def test_models(doctest_namespace):
cz_model = CzjzekDistribution(0.5)
doctest_namespace["cz_model"] = cz_model
S0 = {"Cq": 1e6, "eta": 0.3}
ext_cz_model = ExtCzjzekDistribution(S0, eps=0.35)
doctest_namespace["ext_cz_model"] = ext_cz_model
def test_extended_czjzek_eta_distribution_3():
filename = path.join(MODULE_DIR, "test_data", "eps=0.65.npy")
with open(filename, "rb") as f:
data = np.load(f)
S0 = {"Cq": 1e6, "eta": 0.7}
_, eta1 = ExtCzjzekDistribution(S0, eps=0.65).rvs(size=COUNT)
hist1, _ = np.histogram(eta1, bins=100, range=[0, 1])
message = "failed to compare values with file eps=0.05.npy"
np.testing.assert_almost_equal(hist1 / COUNT, data[3], decimal=2, err_msg=message)
def get_prob_dist(iso, Cq, eta, eps, cov):
pdf = 0
for i in range(len(iso)):
# The 2D amplitudes for Cq and eta is sampled from the extended Czjzek model.
avg_tensor = {"Cq": Cq[i], "eta": eta[i]}
_, _, amp = ExtCzjzekDistribution(avg_tensor,
eps=eps[i]).pdf(pos=[Cq_r, eta_r])
# The 1D amplitudes for isotropic chemical shifts is sampled as a Gaussian.
iso_amp = multivariate_normal(mean=iso[i], cov=[cov[i]]).pdf(iso_r)
# The 3D amplitude grid is generated as an uncorrelated distribution of the
# above two distribution, which is the product of the two distributions.
pdf_t = np.repeat(amp, iso_r.size).reshape(eta_r.size, Cq_r.size,
iso_r.size)
pdf_t *= iso_amp
pdf += pdf_t
return pdf
# %%
# Symmetric shielding tensor
# --------------------------
#
# Create the extended Czjzek distribution
# '''''''''''''''''''''''''''''''''''''''
#
# First, create a distribution of the zeta and eta parameters of the shielding tensors
# using the :ref:`extended_czjzek_distribution` model as follows,
# The range of zeta and eta coordinates over which the distribution is sampled.
z_lim = np.arange(100) * 0.4 + 40 # in ppm
e_lim = np.arange(21) / 20
dominant = {"zeta": 60, "eta": 0.3}
z_dist, e_dist, amp = ExtCzjzekDistribution(dominant, eps=0.14).pdf(pos=[z_lim, e_lim])
# %%
# The following is the plot of the extended Czjzek distribution.
plt.figure(figsize=(4.25, 3.0))
plt.contourf(z_dist, e_dist, amp, levels=10)
plt.xlabel(r"$\zeta$ / ppm")
plt.ylabel(r"$\eta$")
plt.tight_layout()
plt.show()
# %%
# Simulate the spectrum
# '''''''''''''''''''''
#
# Create the spin systems from the above :math:`\zeta` and :math:`\eta` parameters.
def test_extended_czjzek_polar():
S0 = {"zeta": 1, "eta": 0.1}
x, y = ExtCzjzekDistribution(S0, eps=0.05, polar=True).rvs(size=COUNT)
x1, y1 = x_y_from_zeta_eta(*x_y_to_zeta_eta(x, y))
np.testing.assert_almost_equal(x, x1)
np.testing.assert_almost_equal(y, y1)
