CULA - using Python solve() within CULA

Question

How can I incorporate Python's solve() in a Python CULA program? I am using

LA = libculaC.solve() 

Result:

Traceback (most recent call last):
      File "culaTest.py", line 96, in <module>
        LA = libculaC.solve(0)
      File "/usr/lib/python2.7/ctypes/__init__.py", line 378, in __getattr__
        func = self.__getitem__(name)
      File "/usr/lib/python2.7/ctypes/__init__.py", line 383, in __getitem__
        func = self._FuncPtr((name_or_ordinal, self))
    AttributeError: /usr/local/cula/lib64/libcula_lapack.so: undefined symbol:     
    solve

Any combination of liculaC and ctypes give me similar errors. How can I bring this function in? Do I need to use a C function (scanf) or something. Thanks.

Answer 1

This took awhile, but this is what I have so far. Must use ctypes and must convert to column major vice the standard row major. Use matrices not arrays and keep your data types consistent throughout.

import ctypes
from scipy import *
from scipy.linalg import *
import numpy as np
import sys
import csv
print     "___________________________________________________________________________________________________"

libculaC=ctypes.CDLL('libcula_lapack.so',mode=ctypes.RTLD_GLOBAL)
libculaC.culaGetStatusString.restype=ctypes.c_char_p

info=libculaC.culaInitialize()

#Row major-normal form, but must be converted-('4 1;2 5')
Anp = np.matrix('4.0 2.0;1.0 5.0') #Column major
print "This is Anp: "
print Anp
print '___________END Anp______________'

#use ctypes to convert from Py to C
#2x2 matrix
Anp = Anp.astype(numpy.float32)  #astype is array type for ctype
c_float_p = ctypes.POINTER(ctypes.c_float)
A1_p = Anp.ctypes.data_as(c_float_p)
# 2x1 matrix
B1 = np.matrix('5.0 ;7.0')
print "This is B1"
print B1
print '__________________B1 END______________________'
B1 = B1.astype(numpy.float32)
B1_p = B1.ctypes.data_as(c_float_p)

X=np.empty([2])
X=X.astype(numpy.float32)
X_p =X.ctypes.data_as(c_float_p)
print "This is X"
print X
print '__________________X END______________________'


info = 0
libculaC.culaSgesv(2,1,A1_p,2,X_p,B1_p,2)  #libculaC.culaSgesv

a = np.fromiter(B1_p, dtype=np.float32, count=2)
a = np.reshape(a,(-1,2))
print "The solution returning from Sgesv: "
print a
print "-----------------------Program End----------------------------"

libculaC.culaShutdown()

OUTPUT: This is Anp:

[[ 4. 2.] [ 1. 5.]]

___________END Anp______________

This is B1

[[ 5.] [ 7.]]

__________________B1 END______________________

This is X

[ 5. 7.]

__________________X END______________________

The solution returning from Sgesv:

[[ 1. 1.]]

-----------------------Program End----------------------------