[Numpy-discussion] repeat array along new axis without making a copy
Hi I'd like to repeat an array along a new axis (like broadcast): In [8]: a Out[8]: array([[0, 1, 2], [3, 4, 5]]) In [9]: b=repeat(a[None,...], 3, axis=0) In [10]: b Out[10]: array([[[0, 1, 2], [3, 4, 5]], [[0, 1, 2], [3, 4, 5]], [[0, 1, 2], [3, 4, 5]]]) In [18]: id(a); id(b[0,...]); id(b[1,...]); id(b[2,...]) Out[18]: 40129600 Out[18]: 39752080 Out[18]: 40445232 Out[18]: 40510272 Can I do this such that each sub-array b[i,...] is a view and not a copy? Background: I'm working on a container class to store trajectory-like data. The API requires that each array has a time axis (axis=0 here) along which sub-arrays are stored which may be the same in some cases. Then, I don't want to store redundant information if possible. Thanks! best, Steve ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] repeat array along new axis without making a copy
On Feb 15 06:25 -0600, Warren Weckesser wrote: Yes, such an array can be created using the as_strided() function from the module numpy.lib.stride_tricks: Thank you, I will look into that. best, Steve ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] subtle behavior when subtracting sub-arrays
Hi I stumbled upon some numpy behavior which I was not aware of. Say I have an array of shape (2,2,3) and want to subtract the sub-array a[...,0] of shape (2,2) from each a[...,i], i=0,1,2 . ## ok ## In [1]: a=arange(2*2*3).reshape(2,2,3) # Copy the array to be subtracted. In [2]: a0=a[...,0].copy() # Trivial approach. That works. In [3]: for k in range(a.shape[-1]): ...: a[...,k] -= a0 ...: ...: # OK In [4]: a Out[4]: array([[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]]) In [5]: a=arange(2*2*3).reshape(2,2,3) # The same, with broadcasting. In [6]: a=a-a[...,0][...,None] # OK In [7]: a Out[7]: array([[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]]) ## not ok ## In [8]: a=arange(2*2*3).reshape(2,2,3) In [9]: a-=a[...,0][...,None] # NOT OK In [10]: a Out[10]: array([[[ 0, 1, 2], [ 0, 4, 5]], [[ 0, 7, 8], [ 0, 10, 11]]]) In [11]: a=arange(2*2*3).reshape(2,2,3) # NOT OK, same as above In [12]: for k in range(a.shape[-1]): ...: a[...,k] -= a[...,0] ...: ...: In [14]: a Out[14]: array([[[ 0, 1, 2], [ 0, 4, 5]], [[ 0, 7, 8], [ 0, 10, 11]]]) To sum up, I find it a bit subtle that a = a - a[...,0][...,None] works as expected, while a -= a[...,0][...,None] does not. I guess the reason is that in the latter case (and the corresponding loop), a[...,0] itself is changed during the loop, while in the former case, numpy makes a copy of a[...,0] ? Is this intended? This is with numpy 1.3.0. best, Steve ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] subclassing ndarray
Hi all I'm subclassing ndarray following [1] and I'd like to know if i'm doing it right. My goals are - ndarray subclass MyArray with additional methods - replacement for np.array, np.asarray on module level returning MyArray instances - expose new methods as functions on module level import numpy as np class MyArray(np.ndarray): def __new__(cls, arr, **kwargs): return np.asarray(arr, **kwargs).view(dtype=arr.dtype, type=cls) # define new methods here ... def print_shape(self): print self.shape # replace np.array() def array(*args, **kwargs): return MyArray(np.array(*args, **kwargs)) # replace np.asarray() def asarray(*args, **kwargs): return MyArray(*args, **kwargs) # expose array method as function def ps(a): asarray(a).print_shape() Would that work? PS: I found a little error in [1]: In section __new__ and __init__, the class def should read class C(object): def __new__(cls, *args): + print 'cls is:, cls print 'Args in __new__:', args return object.__new__(cls, *args) def __init__(self, *args): + print 'self is:, self print 'Args in __init__:', args [1] http://docs.scipy.org/doc/numpy/user/basics.subclassing.html best, steve ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy array change notifier?
On Oct 27 16:43 -0400, Pierre GM wrote: Erik, may be you could try the trick presented here : http://www.scipy.org/Subclasses in the __array_wrap__ section. Stupid question: How do I find pages like http://www.scipy.org/Subclasses on scipy.org? I can find them with the search function, but only when I know that it's there :) best, steve ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy array change notifier?
On Oct 30 10:06 -0400, Pierre GM wrote: Steve, Right, there's not a lot of visibility for this one. Yes, wouldn't it be better placed in the Cookbook section (or any other suitable place that's visible to anyone entering scipy.org)? Other pages are unfortunately also not visible, like http://www.scipy.org/EricsBroadcastingDoc . best, steve ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] how to save a large array into a file quickly
On Oct 14 15:29 -1000, Eric Firing wrote: frank wang wrote: Hi, I have a large ndarray that I want to dump to a file. I know that I can use a for loop to write one data at a time. Since Python is a very powerfully language, I want to find a way that will dump the data fast and clean. The data can be in floating point or integer. Use numpy.save for a single array, or numpy.savez for multiple ndarrays, assuming you will want to read them back with numpy. If you want to dump to a text file, use numpy.savetxt. If you want to dump to a binary file to be read by another program, you might want to use the tofile method of the ndarray. I've just updated [1] to mention scipy.io.npfile as well as numpy.save friends. Now, I hope that all common ways to read/write arrays are present in one place. [1] http://scipy.org/Cookbook/InputOutput best, steve ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
