def test_correlate_intra(self, rtol=1e-6, atol=0.0):
# test autocorrelator
intra = self.rings.correlate_intra(1.0, 1.0, normed=True)
assert intra.shape == (self.rings.num_phi,)
q_ind = self.rings.q_index(1.0)
ref_corr = np.zeros(self.num_phi)
for i in range(self.num_shots):
x = self.rings.polar_intensities[i,q_ind,:].flatten().copy()
ref_corr += brute_force_masked_correlation(x, np.ones(len(x), dtype=np.bool), normed=True)
ref_corr /= float(self.num_shots)
assert_allclose(intra / intra[0], ref_corr / ref_corr[0], rtol=rtol, atol=atol,
err_msg='doesnt match reference implementation')
assert_allclose(intra, ref_corr, rtol=rtol, atol=atol,
err_msg='doesnt match reference implementation normalization')
# test norming
assert np.abs(intra[0] - 1.0) < rtol
intra_unnorm = self.rings.correlate_intra(1.0, 1.0, normed=False)
assert not np.abs(intra_unnorm[0] - 1.0) < rtol
assert_allclose(intra, intra_unnorm / intra_unnorm[0],
rtol=rtol, atol=atol, err_msg='normalization broken')
# test cross correlator
intra = self.rings.correlate_intra(1.0, 2.0, normed=True)
assert not np.abs(intra_unnorm[0] - 1.0) < 1e-8 # x-corr normed when it shouldnt be
# test the limit on
intra = self.rings.correlate_intra(1.0, 1.0, num_shots=1)
assert intra.shape == (self.rings.num_phi,) # num_shots flag broken
def test_corr_rows_w_mask(self):
q1 = 1.0 # chosen arb.
q_ind = self.rings.q_index(q1)
x = self.rings.polar_intensities[0,q_ind,:].flatten().copy()
x_mask = np.random.binomial(1, 0.9, size=len(x)).astype(np.bool)
no_mask = np.ones_like(x_mask)
corr = self.rings._correlate_rows(x, x, x_mask, x_mask)
true_corr = self.rings._correlate_rows(x, x)
ref_corr = brute_force_masked_correlation(x, x_mask, normed=False)
normed_ref = ref_corr / ref_corr[0]
normed_corr = corr / corr[0]
# big tol, but w/a lot of masking there is a ton of noise
assert_allclose(normed_ref, normed_corr, atol=1e-2,
rtol=1e-2, err_msg='correlation incorrect')
# make sure the normalization is OK
assert np.sum( np.abs(corr - ref_corr) ) / (corr[1]*float(len(corr))) < 1e-2
# make sure masked and unmaksed are somewhat similar
assert_allclose(true_corr / true_corr[0], normed_corr, atol=0.1,
err_msg='masked correlation very different from unmasked version')
def test_corr_rows_w_mask(self):
q1 = 1.0 # chosen arb.
q_ind = self.rings.q_index(q1)
x = self.rings.polar_intensities[0, q_ind, :].flatten().copy()
x_mask = np.random.binomial(1, 0.9, size=len(x)).astype(np.bool)
no_mask = np.ones_like(x_mask)
corr = self.rings._correlate_rows(x, x, x_mask, x_mask)
true_corr = self.rings._correlate_rows(x, x)
ref_corr = brute_force_masked_correlation(x, x_mask, normed=False)
normed_ref = ref_corr / ref_corr[0]
normed_corr = corr / corr[0]
# big tol, but w/a lot of masking there is a ton of noise
assert_allclose(normed_ref,
normed_corr,
atol=1e-2,
rtol=1e-2,
err_msg='correlation incorrect')
# make sure the normalization is OK
assert np.sum(
np.abs(corr - ref_corr)) / (corr[1] * float(len(corr))) < 1e-2
# make sure masked and unmaksed are somewhat similar
assert_allclose(
true_corr / true_corr[0],
normed_corr,
atol=0.1,
err_msg='masked correlation very different from unmasked version')
def test_brute_correlation_wo_mask(self):
# this might be better somewhere else, but is here for now:
# this tests the brute force implementation in testing.py used below
x = np.random.randn(100) + 0.1 * np.arange(100)
mask = np.ones(100, dtype=np.bool)
c = brute_force_masked_correlation(x, mask, normed=False)
c_norm = brute_force_masked_correlation(x, mask, normed=True)
ref = np.zeros(100)
for delta in range(100):
ref[delta] = np.sum( (x - x.mean()) * \
(np.roll(x, delta) - x.mean()) ) \
/ float(len(x))
assert_allclose(ref / ref[0], c_norm, err_msg='normalized fail')
assert_allclose(ref, c, err_msg='unnormalized fail')
def test_brute_correlation_wo_mask(self):
# this might be better somewhere else, but is here for now:
# this tests the brute force implementation in testing.py used below
x = np.random.randn(100) + 0.1 * np.arange(100)
mask = np.ones(100, dtype=np.bool)
c = brute_force_masked_correlation(x, mask, normed=False)
c_norm = brute_force_masked_correlation(x, mask, normed=True)
ref = np.zeros(100)
for delta in range(100):
ref[delta] = np.sum( (x - x.mean()) * \
(np.roll(x, delta) - x.mean()) ) \
/ float(len(x))
assert_allclose(ref / ref[0], c_norm, err_msg='normalized fail')
assert_allclose(ref, c, err_msg='unnormalized fail')
def test_brute_correlation_wo_mask(self):
# this might be better somewhere else, but is here for now:
# this tests the brute force implementation in testing.py used below
x = np.random.randn(100) + 0.1 * np.arange(100)
mask = np.ones(100, dtype=np.bool)
c = brute_force_masked_correlation(x, mask)
ref = np.zeros(100)
for i in range(100):
ref[i] = np.correlate(x, np.roll(x, i))
ref /= float(len(x)) * np.mean(x)**2
ref -= 1.0
assert_allclose(ref, c)
def test_brute_correlation_wo_mask(self):
# this might be better somewhere else, but is here for now:
# this tests the brute force implementation in testing.py used below
x = np.random.randn(100) + 0.1 * np.arange(100)
mask = np.ones(100, dtype=np.bool)
c = brute_force_masked_correlation(x, mask)
ref = np.zeros(100)
for i in range(100):
ref[i] = np.correlate(x, np.roll(x, i))
ref /= float(len(x)) * np.mean(x) ** 2
ref -= 1.0
assert_allclose(ref, c)
def test_corr_rows_w_mask(self):
q1 = 1.0 # chosen arb.
q_ind = self.rings.q_index(q1)
x = self.rings.polar_intensities[0,q_ind,:].flatten().copy()
x_mask = np.random.binomial(1, 0.9, size=len(x)).astype(np.bool)
no_mask = np.ones_like(x_mask)
corr = self.rings._correlate_rows(x, x, x_mask, x_mask, mean_only=True)
true_corr = self.rings._correlate_rows(x, x, no_mask, no_mask, mean_only=True)
ref_corr = brute_force_masked_correlation(x, x_mask)
# big tol, but w/a lot of masking there is a ton of noise
assert_allclose(true_corr, corr, atol=0.1)
assert_allclose(ref_corr, corr, rtol=1e-03)
def test_correlate_intra(self, rtol=1e-6, atol=0.0):
# test autocorrelator
intra = self.rings.correlate_intra(1.0, 1.0, normed=True)
assert intra.shape == (self.rings.num_phi, )
q_ind = self.rings.q_index(1.0)
ref_corr = np.zeros(self.num_phi)
for i in range(self.num_shots):
x = self.rings.polar_intensities[i, q_ind, :].flatten().copy()
ref_corr += brute_force_masked_correlation(x,
np.ones(len(x),
dtype=np.bool),
normed=True)
ref_corr /= float(self.num_shots)
assert_allclose(intra / intra[0],
ref_corr / ref_corr[0],
rtol=rtol,
atol=atol,
err_msg='doesnt match reference implementation')
assert_allclose(
intra,
ref_corr,
rtol=rtol,
atol=atol,
err_msg='doesnt match reference implementation normalization')
# test norming
assert np.abs(intra[0] - 1.0) < rtol
intra_unnorm = self.rings.correlate_intra(1.0, 1.0, normed=False)
assert not np.abs(intra_unnorm[0] - 1.0) < rtol
assert_allclose(intra,
intra_unnorm / intra_unnorm[0],
rtol=rtol,
atol=atol,
err_msg='normalization broken')
# test cross correlator
intra = self.rings.correlate_intra(1.0, 2.0, normed=True)
assert not np.abs(intra_unnorm[0] -
1.0) < 1e-8 # x-corr normed when it shouldnt be
# test the limit on
intra = self.rings.correlate_intra(1.0, 1.0, num_shots=1)
assert intra.shape == (self.rings.num_phi, ) # num_shots flag broken
def test_corr_rows_w_mask(self):
q1 = 1.0 # chosen arb.
q_ind = self.rings.q_index(q1)
x = self.rings.polar_intensities[0, q_ind, :].flatten().copy()
x_mask = np.random.binomial(1, 0.9, size=len(x)).astype(np.bool)
no_mask = np.ones_like(x_mask)
corr = self.rings._correlate_rows(x, x, x_mask, x_mask, mean_only=True)
true_corr = self.rings._correlate_rows(x,
x,
no_mask,
no_mask,
mean_only=True)
ref_corr = brute_force_masked_correlation(x, x_mask)
# big tol, but w/a lot of masking there is a ton of noise
assert_allclose(true_corr, corr, atol=0.1)
assert_allclose(ref_corr, corr, rtol=1e-03)
