at 0x4553fa0, dtype=object) ...then one can type it out? Dag ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
Dag Sverre Seljebotn wrote: On 01/31/2012 03:07 PM, Robert Kern wrote: On Tue, Jan 31, 2012 at 13:26, Neal Beckerndbeck...@gmail.com wrote: I was just bitten by this unexpected behavior: In [24]: all ([i0 for i in xrange (10)]) Out[24]: False In [25]: all (i0 for i in xrange (10)) Out[25]: True Turns out: In [31]: all is numpy.all Out[31]: True So numpy.all doesn't seem to do what I would expect when given a generator. Bug? Expected behavior. numpy.all(), like nearly all numpy functions, converts the input to an array using numpy.asarray(). numpy.asarray() knows nothing special about generators and other iterables that are not sequences, so it thinks it's a single scalar object. This scalar object happens to have a __nonzero__() method that returns True like most Python objects that don't override this. In order to use generic iterators that are not sequences, you need to explicitly use numpy.fromiter() to convert them to ndarrays. asarray() and array() can't do it in general because they need to autodiscover the shape and dtype all at the same time. Perhaps np.asarray could specifically check for a generator argument and raise an exception? I imagine that would save people some time when running into this... If you really want In [7]: x = np.asarray(None) In [8]: x[()] = (i for i in range(10)) In [9]: x Out[9]: array(generator object genexpr at 0x4553fa0, dtype=object) ...then one can type it out? Dag The reason it surprised me, is that python 'all' doesn't behave as numpy 'all' in this respect - and using ipython, I didn't even notice that 'all' was numpy.all rather than standard python all. All in all, rather unfortunate :) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On 1/31/2012 8:26 AM, Neal Becker wrote: I was just bitten by this unexpected behavior: In [24]: all ([i 0 for i in xrange (10)]) Out[24]: False In [25]: all (i 0 for i in xrange (10)) Out[25]: True Turns out: In [31]: all is numpy.all Out[31]: True np.array([i 0 for i in xrange (10)]) array([False, True, True, True, True, True, True, True, True, True], dtype=bool) np.array(i 0 for i in xrange (10)) array(generator object genexpr at 0x0267A210, dtype=object) import this Cheers, Alan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tuesday, January 31, 2012, Alan G Isaac alan.is...@gmail.com wrote: On 1/31/2012 8:26 AM, Neal Becker wrote: I was just bitten by this unexpected behavior: In [24]: all ([i 0 for i in xrange (10)]) Out[24]: False In [25]: all (i 0 for i in xrange (10)) Out[25]: True Turns out: In [31]: all is numpy.all Out[31]: True np.array([i 0 for i in xrange (10)]) array([False, True, True, True, True, True, True, True, True, True], dtype=bool) np.array(i 0 for i in xrange (10)) array(generator object genexpr at 0x0267A210, dtype=object) import this Cheers, Alan Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. If the former, why isn't it using asanyarray()? Ben Root ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 15:13, Benjamin Root ben.r...@ou.edu wrote: Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. Why would you expect that? [~/scratch] |37 np.asanyarray(i5 for i in range(10)) array(generator object genexpr at 0xdc24a08, dtype=object) -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On 01/31/2012 04:13 PM, Benjamin Root wrote: On Tuesday, January 31, 2012, Alan G Isaac alan.is...@gmail.com mailto:alan.is...@gmail.com wrote: On 1/31/2012 8:26 AM, Neal Becker wrote: I was just bitten by this unexpected behavior: In [24]: all ([i 0 for i in xrange (10)]) Out[24]: False In [25]: all (i 0 for i in xrange (10)) Out[25]: True Turns out: In [31]: all is numpy.all Out[31]: True np.array([i 0 for i in xrange (10)]) array([False, True, True, True, True, True, True, True, True, True], dtype=bool) np.array(i 0 for i in xrange (10)) array(generator object genexpr at 0x0267A210, dtype=object) import this Cheers, Alan Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. If the former, why isn't it using asanyarray()? Your expectation is probably wrong: In [12]: np.asanyarray(i for i in range(10)) Out[12]: array(generator object genexpr at 0x455d9b0, dtype=object) Dag Sverre ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 15:13, Benjamin Root ben.r...@ou.edu wrote: Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. Why would you expect that? [~/scratch] |37 np.asanyarray(i5 for i in range(10)) array(generator object genexpr at 0xdc24a08, dtype=object) -- Robert Kern What possible use-case could there be for a numpy array of generators? Furthermore, from the documentation: numpy.asanyarray = asanyarray(a, dtype=None, order=None, maskna=None, ownmaskna=False) Convert the input to an ndarray, but pass ndarray subclasses through. Parameters -- a : array_like *Input data, in any form that can be converted to an array*. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. Emphasis mine. A generator is an input that could be converted into an array. (Setting aside the issue of non-terminating generators such as those from cycle()). Ben Root ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On 01/31/2012 04:35 PM, Benjamin Root wrote: On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern robert.k...@gmail.com mailto:robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 15:13, Benjamin Root ben.r...@ou.edu mailto:ben.r...@ou.edu wrote: Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. Why would you expect that? [~/scratch] |37 np.asanyarray(i5 for i in range(10)) array(generator object genexpr at 0xdc24a08, dtype=object) -- Robert Kern What possible use-case could there be for a numpy array of generators? Furthermore, from the documentation: numpy.asanyarray = asanyarray(a, dtype=None, order=None, maskna=None, ownmaskna=False) Convert the input to an ndarray, but pass ndarray subclasses through. Parameters -- a : array_like *Input data, in any form that can be converted to an array*. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. Emphasis mine. A generator is an input that could be converted into an array. (Setting aside the issue of non-terminating generators such as those from cycle()). Splitting semantic hairs doesn't help here -- it *does* return an array, it just happens to be a completely useless 0-dimensional one. The question is, is the current confusing and less than useful? (I vot for yes). list and tuple are special-cased, why not generators (at least to raise an exception) Going OT, look at this gem: In [3]: a Out[3]: array([1, 2, 3], dtype=object) In [4]: a.shape Out[4]: () ??? In [9]: b Out[9]: array([1, 2, 3], dtype=object) In [10]: b.shape Out[10]: (3,) Figuring out the ??? is left as an exercise to the reader :-) Dag Sverre ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On 1/31/2012 10:35 AM, Benjamin Root wrote: A generator is an input that could be converted into an array. def mygen(): i = 0 while True: yield i i += 1 Alan Isaac ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 15:35, Benjamin Root ben.r...@ou.edu wrote: On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 15:13, Benjamin Root ben.r...@ou.edu wrote: Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. Why would you expect that? [~/scratch] |37 np.asanyarray(i5 for i in range(10)) array(generator object genexpr at 0xdc24a08, dtype=object) -- Robert Kern What possible use-case could there be for a numpy array of generators? Not many. This isn't an intentional feature, just a logical consequence of all of the other intentional features being applied consistently. Furthermore, from the documentation: numpy.asanyarray = asanyarray(a, dtype=None, order=None, maskna=None, ownmaskna=False) Convert the input to an ndarray, but pass ndarray subclasses through. Parameters -- a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. Emphasis mine. A generator is an input that could be converted into an array. (Setting aside the issue of non-terminating generators such as those from cycle()). I'm sorry, but this is not true. In general, it's too hard to do all of the magic autodetermination that asarray() and array() do when faced with an indeterminate-length iterable. We tried. That's why we have fromiter(). By restricting the domain to an iterable yielding scalars and requiring that the user specify the desired dtype, fromiter() can figure out the rest. Like it or not, array_like is practically defined by the behavior of np.asarray(), not vice-versa. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
Le 31 janvier 2012 10:50, Robert Kern robert.k...@gmail.com a écrit : On Tue, Jan 31, 2012 at 15:35, Benjamin Root ben.r...@ou.edu wrote: On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 15:13, Benjamin Root ben.r...@ou.edu wrote: Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. Why would you expect that? [~/scratch] |37 np.asanyarray(i5 for i in range(10)) array(generator object genexpr at 0xdc24a08, dtype=object) -- Robert Kern What possible use-case could there be for a numpy array of generators? Not many. This isn't an intentional feature, just a logical consequence of all of the other intentional features being applied consistently. Furthermore, from the documentation: numpy.asanyarray = asanyarray(a, dtype=None, order=None, maskna=None, ownmaskna=False) Convert the input to an ndarray, but pass ndarray subclasses through. Parameters -- a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. Emphasis mine. A generator is an input that could be converted into an array. (Setting aside the issue of non-terminating generators such as those from cycle()). I'm sorry, but this is not true. In general, it's too hard to do all of the magic autodetermination that asarray() and array() do when faced with an indeterminate-length iterable. We tried. That's why we have fromiter(). By restricting the domain to an iterable yielding scalars and requiring that the user specify the desired dtype, fromiter() can figure out the rest. Like it or not, array_like is practically defined by the behavior of np.asarray(), not vice-versa. In that case I agree with whoever said ealier it would be best to detect this case and throw an exception, as it'll probably save some headaches. -=- Olivier ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 15:35, Benjamin Root ben.r...@ou.edu wrote: Furthermore, from the documentation: numpy.asanyarray = asanyarray(a, dtype=None, order=None, maskna=None, ownmaskna=False) Convert the input to an ndarray, but pass ndarray subclasses through. Parameters -- a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. I should also add that this verbiage is also in np.asarray(). The only additional feature of np.asanyarray() is that is does not convert ndarray subclasses like matrix to ndarray objects. np.asanyarray() does not accept more types of objects than np.asarray(). -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 10:05 AM, Olivier Delalleau sh...@keba.be wrote: Le 31 janvier 2012 10:50, Robert Kern robert.k...@gmail.com a écrit : On Tue, Jan 31, 2012 at 15:35, Benjamin Root ben.r...@ou.edu wrote: On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 15:13, Benjamin Root ben.r...@ou.edu wrote: Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. Why would you expect that? [~/scratch] |37 np.asanyarray(i5 for i in range(10)) array(generator object genexpr at 0xdc24a08, dtype=object) -- Robert Kern What possible use-case could there be for a numpy array of generators? Not many. This isn't an intentional feature, just a logical consequence of all of the other intentional features being applied consistently. Furthermore, from the documentation: numpy.asanyarray = asanyarray(a, dtype=None, order=None, maskna=None, ownmaskna=False) Convert the input to an ndarray, but pass ndarray subclasses through. Parameters -- a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. Emphasis mine. A generator is an input that could be converted into an array. (Setting aside the issue of non-terminating generators such as those from cycle()). I'm sorry, but this is not true. In general, it's too hard to do all of the magic autodetermination that asarray() and array() do when faced with an indeterminate-length iterable. We tried. That's why we have fromiter(). By restricting the domain to an iterable yielding scalars and requiring that the user specify the desired dtype, fromiter() can figure out the rest. Like it or not, array_like is practically defined by the behavior of np.asarray(), not vice-versa. In that case I agree with whoever said ealier it would be best to detect this case and throw an exception, as it'll probably save some headaches. -=- Olivier I'll agree with this statement. This bug has popped up a few times in the mpl bug tracker due to the pylab mode. While I would prefer if it were possible to evaluate the generator into an array, silently returning True incorrectly for all() and any() is probably far worse. That said, is it still impossible to make np.all() and np.any() special to have similar behavior to the built-in all() and any()? Maybe it could catch the above exception and then return the result from python's built-ins? Cheers! Ben Root ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 6:33 AM, Neal Becker ndbeck...@gmail.com wrote: The reason it surprised me, is that python 'all' doesn't behave as numpy 'all' in this respect - and using ipython, I didn't even notice that 'all' was numpy.all rather than standard python all. namespaces are one honking great idea -- sorry, I couldn't help myself -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
I also agree that an exception should be raised at the very least. It might also be possible to make the NumPy any, all, and sum functions behave like the builtins when given a generator. It seems worth exploring at least. Travis -- Travis Oliphant (on a mobile) 512-826-7480 On Jan 31, 2012, at 10:46 AM, Benjamin Root ben.r...@ou.edu wrote: On Tue, Jan 31, 2012 at 10:05 AM, Olivier Delalleau sh...@keba.be wrote: Le 31 janvier 2012 10:50, Robert Kern robert.k...@gmail.com a écrit : On Tue, Jan 31, 2012 at 15:35, Benjamin Root ben.r...@ou.edu wrote: On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 15:13, Benjamin Root ben.r...@ou.edu wrote: Is np.all() using np.array() or np.asanyarray()? If the latter, I would expect it to return a numpy array from a generator. Why would you expect that? [~/scratch] |37 np.asanyarray(i5 for i in range(10)) array(generator object genexpr at 0xdc24a08, dtype=object) -- Robert Kern What possible use-case could there be for a numpy array of generators? Not many. This isn't an intentional feature, just a logical consequence of all of the other intentional features being applied consistently. Furthermore, from the documentation: numpy.asanyarray = asanyarray(a, dtype=None, order=None, maskna=None, ownmaskna=False) Convert the input to an ndarray, but pass ndarray subclasses through. Parameters -- a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. Emphasis mine. A generator is an input that could be converted into an array. (Setting aside the issue of non-terminating generators such as those from cycle()). I'm sorry, but this is not true. In general, it's too hard to do all of the magic autodetermination that asarray() and array() do when faced with an indeterminate-length iterable. We tried. That's why we have fromiter(). By restricting the domain to an iterable yielding scalars and requiring that the user specify the desired dtype, fromiter() can figure out the rest. Like it or not, array_like is practically defined by the behavior of np.asarray(), not vice-versa. In that case I agree with whoever said ealier it would be best to detect this case and throw an exception, as it'll probably save some headaches. -=- Olivier I'll agree with this statement. This bug has popped up a few times in the mpl bug tracker due to the pylab mode. While I would prefer if it were possible to evaluate the generator into an array, silently returning True incorrectly for all() and any() is probably far worse. That said, is it still impossible to make np.all() and np.any() special to have similar behavior to the built-in all() and any()? Maybe it could catch the above exception and then return the result from python's built-ins? Cheers! Ben Root ___ 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] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 22:17, Travis Oliphant tra...@continuum.io wrote: I also agree that an exception should be raised at the very least. It might also be possible to make the NumPy any, all, and sum functions behave like the builtins when given a generator. It seems worth exploring at least. I would rather we deprecate the all() and any() functions in favor of the alltrue() and sometrue() aliases that date back to Numeric. Renaming them to match the builtin names was a mistake. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 4:22 PM, Robert Kern robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 22:17, Travis Oliphant tra...@continuum.io wrote: I also agree that an exception should be raised at the very least. It might also be possible to make the NumPy any, all, and sum functions behave like the builtins when given a generator. It seems worth exploring at least. I would rather we deprecate the all() and any() functions in favor of the alltrue() and sometrue() aliases that date back to Numeric. +1 (Maybe 'anytrue' for consistency? (And a royal blue bike shed?)) Warren ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy all unexpected result (generator)
Actually i believe the NumPy 'any' and 'all' names pre-date the Python usage which first appeared in Python 2.5 I agree with Chris that namespaces are a great idea. I don't agree with deprecating 'any' and 'all' It also seems useful to revisit under what conditions 'array' could correctly interpret a generator expression, but in the context of streaming or deferred arrays. Travis -- Travis Oliphant (on a mobile) 512-826-7480 On Jan 31, 2012, at 4:22 PM, Robert Kern robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 22:17, Travis Oliphant tra...@continuum.io wrote: I also agree that an exception should be raised at the very least. It might also be possible to make the NumPy any, all, and sum functions behave like the builtins when given a generator. It seems worth exploring at least. I would rather we deprecate the all() and any() functions in favor of the alltrue() and sometrue() aliases that date back to Numeric. Renaming them to match the builtin names was a mistake. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ 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] numpy all unexpected result (generator)
On Tue, Jan 31, 2012 at 5:35 PM, Travis Oliphant tra...@continuum.io wrote: Actually i believe the NumPy 'any' and 'all' names pre-date the Python usage which first appeared in Python 2.5 I agree with Chris that namespaces are a great idea. I don't agree with deprecating 'any' and 'all' I completely agree here. I also like to keep np.all, np.any, np.max, ... np.max((i 0 for i in xrange (10))) generator object genexpr at 0x046493F0 max((i 0 for i in xrange (10))) True I used an old-style matplotlib example as recipe yesterday, and the first thing I did is getting rid of the missing name spaces, and I had to think twice what amax and amin are. aall, aany ??? ;) Josef It also seems useful to revisit under what conditions 'array' could correctly interpret a generator expression, but in the context of streaming or deferred arrays. Travis -- Travis Oliphant (on a mobile) 512-826-7480 On Jan 31, 2012, at 4:22 PM, Robert Kern robert.k...@gmail.com wrote: On Tue, Jan 31, 2012 at 22:17, Travis Oliphant tra...@continuum.io wrote: I also agree that an exception should be raised at the very least. It might also be possible to make the NumPy any, all, and sum functions behave like the builtins when given a generator. It seems worth exploring at least. I would rather we deprecate the all() and any() functions in favor of the alltrue() and sometrue() aliases that date back to Numeric. Renaming them to match the builtin names was a mistake. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ 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
