def testWisdom(self):
i=0
for lib in libs:
lib.forget_wisdom()
inputa = lib.create_aligned_array(1024,np.typeDict[_complex[i]])
outputa = lib.create_aligned_array(1024,np.typeDict[_complex[i]])
plan = lib.Plan(inputa,outputa,flags=['patient'])
soriginal = lib.export_wisdom_to_string()
lib.import_wisdom_from_string(soriginal)
lib.export_wisdom_to_file('test.wisdom')
lib.forget_wisdom()
lib.import_wisdom_from_file('test.wisdom')
os.remove('test.wisdom')
del inputa
del outputa
i+=1
def testWisdom(self):
i = 0
for lib in libs:
lib.forget_wisdom()
inputa = lib.create_aligned_array(1024, np.typeDict[_complex[i]])
outputa = lib.create_aligned_array(1024, np.typeDict[_complex[i]])
plan = lib.Plan(inputa, outputa, flags=['patient'])
soriginal = lib.export_wisdom_to_string()
lib.import_wisdom_from_string(soriginal)
lib.export_wisdom_to_file('test.wisdom')
lib.forget_wisdom()
lib.import_wisdom_from_file('test.wisdom')
os.remove('test.wisdom')
del inputa
del outputa
i += 1
def fftw_propagation_aligned(N,repeats, lib, dtype):
t = np.linspace(-5,5,N)
dt = t[1]-t[0]
f = np.linspace(-1/dt/2.,1/dt/2.,N)
f = fftshift(f)
t = fftshift(t)
farray = lib.create_aligned_array(f.shape,dtype=dtype)
tarray = lib.create_aligned_array(t.shape,dtype=dtype)
fftplan = lib.Plan(tarray,farray,'forward')
ifftplan = lib.Plan(farray,tarray,'backward')
farray[:] = 0
tarray[:] = 0
tarray += np.exp(-t**2/0.5)
dispersion = np.exp(-1.j*h*beta*f)
ti = time.time()
for i in xrange(repeats):
fftplan()
farray *= dispersion/N
ifftplan()
to = time.time()-ti
return fftshift(t),fftshift(tarray),fftshift(f),fftshift(farray), to
def fftw_propagation_aligned(N, repeats, lib, dtype):
t = np.linspace(-5, 5, N)
dt = t[1] - t[0]
f = np.linspace(-1 / dt / 2., 1 / dt / 2., N)
f = fftshift(f)
t = fftshift(t)
farray = lib.create_aligned_array(f.shape, dtype=dtype)
tarray = lib.create_aligned_array(t.shape, dtype=dtype)
fftplan = lib.Plan(tarray, farray, 'forward')
ifftplan = lib.Plan(farray, tarray, 'backward')
farray[:] = 0
tarray[:] = 0
tarray += np.exp(-t**2 / 0.5)
dispersion = np.exp(-1.j * h * beta * f)
ti = time.time()
for i in xrange(repeats):
fftplan()
farray *= dispersion / N
ifftplan()
to = time.time() - ti
return fftshift(t), fftshift(tarray), fftshift(f), fftshift(farray), to
