Mailman 3 September 2010 - NumPy-Discussion

vectorize doesn't like functools.partial
by Neal Becker 08 Sep '10

08 Sep '10

If I try to use vectorize on the result of functools.partial, I seem to get: ValueError: failed to determine the number of arguments for <functools.partial object at 0x4e396d8> Anything I can do about it?

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ValueError: objects are not aligned
by RuiDC 08 Sep '10

08 Sep '10

I'm getting this error, which must be a simple error in getting the result from the cursor in the right shape, how do I get the cursor into the array as a single dimension?: import numpy as np import sqlite3 conn = sqlite3.connect("simpledb") cursor1 = conn.execute("select LogReturn from tblReturns limit 3") rf1 = np.asarray(cursor1.fetchall(), dtype=np.float64) print (rf1) print (np.corrcoef([rf1, rf1])) result: [[-0.00641625] [-0.00498411] [-0.0038015 ]] Traceback (most recent call last): File "C:\Documents and Settings\rui\workspace\numpytest\src\numpytest.py", line 38, in <module> print (np.corrcoef([rf1, rf1])) File "C:\Python26\lib\site-packages\numpy\lib\function_base.py", line 2003, in corrcoef c = cov(x, y, rowvar, bias, ddof) File "C:\Python26\lib\site-packages\numpy\lib\function_base.py", line 1953, in cov return (dot(X, X.T.conj()) / fact).squeeze() -- View this message in context: http://old.nabble.com/ValueError%3A-objects-are-not-aligned-tp29641661p2964… Sent from the Numpy-discussion mailing list archive at Nabble.com.

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new warning in 1.5.0: divide by zero encountered in log
by John Reid 08 Sep '10

08 Sep '10

Hi, I recently upgraded to numpy 1.5.0 and now I get warnings when I take the logarithm of 0. In [5]: np.log(0.) Warning: divide by zero encountered in log Out[5]: -inf I want to evaluate x * log(x) where its value is defined as 0 when x=0 so I have the following function: def safe_x_log_x(x): "@return: x log(x) but replaces -inf with 0." l = np.log(x) result = x * l result[np.isneginf(l)] = 0. return result Is there a better way of doing this that won't give me all these warnings? Can I turn the warnings off? Thanks, John.

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ndarray**maskedArray with broadcasting bug
by Benjamin Root 07 Sep '10

07 Sep '10

Here is a fun one... import numpy as np a_2d = np.random.random((3, 5)) b_1d = np.random.random(5) b_2d = np.vstack((b_1d, b_1d, b_1d)) a_ma_2d = np.ma.masked_array(a_2d, mask=(numpy.random.random((3, 5)) < 0.25)) b_ma_1d = np.ma.masked_array(b_1d, mask=(numpy.random.random(5) < 0.25)) b_ma_2d = np.ma.masked_array(b_2d, mask=(numpy.random.random((3, 5)) < 0.25)) # a**b without broadcasting (works) print a_2d**b_2d # a**b with broadcasting (works) print a_2d**b_1d # a_ma**b_ma without broadcasting (works) print a_ma_2d**b_ma_2d # a_ma**b_ma with broadcasting (works) print a_ma_2d**b_ma_1d # a**b_ma without broadcasting (works) print a_2d**b_ma_2d # a**b_ma with broadcasting (FAILS) print a_2d**b_ma_1d Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/bvr/Programs/numpy/numpy/ma/core.py", line 3697, in __rpow__ return power(other, self) File "/home/bvr/Programs/numpy/numpy/ma/core.py", line 6043, in power m |= invalid ValueError: invalid return array shape # Now, test broadcasting of the other side of the operator c = np.random.random(5) c_ma = np.ma.masked_array(c, mask=(np.random.random(10) < 0.25)) # c**b_ma with broadcasting (works) print c**b_ma_2d # c_ma**b with broadcasting (FAILS) print c_ma**b_2d Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/bvr/Programs/numpy/numpy/ma/core.py", line 3693, in __pow__ return power(self, other) File "/home/bvr/Programs/numpy/numpy/ma/core.py", line 6043, in power m |= invalid ValueError: invalid return array shape So, this fails if we broadcast the masked array portion of a power expression and the other value is a ndarray. Note, the same also occurs if we broadcast a masked array in order to raise it to powers indicated by an ndarray. Took me the longest time to nail this one down... Ben Root

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Help with the tensordot function?
by Rick Muller 07 Sep '10

07 Sep '10

Can someone help me replace a slow expression with a faster one based on tensordot? I've read the documentation and I'm still confused. I have two matrices b and d. b is n x m and d is m x m. I want to replace the expression >>> bdb = zeros(n,'d') >>> for i in xrange(n): >>> bdb[i,:] = dot(b[i,:],dot(d,b[i,:]) with something that doesn't have the for loop and thus is a bit faster. The first step is >>> bd = dot(b,d) However, following this with >>> bdb = dot(bd,b.T) doesn't work, since it yields a n x n matrix instead of an n x 1 vector. Reading the notes on tensordot makes me think it's the function to use, but I'm having trouble grokking the axes argument. Can anyone help? Thanks in advance! -- Rick Muller rpmuller(a)gmail.com

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EPD Webinar: How to solve ODEs with SciPy
by Amenity Applewhite 07 Sep '10

07 Sep '10

Enthought Python Distribution Webinar September 10 This Friday,Warren Weckesser will host the first of three webinars in a series on solving differential equations in Python. We will take a close look at the two tools available for solving ordinary differential equations in SciPy: the "odeint" function and the "ode" class. Two examples will be discussed: (1) the famous Lorenz equations that exhibit chaos, and (2) the Gray-Scott reaction-diffusion equations in 1D, from which we will obtain a system of ordinary differential equations by using the "Method of Lines". The demonstrations also include 3D plots and animation using Matplotlib. Enthought Python Distribution Webinar How do I...solve differential equations with Python? Part I: SciPy Tools Friday, September 10: 1pm CST/6pm UTC Wait list (for non EPD subscribers): email:amenity@enthought.com Early in 2011, Warren will host Part II: boundary value problems, and in the spring he'll follow up with a third installment to the series. Have a fantastic September, The Enthought Team

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Pthreads ATLAS
by Andreas Kloeckner 07 Sep '10

07 Sep '10

Hi all, I've just tried to set up a Python 3 test environment for me to begin porting PyCUDA and PyOpenCL to Python 3, but numpy installation managed to foil my. I'm on Debian, and I believe that Debian purposefully compiles ATLAS without pthreads support. Whenever I build numpy 1.5, I encounter this error: Python 3.1.2 (release31-maint, Jul 8 2010, 09:18:08) [GCC 4.4.4] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/andreas/src3/lib/python3.1/site-packages/numpy/__init__.py", line 136, in <module> from . import add_newdocs File "/home/andreas/src3/lib/python3.1/site-packages/numpy/add_newdocs.py", line 9, in <module> from numpy.lib import add_newdoc File "/home/andreas/src3/lib/python3.1/site-packages/numpy/lib/__init__.py", line 13, in <module> from .polynomial import * File "/home/andreas/src3/lib/python3.1/site-packages/numpy/lib/polynomial.py", line 17, in <module> from numpy.linalg import eigvals, lstsq File "/home/andreas/src3/lib/python3.1/site-packages/numpy/linalg/__init__.py", line 48, in <module> from .linalg import * File "/home/andreas/src3/lib/python3.1/site-packages/numpy/linalg/linalg.py", line 23, in <module> from numpy.linalg import lapack_lite ImportError: /home/andreas/src3/lib/python3.1/site-packages/numpy/linalg/lapack_lite.so: undefined symbol: ATL_dptsyrk I gather that "pt" in the symbol name means that this is a pthreads-accelerated version of dsyrk. How do I convey to numpy that I don't have (nor want) Pthreads-accelerated ATLAS bits? Failing that, how do I tell it to just not use ATLAS? I tried looking for information at - numpy-1.5.0/doc/source/user/install.rst - http://docs.scipy.org/doc/ - http://new.scipy.org/building/linux.html - http://www.scipy.org/Installing_SciPy/Linux but nothing seemed to apply, and Google only gave me an instance of Skipper Seabold asking the same question (apparently when building his own Atlas) only to reply with a somewhat opaque "I figured it out" message. Thanks in advance, Andreas

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saving masked array to a text file
by Guillaume Chérel 07 Sep '10

07 Sep '10

Hello, Is there a function like savetxt that can deal with missing values in masked arrays? I'd like to control the way the missing values are written to the file. Thanks, Guillaume

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kron produces F-contiguous?
by Neal Becker 06 Sep '10

06 Sep '10

It seems if I call kron with 2 C-contiguous arrays, it returns an F- contiguous array. Any reason for this (it's not what I wanted)?

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dll load failed when loading numpy in embedded python
by Jean-François MAUREL 06 Sep '10

06 Sep '10

Hi everyone, I get the error: 'DLL load failed' when loading numpy in embedded python. I could not find a solution in the archive. Any help appreciated. I have written a test as follows (borrowed from an other post): #include <stdlib.h> #include <stdio.h> #include <Python.h> int load(){ PyObject *errobj, *errdata, *errtraceback; char* code = "import numpy\n"; Py_Initialize(); PyObject *m = PyImport_AddModule("__main__"); PyObject* d = PyModule_GetDict( m ); PyObject * resu = PyRun_String( code, Py_file_input, d, d); PyErr_Fetch (&errobj, &errdata, &errtraceback); if (errdata != NULL){ PyObject *s = PyObject_Str(errdata); char * c = PyString_AS_STRING(s); printf("%s\n",c); Py_DECREF(s); }else{ printf("no errors.\n"); } Py_XDECREF(errobj); Py_XDECREF(errdata); Py_XDECREF(errtraceback); Py_Finalize(); return 0; } int main(int argc, char **argv){ load(); return 0; } this is build using: gcc -I/openSource/python26/include -ggdb -c t.c -oc.o -Wall gcc c.o C:\openSource\python26\libs\libpython26.a -otest.exe -Wall configuration is: windows vista mingw gcc 4.5.0 python 2.6 numpy 1.5 -- Jean-François MAUREL

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