def test_array():
sim.run()
dataset = processor.apply_operations(sim.methods[0].simulation)
data_sum = 0
for dv in dataset.y:
data_sum += dv.components[0]
params = sf.make_LMFIT_params(sim, processor)
a = sf.LMFIT_min_function(params, sim, processor)
np.testing.assert_almost_equal(-a, data_sum, decimal=8)
dat = sf.add_csdm_dvs(dataset.real)
fits = sf.bestfit(sim, processor)
assert sf.add_csdm_dvs(fits[0]) == dat
res = sf.residuals(sim, processor)
assert res[0] == -dat
# setup of the fitting parameters.
params = sf.make_LMFIT_params(sim, processor)
params.pop("sys_0_abundance")
print(params.pretty_print(columns=["value", "min", "max", "vary", "expr"]))
# %%
# **Solve the minimizer using LMFIT**
minner = Minimizer(sf.LMFIT_min_function,
params,
fcn_args=(sim, processor, sigma))
result = minner.minimize()
result
# %%
# The best fit solution
# ---------------------
best_fit = sf.bestfit(sim, processor)[0].real
residuals = sf.residuals(sim, processor)[0].real
# Plot the spectrum
plt.figure(figsize=(4.25, 3.0))
ax = plt.subplot(projection="csdm")
ax.plot(experiment, "k", linewidth=1, label="Experiment")
ax.plot(best_fit, "r", alpha=0.75, linewidth=1, label="Best Fit")
ax.plot(residuals, alpha=0.75, linewidth=1, label="Residuals")
ax.set_xlim(100, -100)
plt.grid()
plt.legend()
plt.tight_layout()
plt.show()
# argument, respectively.
params = sf.make_LMFIT_params(sim, processors, include={"rotor_frequency"})
print(params.pretty_print(columns=["value", "min", "max", "vary", "expr"]))
# %%
# **Solve the minimizer using LMFIT**
minner = Minimizer(sf.LMFIT_min_function,
params,
fcn_args=(sim, processors, sigmas))
result = minner.minimize()
result
# %%
# The best fit solution
# ---------------------
all_best_fit = sf.bestfit(sim, processors) # a list of best fit simulations
all_residuals = sf.residuals(sim, processors) # a list of residuals
# Plot the spectrum
fig, ax = plt.subplots(1,
3,
figsize=(12, 3),
subplot_kw={"projection": "csdm"})
for i, proc in enumerate(processors):
ax[i].plot(experiments[i],
color="black",
linewidth=0.5,
label="Experiment")
ax[i].plot(all_residuals[i].real,
color="gray",
linewidth=0.5,
