Mailman 3 February 2009 - NumPy-Discussion

Re: [Numpy-discussion] Building on WinXP 64-bit, Intel Compilers
by Mike Colonno Feb. 2, 2009

Feb. 2, 2009

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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numpy.load and gzip file handles
by Matthew Miller Feb. 2, 2009

Feb. 2, 2009

Hi everyone. I'd like to log the state of my program as it progresses. Using the numpy.save / numpy.load functions on the same filehandle repeatedly works very well for this -- but ends up making a file which very quickly grows to gigabytes. The data compresses well, though, so I thought I'd use Python's built-in gzip module underneath. This works great for saving -- but when it comes time to play back, there's an issue: >>> import numpy >>> import gzip >>> f=open("test.gz") >>> g=gzip.GzipFile(None,"rb",9,f) >>> g <gzip open file 'test.gz', mode 'r' at 0xbaad50 0xc0ab90> >>> numpy.load(g) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib64/python2.5/site-packages/numpy/lib/io.py", line 133, in load fid.seek(-N,1) # back-up TypeError: seek() takes exactly 2 arguments (3 given) Turns out you can't rewind gzip file handles in Python. Oops. The offending code is that which distinguishes between npy and npz files. Could there maybe be something added to just trust me that it's an npy? Or better yet, is there something I'm doing wrong / overlooking? Thanks! -- Matthew Miller mattdm(a)mattdm.org <http://mattdm.org/>

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using numpy functions on an array of objects
by Sebastian Walter Feb. 1, 2009

Feb. 1, 2009

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 Feb. 1, 2009

Feb. 1, 2009

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 Feb. 1, 2009

Feb. 1, 2009

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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