`NuclearReflectivityEnergy`

class nexus.NuclearReflectivityEnergy(Measurement self, Experiment experiment, std::string const id="")

class nexus.NuclearReflectivityEnergy(Measurement self, Experiment experiment, DoubleVector detuning, std::string const id="") → Measurement

Bases: FitMeasurement

Constructor for the NuclearReflectivityEnergy class. Class to calculate the angular-dependent and energy-integrated total scattering intensity of the experiment while for one sample the gracing-incidence angle is changed.

Parameters:

id (string) – User identifier.
experiment (Experiment) – Experiment.
sample (GrazingSample) –
Sample whose angle is changed.

Changed in version 2.0.0: changed from old Sample class to GrazingSample.
angles (list) – List of the angles.
detuning (list or ndarray) – Detuning values for the calculation.
intensity_data (list or ndarray) – Intensity data for fitting.
scaling (float or Var or string) – Intensity scaling factor for fitting. Default is auto.
background (float or Var or string) – Background for fitting. Default is 0.
offset (float or Var) –
Angular offset of the model. Default is 0.

Added in version 1.1.0.
resolution (float or Var) –
Resolution value for convolution.

Removed in version 2.0.0.
fit_weight (float) – Relative weight for the cost function in multi measurement fitting.
instrument (Instrument) –
Instrument object with the implementation of a resolution function. Default is a Gaussian instrument function with 0.001 degree FWHM.

Added in version 2.0.0.
residual (Residual) – Implementation of the residual calculation used for fitting.
time_gate (list) –
An empty or two element list. If a two element list is passed these two values [start, stop] are taken as a time gating. The spectrum is Fourier transformed, the time gate is applied, and time response is transformed back to a spectrum. This does not give a time gated Moessbauer spectrum. Given in ns.

Added in version 1.0.1.
coherence (bool) –
Determines if the summation over the thickness and divergence distributions is performed coherently (True) or incoherently (False). Default is False.

Added in version 1.0.3.
print_progrees (bool) –
Determines if the progress bar is printed. Default is False.

Added in version 1.2.0.

id

User identifier.

Type:: string

experiment

Experiment.

Type:: Experiment

sample

Sample whose angle is changed.

Changed in version 2.0.0: changed from old Sample class to GrazingSample.

Type:: GrazingSample

angles

List of the angles.

Type:: list

detuning

Detuning values for the calculation.

Type:: list or ndarray

result

List of angle-dependent energy-integrated intensity values in units of (\(\Gamma\)).

Type:: list

intensity_data

Intensity data for fitting.

Type:: list

data_size

Number of data points of experimental data.

Type:: int

scaling

Intensity scaling factor for fitting.

Type:: Var

background

Background for fitting.

Type:: Var

offset

Angular offset of the model. Default is 0.

Added in version 1.1.0.

Type:: float or Var

resolution

Resolution value for convolution.

Removed in version 2.0.0.

Type:: Var

resolution_kernel

Weight of the Gaussian resolution same step size as detuning.

Removed in version 2.0.0.

Type:: list

fit_weight

Relative weight for the cost function in multi measurement fitting.

Type:: float

instrument

Instrument object with the implementation of a resolution function. Default is a Gaussian instrument function with 0.001 degree FWHM.

Added in version 2.0.0.

Type:: Instrument

residual

Implementation of the residual calculation used for fitting.

Type:: Residual

time_gate

An empty or two element list. If a two element list is passed these two values [start, stop] are taken as a time gating. The spectrum is Fourier transformed, the time gate is applied, and time response is transformed back to a spectrum. This does not give a time gated Moessbauer spectrum. Given in ns.

Added in version 1.0.1.

Type:: list

coherence

Determines if the summation over the thickness and divergence distributions is performed coherently (True) or incoherently (False). Default is False.

Added in version 1.0.3.

Type:: bool

print_progrees

Determines if the progress bar is printed. Default is False.

Added in version 1.2.0.

Type:: bool

Calculate()

Calculates the class method. Also callable via operator ().

Returns:: Array of intensity values.
Return type:: ndarray

Cost()

Get the cost function of the measurement

\[cost = \frac{1}{n} \sum_i r(y_i, \hat{y}_i)\]

Added in version 2.0.0.

Returns:: cost function value.
Return type:: float

MeanSquaredError()

Get the mean squared error (MSE) of the measurement

\[cost = \frac{1}{n} \sum_i (y_i - \hat{y}_i)^2\]

Added in version 2.0.0.

Returns:: MSE value.
Return type:: float

Plot(data=True, residuals=True, datacolor='black', theorycolor='r', theorywidth=2, datalinestyle='none', datamarker='+', datamarkersize=2, datafillstyle='full', legend=True, errors=False, errorcap=2, name=None)

Plot the NuclearReflectivityEnergy.

Added in version 1.0.3.

Changed in version 1.2.0.

Parameters:

data (bool) – Select if intensity_data should be plot.
residuals (bool) – Select if residuals from fit should be plot.
datacolor (string) – See matplotlib documentation.
theorycolor (string) – See matplotlib documentation.
theorywidth (float) – See matplotlib documentation.
datalinestyle (string) – See matplotlib documentation.
datamarker (string) – See matplotlib documentation.
datamarkersize (float) – See matplotlib documentation.
datafillstyle (string) – See matplotlib documentation.
legend (bool) – Select if legend should be plot when model and data are plot.
errors (bool) – Select if error bars should be plot.
errorcap (float) – See matplotlib documentation (capsize).
name (string) –
If given, the plot is saved under this name.

Added in version 1.1.0.

PopulateFitHandler(FitMeasurement self, OptimizerHandler fit_handler)

PopulateOptimizerHandler(Measurement self, OptimizerHandler optimizer_handler)

Residuals(NuclearReflectivityEnergy self) → double

SetCeresFitParameterPointers(FitMeasurement self, double const *const * parameters)

SetFitEqualities(FitMeasurement self)

SetNLoptFitParameterPointers(FitMeasurement self)

SetPagmoFitParameterPointers(FitMeasurement self, DoubleVector inputs)

Update(Measurement self)

Velocity()

Returns the velocity detuning of the measurement in units of mm/s.

Returns:: Velocity detuning (mm/s).
Return type:: ndarray

property angles

std::vector<(double,std::allocator<(double)>)>

Type:: angles

property background

p.Var

Type:: background

property bare_residuals

std::vector<(double,std::allocator<(double)>)>

Type:: bare_residuals

property bootstrap_fit_result

std::vector<(double,std::allocator<(double)>)>

Type:: bootstrap_fit_result

property bootstrap_residuals

std::vector<(double,std::allocator<(double)>)>

Type:: bootstrap_residuals

property coherence

bool

Type:: coherence

property data_size

size_t

Type:: data_size

property detuning

std::vector<(double,std::allocator<(double)>)>

Type:: detuning

property distribution_points

unsigned int

Type:: distribution_points

property experiment

p.Experiment

Type:: experiment

property fit_equalities

std::vector<(p.Var,std::allocator<(p.Var)>)>

Type:: fit_equalities

property fit_parameter_pointers

std::vector<(p.double,std::allocator<(p.double)>)>

Type:: fit_parameter_pointers

property fit_residuals

std::vector<(double,std::allocator<(double)>)>

Type:: fit_residuals

property fit_weight

double

Type:: fit_weight

property id

std::string

Type:: id

property inequality

p.Inequality

Type:: inequality

property input_intensity_data

std::vector<(double,std::allocator<(double)>)>

Type:: input_intensity_data

property instrument

p.Instrument

Type:: instrument

property intensity_data

std::vector<(double,std::allocator<(double)>)>

Type:: intensity_data

property nlopt_fit_parameters_ptr

p.double

Type:: nlopt_fit_parameters_ptr

property offset

p.Var

Type:: offset

property print_progress

bool

Type:: print_progress

property residual

p.Residual

Type:: residual

property result

std::vector<(double,std::allocator<(double)>)>

Type:: result

property sample

p.GrazingSample

Type:: sample

property scaling

p.Var

Type:: scaling

property sum_user_residuals

double

Type:: sum_user_residuals

property time

std::vector<(double,std::allocator<(double)>)>

Type:: time

property time_gate

std::vector<(double,std::allocator<(double)>)>

Type:: time_gate

NuclearReflectivityEnergy

`NuclearReflectivityEnergy`