Re: [Numpy-discussion] Getting an item in an array with its coordinates given by another array
Take a look at numpy.ix_ http://www.scipy.org/Numpy_Example_List_With_Doc#head-603de8bdb62d0412798c45fe1db0648d913c8a9c This method creates the index array for you. You only have to specify the coordinates in each dimesion. Bernhard On Oct 29, 8:46 am, Matthieu Brucher [EMAIL PROTECTED] wrote: Little correction, only c[(2,3)] gives me what I expect, not c[[2,3]], which is even stranger. c[(2,3)] is the same as c[2,3] and obviously works as you expected. Well, this is not indicated in the documentation. c[[2,3]] is refered to as 'advanced indexing' in the numpy book. It will return elements 2 and 3 along the first dimension. To get what you want, you need to put it like this: c[[2],[3]] In [118]: c = N.arange(0.,3*4*5).reshape((3,4,5)) In [119]: c[[2],[3]] Out[119]: array([[ 55., 56., 57., 58., 59.]]) This is very strange to say the least, as tuple do not work in the same way. This does not work however using a ndarray holding the indices. In [120]: ind = N.array([[2],[3]]) In [121]: c[ind] --- type 'exceptions.IndexError'Traceback (most recent call last) /media/hda6/home/ck/ipython console in module() type 'exceptions.IndexError': index (3) out of range (0=index=2) in dimension 0 so you have to convert it to a list before: In [122]: c[ind.tolist()] Out[122]: array([[ 55., 56., 57., 58., 59.]]) But if I have the coordinates of the points in an array, I have to reshape it and then convert it into a list. Or convert it into a list and then convert it to a tuple. I know that advanced indexing is useful, but here it is not coherent. tuples and lists should have the same result on the array, or at least it should be documented. So there is not way to get a sub-array based on coordinates in an array ? Matthieu -- French PhD student Website :http://miles.developpez.com/ Blogs :http://matt.eifelle.comandhttp://blog.developpez.com/?blog=92 ___ 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] Getting an item in an array with its coordinates given by another array
Matthieu Brucher matthieu.brucher at gmail.com writes: But if I have the coordinates of the points in an array, I have to reshape it and then convert it into a list. Or convert it into a list and then convert it to a tuple. I know that advanced indexing is useful, but here it is not coherent. tuples and lists should have the same result on the array, or at least it should be documented. I can't give you the reasons why that behaviour was chosen. But it's simply wrong that it's not documented. Before answering you I read the corresponding chapter in the numpy book to be sure not to tell you nonsense. So go ahead and do so, too. So there is not way to get a sub-array based on coordinates in an array? It's just a guess, but probably you can't use an ndarray as index because indices like this [[2],[3,4]] aren't valid input for a ndarray. Christian ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Getting an item in an array with its coordinates given by another array
But if I have the coordinates of the points in an array, I have to reshape it and then convert it into a list. Or convert it into a list and then convert it to a tuple. I know that advanced indexing is useful, but here it is not coherent. tuples and lists should have the same result on the array, or at least it should be documented. I can't give you the reasons why that behaviour was chosen. But it's simply wrong that it's not documented. Before answering you I read the corresponding chapter in the numpy book to be sure not to tell you nonsense. So go ahead and do so, too. Thank you for this answer, I suppose we'll have to wait the answer of a numpy guru ;) Matthieu -- French PhD student Website : http://miles.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy distutils patch
Hey, I was looking at numpy/distutils/ccompiler.py and noticed that it has a fix for distutils.util.split_quoted. Here is the relevant code from split_quoted in numpy.distutils.ccompiler: --- def split_quoted(s): snip if _has_white_re.search(s[beg+1:end-1]): s = s[:beg] + s[beg+1:end-1] + s[end:] pos = m.end() - 2 else: # Keeping quotes when a quoted word does not contain # white-space. XXX: send a patch to distutils pos = m.end() snip --- Here is the relevant code from split_quoted in distutils.util: --- def split_quoted(s): snip s = s[:beg] + s[beg+1:end-1] + s[end:] pos = m.end() - 2 snip --- Does anyone know if a patch was ever submitted upstream? If not, is there any reason that a patch shouldn't be submitted? Thanks, -- 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
Re: [Numpy-discussion] Getting an item in an array with its coordinates given by another array
On 10/28/07, Matthieu Brucher [EMAIL PROTECTED] wrote: Little correction, only c[(2,3)] gives me what I expect, not c[[2,3]], which is even stranger. c[(2,3)] is the same as c[2,3] and obviously works as you expected. Well, this is not indicated in the documentation. This is true at the Python level and is not related to numpy. x=c[2,3] is equivalent to x=c.__getitem__((2,3)). Note that the index pair is passed as a tuple. On the other hand, a single index is not passed as a tuple, but is instead passed as is. For example: x = c[a] gets passed as x=c.__getitem__(a). If 'a' happens to be '(2,3)' you get the behavior above. So, although lists and arrays can be used for fancy-indexing, tuples cannot be since you can't tell the difference between a tuple of indices and multiple indices inside square brackets. [SNIP] -- . __ . |-\ . . [EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Getting an item in an array with its coordinates given by another array
In this case the constructor tuple(arr) should work just fine. Sorry, I didn't know it could work (shame on me I should have tested). -- French PhD student Website : http://miles.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy version and numpy.asarray behavior issue
I have 2 PCs with 2 different installs: ActivePython 2.4.3 Build 12 with numpy version '1.0b1' and Enthought 2.4.3 (1.0.0 #69) with numpy version '0.9.7.2476' The attached runs Ok on numpy v1.0, but on Enthought's, print a1[0] gives: IndexError: 0-d arrays can't be indexed. It seems that the 0.9.7 numpy.asarray is not creating a true array from Dummy class in the code below. Enthought only comes with 0.9.9.2706 (now). When was the asarray behavior supported, or, is there some other issue I'm missing? I'll use Activestate's distro if needed, but I'd like to keep Enthought for that one... def fromaddress(address, dtype, shape, strides=None): Create a numpy array from an integer address, a dtype or dtype string, a shape tuple, and possibly strides. import numpy # Make sure our dtype is a dtype, not just f or whatever. dtype = numpy.dtype(dtype) class Dummy(object): pass d = Dummy() d.__array_interface__ = dict( data = (address, False), typestr = dtype.str, descr = dtype.descr, shape = shape, strides = strides, version = 3, ) return numpy.asarray(d) Thanks, Ray nFromAddress.py def fromaddress(address, dtype, shape, strides=None): Create a numpy array from an integer address, a dtype or dtype string, a shape tuple, and possibly strides. import numpy # Make sure our dtype is a dtype, not just f or whatever. dtype = numpy.dtype(dtype) class Dummy(object): pass d = Dummy() d.__array_interface__ = dict( data = (address, False), typestr = dtype.str, descr = dtype.descr, shape = shape, strides = strides, version = 3, ) return numpy.asarray(d) ##Numeric example, with address kludge import Numeric, numpy, ctypes, string a0 = Numeric.zeros((1), Numeric.Int16) nAddress = int(string.split(repr(a0.__copy__))[-1][:-1], 16) tmp=(ctypes.c_long*1)(0) ctypes.memmove(tmp, nAddress+8, 4) nAddress = tmp[0] a1 = fromaddress(nAddress, numpy.int16, (1,)) a0[0] = 5 print a1[0] ## numpy example a2 = numpy.zeros(1, numpy.int16) nAddress = a2.__array_interface__['data'][0] nType = a2.__array_interface__['typestr'] nShape = a2.__array_interface__['shape'] a3 = fromaddress(nAddress, nType, nShape) a2[0] = 5 print a3[0]___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy version and numpy.asarray behavior issue
I just installed 1.0.3.1 on top of Enthought's and asarray() works. But... Although the creation of an array from an address via a Dummy class is kosher in one process (as in the previous attachment), it fails across processes - the array is created, but gives a Python has generated errors window if the second process even attempts to read. It can seem to work across processes with mmap.mmap() and tags (used in Windows) def arrSharedMemory(shape, dtype, tag=PySharedMemory, access=None): ## Windows only ! share memory between different ## processes if same `tag` is used. itemsize = N.dtype(dtype).itemsize count = N.product(shape) size = count * itemsize import mmap sharedmem = mmap.mmap(0, size, tag, access) a=N.frombuffer(sharedmem, dtype, count) a.shape = shape return a I guess I'll use mmap unless someone can point out otherwise Thanks, Ray nFromAddress.py ##Numeric example, with address kludge import Numeric, numpy, ctypes, subprocess from time import clock, sleep a = Numeric.zeros((4), Numeric.Int16) nAddress = int(repr(.__copy__).split()[-1][:-1], 16) tmp=(ctypes.c_long*1)(0) ctypes.memmove(tmp, nAddress+8, 4) nAddress = tmp[0] a = numpy.zeros(4, numpy.int16) nAddress = a.__array_interface__['data'][0] print nAddress pid = subprocess.Popen( [r'C:\python24\python.exe', ['nFromAddress2.py '+str(nAddress)] ]).pid while clock()5: sleep(.1) if a[0]!=0: ## wait for a change... print a0[0] nFromAddress.py import numpy import time, sys def fromaddress(address, dtype, shape, strides=None): Create a numpy array from an integer address, a dtype or dtype string, a shape tuple, and possibly strides. # Make sure our dtype is a dtype, not just f or whatever. dtype = numpy.dtype(dtype) class Dummy(object): pass d = Dummy() d.__array_interface__ = dict( data = (address, False), typestr = dtype.str, descr = dtype.descr, shape = shape, strides = strides, version = 3, ) return numpy.asarray(d) nAddress = sys.argv[1] print 'recvd addr', nAddress a3 = fromaddress(nAddress, numpy.int16, (4,)) ## any of the following cause a Python/Windows error on access print a3 #a3[0] = 5___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] vectorizing loops
Timothy Hochberg wrote: On 10/29/07, *Christopher Barker* [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: (incidently, the kind of things 'we' are doing seem like the most simple things to JIT). Wouldn't a numpy-aware psyco be cool then? Oh well, I'm not going to write it! (though as I think about it, for the special case of a contiguous array, it would be awfully similar to an array.array --- hmmm.) Psyco is aware of array.array and can operate on array.array's quite fast. [In fact, somewhere I have a ndarray-like class based on Psyco that runs pretty fast for integers, but not so fast for floats]. The problem that Psyco has for numeric purposes is that it has no concept of floating point numbers. It can natively store only a few different things: ints, pointers, and arrays of ints or pointers. To put, for example, doubles, onto the Psyco stack or into one of it's registers, you need to break the bits of the double up, and stuff them into a couple of different int registers. Then to operate on them you need to put them back together, since they may get separated. All of this adds a fair amount of overhead. I've been hoping that the PyPy jit will address this, but I haven't had time to follow that project closely enough to see if that's on the agenda. My impression is that it is hard to follow pypy when you are not 'part of it', but just from the ML, and a couple of questions of mine, my understanding is that it is on the agenda. They have a JIT (maybe does not work on float, though), there is work on numpy arrays at the rpython level (rpython being the typed subset of python pypy is using at their core). cheers, David -- . __ . |-\ . . [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
