Python CULA Sgesv Ax=B solving but gives negatives for positives? Why?

Question

I am using Python CULA Sgesv to solve a for a matrix operation. When I compare the answer from CULA to linear algebra solution CULA returns the correct numbers, but reverses the sign of the number. So if the real solution is positive the CULA solution is negative. I have tried float32 (SGESV) and doubles (DGESV) and both produce the same results. I do not know what I am doing wrong.

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

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

info=libculaC.culaInitialize()


def fileCov(na):
    rmean = zeros(na)
    y = zeros((na, na))
    with open('covMatrix.csv', 'r') as f1:
        reader = csv.reader(f1)
        i = 0
        for row in reader:
            y[i] = np.array(row, float64)
            i = i +1

    with open('rMean.csv', 'r') as f2:
        reader = csv.reader(f2)
        for row in reader:
            rmean = np.array(row, float64)
    return rmean, y

def generateCov(na, ns):
    rmean = zeros(na)
    rvolat = zeros(na)
    for i in range(na):
        t1 = np.random.uniform(-7.0, 7.0)
        rmean[i] = t1
        rvolat[i] = math.fabs(t1)/7.0
    print rmean
    print rvolat
    z = zeros((na,ns))
    for j in range(na):
        r = np.random.normal(rmean[j], rvolat[j], ns)
        z[j] = r
   return rmean, np.cov(z)
na = 256
ns = 1000
na = 8
rmean, y = fileCov(na)
print "This is Y:"
print y
print "This is rmean:"
print rmean
#sys.exit()
np.random.seed(0)
#rmean, y = generateCov(na, ns)
A = zeros((na+2,na+2))
for i in range(na):
    for j in range(na):
        A[i][j] = y[i][j]
    A[i][na] = -rmean[i]  
A[i][na+1] = -1.0
for j in range(na):
    A[na][j] = rmean[j]
A[na+1][j] = 1.0
x = zeros(na+2)
b = zeros(na+2)
b[na] = 57.64
b[na+1] = 1.0
print "This is A: "
print A
print "This is b: "
print b

# The 10x10 matrix
A_t = A.T
A_t = A_t.astype(numpy.float32) #float32
c_float_p = ctypes.POINTER(ctypes.c_float)
A1_p = A_t.ctypes.data_as(c_float_p)
# The 10x1 matrix
x1= np.empty([na+2])
x1= x1.astype(numpy.float32)
X_p = x1.ctypes.data_as(c_float_p)
# The 10x1 matrix with covariance
b_t=b.T
b_t = b_t.astype(numpy.float32)
b1_p = b_t.ctypes.data_as(c_float_p)

x = solve(A, b) #used to compare results to CULA SGESV

libculaC.culaSgesv(10,1,A1_p,10,X_p,b1_p,10)
a = np.fromiter(b1_p, dtype=np.float32, count=10)
print a
print "THIS IS X from solver:"
print x
print "x is over"
ym = np.matrix(y)
xm = np.matrix(a[0:na])  #x changed to a
print "This is ym: "
print ym
print "This is xm: "
print xm
print "This is sqrt(xm * ym * xm.T): "
print sqrt(xm * ym * xm.T)
sum = 0.0
for j in range(na):
    sum = sum + a[j]  #x changed to a
print sum


libculaC.culaShutdown()