def test_method_exp_sim(): spin_system = SpinSystem(sites=[ Site( isotope="27Al", isotropic_chemical_shift=64.5, # in ppm quadrupolar={ "Cq": 3.22e6, "eta": 0.66 }, # Cq is in Hz ) ]) data = cp.as_csdm(np.random.rand(1024, 512)) method = ThreeQ_VAS( channels=["27Al"], magnetic_flux_density=7, spectral_dimensions=[ { "count": 1024, "spectral_width": 5000, "reference_offset": -3e3 }, { "count": 512, "spectral_width": 10000, "reference_offset": 4e3 }, ], experiment=data, ) sim = Simulator() sim.spin_systems = [spin_system] sim.methods = [method] sim.run()
def pre_setup(isotope, shift, reference_offset): spin_system = SpinSystem( sites=[Site(isotope=isotope, isotropic_chemical_shift=shift)]) method = Method.parse_dict_with_units( dict( channels=[isotope], spectral_dimensions=[{ "count": 2048, "spectral_width": "25 kHz", "reference_offset": f"{reference_offset} Hz", "events": [{ "magnetic_flux_density": "9.4 T", "transition_queries": [{ "ch1": { "P": [-1] } }], }], }], )) sim = Simulator() sim.spin_systems.append(spin_system) sim.methods = [method] sim.run() x, y = sim.methods[0].simulation.to_list() return x[np.argmax(y)], abs(x[1] - x[0])
def test_empty_spin_sys_simulator(): sim = Simulator() sim.methods = [ BlochDecaySpectrum(channels=["1H"], spectral_dimensions=[{"count": 10}]) ] sim.config.decompose_spectrum = "spin_system" sim.run() assert np.allclose(sim.methods[0].simulation.y[0].components[0], 0)
def test_simulator_assignments(): a = Simulator() assert a.spin_systems == [] error = "value is not a valid list" with pytest.raises(Exception, match=f".*{error}.*"): a.spin_systems = "" with pytest.raises(Exception, match=f".*{error}.*"): a.methods = ""
def generate_simulator(spin_systems, method, integration_volume="octant", number_of_sidebands=64): sim = Simulator() sim.spin_systems = spin_systems sim.methods = [method] sim.config.integration_volume = integration_volume sim.config.number_of_sidebands = number_of_sidebands return sim
def pre_setup(): site_1 = Site(isotope="13C", shielding_symmetric={"zeta": 50, "eta": 0.5}) spin_system = SpinSystem(sites=[site_1]) method = BlochDecaySpectrum(channels=["13C"], spectral_dimensions=[{ "count": 1024, "spectral_width": 25000 }]) sim = Simulator() sim.spin_systems.append(spin_system) sim.methods = [method] return sim
def generate_shielding_kernel(zeta_, eta_, angle, freq, n_sidebands, to_ppm=True): method = BlochDecaySpectrum( channels=["29Si"], magnetic_flux_density=9.4, spectral_dimensions=[ dict(count=96, spectral_width=208.33 * 96, reference_offset=0) ], rotor_angle=angle, rotor_frequency=freq, ) if to_ppm: larmor_frequency = -method.channels[ 0].gyromagnetic_ratio * 9.4 # in MHz zeta_ /= larmor_frequency spin_systems = [ SpinSystem(sites=[ Site(isotope="29Si", shielding_symmetric={ "zeta": z, "eta": e }) ]) for z, e in zip(zeta_, eta_) ] sim = Simulator() sim.spin_systems = spin_systems sim.methods = [method] sim.config.decompose_spectrum = "spin_system" sim.config.number_of_sidebands = n_sidebands sim.run(pack_as_csdm=False) sim_lineshape = sim.methods[0].simulation.real sim_lineshape = np.asarray(sim_lineshape).reshape(4, 4, 96) sim_lineshape = sim_lineshape / sim_lineshape[0, 0].sum() sim_lineshape[0, :, :] /= 2.0 sim_lineshape[:, 0, :] /= 2.0 sim_lineshape.shape = (16, 96) return sim_lineshape
def test_simulator_2(): sim = Simulator() sim.spin_systems = [ SpinSystem( sites=[Site(isotope="1H"), Site(isotope="23Na")], couplings=[Coupling(site_index=[0, 1], isotropic_j=15)], ) ] sim.methods = [ BlochDecaySpectrum( channels=["1H"], spectral_dimensions=[{ "count": 10 }], experiment=cp.as_csdm(np.arange(10)), ) ] sim.name = "test" sim.label = "test0" sim.description = "testing-testing 1.2.3" sim.run() # save sim.save("test_sim_save.temp") sim_load = Simulator.load("test_sim_save.temp") sim_load_data = sim_load.methods[0].simulation sim_data = sim.methods[0].simulation sim_load_data._timestamp = "" assert sim_load_data.dict() == sim_data.dict() sim_load.methods[0].simulation = None sim.methods[0].simulation = None assert sim_load.spin_systems == sim.spin_systems assert sim_load.methods == sim.methods assert sim_load.name == sim.name assert sim_load.description == sim.description os.remove("test_sim_save.temp")
def kernel(self, supersampling=1): """ Return the NMR nuclear shielding anisotropic line-shape kernel. Args: supersampling: An integer. Each cell is supersampled by the factor `supersampling` along every dimension. Returns: A numpy array containing the line-shape kernel. """ args_ = deepcopy(self.method_args) method = BlochDecaySpectrum.parse_dict_with_units(args_) isotope = args_["channels"][0] zeta, eta = self._get_zeta_eta(supersampling) x_csdm = self.inverse_kernel_dimension[0] if x_csdm.coordinates.unit.physical_type == "frequency": # convert zeta to ppm if given in frequency units. zeta /= self.larmor_frequency # zeta in ppm for dim_i in self.inverse_kernel_dimension: if dim_i.origin_offset.value == 0: dim_i.origin_offset = f"{abs(self.larmor_frequency)} MHz" spin_systems = [ SpinSystem(sites=[ dict(isotope=isotope, shielding_symmetric=dict(zeta=z, eta=e)) ]) for z, e in zip(zeta, eta) ] sim = Simulator() sim.config.number_of_sidebands = self.number_of_sidebands sim.config.decompose_spectrum = "spin_system" sim.spin_systems = spin_systems sim.methods = [method] sim.run(pack_as_csdm=False) amp = sim.methods[0].simulation.real return self._averaged_kernel(amp, supersampling)
def test_7(): site = Site(isotope="23Na") sys = SpinSystem(sites=[site], abundance=50) sim = Simulator() sim.spin_systems = [sys, sys] sim.methods = [BlochDecayCTSpectrum(channels=["23Na"])] sim.methods[0].experiment = cp.as_csdm(np.zeros(1024)) processor = sp.SignalProcessor(operations=[ sp.IFFT(dim_index=0), sp.apodization.Gaussian(FWHM="0.2 kHz", dim_index=0), sp.FFT(dim_index=0), ]) 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 test_array() sim.config.decompose_spectrum = "spin_system" test_array()
def test_ThreeQ_VAS_spin_3halves(): site = Site( isotope="87Rb", isotropic_chemical_shift=-9, shielding_symmetric={ "zeta": 100, "eta": 0 }, quadrupolar={ "Cq": 3.5e6, "eta": 0.36, "beta": 70 / 180 * np.pi }, ) spin_system = SpinSystem(sites=[site]) method = ThreeQ_VAS( channels=["87Rb"], magnetic_flux_density=9.4, spectral_dimensions=[ { "count": 1024, "spectral_width": 20000 }, { "count": 512, "spectral_width": 20000 }, ], ) sim = Simulator() sim.spin_systems = [spin_system] sim.methods = [method] sim.config.integration_volume = "hemisphere" sim.run() data = sim.methods[0].simulation dat = data.y[0].components[0] index = np.where(dat == dat.max())[0] # The isotropic coordinate of this peak is given by # v_iso = (17/8)*iso_shift + 1e6/8 * (vq/v0)^2 * (eta^2 / 3 + 1) # ref: D. Massiot et al. / Solid State Nuclear Magnetic Resonance 6 (1996) 73-83 spin = method.channels[0].spin v0 = method.channels[0].gyromagnetic_ratio * 9.4 * 1e6 vq = (3 * 3.5e6) / (2 * spin * (2 * spin - 1)) v_iso = -9 * 17 / 8 + 1e6 / 8 * ((vq / v0)**2) * ((0.36**2) / 3 + 1) # the coordinate from spectrum along the iso dimension must be equal to v_iso v_iso_spectrum = data.x[1].coordinates[index[0]].value np.testing.assert_almost_equal(v_iso, v_iso_spectrum, decimal=2) # The projection onto the MAS dimension should be the 1D block decay central # transition spectrum mas_slice = data.sum(axis=1).y[0].components[0] # MAS spectrum method = BlochDecayCTSpectrum( channels=["87Rb"], magnetic_flux_density=9.4, rotor_frequency=1e9, spectral_dimensions=[{ "count": 512, "spectral_width": 20000 }], ) sim = Simulator() sim.spin_systems = [spin_system] sim.methods = [method] sim.config.integration_volume = "hemisphere" sim.run() data = sim.methods[0].simulation.y[0].components[0] assert np.allclose(data / data.max(), mas_slice / mas_slice.max())
label="Fast MAS dimension", ), ], ) # A graphical representation of the method object. plt.figure(figsize=(5, 3.5)) qmat.plot() plt.show() # %% # Create the Simulator object, add the method and spin system objects, and # run the simulation. sim = Simulator() sim.spin_systems = spin_systems # add the spin systems sim.methods = [qmat] # add the method. # For 2D spinning sideband simulation, set the number of spinning sidebands in the # Simulator.config object to `spectral_width/rotor_frequency` along the sideband # dimension. sim.config.number_of_sidebands = 32 sim.run() # %% # The plot of the simulation. plt.figure(figsize=(4.25, 3.0)) data = sim.methods[0].simulation.real ax = plt.subplot(projection="csdm") cb = ax.imshow(data / data.max(), aspect="auto", cmap="gist_ncar_r", vmax=0.15) plt.colorbar(cb) ax.invert_xaxis()
"spectral_width": 50000, # in Hz "label": r"$^{17}$O resonances", }], ) # %% # The above method is set up to record the :math:`^{17}\text{O}` resonances at the # magic angle, spinning at 14 kHz and 9.4 T (default, if the value is not provided) # external magnetic flux density. The resonances are recorded over 50 kHz spectral # width using 2048 points. # %% # **Step 4:** Create the Simulator object and add the method and spin system objects. sim = Simulator() sim.spin_systems = spin_systems # add the spin systems sim.methods = [method] # add the method # %% # **Step 5:** Simulate the spectrum. sim.run() # The plot of the simulation before signal processing. ax = plt.subplot(projection="csdm") ax.plot(sim.methods[0].simulation.real, color="black", linewidth=1) ax.invert_xaxis() plt.tight_layout() plt.show() # %% # **Step 6:** Add post-simulation signal processing. processor = sp.SignalProcessor(operations=[
2e4, # in Hz "reference_offset": 0, # in Hz "label": "MAS dimension", "events": [{ "rotor_angle": 54.735 * 3.14159 / 180, "transition_query": [{ "P": [-1], "D": [0] }], }], }, ], ) sim.methods = [das] # add the method. # %% # Run the simulation sim.run() # %% # The plot of the simulation. data = sim.methods[0].simulation plt.figure(figsize=(4.25, 3.0)) ax = plt.subplot(projection="csdm") cb = ax.imshow(data / data.max(), aspect="auto", cmap="gist_ncar_r") plt.colorbar(cb) ax.invert_xaxis() ax.invert_yaxis()
}, { "count": 512, "spectral_width": 5e3, # in Hz "reference_offset": -4e3, # in Hz "label": "MAS dimension", }, ], )) # %% # Create the Simulator object, add the method and spin system objects, and # run the simulation. sim = Simulator() sim.spin_systems = spin_systems # add the spin systems sim.methods = method # add the methods. sim.run() # %% # The plot of the simulation. data = [sim.methods[0].simulation, sim.methods[1].simulation] fig, ax = plt.subplots(1, 2, figsize=(8.5, 3), subplot_kw={"projection": "csdm"}) titles = ["STVAS @ magic-angle", "STVAS @ 0.0059 deg off magic-angle"] for i, item in enumerate(data): cb1 = ax[i].imshow(item / item.max(), aspect="auto", cmap="gist_ncar_r") ax[i].set_title(titles[i]) plt.colorbar(cb1, ax=ax[i])
"reference_offset": -1.05e4, # in Hz "label": "Isotropic dimension", "events": [{ "rotor_angle": 54.735 * 3.14159 / 180 }], }, ], affine_matrix=[[1, -1], [0, 1]], ) # %% # Create the Simulator object, add the method and spin system objects, and run the # simulation. sim = Simulator() sim.spin_systems = spin_systems # add the spin systems sim.methods = [maf] # add the method sim.run() # %% # Add post-simulation signal processing. csdm_data = sim.methods[0].simulation processor = sp.SignalProcessor(operations=[ sp.IFFT(dim_index=(0, 1)), apo.Gaussian(FWHM="50 Hz", dim_index=0), apo.Gaussian(FWHM="50 Hz", dim_index=1), sp.FFT(dim_index=(0, 1)), ]) processed_data = processor.apply_operations(data=csdm_data).real processed_data /= processed_data.max() # %%
rotor_frequency=2000, spectral_dimensions=[ { "count": 20 * 4, "spectral_width": 2000 * 20, # value in Hz "label": "Anisotropic dimension", }, { "count": 1024, "spectral_width": 3e4, # value in Hz "reference_offset": 1.1e4, # value in Hz "label": "Isotropic dimension", }, ], ) sim.methods = [PASS] # add the method. # For 2D spinning sideband simulation, set the number of spinning sidebands in the # Simulator.config object to `spectral_width/rotor_frequency` along the sideband # dimension. sim.config.number_of_sidebands = 20 # run the simulation. sim.run() # %% # Apply post-simulation processing. Here, we apply a Lorentzian line broadening to the # isotropic dimension. data = sim.methods[0].simulation processor = sp.SignalProcessor(operations=[ sp.IFFT(dim_index=0),
], ), ], ) # A graphical representation of the method object. plt.figure(figsize=(5, 3.5)) coaster.plot() plt.show() # %% # Create the Simulator object, add the method and spin system objects, and # run the simulation. sim = Simulator() sim.spin_systems = [spin_system] # add the spin systems sim.methods = [coaster] # add the method. # configure the simulator object. For non-coincidental tensors, set the value of the # `integration_volume` attribute to `hemisphere`. sim.config.integration_volume = "hemisphere" sim.run() # %% # The plot of the simulation. data = sim.methods[0].simulation plt.figure(figsize=(4.25, 3.0)) ax = plt.subplot(projection="csdm") cb = ax.imshow(data.real / data.real.max(), aspect="auto", cmap="gist_ncar_r") plt.colorbar(cb) ax.invert_xaxis()
label="Isotropic dimension", ), dict( count=512, spectral_width=2e4, # in Hz reference_offset=2e3, # in Hz label="MAS dimension", ), ], ) # %% # Create the simulator object, add the spin systems and method, and run the simulation. sim = Simulator() sim.spin_systems = spin_systems # add the spin systems sim.methods = [mqvas] # add the method sim.config.number_of_sidebands = 1 sim.run() data = sim.methods[0].simulation.real # %% # The plot of the corresponding spectrum. plt.figure(figsize=(4.25, 3.0)) ax = plt.subplot(projection="csdm") cb = ax.imshow(data / data.max(), cmap="gist_ncar_r", aspect="auto") plt.colorbar(cb) ax.set_ylim(-20, -50) ax.set_xlim(80, 20) plt.tight_layout() plt.show()
channels=["15N"], magnetic_flux_density=11.74, # in T rotor_frequency=3000, # in Hz spectral_dimensions=[{ "count": 8192, "spectral_width": 4e4, # in Hz "reference_offset": 7e3, # in Hz "label": r"$^{15}$N resonances", }], ) # %% # Add the methods to the Simulator object and run the simulation # Add the methods. sim.methods = [method_13C, method_15N] # Run the simulation. sim.run() # Get the simulation data from the respective methods. data_13C = sim.methods[ 0].simulation # method at index 0 is 13C Bloch decay method. data_15N = sim.methods[ 1].simulation # method at index 1 is 15N Bloch decay method. # %% # Add post-simulation signal processing. processor = sp.SignalProcessor( operations=[sp.IFFT(), sp.apodization.Exponential(FWHM="10 Hz"),
def setup_simulation(site, affine_matrix, class_id=0): isotope = site.isotope.symbol spin_system = [SpinSystem(sites=[site])] method_C = method_class[class_id] method = method_C( channels=[isotope], magnetic_flux_density=7, # in T rotor_angle=54.735 * np.pi / 180, spectral_dimensions=[ { "count": 512, "spectral_width": 4e4, # in Hz "reference_offset": -3e3, # in Hz }, { "count": 1024, "spectral_width": 1e4, # in Hz "reference_offset": -4e3, # in Hz }, ], affine_matrix=affine_matrix, ) method_1 = Method( channels=[isotope], magnetic_flux_density=7, # in T rotor_angle=54.735 * np.pi / 180, rotor_frequency=np.inf, spectral_dimensions=[ { "count": 1024, "spectral_width": 1e4, # in Hz "reference_offset": -4e3, # in Hz "events": [ { "fraction": 27 / 17, "freq_contrib": ["Quad2_0"], "transition_queries": [{"ch1": {"P": [-1], "D": [0]}}], }, { "fraction": 1, "freq_contrib": ["Quad2_4"], "transition_queries": [{"ch1": {"P": [-1], "D": [0]}}], }, ], } ], ) sim = Simulator() sim.spin_systems = spin_system # add the spin-system sim.methods = [method, method_1] # add the method # run the simulation sim.config.number_of_sidebands = 1 sim.run() csdm_data1 = sim.methods[0].simulation.real.sum(axis=1) csdm_data2 = sim.methods[1].simulation.real csdm_data1 /= csdm_data1.max() csdm_data2 /= csdm_data2.max() np.testing.assert_almost_equal( csdm_data1.y[0].components, csdm_data2.y[0].components, decimal=3 )
# ''''''''''''''''''''' # # Create the spin systems from the above :math:`\zeta` and :math:`\eta` parameters. systems = single_site_system_generator(isotopes="13C", shielding_symmetric={ "zeta": z_dist, "eta": e_dist }, abundance=amp) print(len(systems)) # %% # Create a simulator object and add the above system. sim = Simulator() sim.spin_systems = systems # add the systems sim.methods = [BlochDecaySpectrum(channels=["13C"])] # add the method sim.run() # %% # The following is the static spectrum arising from a Czjzek distribution of the # second-rank traceless shielding tensors. plt.figure(figsize=(4.25, 3.0)) ax = plt.subplot(projection="csdm") ax.plot(sim.methods[0].simulation, color="black", linewidth=1) plt.tight_layout() plt.show() # %% # Quadrupolar tensor # ------------------ #
def test_MQMAS(): site = Site( isotope="87Rb", isotropic_chemical_shift=-9, shielding_symmetric={ "zeta": 100, "eta": 0 }, quadrupolar={ "Cq": 3.5e6, "eta": 0.36, "beta": 70 / 180 * np.pi }, ) spin_system = SpinSystem(sites=[site]) method = Method2D( channels=["87Rb"], magnetic_flux_density=9.4, spectral_dimensions=[ { "count": 128, "spectral_width": 20000, "events": [{ "transition_query": [{ "P": [-3], "D": [0] }] }], }, { "count": 128, "spectral_width": 20000, "events": [{ "transition_query": [{ "P": [-1], "D": [0] }] }], }, ], ) sim = Simulator() sim.spin_systems = [spin_system] sim.methods = [method] sim.config.integration_volume = "hemisphere" sim.run() # process k = 21 / 27 # shear factor processor = sp.SignalProcessor(operations=[ sp.IFFT(dim_index=1), aft.Shear(factor=-k, dim_index=1, parallel=0), aft.Scale(factor=1 + k, dim_index=1), sp.FFT(dim_index=1), ]) processed_data = processor.apply_operations( data=sim.methods[0].simulation).real # Since there is a single site, after the shear and scaling transformations, there # should be a single perak along the isotropic dimension at index 70. # The isotropic coordinate of this peak is given by # w_iso = (17.8)*iso_shift + 1e6/8 * (vq/v0)^2 * (eta^2 / 3 + 1) # ref: D. Massiot et al. / Solid State Nuclear Magnetic Resonance 6 (1996) 73-83 iso_slice = processed_data[40, :] assert np.argmax(iso_slice.y[0].components[0]) == 70 # calculate the isotropic coordinate spin = method.channels[0].spin w0 = method.channels[0].gyromagnetic_ratio * 9.4 * 1e6 wq = 3 * 3.5e6 / (2 * spin * (2 * spin - 1)) w_iso = -9 * 17 / 8 + 1e6 / 8 * (wq / w0)**2 * ((0.36**2) / 3 + 1) # the coordinate from spectrum w_iso_spectrum = processed_data.x[1].coordinates[70].value np.testing.assert_almost_equal(w_iso, w_iso_spectrum, decimal=2) # The projection onto the MAS dimension should be the 1D block decay central # transition spectrum mas_slice = processed_data.sum(axis=1).y[0].components[0] # MAS spectrum method = BlochDecayCTSpectrum( channels=["87Rb"], magnetic_flux_density=9.4, rotor_frequency=1e9, spectral_dimensions=[{ "count": 128, "spectral_width": 20000 }], ) sim = Simulator() sim.spin_systems = [spin_system] sim.methods = [method] sim.config.integration_volume = "hemisphere" sim.run() data = sim.methods[0].simulation.y[0].components[0] np.testing.assert_almost_equal(data / data.max(), mas_slice / mas_slice.max(), decimal=2, err_msg="not equal")
} method2 = { "channels": ["1H"], "magnetic_flux_density": "9.4 T", "rotor_frequency": "1 kHz", "rotor_angle": "54.735 deg", "spectral_dimensions": [ {"count": 2048, "spectral_width": "25 kHz", "reference_offset": "0 Hz",} ], } sim = Simulator() sim.spin_systems = [SpinSystem.parse_dict_with_units(item) for item in spin_systems] sim.methods = [ BlochDecaySpectrum.parse_dict_with_units(method1), BlochDecaySpectrum.parse_dict_with_units(method2), ] sim.run() freq1, amp1 = sim.methods[0].simulation.to_list() freq2, amp2 = sim.methods[1].simulation.to_list() fig, ax = plt.subplots(1, 2, figsize=(6, 3)) ax[0].plot(freq1, amp1, linewidth=1.0, color="k") ax[0].set_xlabel(f"frequency ratio / {freq2.unit}") ax[0].grid(color="gray", linestyle="--", linewidth=0.5, alpha=0.5) ax[0].set_title("Static") ax[1].plot(freq2, amp2, linewidth=1.0, color="k") ax[1].set_xlabel(f"frequency ratio / {freq2.unit}") ax[1].grid(color="gray", linestyle="--", linewidth=0.5, alpha=0.5)
def test_MQMAS_spin_5halves(): spin_system = SpinSystem(sites=[ Site( isotope="27Al", isotropic_chemical_shift=64.5, # in ppm quadrupolar={ "Cq": 3.22e6, "eta": 0.66 }, # Cq is in Hz ) ]) method = ThreeQ_VAS( channels=["27Al"], magnetic_flux_density=7, spectral_dimensions=[ { "count": 1024, "spectral_width": 5000, "reference_offset": -3e3 }, { "count": 512, "spectral_width": 10000, "reference_offset": 4e3 }, ], ) sim = Simulator() sim.spin_systems = [spin_system] sim.methods = [method] sim.run() data = sim.methods[0].simulation dat = data.y[0].components[0] index = np.where(dat == dat.max())[0] # The isotropic coordinate of this peak is given by # v_iso = -(17/31)*iso_shift + 8e6/93 * (vq/v0)^2 * (eta^2 / 3 + 1) # ref: D. Massiot et al. / Solid State Nuclear Magnetic Resonance 6 (1996) 73-83 spin = method.channels[0].spin v0 = method.channels[0].gyromagnetic_ratio * 7 * 1e6 vq = 3 * 3.22e6 / (2 * spin * (2 * spin - 1)) v_iso = -(17 / 31) * 64.5 - (8e6 / 93) * (vq / v0)**2 * ((0.66**2) / 3 + 1) # the coordinate from spectrum along the iso dimension must be equal to v_iso v_iso_spectrum = data.x[1].coordinates[index[0]].value np.testing.assert_almost_equal(v_iso, v_iso_spectrum, decimal=2) # The projection onto the MAS dimension should be the 1D block decay central # transition spectrum mas_slice = data.sum(axis=1).y[0].components[0] # MAS spectrum method = BlochDecayCTSpectrum( channels=["27Al"], magnetic_flux_density=7, rotor_frequency=1e9, spectral_dimensions=[{ "count": 512, "spectral_width": 10000, "reference_offset": 4e3 }], ) sim = Simulator() sim.spin_systems = [spin_system] sim.methods = [method] sim.config.integration_volume = "hemisphere" sim.run() data = sim.methods[0].simulation.y[0].components[0] assert np.allclose(data / data.max(), mas_slice / mas_slice.max())
def generate_simulator(spin_systems, method): sim = Simulator() sim.spin_systems = spin_systems sim.methods = [method] return sim
def SSB2D_setup(ist, vr, method_type): sites = [ Site( isotope=ist, isotropic_chemical_shift=29, shielding_symmetric={ "zeta": -70, "eta": 0.000 }, ), Site( isotope=ist, isotropic_chemical_shift=44, shielding_symmetric={ "zeta": -96, "eta": 0.166 }, ), Site( isotope=ist, isotropic_chemical_shift=57, shielding_symmetric={ "zeta": -120, "eta": 0.168 }, ), ] spin_systems = [SpinSystem(sites=[s]) for s in sites] B0 = 11.7 if method_type == "PASS": method = SSB2D( channels=[ist], magnetic_flux_density=B0, # in T rotor_frequency=vr, spectral_dimensions=[ { "count": 32, "spectral_width": 32 * vr, # in Hz "label": "Anisotropic dimension", }, # The last spectral dimension block is the direct-dimension { "count": 2048, "spectral_width": 2e4, # in Hz "reference_offset": 5e3, # in Hz "label": "Fast MAS dimension", }, ], ) else: method = Method2D( channels=[ist], magnetic_flux_density=B0, # in T spectral_dimensions=[ { "count": 64, "spectral_width": 8e4, # in Hz "label": "Anisotropic dimension", "events": [{ "rotor_angle": 90 * 3.14159 / 180 }], }, # The last spectral dimension block is the direct-dimension { "count": 2048, "spectral_width": 2e4, # in Hz "reference_offset": 5e3, # in Hz "label": "Fast MAS dimension", }, ], affine_matrix=[[1, -1], [0, 1]], ) sim = Simulator() sim.spin_systems = spin_systems # add spin systems sim.methods = [method] # add the method. sim.run() data_ssb = sim.methods[0].simulation dim_ssb = data_ssb.x[0].coordinates.value if method_type == "PASS": bloch = BlochDecaySpectrum( channels=[ist], magnetic_flux_density=B0, # in T rotor_frequency=vr, # in Hz spectral_dimensions=[ { "count": 32, "spectral_width": 32 * vr, # in Hz "reference_offset": 0, # in Hz "label": "MAS dimension", }, ], ) else: bloch = BlochDecaySpectrum( channels=[ist], magnetic_flux_density=B0, # in T rotor_frequency=vr, # in Hz rotor_angle=90 * 3.14159 / 180, spectral_dimensions=[ { "count": 64, "spectral_width": 8e4, # in Hz "reference_offset": 0, # in Hz "label": "MAS dimension", }, ], ) for i in range(3): iso = spin_systems[i].sites[0].isotropic_chemical_shift sys = spin_systems[i].copy() sys.sites[0].isotropic_chemical_shift = 0 sim2 = Simulator() sim2.spin_systems = [sys] # add spin systems sim2.methods = [bloch] # add the method. sim2.run() index = np.where(dim_ssb < iso)[0][-1] one_d_section = data_ssb.y[0].components[0][:, index] one_d_section /= one_d_section.max() one_d_sim = sim2.methods[0].simulation.y[0].components[0] one_d_sim /= one_d_sim.max() np.testing.assert_almost_equal(one_d_section, one_d_sim, decimal=6)
# As mentioned before, a method object is decoupled from the spin system object. Notice, # when we get the transition pathways from this method for a single-site spin system, we # get a single transition pathway. print(hahn_echo.get_transition_pathways(spin_system_1)) # %% # In the case of a homonuclear two-site spin 1/2 spin system, the same method returns # four transition pathways. print(hahn_echo.get_transition_pathways(spin_system_2)) # %% # Create the Simulator object, add the method and spin system objects, and run the # simulation. sim = Simulator() sim.spin_systems = [spin_system_1, spin_system_2] # add the spin systems sim.methods = [hahn_echo] # add the method sim.config.decompose_spectrum = "spin_system" sim.run() # %% # The simulation from each spin system is stored as a dependent variable within the # CSDM object. Use the `split` function to split the list of the dependent variables # into a list of CSDM objects. simulation_results = sim.methods[0].simulation.split() # The plot of the two simulations. fig, ax = plt.subplots(1, 2, figsize=(8.0, 3.0), subplot_kw={"projection": "csdm"})
# %% # Simulate the spectrum # ''''''''''''''''''''' # # Create the spin systems from the above :math:`\zeta` and :math:`\eta` parameters. systems = single_site_system_generator( isotope="13C", shielding_symmetric={"zeta": z_dist, "eta": e_dist}, abundance=amp ) print(len(systems)) # %% # Create a simulator object and add the above system. sim = Simulator() sim.spin_systems = systems # add the systems sim.methods = [BlochDecaySpectrum(channels=["13C"])] # add the method sim.run() # %% # The following is the static spectrum arising from a Czjzek distribution of the # second-rank traceless shielding tensors. plt.figure(figsize=(4.25, 3.0)) ax = plt.subplot(projection="csdm") ax.plot(sim.methods[0].simulation.real, color="black", linewidth=1) plt.tight_layout() plt.show() # %% # Quadrupolar tensor # ------------------ #
abundance=pdf, ) # %% # Static spectrum # --------------- # Observe the static :math:`^{27}\text{Al}` NMR spectrum simulation. First, # create a central transition selective Bloch decay spectrum method. static_method = BlochDecayCTSpectrum( channels=["27Al"], spectral_dimensions=[dict(spectral_width=80000)]) # %% # Create the simulator object and add the spin systems and method. sim = Simulator() sim.spin_systems = spin_systems # add the spin systems sim.methods = [static_method] # add the method sim.run() # %% # The plot of the corresponding spectrum. plt.figure(figsize=(4.25, 3.0)) ax = plt.subplot(projection="csdm") ax.plot(sim.methods[0].simulation.real, color="black", linewidth=1) ax.invert_xaxis() plt.tight_layout() plt.show() # %% # Spinning sideband simulation at the magic angle # ----------------------------------------------- # Simulation of the same spin systems at the magic angle and spinning at 25 kHz.