`EnergySpectrum`

class nexus.EnergySpectrum(experiment, detuning, electronic=True, intensity_data=[], scaling='auto', background='auto', resolution=0.0, distribution_points=1, fit_weight=1.0, kernel_type='Gauss', residual=<nexus.clib.cnexus.Sqrt; proxy of <Swig Object of type 'Residual *'> >, time_gate=[], coherence=False, id='')

Bases: FitMeasurement

Constructor for the EnergySpectrum class. Class to calculate the energy-dependent intensity of the experiment.

Parameters:

id (string) – User identifier.
experiment (Experiment) – Experiment for the calculation.
detuning (list or ndarray) – Detuning values for the calculation (Gamma).
electronic (bool) – If True electronic scattering is included.
intensity_data (list or ndarray) – Intensity data for fitting. Default is an empty list.
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.
resolution (float or Var) – Resolution value for convolution (Gamma). Default is 0.
distribution_points (int) – Number of points for thickness distributions in forward geometry and angular divergence in grazing geometry. Both distributions assume incoherent summation over the weighted contributions. Default is 1.
fit_weight (float) – Relative weight for the cost function in multi measurement fitting. Default is 1.
kernel_type (string) – Type of the resolution kernel. Is used for the convolution of the calculated energy spectrum with the resolution kernel. Can be Gauss or Lorentz. Default is Gauss.
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.

id

User identifier.

Type:: string

experiment

Experiment for the calculation.

Type:: Experiment

detuning

Detuning values for the calculation (Gamma).

Type:: list or ndarray

electronic

If True electronic scattering is included.

Type:: bool

result

List of detuning dependent intensity values.

Type:: list

intensity_data

Intensity data for fitting.

Type:: list

data_size

Number of data points of experimental intensity data.

Type:: int

scaling

Intensity scaling factor for fitting.

Type:: Var

background

Background for fitting.

Type:: Var

resolution

Resolution value for convolution.

Type:: Var

resolution_kernel

The used kernel, same step size as detuning.

Type:: list

distribution_points

Number of points for thickness distributions in forward geometry and angular divergence in grazing geometry. Both distributions assume incoherent summation over the weighted contributions.

Type:: int

fit_weight

Relative weight for the cost function in multi measurement fitting.

Type:: float

kernel_type

Type of the resolution kernel. Is used for the convolution of the calculated energy spectrum with the resolution kernel. Can be Gauss or Lorentz.

Type:: string

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

Calculate()

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

Returns:: Array of float 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, velocity=False, datacolor='black', theorycolor='r', theorywidth=2, datalinestyle='none', datamarker='+', datamarkersize=2, datafillstyle='full', legend=True, errors=False, errorcap=2, name=None)

Plot the EnergySpectrum.

Added in version 1.0.3.

Changed in version 1.2.0.

Parameters:

data (bool) – True if intensity_data should be plot.
residuals (bool) – True if residuals from fit should be plot.
velocity (bool) – Plot velocity instead of detuning.
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.

Velocity()

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

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

EnergySpectrum

`EnergySpectrum`