[Numpy-discussion] Vectorizing a function
Hi, I've got some questions on the numpy.vectorize function. Currently, i'm doing this kind of work : [ code] def calc_0d(x, y): make complexe calculation using two scalars x and y [ ... ] return res1, res2, res 3 # vectorize the function calc = vectorize(calc_0d) res1, res_2, res_3 = calc(array_x, array_y) [/code] This works fine. Really impressive. Good work for this ! My problem is that the complexe calculations made in calc_0d use some parameters, which are currently defined at the head of my python file. This is not very nice and I can't define a module containing theses two functions and call them with different parameters. I would like to make this cleaner and pass theses parameter as keyword argument, but this don't seems to be possible with vectorize. Indeed, some of theses parameters are array parameters and only the x and y arguments should be interpreted with the broadcasting rules What is the good way for doing this ? Regards, -- LB ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Vectorizing a function
On Wed, Jan 30, 2008 at 12:49:44AM -0800, LB wrote: My problem is that the complexe calculations made in calc_0d use some parameters, which are currently defined at the head of my python file. This is not very nice and I can't define a module containing theses two functions and call them with different parameters. I would like to make this cleaner and pass theses parameter as keyword argument, but this don't seems to be possible with vectorize. Indeed, some of theses parameters are array parameters and only the x and y arguments should be interpreted with the broadcasting rules What is the good way for doing this ? I don't know what the good way is, but you can always use functional programming style (Oh, no, CaML is getting on me !): def calc_0d_params(param1, param2, param3): def calc_0d(x, y): # Here your code making use of param1, param2, param3) ... return calc_0d(x, y) you call the function like this: calc_0d_params(param1, param2, param3)(x, y) To vectorize it you can do: calc_0d_vect = lambda *params: vectorize(calc_0d_params(*params)) This is untested code, but I hope you get the idea. It all about partial evaluation of arguments. By the way, the parameters can now be keyword arguments. HTH, Gaël ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] argsort memory problem?
* Robin [EMAIL PROTECTED] [2008-01-29 19:23:11 +]: On Jan 29, 2008 7:16 PM, Lou Pecora [EMAIL PROTECTED] wrote: Hmmm... Interesting. I am using Python 2.4.4. It would be nice to have other Mac people with same/other Python and numpy versions try the argsort bug code. I don't see any memory leak with the test code. Mac OS X 10.5.1 Python 2.5.1 (not apple one) Numpy 1.0.5.dev4722 I have run the test1 code again, this time on my laptop PC (no MAC-user, sorry) using the last stable numpy release. The memory problem does _not_ show up now. I'm running with: - Ubuntu Feisty (kernel 2.6.17-12-generic i686) - python 2.5.1 (Feisty package) - numpy 1.0.4 (compiled with gcc version 4.1.2) However, the memory leak appears on my laptop if I use python 2.5.1 with numpy 1.0.1. At least here, this seems to be an issue dependent only on the numpy version, and a solved one. -- oriol ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Vectorizing a function
Try use a closure. On Jan 30, 12:49 am, LB [EMAIL PROTECTED] wrote: Hi, I've got some questions on the numpy.vectorize function. Currently, i'm doing this kind of work : [ code] def calc_0d(x, y): make complexe calculation using two scalars x and y [ ... ] return res1, res2, res 3 # vectorize the function calc = vectorize(calc_0d) res1, res_2, res_3 = calc(array_x, array_y) [/code] This works fine. Really impressive. Good work for this ! My problem is that the complexe calculations made in calc_0d use some parameters, which are currently defined at the head of my python file. This is not very nice and I can't define a module containing theses two functions and call them with different parameters. I would like to make this cleaner and pass theses parameter as keyword argument, but this don't seems to be possible with vectorize. Indeed, some of theses parameters are array parameters and only the x and y arguments should be interpreted with the broadcasting rules What is the good way for doing this ? Regards, -- LB ___ Numpy-discussion mailing list [EMAIL PROTECTED]://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Vectorizing a function
On a side note, given that I've seen quite a few posts about vectorize() over the past several months... I've written hundreds or thousands of functions that are intended to work with numeric/numpy arrays and/or scalars and I've _never_ (not once!) found a need for the vectorize function. Python's duck-typing has almost always allowed things to work without any (or sometimes with only minor) changes to the function. For example: In [12]: def foo(x, y): : return 2.0*x + y In [13]: foo(3.0, 5.0) Out[13]: 11.0 In [14]: foo(arange(4), ones(4)+3.0) Out[14]: array([ 4., 6., 8., 10.]) In [15]: foo(arange(4), 3.0) Out[15]: array([ 3., 5., 7., 9.]) That works fine with arrays, scalars, or array/scalar mixes in the calling. I do understand that more complicated functions might require vectorize(), however, I wonder if sometimes it is used when it doesn't need to be? Scott On Wed, Jan 30, 2008 at 10:22:15AM +0100, Gael Varoquaux wrote: On Wed, Jan 30, 2008 at 12:49:44AM -0800, LB wrote: My problem is that the complexe calculations made in calc_0d use some parameters, which are currently defined at the head of my python file. This is not very nice and I can't define a module containing theses two functions and call them with different parameters. I would like to make this cleaner and pass theses parameter as keyword argument, but this don't seems to be possible with vectorize. Indeed, some of theses parameters are array parameters and only the x and y arguments should be interpreted with the broadcasting rules What is the good way for doing this ? I don't know what the good way is, but you can always use functional programming style (Oh, no, CaML is getting on me !): def calc_0d_params(param1, param2, param3): def calc_0d(x, y): # Here your code making use of param1, param2, param3) ... return calc_0d(x, y) you call the function like this: calc_0d_params(param1, param2, param3)(x, y) To vectorize it you can do: calc_0d_vect = lambda *params: vectorize(calc_0d_params(*params)) This is untested code, but I hope you get the idea. It all about partial evaluation of arguments. By the way, the parameters can now be keyword arguments. HTH, Gaël ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- -- Scott M. RansomAddress: NRAO Phone: (434) 296-0320 520 Edgemont Rd. email: [EMAIL PROTECTED] Charlottesville, VA 22903 USA GPG Fingerprint: 06A9 9553 78BE 16DB 407B FFCA 9BFA B6FF FFD3 2989 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] load movie frames in python?
On Jan 29, 2008, at Jan 29:9:25 PM, Andrew Straw wrote: I'm pretty sure there's code floating around the pyglet mailing list. I'd be happy to add it to http://code.astraw.com/projects/motmot/wiki/pygarrayimage if it seems reasonable. (pygarrayimage goes from numpy array to pyglet texture). I checked the pyglet users group, and the only mention I see is from: http://groups.google.com/group/pyglet-users/browse_thread/thread/ 5981f764902c7df/888328e19653be1a?lnk=gstq=numpy#888328e19653be1a there, he has the solution: a = numpy.frombuffer(surf_clip.get_data(), numpy.uint8) a.shape = (sw, sh, 4) a = a[:,:,0:3] #drop alpha channel but when I do it (see code below) the image is clearly munged, so I think the decoding is not quite right. any ideas? thanks, Brian Blais -- Brian Blais [EMAIL PROTECTED] http://web.bryant.edu/~bblais import pylab import numpy from pyglet import media,window win = window.Window(resizable=True) player = media.Player() filename='ica_mvl.avi' # from http://hlab.phys.rug.nl/demos/ica/ jiminy.html source = media.load(filename) player.queue(source) player.play() if True: player.dispatch_events() imdata=player.texture.get_image_data() a = numpy.frombuffer(player.texture.get_image_data().data, numpy.uint8) a.shape = (imdata.width, imdata.height, 4) a = a[:,:,0:3] #drop alpha channel # make gray im=a.sum(axis=1)/3 pylab.imshow(im,cmap=pylab.cm.gray) pylab.show() pylab.draw() ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Can not update a submatrix
In the following piece of code: import numpy as N R = N.arange(9).reshape(3,3) ax = [1,2] R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) R[ax,:][:,ax] = 100 R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Why R is not updated? I was expecting: R array([[0, 1, 2], [3, 100, 100], [6, 100, 100]]) I am using numpy 1.04 on gentoo-linux/amd64 Nadav ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
A Wednesday 30 January 2008, Nadav Horesh escrigué: In the following piece of code: import numpy as N R = N.arange(9).reshape(3,3) ax = [1,2] R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) R[ax,:][:,ax] = 100 R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Why R is not updated? Because R[ax] is not a view of R, but another copy of the original object (fancy indexing does return references to different objects). In order to get views, you must specify only a slice of the original array. For example: In [50]: S = R[::2] In [51]: S[:] = 2 In [52]: R Out[52]: array([[2, 2, 2], [3, 4, 5], [2, 2, 2]]) So, what you need is something like: In [68]: R = N.arange(9).reshape(3,3) In [69]: S = R[1:3,:][:,1:3] In [70]: S[:] = 2 In [71]: R Out[71]: array([[0, 1, 2], [3, 2, 2], [6, 2, 2]]) Cheers, -- 0,0 Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data - ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
But: R[ax,:] = 100 R array([[ 0, 1, 2], [100, 100, 100], [100, 100, 100]]) R[:,ax] = 200 R array([[ 0, 200, 200], [100, 200, 200], [100, 200, 200]]) Do I get an array view only if the array is contiguous? Nadav. On Wed, 2008-01-30 at 16:08 +0100, Francesc Altet wrote: A Wednesday 30 January 2008, Nadav Horesh escrigué: In the following piece of code: import numpy as N R = N.arange(9).reshape(3,3) ax = [1,2] R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) R[ax,:][:,ax] = 100 R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Why R is not updated? Because R[ax] is not a view of R, but another copy of the original object (fancy indexing does return references to different objects). In order to get views, you must specify only a slice of the original array. For example: In [50]: S = R[::2] In [51]: S[:] = 2 In [52]: R Out[52]: array([[2, 2, 2], [3, 4, 5], [2, 2, 2]]) So, what you need is something like: In [68]: R = N.arange(9).reshape(3,3) In [69]: S = R[1:3,:][:,1:3] In [70]: S[:] = 2 In [71]: R Out[71]: array([[0, 1, 2], [3, 2, 2], [6, 2, 2]]) Cheers, ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
Am Mittwoch, 30. Januar 2008 16:21:40 schrieb Nadav Horesh: But: R[ax,:] = 100 This is calling __setitem__, i.e. does not create either a view or a copy. Non-contiguous views (e.g. using [::2]) are also possible AFAIK, but fancy indexing is something different. -- Ciao, / / /--/ / / ANS ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
you simply need to change the definition of ax: ax = slice(1,3) and all works fine. L. On 1/30/08, Francesc Altet [EMAIL PROTECTED] wrote: A Wednesday 30 January 2008, Nadav Horesh escrigué: In the following piece of code: import numpy as N R = N.arange(9).reshape(3,3) ax = [1,2] R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) R[ax,:][:,ax] = 100 R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Why R is not updated? Because R[ax] is not a view of R, but another copy of the original object (fancy indexing does return references to different objects). In order to get views, you must specify only a slice of the original array. For example: In [50]: S = R[::2] In [51]: S[:] = 2 In [52]: R Out[52]: array([[2, 2, 2], [3, 4, 5], [2, 2, 2]]) So, what you need is something like: In [68]: R = N.arange(9).reshape(3,3) In [69]: S = R[1:3,:][:,1:3] In [70]: S[:] = 2 In [71]: R Out[71]: array([[0, 1, 2], [3, 2, 2], [6, 2, 2]]) Cheers, -- 0,0 Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data - ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- Lorenzo Bolla [EMAIL PROTECTED] http://lorenzobolla.emurse.com/ ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
On Jan 30, 2008 8:21 AM, Nadav Horesh [EMAIL PROTECTED] wrote: But: R[ax,:] = 100 R array([[ 0, 1, 2], [100, 100, 100], [100, 100, 100]]) R[:,ax] = 200 R array([[ 0, 200, 200], [100, 200, 200], [100, 200, 200]]) Do I get an array view only if the array is contiguous? You get an array view if the elements can be addressed with offsets, strides, and counts. Think nested for loops. In [1]: a = zeros((4,4)) In [2]: a[::2,::2] = 1 In [3]: a Out[3]: array([[ 1., 0., 1., 0.], [ 0., 0., 0., 0.], [ 1., 0., 1., 0.], [ 0., 0., 0., 0.]]) Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
or you can maybe use numpy.ix_: ax = [1,2] R[numpy.ix_(ax,ax)] = 100 hth, L. On 1/30/08, lorenzo bolla [EMAIL PROTECTED] wrote: you simply need to change the definition of ax: ax = slice(1,3) and all works fine. L. On 1/30/08, Francesc Altet [EMAIL PROTECTED] wrote: A Wednesday 30 January 2008, Nadav Horesh escrigué: In the following piece of code: import numpy as N R = N.arange(9).reshape(3,3) ax = [1,2] R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) R[ax,:][:,ax] = 100 R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Why R is not updated? Because R[ax] is not a view of R, but another copy of the original object (fancy indexing does return references to different objects). In order to get views, you must specify only a slice of the original array. For example: In [50]: S = R[::2] In [51]: S[:] = 2 In [52]: R Out[52]: array([[2, 2, 2], [3, 4, 5], [2, 2, 2]]) So, what you need is something like: In [68]: R = N.arange(9).reshape(3,3) In [69]: S = R[1:3,:][:,1:3] In [70]: S[:] = 2 In [71]: R Out[71]: array([[0, 1, 2], [3, 2, 2], [6, 2, 2]]) Cheers, -- 0,0 Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data - ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- Lorenzo Bolla [EMAIL PROTECTED] http://lorenzobolla.emurse.com/ -- Lorenzo Bolla [EMAIL PROTECTED] http://lorenzobolla.emurse.com/ ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Vectorizing a function
On Jan 30, 2008 10:10 AM, Charles R Harris [EMAIL PROTECTED] wrote: [SNIP] IIRC, the way to do closures in Python is something like In [5]: def factory(x) : ...: def f() : ...: print x ...: f.x = x ...: return f ...: In [6]: f = factory(Hello world.) In [7]: f() Hello world. There is a reason to do it that way, but I don't recall what it is. You don't need the f.x = x line. It's possible it has some beneficial side effect that I don't recall, but basic closures work fine without out. Nor do I know if the result can be vectorized, I've never used vectorize. I expect they could be, but I don't use vectorize either. -- . __ . |-\ . . [EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Vectorizing a function
On Jan 30, 2008 10:10 AM, Charles R Harris [EMAIL PROTECTED] wrote: On Jan 30, 2008 2:22 AM, Gael Varoquaux [EMAIL PROTECTED] wrote: On Wed, Jan 30, 2008 at 12:49:44AM -0800, LB wrote: My problem is that the complexe calculations made in calc_0d use some parameters, which are currently defined at the head of my python file. This is not very nice and I can't define a module containing theses two functions and call them with different parameters. I would like to make this cleaner and pass theses parameter as keyword argument, but this don't seems to be possible with vectorize. Indeed, some of theses parameters are array parameters and only the x and y arguments should be interpreted with the broadcasting rules What is the good way for doing this ? I don't know what the good way is, but you can always use functional programming style (Oh, no, CaML is getting on me !): def calc_0d_params(param1, param2, param3): def calc_0d(x, y): # Here your code making use of param1, param2, param3) ... return calc_0d(x, y) you call the function like this: calc_0d_params(param1, param2, param3)(x, y) To vectorize it you can do: calc_0d_vect = lambda *params: vectorize(calc_0d_params(*params)) This is untested code, but I hope you get the idea. It all about partial evaluation of arguments. By the way, the parameters can now be keyword arguments. IIRC, the way to do closures in Python is something like In [5]: def factory(x) : ...: def f() : ...: print x ...: f.x = x ...: return f ...: Oops, looks like that needs to be: In [5]: def factory(x) : ...: def f() : ...: print f.x ...: f.x = x ...: return f ...: You can also do something simpler: In [51]: def f() : print f.x : In [52]: f.x = Hello World! In [53]: f() Hello World! Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Vectorizing a function
On Jan 30, 2008 10:18 AM, Timothy Hochberg [EMAIL PROTECTED] wrote: On Jan 30, 2008 10:10 AM, Charles R Harris [EMAIL PROTECTED] wrote: [SNIP] IIRC, the way to do closures in Python is something like In [5]: def factory(x) : ...: def f() : ...: print x ...: f.x = x ...: return f ...: In [6]: f = factory(Hello world.) In [7]: f() Hello world. There is a reason to do it that way, but I don't recall what it is. You don't need the f.x = x line. It's possible it has some beneficial side effect that I don't recall, but basic closures work fine without out. You're right. It looks like the context gets stored in f.func_closure. In [14]: f.func_closure[0].cell_contents Out[14]: 'Hello world.' Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
Thank you very much, that what I was looking for. Charles made a good point about offsets, counts and strides --- I really should go and reread the documentation. Nadav. -Original Message- From: [EMAIL PROTECTED] on behalf of lorenzo bolla Sent: Wed 30-Jan-08 17:31 To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] Can not update a submatrix or you can maybe use numpy.ix_: ax = [1,2] R[numpy.ix_(ax,ax)] = 100 hth, L. On 1/30/08, lorenzo bolla [EMAIL PROTECTED] wrote: you simply need to change the definition of ax: ax = slice(1,3) and all works fine. L. On 1/30/08, Francesc Altet [EMAIL PROTECTED] wrote: A Wednesday 30 January 2008, Nadav Horesh escrigué: In the following piece of code: import numpy as N R = N.arange(9).reshape(3,3) ax = [1,2] R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) R[ax,:][:,ax] = 100 R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Why R is not updated? Because R[ax] is not a view of R, but another copy of the original object (fancy indexing does return references to different objects). In order to get views, you must specify only a slice of the original array. For example: In [50]: S = R[::2] In [51]: S[:] = 2 In [52]: R Out[52]: array([[2, 2, 2], [3, 4, 5], [2, 2, 2]]) So, what you need is something like: In [68]: R = N.arange(9).reshape(3,3) In [69]: S = R[1:3,:][:,1:3] In [70]: S[:] = 2 In [71]: R Out[71]: array([[0, 1, 2], [3, 2, 2], [6, 2, 2]]) Cheers, -- 0,0 Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data - ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- Lorenzo Bolla [EMAIL PROTECTED] http://lorenzobolla.emurse.com/ -- Lorenzo Bolla [EMAIL PROTECTED] http://lorenzobolla.emurse.com/ winmail.dat___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Median again
Timothy Hochberg wrote: On Jan 29, 2008 5:48 PM, Travis E. Oliphant [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: Joris De Ridder wrote: On 30 Jan 2008, at 00:32, Travis E. Oliphant wrote: Matthew Brett wrote: Hi, median moved mediandim0 implementation of medianwithaxis or similar, with same call signature as mean. But - for the median function change - do we agree that this should be changed? I think it is a significant wart in the numpy API, and has caught quite a few people... I'm fine with a median API change for 1.1. We can add the axis keyword for 1.0.5 as long as the default stays the same. We can also add the other keywords as well if appropriate defaults can be determined. Do you mean creating a median(a, axis=0) for 1.0.5, and changing it to median(a,axis=None) for 1.1? (Modulo other keywords). Yes. That is the approach I prefer. I'm all for fixing this, but the prospect of going straight from one default to another makes me nervous. Is there is any prospect we could spit out a warning when an axis is not specified for median starting in 1.05 up till 1.1. It could even be a PendingDeprecationWarning, which by default doesn't print anything I believe, but would allow people to check there code for potential failure points. Yes, we could start to do that (spit out a warning in 1.0.5). This should definitely be done in 1.0.6 Perhaps we use axis=None to start with and then check for that and spit out the warning (and change axis to 0). Thanks for the pointer to PendingDeprecationWarning. We could also have an APIChangeWarning that we define in NumPy which would allow SciPy to use it as well. -Travis ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Vectorizing a function
Thank you Gael, I think this could work for my case. It will be a bit tricky, since calc_0d is already a closure in which I've defined a function : the parameters x and y are to main parameters of an ODE. So calc_0d define a function, integrate it sing scipy.integrate.odeint and returns some characteristics of the solution. Numpy.vectorize allows me to draw very easily map and contours of theses characteristics. So I will have a function, defining a function, defining another one... ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
On 30/01/2008, Francesc Altet [EMAIL PROTECTED] wrote: A Wednesday 30 January 2008, Nadav Horesh escrigué: In the following piece of code: import numpy as N R = N.arange(9).reshape(3,3) ax = [1,2] R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) R[ax,:][:,ax] = 100 R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Why R is not updated? Because R[ax] is not a view of R, but another copy of the original object (fancy indexing does return references to different objects). In order to get views, you must specify only a slice of the original array. For example: This is not exactly correct. There are two kinds of fancy indexing in numpy, which behave similarly. There is normal fancy indexing, which produces a new array, not sharing data with the old array: a = N.random.normal(size=10) b = a[a0] There is also fancy indexing of lvalues (to use a C term): a = N.random.normal(size=10) a[a0] *= -1 Here no copy is made; instead, the data is modified in-place. This requires some clever hacks under the hood, since a[a0] *can't* be a view of a. The problem the OP had is that they are going beyond what the clever hacks can deal with. That's why R[ax,:] = 100 works but R[ax,:][:,ax] = 100 doesn't. The problem is that you have two indexing operations in the second situation, and the inplace operators can't deal with that. Solutions include working on a flattened version of the array (for which a single indexing operation suffices), using the (not in-place) where() construct, or using the ix_ function: R[N.ix_(ax,ax)] = 100 N.ix_ is necessary because R[ax,ax] doesn't do what I, for one, would have expected: R[[1,2],[3,4]] yields [R[1,3],R[2,4]], not [[R[1,3],R[1,4]],[R[2,3],R[2,4]]]. But in any case, once you reduce it to a single fancy-indexing operation, you can successfully use it as an lvalue. Striding and views are not really the issue. Anne ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Can not update a submatrix
On Jan 30, 2008 12:43 PM, Anne Archibald [EMAIL PROTECTED] wrote: On 30/01/2008, Francesc Altet [EMAIL PROTECTED] wrote: A Wednesday 30 January 2008, Nadav Horesh escrigué: In the following piece of code: import numpy as N R = N.arange(9).reshape(3,3) ax = [1,2] R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) R[ax,:][:,ax] = 100 R array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Why R is not updated? Because R[ax] is not a view of R, but another copy of the original object (fancy indexing does return references to different objects). In order to get views, you must specify only a slice of the original array. For example: This is not exactly correct. [...a fine explanation by Anne...] Another way to look at this is note that: R[ax,:][:,ax] = 100 is translated to: R.__getitem__((ax, :)).__setitem__((:,ax), 100) '__setitem__' is capable of setting values using fancy indexing, but as has been noted, '__getitem__' makes a copy of R when using fancy indexing, and therefore the original does not get modified. [Technically, ':' gets translated to slice(None,None,None), but I didn't want to write that out]. -- . __ . |-\ . . [EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Median again
On Jan 30, 2008 10:41 AM, Travis E. Oliphant [EMAIL PROTECTED] wrote: Yes, we could start to do that (spit out a warning in 1.0.5). This should definitely be done in 1.0.6 Perhaps we use axis=None to start with and then check for that and spit out the warning (and change axis to 0). Thanks for the pointer to PendingDeprecationWarning. We could also have an APIChangeWarning that we define in NumPy which would allow SciPy to use it as well. +1 I think this sounds very reasonable and will fix a major NumPy wart. -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/ ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Visual studio with mingw g77 built libraries
Hi, I would like to ask some details about the build process of numpy when visual studio is used for C compilation, and g77 for blas/lapack. As I understand it, the current situation consists in using libraries built the Unix way (e.g. libblas.a, static library built with ar + ranlib), taking the gcc runtimes on the fly (libgcc and g2c), and put everything together to feed VS compiler. I wonder if we should not use another way, that is use libraries built the windows way (dll + lib). I think it is more natural (and would also make my life easier for numscons): - it would means that on windows, all compilers combination would follow a similar process. - dll and lib are more natural for windows developers (who are the ones using VS in the first place). The only difference for users would be in the way blas/lapack should be built. Is there a reason why this was not the chosen path ? Is it because it would have been too difficult to do it with distutils (using dlltool and co) ? Or other reasons I am not aware of ? cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
