NumPy-Discussion January 2009

numpy-discussion@python.org

116 participants
133 discussions

strange multiplication behavior with numpy.float64 and ndarray subclass

by Darren Dale

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

12 years, 10 months

linalg.norm missing an 'axis' kwarg?!

by Hans Meine

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

12 years, 11 months

default float type of array not accepted by SWIG wrapped C functions

by Rich E

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

12 years, 11 months

porting NumPy to Python 3

by James Watson

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.

12 years, 11 months

Re: [Numpy-discussion] Building on WinXP 64-bit, Intel Compilers

by Mike Colonno

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.

12 years, 11 months

using numpy functions on an array of objects

by Sebastian Walter

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

12 years, 12 months

Re: [Numpy-discussion] puzzle: generate index with many ranges

by Rick White

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

12 years, 12 months

minor improvment to ones

by Neal Becker

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

12 years, 12 months

ndarray.resize method and reference counting

by Scott Sinclair

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

12 years, 12 months

A buildbot farm with shell access - for free ?

by David Cournapeau

Hi, Just saw that on one ML: http://www.snakebite.org/ http://mail.python.org/pipermail/python-committers/2009-January/000331.html Bottom line: it looks like there is a set of machines which were donated to the PSF for buildbot *with shell access* so that people can fix problems appearing on some platforms. If you look at the email, there are some 'exotic' machines that mere mortals cannot have access to (like True64 on Itanium: to quote the email "massive quad Itanium 2 RX-5670s, chock full of 73GB 15k disks and no less than 78GB of RAM between the two servers; 32GB in one and 46GB in the other"). There are also windows machines available. It is said in the email that this is reserved to the python project, and prominent python projects like Twisted and Django. Would it be ok to try to be qualified as a prominent python project as well ? cheers, David

12 years, 12 months

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NumPy-Discussion January 2009