def test_MAF_lineshape_kernel():
for dim in anisotropic_dims:
ns_obj = ShieldingPALineshape(
anisotropic_dimension=dim,
inverse_dimension=inverse_dimension,
channel="29Si",
magnetic_flux_density="9.4 T",
rotor_angle="90 deg",
rotor_frequency="14 kHz",
number_of_sidebands=1,
)
zeta, eta = ns_obj._get_zeta_eta(supersampling=1)
K = ns_obj.kernel(supersampling=1)
sim_lineshape = generate_shielding_kernel(zeta, eta, np.pi / 2, 14000,
1).T
assert np.allclose(K, sim_lineshape, rtol=1.0e-3, atol=1e-3)
ns_obj = MAF(
anisotropic_dimension=dim,
inverse_dimension=inverse_dimension,
channel="29Si",
magnetic_flux_density="9.4 T",
)
zeta, eta = ns_obj._get_zeta_eta(supersampling=1)
K = ns_obj.kernel(supersampling=1)
sim_lineshape = generate_shielding_kernel(zeta, eta, np.pi / 2, 14000,
1).T
assert np.allclose(K, sim_lineshape, rtol=1.0e-3, atol=1e-3)
_ = TSVDCompression(K, s=np.arange(96))
assert _.truncation_index == 15
def test_spinning_sidebands_kernel():
# 1
for dim in anisotropic_dims:
ns_obj = ShieldingPALineshape(
anisotropic_dimension=dim,
inverse_dimension=inverse_dimension,
channel="29Si",
magnetic_flux_density="9.4 T",
rotor_angle="54.735 deg",
rotor_frequency="100 Hz",
number_of_sidebands=96,
)
zeta, eta = ns_obj._get_zeta_eta(supersampling=1)
K = ns_obj.kernel(supersampling=1)
sim_lineshape = generate_shielding_kernel(zeta, eta,
0.9553059660790962, 100,
96).T
assert np.allclose(K, sim_lineshape, rtol=1.0e-3, atol=1e-3)
# 2
ns_obj = ShieldingPALineshape(
anisotropic_dimension=dim,
inverse_dimension=inverse_dimension_ppm,
channel="29Si",
magnetic_flux_density="9.4 T",
rotor_angle="54.735 deg",
rotor_frequency="100 Hz",
number_of_sidebands=96,
)
zeta, eta = ns_obj._get_zeta_eta(supersampling=1)
K = ns_obj.kernel(supersampling=1)
sim_lineshape = generate_shielding_kernel(zeta,
eta,
0.9553059660790962,
100,
96,
to_ppm=False).T
assert np.allclose(K, sim_lineshape, rtol=1.0e-3, atol=1e-3)
# 3
ns_obj = SpinningSidebands(
anisotropic_dimension=dim,
inverse_dimension=inverse_dimension,
channel="29Si",
magnetic_flux_density="9.4 T",
)
zeta, eta = ns_obj._get_zeta_eta(supersampling=1)
K = ns_obj.kernel(supersampling=1)
sim_lineshape = generate_shielding_kernel(zeta, eta,
0.9553059660790962, 208.33,
96).T
assert np.allclose(K, sim_lineshape, rtol=1.0e-3, atol=1e-3)
_ = TSVDCompression(K, s=np.arange(96))
assert _.truncation_index == 15
# Here, ``lineshape`` is an instance of the # :class:`~mrinversion.kernel.nmr.ShieldingPALineshape` class. The required # arguments of this class are the `anisotropic_dimension`, `inverse_dimension`, and # `channel`. We have already defined the first two arguments in the previous # sub-section. The value of the `channel` argument is the nucleus observed in the MAF # experiment. In this example, this value is '29Si'. # The remaining arguments, such as the `magnetic_flux_density`, `rotor_angle`, # and `rotor_frequency`, are set to match the conditions under which the 2D MAF # spectrum was acquired. The value of the `number_of_sidebands` argument is the number # of sidebands calculated for each line-shape within the kernel. Unless, you have a lot # of spinning sidebands in your MAF dataset, four sidebands should be enough. # # Once the ShieldingPALineshape instance is created, use the # :meth:`~mrinversion.kernel.nmr.ShieldingPALineshape.kernel` method of the # instance to generate the MAF line-shape kernel. K = lineshape.kernel(supersampling=1) print(K.shape) # %% # The kernel ``K`` is a NumPy array of shape (128, 625), where the axes with 128 and # 625 points are the anisotropic dimension and the features (x-y coordinates) # corresponding to the :math:`25\times 25` `x`-`y` grid, respectively. # %% # Data Compression # '''''''''''''''' # # Data compression is optional but recommended. It may reduce the size of the # inverse problem and, thus, further computation time. new_system = TSVDCompression(K, data_object_truncated) compressed_K = new_system.compressed_K
# is the same as the physical rotor frequency. For other measurements like the extended # chemical shift modulation sequences (XCS) [#f3]_, or its variants, the effective # anisotropic modulation frequency is lower than the physical rotor frequency and # should be set appropriately. # # The argument `number_of_sidebands` is the maximum number of computed # sidebands in the kernel. For most two-dimensional isotropic v.s pure # anisotropic spinning-sideband correlation measurements, the sampling along the # sideband dimension is the rotor or the effective anisotropic modulation # frequency. Therefore, the value of the `number_of_sidebands` argument is # usually the number of points along the sideband dimension. # In this example, this value is 32. # # Once the ShieldingPALineshape instance is created, use the kernel() # method to generate the spinning sideband lineshape kernel. K = sidebands.kernel(supersampling=2) print(K.shape) # %% # The kernel ``K`` is a NumPy array of shape (32, 625), where the axes with 32 and # 625 points are the spinning sidebands dimension and the features (x-y coordinates) # corresponding to the :math:`25\times 25` `x`-`y` grid, respectively. # %% # Data Compression # '''''''''''''''' # # Data compression is optional but recommended. It may reduce the size of the # inverse problem and, thus, further computation time. new_system = TSVDCompression(K, data_object_truncated) compressed_K = new_system.compressed_K
def test_01():
domain = "https://sandbox.zenodo.org/record/1065347/files"
filename = f"{domain}/8lnwmg0dr7y6egk40c2orpkmmugh9j7c.csdf"
data_object = cp.load(filename)
data_object = data_object.real
_ = [item.to("ppm", "nmr_frequency_ratio") for item in data_object.dimensions]
data_object = data_object.T
data_object_truncated = data_object[:, 155:180]
anisotropic_dimension = data_object_truncated.dimensions[0]
inverse_dimensions = [
cp.LinearDimension(count=25, increment="400 Hz", label="x"),
cp.LinearDimension(count=25, increment="400 Hz", label="y"),
]
lineshape = ShieldingPALineshape(
anisotropic_dimension=anisotropic_dimension,
inverse_dimension=inverse_dimensions,
channel="29Si",
magnetic_flux_density="9.4 T",
rotor_angle="87.14°",
rotor_frequency="14 kHz",
number_of_sidebands=4,
)
K = lineshape.kernel(supersampling=2)
new_system = TSVDCompression(K, data_object_truncated)
compressed_K = new_system.compressed_K
compressed_s = new_system.compressed_s
assert new_system.truncation_index == 87
s_lasso = SmoothLasso(
alpha=2.07e-7, lambda1=7.85e-6, inverse_dimension=inverse_dimensions
)
s_lasso.fit(K=compressed_K, s=compressed_s)
f_sol = s_lasso.f
residuals = s_lasso.residuals(K=K, s=data_object_truncated)
# assert np.allclose(residuals.mean().value, 0.00048751)
np.testing.assert_almost_equal(residuals.std().value, 0.00336372, decimal=2)
f_sol /= f_sol.max()
[item.to("ppm", "nmr_frequency_ratio") for item in f_sol.dimensions]
Q4_region = f_sol[0:8, 0:8, 3:18]
Q4_region.description = "Q4 region"
Q3_region = f_sol[0:8, 11:22, 8:20]
Q3_region.description = "Q3 region"
# Analysis
int_Q4 = stats.integral(Q4_region) # volume of the Q4 distribution
mean_Q4 = stats.mean(Q4_region) # mean of the Q4 distribution
std_Q4 = stats.std(Q4_region) # standard deviation of the Q4 distribution
int_Q3 = stats.integral(Q3_region) # volume of the Q3 distribution
mean_Q3 = stats.mean(Q3_region) # mean of the Q3 distribution
std_Q3 = stats.std(Q3_region) # standard deviation of the Q3 distribution
np.testing.assert_almost_equal(
(100 * int_Q4 / (int_Q4 + int_Q3)).value, 60.45388973909665, decimal=1
)
np.testing.assert_almost_equal(
np.asarray([mean_Q4[0].value, mean_Q4[1].value, mean_Q4[2].value]),
np.asarray([8.604842824865958, 9.05845796147297, -103.6976331077773]),
decimal=0,
)
np.testing.assert_almost_equal(
np.asarray([mean_Q3[0].value, mean_Q3[1].value, mean_Q3[2].value]),
np.asarray([10.35036818411856, 79.02481579085152, -90.58326773441284]),
decimal=0,
)
np.testing.assert_almost_equal(
np.asarray([std_Q4[0].value, std_Q4[1].value, std_Q4[2].value]),
np.asarray([4.525457744683861, 4.686253809896416, 5.369228151035292]),
decimal=0,
)
np.testing.assert_almost_equal(
np.asarray([std_Q3[0].value, std_Q3[1].value, std_Q3[2].value]),
np.asarray([6.138761032132587, 7.837190479891721, 4.210912435356488]),
decimal=0,
)