[Numpy-discussion] [ANN] Summer School Advanced Scientific Programming in Python in Kiel, Germany
Advanced Scientific Programming in Python = a Summer School by the G-Node and the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques, incorporating theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python is assumed. Participants without any prior experience with Python should work through the proposed introductory materials before the course. Date and Location = September 2—7, 2012. Kiel, Germany. Preliminary Program === Day 0 (Sun Sept 2) — Best Programming Practices - Best Practices, Development Methodologies and the Zen of Python - Version control with git - Object-oriented programming design patterns Day 1 (Mon Sept 3) — Software Carpentry - Test-driven development, unit testing quality assurance - Debugging, profiling and benchmarking techniques - Best practices in data visualization - Programming in teams Day 2 (Tue Sept 4) — Scientific Tools for Python - Advanced NumPy - The Quest for Speed (intro): Interfacing to C with Cython - Advanced Python I: idioms, useful built-in data structures, generators Day 3 (Wed Sept 5) — The Quest for Speed - Writing parallel applications in Python - Programming project Day 4 (Thu Sept 6) — Efficient Memory Management - When parallelization does not help: the starving CPUs problem - Advanced Python II: decorators and context managers - Programming project Day 5 (Fri Sept 7) — Practical Software Development - Programming project - The Pelita Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects. Applications You can apply on-line at http://python.g-node.org Applications must be submitted before 23:59 UTC, May 1, 2012. Notifications of acceptance will be sent by June 1, 2012. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate last time was around 20%. Prerequisites: You are supposed to know the basics of Python to participate in the lectures. You are encouraged to go through the introductory material available on the website. Faculty === - Francesc Alted, Continuum Analytics Inc., USA - Pietro Berkes, Enthought Inc., UK - Valentin Haenel, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland - Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland - Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy - Rike-Benjamin Schuppner, Technologit GbR, Germany - Bartosz Teleńczuk, Unité de Neurosciences Information et Complexité, Centre National de la Recherche Scientifique, France - Stéfan van der Walt, Helen Wills Neuroscience Institute, University of California Berkeley, USA - Bastian Venthur, Berlin Institute of Technology and Bernstein Focus Neurotechnology, Germany - Niko Wilbert, TNG Technology Consulting GmbH, Germany - Tiziano Zito, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany Organized by Christian T. Steigies and Christian Drews of the Institute of Experimental and Applied Physics, Christian-Albrechts-Universität zu Kiel , and by Zbigniew Jędrzejewski-Szmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://python.g-node.org Contact: python-i...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org
[Numpy-discussion] numpy all unexpected result (generator)
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? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] histogram help
Do you want a histogramm of z for each (x,y) ? Nadav From: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org] On Behalf Of Ruby Stevenson [ruby...@gmail.com] Sent: 30 January 2012 21:27 To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] histogram help Sorry, I realize I didn't describe the problem completely clear or correct. the (x,y) in this case is just many co-ordinates, and each coordinate has a list of values (Z value) associated with it. The bins are allocated for the Z. I hope this clarify things a little. Thanks again. Ruby On Mon, Jan 30, 2012 at 2:21 PM, Ruby Stevenson ruby...@gmail.com wrote: hi, all I am trying to figure out how to do histogram with numpy I have a three-dimension array A[x,y,z], another array (bins) has been allocated along Z dimension, z' how can I get the histogram of H[ x, y, z' ]? thanks for your help. Ruby ___ 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] Unexpected reorganization of internal data
Hi,
I am confused. Here's the reason:
The following structure is a representation of N points in 3D space:
U = numpy.array([[x1,y1,z1], [x1,y1,z1],...,[xn,yn,zn]])
So the array U has shape (N,3). This order makes sense to me since U[i]
will give you the i'th point in the set. Now, I want to pass this array
to a C++ function that does some stuff with the points. Here's how I do
that
void Foo::doStuff(int n, PyObject * numpy_data)
{
// Get pointer to data
double * const positions = (double *) PyArray_DATA(numpy_data);
// Print positions
for (int i=0; in; ++i)
{
float x = static_castfloat(positions[3*i+0])
float y = static_castfloat(positions[3*i+1])
float z = static_castfloat(positions[3*i+2])
printf(Pos[%d] = %f %f %f\n, x, y, z);
}
}
When I call this routine, using a swig wrapped Python interface to the
C++ class, everything prints out nice.
Now, I want to apply a rotation to all the positions. So I set up some
rotation matrix R like this:
R = numpy.array([[r11,r12,r13],
[r21,r22,r23],
[r31,r32,r33]])
To apply the matrix to the data in one crunch, I do
V = numpy.dot(R, U.transpose()).transpose()
Now when I call my C++ function from the Python side, all the data in V
is printed, but it has been transposed. So apparently the internal data
structure handled by numpy has been reorganized, even though I called
transpose() twice, which I would expect to cancel out each other.
However, if I do:
V = numpy.array(U.transpose()).transpose()
and call the C++ routine, everything is perfectly fine, ie. the data
structure is as expected.
What went wrong?
Best regards,
Mads
--
+-+
| Mads Ipsen |
+--+--+
| Gåsebæksvej 7, 4. tv | |
| DK-2500 Valby| phone: +45-29716388 |
| Denmark | email: mads.ip...@gmail.com |
+--+--+
___
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 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] Unexpected reorganization of internal data
Not exactly an answer to your question, but I can highly recommend using Boost.python, PyUblas and Ublas for your C++ vectors and matrices. It gives you a really good interface on the C++ side to numpy arrays and matrices, which can be passed in both directions over the language threshold with no copying. If I had to guess I'd say sometimes when transposing numpy simply sets a flag internally to avoid copying the data, but in some cases (such as perhaps when multiplication needs to take place) the data has to be placed in a new object. Accessing the data via raw pointers in C++ may not be checking for the 'transpose' flag and therefore you see an unexpected result. Disclaimer: this is just a guess, someone more familiar with Numpy internals will no doubt be able to correct me. Malcolm ___ 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] Unexpected reorganization of internal data
Hi,
On Tue, Jan 31, 2012 at 8:29 AM, Mads Ipsen madsip...@gmail.com wrote:
Hi,
I am confused. Here's the reason:
The following structure is a representation of N points in 3D space:
U = numpy.array([[x1,y1,z1], [x1,y1,z1],...,[xn,yn,zn]])
So the array U has shape (N,3). This order makes sense to me since U[i] will
give you the i'th point in the set. Now, I want to pass this array to a C++
function that does some stuff with the points. Here's how I do that
void Foo::doStuff(int n, PyObject * numpy_data)
{
// Get pointer to data
double * const positions = (double *) PyArray_DATA(numpy_data);
// Print positions
for (int i=0; in; ++i)
{
float x = static_castfloat(positions[3*i+0])
float y = static_castfloat(positions[3*i+1])
float z = static_castfloat(positions[3*i+2])
printf(Pos[%d] = %f %f %f\n, x, y, z);
}
}
When I call this routine, using a swig wrapped Python interface to the C++
class, everything prints out nice.
Now, I want to apply a rotation to all the positions. So I set up some
rotation matrix R like this:
R = numpy.array([[r11,r12,r13],
[r21,r22,r23],
[r31,r32,r33]])
To apply the matrix to the data in one crunch, I do
V = numpy.dot(R, U.transpose()).transpose()
Now when I call my C++ function from the Python side, all the data in V is
printed, but it has been transposed. So apparently the internal data
structure handled by numpy has been reorganized, even though I called
transpose() twice, which I would expect to cancel out each other.
However, if I do:
V = numpy.array(U.transpose()).transpose()
and call the C++ routine, everything is perfectly fine, ie. the data
structure is as expected.
What went wrong?
The numpy array reserves the right to organize its data internally.
For example, a numpy array can be in Fortran order in memory, or C
order in memory, and many more complicated schemes. You might want to
have a look at:
http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html#internal-memory-layout-of-an-ndarray
If you depend on a particular order for your array memory, you might
want to look at:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.ascontiguousarray.html
Best,
Matthew
___
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] Unexpected reorganization of internal data
On Tue, Jan 31, 2012 at 6:14 AM, Malcolm Reynolds malcolm.reyno...@gmail.com wrote: Not exactly an answer to your question, but I can highly recommend using Boost.python, PyUblas and Ublas for your C++ vectors and matrices. It gives you a really good interface on the C++ side to numpy arrays and matrices, which can be passed in both directions over the language threshold with no copying. or use Cython... If I had to guess I'd say sometimes when transposing numpy simply sets a flag internally to avoid copying the data, but in some cases (such as perhaps when multiplication needs to take place) the data has to be placed in a new object. good guess: V = numpy.dot(R, U.transpose()).transpose() a array([[1, 2], [3, 4], [5, 6]]) a.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False b = a.transpose() b.flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False so the transpose() simple re-arranges the strides to Fortran order, rather than changing anything in memory. np.dot() produces a new array, so it is C-contiguous, then you transpose it, so you get a fortran-ordered array. Now when I call my C++ function from the Python side, all the data in V is printed, but it has been transposed. as mentioned, if you are working with arrays in C++ (or fortran, orC, or...) and need to count on the ordering of the data, you need to check it in your extension code. There are utilities for this. However, if I do: V = numpy.array(U.transpose()).transpose() right: In [7]: a.flags Out[7]: C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False In [8]: a.transpose().flags Out[8]: C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False In [9]: np.array( a.transpose() ).flags Out[9]: C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False so the np.array call doesn't re-arrange the order if it doesn't need to. If you want to force it, you can specify the order: In [10]: np.array( a.transpose(), order='C' ).flags Out[10]: C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False (note: this does surprise me a bit, as it is making a copy, but there you go -- if order matters, specify it) In general, numpy does a lot of things for the sake of efficiency -- avoiding copies when it can, for instance -- this give efficiency and flexibility, but you do need to be careful, particularly when interfacing with the binary data directly. -Chris -- 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
