Mailman 3 January 2009 - NumPy-Discussion

N-D array interface page is out of date
by Andrew Straw 09 Mar '09

09 Mar '09

Hi, I just noticed that the N-D array interface page is outdated and doesn't mention the buffer interface that is standard with Python 2.6 and Python 3.0: http://numpy.scipy.org/array_interface.shtml This page is linked to from http://numpy.scipy.org/ I suggest, at the minimum, modifying the page with really annoying blinking red letters at the top (or other suitable warning) that this is deprecated at that people should use http://www.python.org/dev/peps/pep-3118/ instead.

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strange multiplication behavior with numpy.float64 and ndarray subclass
by Darren Dale 08 Mar '09

08 Mar '09

I have a simple test script here that multiplies an ndarray subclass with another number. Can anyone help me understand why each of these combinations returns a new instance of MyArray: mine = MyArray() print type(np.float32(1)*mine) print type(mine*np.float32(1)) print type(mine*np.float64(1)) print type(1*mine) print type(mine*1) but this one returns a np.float64 instance? print type(np.float64(1)*mine) Here is the full script: import numpy as np class MyArray(np.ndarray): __array_priority__ = 20 def __new__(cls): return np.asarray(1).view(cls).copy() def __repr__(self): return 'my_array' __str__ = __repr__ def __mul__(self, other): return super(MyArray, self).__mul__(other) def __rmul__(self, other): return super(MyArray, self).__rmul__(other) mine = MyArray() print type(np.float32(1)*mine) print type(mine*np.float32(1)) print type(mine*np.float64(1)) print type(1*mine) print type(mine*1) print type(np.float64(1)*mine) Thanks, Darren

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linalg.norm missing an 'axis' kwarg?!
by Hans Meine 10 Feb '09

10 Feb '09

Hi, I have a 2D matrix comprising a sequence of vectors, and I want to compute the norm of each vector. np.linalg.norm seems to be the best bet, but it does not support axis. Wouldn't this be a nice feature? Greetings, Hans

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default float type of array not accepted by SWIG wrapped C functions
by Rich E 06 Feb '09

06 Feb '09

Hi all, I have a SWIG wrapped C library that uses 32bit floating point arrays, using the numpy.i typemapping system for passing the arrays. For every array that I make, I have to convert it using astype('float32'), else python complains that I tried to pass a double-precision array. Is there any way to set the default floating point precision to 32bit, in python or in the SWIG interface file? regards, Rich

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porting NumPy to Python 3
by James Watson 03 Feb '09

03 Feb '09

Hi, I am interested in contributing to the port of NumPy to Python 3. Who I should coordinate effort with? I have started at the Python end of the problem (as opposed to http://www.scipy.org/Python3k), e.g. I have several patches to get 2to3 to work on NumPy's Python source code. James.

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Re: [Numpy-discussion] Building on WinXP 64-bit, Intel Compilers
by Mike Colonno 02 Feb '09

02 Feb '09

Maybe I declared success too soon... The build / install of numpy seems to work (using MS C++ / Intel Fortran), but upon trying to import numpy I get: >>>from numpy import * Traceback (most recent call last): File "C:\<string>", line 1, in <module> File "C:\Python26\Lib\site-packages\numpy\__init__.py", line 130, in <module> import add_newdocs File "C:\Python26\Lib\site-packages\numpy\add_newdocs.py", line 9, in <module> from lib import add_newdoc File "C:\Python26\Lib\site-packages\numpy\lib\__init__.py", line 161, in <module> from polynomial import * File "C:\Python26\Lib\site-packages\numpy\lib\polynomial.py", line 18, in <module> from numpy.linalg import eigvals, lstsq File "C:\Python26\Lib\site-packages\numpy\linalg\__init__.py", line 47, in <module> from linalg import * File "C:\Python26\Lib\site-packages\numpy\linalg\linalg.py", line 22, in <module> from numpy.linalg import lapack_lite ImportError: DLL load failed: Invalid access to memory location. To make matters more interesting, a second attempt leads to something different: >>> from numpy import * Traceback (most recent call last): File "C:\<string>", line 1, in <module> File "C:\Python26\Lib\site-packages\numpy\__init__.py", line 130, in <module> import add_newdocs File "C:\Python26\Lib\site-packages\numpy\add_newdocs.py", line 9, in <module> from lib import add_newdoc File "C:\Python26\Lib\site-packages\numpy\lib\__init__.py", line 161, in <module> from polynomial import * File "C:\Python26\Lib\site-packages\numpy\lib\polynomial.py", line 11, in <module> import numpy.core.numeric as NX AttributeError: 'module' object has no attribute 'core' Let me know if anyone has any thoughts here. Thanks for the help, ~Mike C.

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using numpy functions on an array of objects
by Sebastian Walter 01 Feb '09

01 Feb '09

Hey, What is the best solution to get this code working? Anyone a good idea? ------------------ test.py ----------------------------------------------- import numpy import numpy.linalg class afloat: def __init__(self,x): self.x = x def __add__(self,rhs): return self.x + rhs.x def sin(self): return numpy.sin(self.x) def inv(self): return numpy.linalg.inv(self.x) def trace(self): return 0 y = afloat(numpy.eye(3)) z = afloat(numpy.ones(3)) print y + z # works print numpy.sin(y) # works print numpy.trace(y) # doesn't work...??? print numpy.linalg.inv(y) # doesn't work ...??? -------------------- end test.py -------------------------------- === Explanation why I need that === I have the following problem. I need to do numerical calculations on generalized versions of real numbers. In particular with truncated Taylor polynomials. I've implemented that as a class that I called TC. To define what the multiplication of two Taylor polynomials is I use operator overloading. Additionally, I need to compute sine, cosine, exp, etc. of Taylor polynomials. For that, I can use the numpy functions. Numpy is apparently smart enough to call the member function sin(self) of my class afloat when it realizes that the argument of numpy.sin is not a known type. This is really great. However, some functions are not as smart: Among them trace inv dot As a workaround, i could this: def inv(X): if X.__class__ == afloat: return X.inv() else: return numpy.linalg.inv(X) This is somewhat OK, but I'd like to use already existing Python code that uses Numpy internally. So I have to rely that numpy.inv(X) calls X.inv() when X is object of my class. best regards, Sebastian

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Re: [Numpy-discussion] puzzle: generate index with many ranges
by Rick White 01 Feb '09

01 Feb '09

Here's a technique that works: Python 2.4.2 (#5, Nov 21 2005, 23:08:11) [GCC 4.0.0 20041026 (Apple Computer, Inc. build 4061)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as np >>> a = np.array([0,4,0,11]) >>> b = np.array([-1,11,4,15]) >>> rangelen = b-a+1 >>> cumlen = rangelen.cumsum() >>> c = np.arange(cumlen[-1],dtype=np.int32) >>> c += np.repeat(a[1:]-c[cumlen[0:-1]], rangelen[1:]) >>> print c [ 4 5 6 7 8 9 10 11 0 1 2 3 4 11 12 13 14 15] The basic idea is that the difference of your desired output from a simple range is an array with a bunch of constant values appended together, and that is what repeat() does. I'm assuming that you'll never have b < a. Notice the slight ugliness of prepending the elements at the beginning so that the cumsum starts with zero. (Maybe there is a cleaner way to do that.) This does create a second array (via the repeat) that is the same length as the result. If that uses too much memory, you could break up the repeat and update of c into segments using a loop. (You wouldn't need a loop for every a,b element -- do a bunch in each iteration.) -- Rick Raik Gruenberg wrote: > Hi there, > > perhaps someone has a bright idea for this one: > > I want to concatenate ranges of numbers into a single array (for > indexing). So I > have generated an array "a" with starting positions, for example: > > a = [4, 0, 11] > > I have an array b with stop positions: > > b = [11, 4, 15] > > and I would like to generate an index array that takes 4..11, then > 0..4, then > 11..15. > > In reality, a and b have 10000+ elements and the arrays to be > "sliced" are very > large so I want to avoid any for loops etc. Any idea how this could > be done? I > thought some combination of *repeat* and adding of *arange* should > do the trick > but just cannot nail it down. > > Thanks in advance for any hints! > > Greetings, > Raik

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minor improvment to ones
by Neal Becker 01 Feb '09

01 Feb '09

A nit, but it would be nice if 'ones' could fill with a value other than 1. Maybe an optional val= keyword?

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ndarray.resize method and reference counting
by Scott Sinclair 31 Jan '09

31 Jan '09

Hi, I'm confused by the following: >>> import numpy as np >>> np.__version__ '1.3.0.dev6116' # I expect this >>> x = np.eye(3) >>> x.resize((5,5)) >>> x = np.eye(3) >>> y = x >>> x.resize((5,5)) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: cannot resize an array that has been referenced or is referencing another array in this way. Use the resize function # I don't expect this >>> x = np.eye(3) >>> x array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> x.resize((5,5), refcheck=True) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: cannot resize an array that has been referenced or is referencing another array in this way. Use the resize function >>> x.resize((5,5), refcheck=False) >>> x array([[ 1., 0., 0., 0., 1.], [ 0., 0., 0., 1., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.]]) Is there a reference counting bug, or am I misunderstanding something about how Python works when I type a variable's name at the prompt? Cheers, Scott

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