def test_raise_messages():
e = "Expecting a `Simulator` object, found"
with pytest.raises(ValueError, match=f".*{e}.*"):
sf.make_LMFIT_params(12, 21)
e = "Expecting a list of `SignalProcessor` objects."
with pytest.raises(ValueError, match=f".*{e}.*"):
sf.make_LMFIT_params(Simulator(spin_systems=[SpinSystem()]), 21)
def on_submit_signal_processor_button():
existing_data, method_index = setup()
existing_process_data = existing_data["signal_processors"]
n_dims = len(existing_data["methods"][method_index]["spectral_dimensions"])
processor_dict = generate_signal_processor_dict(n_dims)
existing_process_data[method_index] = processor_dict
print("submit process_data", existing_process_data)
# refresh lmfit parameters for signal processor processor
sim, pd, _ = parse(existing_data)
processors = [
sp.SignalProcessor.parse_dict_with_units(mth_proc)
for mth_proc in existing_process_data
]
params = make_LMFIT_params(sim, processors, include={"rotor_frequency"})
existing_data["params"] = params.dumps()
existing_data["trigger"] = {"simulation": True, "method_index": False}
out = {
"alert": ["", False],
"mrsim": [existing_data, no_update],
"children": [no_update] * 3,
"mrsim_config": [no_update] * 4,
"processor": [no_update],
}
return expand_output(out)
def test_6_coupled():
sim = Simulator()
spin_system = {"sites": [H, C], "couplings": [CH], "abundance": "100%"}
system_object = SpinSystem.parse_dict_with_units(spin_system)
sim.spin_systems += [system_object]
post_sim = sp.SignalProcessor(operations=op_list)
params = sf.make_LMFIT_params(sim, post_sim)
valuesdict_system = {
"sys_0_site_0_isotropic_chemical_shift": 10,
"sys_0_site_0_shielding_symmetric_zeta": 5,
"sys_0_site_0_shielding_symmetric_eta": 0.1,
"sys_0_site_0_shielding_symmetric_alpha": 3.12,
"sys_0_site_0_shielding_symmetric_gamma": 0.341,
"sys_0_site_1_isotropic_chemical_shift": -10,
"sys_0_site_1_shielding_symmetric_zeta": 15,
"sys_0_site_1_shielding_symmetric_eta": 0.2,
"sys_0_site_1_shielding_symmetric_beta": 4.12,
"sys_0_coupling_0_isotropic_j": 10,
"sys_0_coupling_0_j_symmetric_zeta": 60,
"sys_0_coupling_0_j_symmetric_eta": 0.4,
"sys_0_abundance": 100,
}
valuedict_proc = {
"SP_0_operation_1_Exponential_FWHM": 100,
"SP_0_operation_3_Scale_factor": 10,
}
assert params.valuesdict() == {
**valuesdict_system,
**valuedict_proc,
}, "Parameter creation failed"
params = sf.make_LMFIT_params(sim)
assert params.valuesdict(
) == valuesdict_system, "Parameter creation failed"
# alias
params = sf.make_LMFIT_parameters(sim)
assert params.valuesdict(
) == valuesdict_system, "Parameter creation failed"
def add_params(mrsim_data):
"""Adds updated params to mrsim_data"""
if mrsim_data is None:
return no_update
if mrsim_data["spin_systems"] is None or len(
mrsim_data["spin_systems"]) == 0:
return no_update
if mrsim_data["methods"] is None or len(mrsim_data["methods"]) == 0:
return no_update
sim, processor, _ = parse(mrsim_data)
params_obj = make_LMFIT_params(sim, processor, include={"rotor_frequency"})
mrsim_data["params"] = params_obj.dumps()
return mrsim_data
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
def compare_result(params, valuesdict_system, sim):
valuesdict_proc = {
"SP_0_operation_1_Exponential_FWHM": 100,
"SP_0_operation_3_Scale_factor": 10,
}
assert params.valuesdict() == {
**valuesdict_system,
**valuesdict_proc,
}, "Parameter creation failed"
params = sf.make_LMFIT_params(sim)
assert params.valuesdict(
) == valuesdict_system, "Parameter creation failed"
# alias
params = sf.make_LMFIT_parameters(sim)
assert params.valuesdict(
) == valuesdict_system, "Parameter creation failed"
def test_6_coupled():
sim = Simulator()
spin_system = {"sites": [H, C], "couplings": [CH], "abundance": "100%"}
system_object = SpinSystem.parse_dict_with_units(spin_system)
sim.spin_systems += [system_object]
post_sim = sp.SignalProcessor(operations=op_list)
params = sf.make_LMFIT_params(sim, post_sim)
valuesdict_system = {
"sys_0_site_0_isotropic_chemical_shift": 10,
"sys_0_site_0_shielding_symmetric_zeta": 5,
"sys_0_site_0_shielding_symmetric_eta": 0.1,
"sys_0_site_0_shielding_symmetric_alpha": 3.12,
"sys_0_site_0_shielding_symmetric_gamma": 0.341,
"sys_0_site_1_isotropic_chemical_shift": -10,
"sys_0_site_1_shielding_symmetric_zeta": 15,
"sys_0_site_1_shielding_symmetric_eta": 0.2,
"sys_0_site_1_shielding_symmetric_beta": 4.12,
"sys_0_coupling_0_isotropic_j": 10,
"sys_0_coupling_0_j_symmetric_zeta": 60,
"sys_0_coupling_0_j_symmetric_eta": 0.4,
"sys_0_abundance": 100,
}
compare_result(params, valuesdict_system, sim)
def test_5_multi_spin_systems():
sim = Simulator()
spin_system1 = {"sites": [H], "abundance": "100%"}
system_object1 = SpinSystem.parse_dict_with_units(spin_system1)
spin_system2 = {"sites": [C], "abundance": "60%"}
system_object2 = SpinSystem.parse_dict_with_units(spin_system2)
sim.spin_systems += [system_object1, system_object2]
post_sim = sp.SignalProcessor(operations=op_list)
params = sf.make_LMFIT_params(sim, post_sim)
valuesdict_system = {
"sys_0_site_0_isotropic_chemical_shift": 10,
"sys_0_site_0_shielding_symmetric_zeta": 5,
"sys_0_site_0_shielding_symmetric_eta": 0.1,
"sys_0_site_0_shielding_symmetric_alpha": 3.12,
"sys_0_site_0_shielding_symmetric_gamma": 0.341,
"sys_0_abundance": 62.5,
"sys_1_site_0_isotropic_chemical_shift": -10,
"sys_1_site_0_shielding_symmetric_zeta": 15,
"sys_1_site_0_shielding_symmetric_eta": 0.2,
"sys_1_site_0_shielding_symmetric_beta": 4.12,
"sys_1_abundance": 37.5,
}
compare_result(params, valuesdict_system, sim)
"r",
alpha=0.75,
linewidth=1,
label="guess spectrum")
ax.set_xlim(100, -100)
plt.grid()
plt.legend()
plt.tight_layout()
plt.show()
# %%
# Least-squares minimization with LMFIT
# -------------------------------------
# Use the :func:`~mrsimulator.utils.spectral_fitting.make_LMFIT_params` for a quick
# 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
# -------------------------------------
#
# Once you have a fitting model, you need to create the list of parameters to use in the
# least-squares minimization. For this, you may use the
# `Parameters <https://lmfit.github.io/lmfit-py/parameters.html>`_ class from *LMFIT*,
# as described in the previous example.
# Here, we make use of a utility function,
# :func:`~mrsimulator.utils.spectral_fitting.make_LMFIT_params`, to simplifies the
# LMFIT parameters generation process. By default, the function only creates parameters
# from the SpinSystem and SignalProcessor objects. Often, in spectrum with sidebands,
# spinning speed may not be accurately known; and is, therefore, included as a fitting
# parameter. To include a keyword from the method object, use the *include* argument
# of the function, as follows,
#
# **Step 6:** Create a list of parameters.
params = sf.make_LMFIT_params(sim, processor, include={"rotor_frequency"})
# %%
# The `make_LMFIT_params` parses the instances of the ``Simulator`` and the
# ``PostSimulator`` objects for parameters and returns a LMFIT `Parameters` object.
#
# **Customize the Parameters:**
# You may customize the parameters list, ``params``, as desired. Here, we remove the
# abundance parameter.
params.pop("sys_0_abundance")
print(params.pretty_print(columns=["value", "min", "max", "vary", "expr"]))
# %%
# **Step 7:** Perform the least-squares minimization. For the user's convenience, we
# also provide a utility function,
# :func:`~mrsimulator.utils.spectral_fitting.LMFIT_min_function`, for evaluating the
