def test_kk_no_bg_norm():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
kk = KramersKronig(norm_to_nrb=False)
kkd = kk.calculate(y, 0*y + 1)
assert_array_almost_equal(np.abs(kkd), np.sqrt(y))
def test_kk_no_bg_norm():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
kk = KramersKronig(norm_to_nrb=False)
kkd = kk.calculate(y, 0*y + 1)
assert_array_almost_equal(np.abs(kkd), np.sqrt(y))
def test_kk_transform_fail():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
y_complex = y.astype(np.complex)
kk = KramersKronig(norm_to_nrb=False)
success = kk._transform(y, 0*y + 1)
assert not success
def test_kk_transform_fail():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
y_complex = y.astype(np.complex)
kk = KramersKronig(norm_to_nrb=False)
success = kk._transform(y, 0*y + 1)
assert not success
def test_kk_transform():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
y_complex = y.astype(np.complex)
kk = KramersKronig(norm_to_nrb=False)
success = kk._transform(y_complex, 0*y_complex + 1)
assert success
assert_array_almost_equal(np.abs(y_complex), np.sqrt(y))
def test_kk_rng():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
rng = np.arange(5, x.size)
kk = KramersKronig(norm_to_nrb=False, rng=rng)
kkd = kk.calculate(y, 0*y + 1)
assert_array_almost_equal(np.abs(kkd[rng]), np.sqrt(y[rng]))
def test_kk():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
hilb_y_analytical = x/(2**2 + x**2)
kk = KramersKronig(pad_factor=10)
kkd = kk.calculate(np.exp(2*y), 0*y + 1)
kkd_angle = np.angle(kkd)
assert_array_almost_equal(hilb_y_analytical, kkd_angle, decimal=4)
def test_kk_transform():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
y_complex = y.astype(np.complex)
kk = KramersKronig(norm_to_nrb=False)
success = kk._transform(y_complex, 0*y_complex + 1)
assert success
assert_array_almost_equal(np.abs(y_complex), np.sqrt(y))
def test_kk_rng():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
rng = np.arange(5, x.size)
kk = KramersKronig(norm_to_nrb=False, rng=rng)
kkd = kk.calculate(y, 0*y + 1)
assert_array_almost_equal(np.abs(kkd[rng]), np.sqrt(y[rng]))
def test_kk():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
hilb_y_analytical = x/(2**2 + x**2)
kk = KramersKronig(pad_factor=10)
kkd = kk.calculate(np.exp(2*y), 0*y + 1)
kkd_angle = np.angle(kkd)
assert_array_almost_equal(hilb_y_analytical, kkd_angle, decimal=4)
def test_kk_properties_setter():
x = np.linspace(-100, 100, 1000)
y = 2/(2**2 + x**2)
hilb_y_analytical = x/(2**2 + x**2)
kk = KramersKronig()
kk.pad_factor=10
kk.norm_to_nrb=True
kk.phase_offset=3.0
assert kk.pad_factor == 10
assert kk.norm_to_nrb
assert kk.phase_offset == 3.0
SPECT_LEN = 878
WN = _np.linspace(4000, 500, SPECT_LEN)
chi = (1 / ((WN - 1000 - 1j * 10)) +
1 / ((WN - 1020 - 1j * 10)) +
1 / ((WN - 2800 - 1j * 10)))
chiNR = 0*chi + 0.055
exc = WN
sig = _np.abs(chi + chiNR)**2
sigNR = _np.abs(chiNR)**2
sigRef = chiNR*(WN/1e3)**.5
NUM_REPS = 10
kk = KramersKronig()
kkd = kk.calculate(sig, sigRef)
kkd = _np.dot(_np.random.rand(NUM_REPS,NUM_REPS,1)*_np.ones((NUM_REPS, NUM_REPS, 1)), kkd[None, :])
# plt.plot(chi.imag/chiNR.real, label='Ideal')
plt.plot(kkd[5, 5, :].imag, label='Before Correction')
start = timeit.default_timer()
phase_err_correct_als = PhaseErrCorrectALS(fix_end_points=True,
smoothness_param=1e8,
asym_param=1e-3,
redux=1)
success = phase_err_correct_als.transform(kkd, verbose=False)
print('Success? : {}'.format(success))
stop = timeit.default_timer()
print('Sec/spectrum: {:.3g}'.format((stop-start)/NUM_REPS**2))