On 01/31/2012 03:07 PM, Robert Kern wrote:

On Tue, Jan 31, 2012 at 13:26, 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

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(, dtype=object) ...then one can type it out? Dag

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(, dtype=object) import this

Cheers, Alan

On Tue, Jan 31, 2012 at 15:13, Benjamin Root 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(i>5 for i in range(10)) array(, 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

On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern wrote:

On Tue, Jan 31, 2012 at 15:13, Benjamin Root 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(i>5 for i in range(10)) array(, 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

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

Le 31 janvier 2012 10:50, Robert Kern a écrit :

On Tue, Jan 31, 2012 at 15:35, Benjamin Root wrote:

...

On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern

wrote:

...

...

On Tue, Jan 31, 2012 at 15:13, Benjamin Root 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(i>5 for i in range(10)) array(, 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

On Tue, Jan 31, 2012 at 10:05 AM, Olivier Delalleau wrote:

Le 31 janvier 2012 10:50, Robert Kern a écrit :

...

On Tue, Jan 31, 2012 at 15:35, Benjamin Root wrote:

...

On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern

wrote:

...

...

On Tue, Jan 31, 2012 at 15:13, Benjamin Root 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(i>5 for i in range(10)) array(, 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

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 wrote:

On Tue, Jan 31, 2012 at 10:05 AM, Olivier Delalleau wrote: Le 31 janvier 2012 10:50, Robert Kern a écrit : On Tue, Jan 31, 2012 at 15:35, Benjamin Root wrote:

...

On Tue, Jan 31, 2012 at 9:18 AM, Robert Kern wrote:

...

On Tue, Jan 31, 2012 at 15:13, Benjamin Root 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(i>5 for i in range(10)) array(, 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

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On Tue, Jan 31, 2012 at 4:22 PM, Robert Kern wrote:

On Tue, Jan 31, 2012 at 22:17, Travis Oliphant 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

On Tue, Jan 31, 2012 at 5:35 PM, Travis Oliphant 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))) 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 wrote:

...

On Tue, Jan 31, 2012 at 22:17, Travis Oliphant 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

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