def test_reginv(self):
# make sure the fancy code is equal to the more literal, but slow way
k = self.ref_kernel[10, 20].copy()
lm = 8.
x = slow_inv(k, lm)
k = self.ref_kernel[10, 20].copy()
xf = GU.regularized_inverse(k, lm)
assert_array_almost_equal(x, xf)
def test_reginv(self):
# make sure the fancy code is equal to the more literal, but slow way
k = self.ref_kernel[10,20].copy()
lm = 8.
x = slow_inv(k, lm)
k = self.ref_kernel[10,20].copy()
xf = GU.regularized_inverse(k, lm)
assert_array_almost_equal(x, xf)
def test_getkernel(self):
(ns, M2, M1) = self.fmap.shape
N2 = M2
fmap = np.swapaxes(self.fmap, -1, -2)
chi = np.swapaxes(self.chi, -1, -2)
b = np.arange(M2) - M2 / 2
n2 = np.arange(M2) - M2 / 2
Tl = 0.000742
K = GU.get_kernel(M2, Tl, b, n2, fmap, chi)
assert_array_almost_equal(K, self.ref_kernel)
def test_getkernel(self):
(ns, M2, M1) = self.fmap.shape
N2 = M2
fmap = np.swapaxes(self.fmap, -1, -2)
chi = np.swapaxes(self.chi, -1, -2)
b = np.arange(M2)-M2/2
n2 = np.arange(M2)-M2/2
Tl = 0.000742
K = GU.get_kernel(M2, Tl, b, n2, fmap, chi)
assert_array_almost_equal(K, self.ref_kernel)
def test_invspeed(self):
nsl, nfe = self.ref_kernel.shape[:2]
lm = 8.0
slcs = range(nsl)
cols = range(nfe)
t0_slow = time.time()
for s in slcs:
for fe in cols:
ik = slow_inv(self.ref_kernel[s, fe], lm)
tf_slow = time.time()
t0_fast = time.time()
for s in slcs:
for fe in cols:
ik = GU.regularized_inverse(self.ref_kernel[s, fe], lm)
tf_fast = time.time()
print "literal numpy solution for %d inverses: %2.4fs" % (
nsl * nfe, tf_slow - t0_slow)
print "slick solution for %d inverses: %2.4fs" % (nsl * nfe,
tf_fast - t0_fast)
print "speedup:", (tf_slow - t0_slow) / (tf_fast - t0_fast)
def test_invspeed(self):
nsl, nfe = self.ref_kernel.shape[:2]
lm = 8.0
slcs = range(nsl)
cols = range(nfe)
t0_slow = time.time()
for s in slcs:
for fe in cols:
ik = slow_inv(self.ref_kernel[s,fe], lm)
tf_slow = time.time()
t0_fast = time.time()
for s in slcs:
for fe in cols:
ik = GU.regularized_inverse(self.ref_kernel[s,fe], lm)
tf_fast = time.time()
print "literal numpy solution for %d inverses: %2.4fs"%(nsl*nfe,
tf_slow-t0_slow)
print "slick solution for %d inverses: %2.4fs"%(nsl*nfe,
tf_fast-t0_fast)
print "speedup:", (tf_slow-t0_slow)/(tf_fast-t0_fast)
