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aspecd.model module¶
Numerical models
Models are defined by (constant) parameters and variables the model is evaluated for. The variables can be thought of as the axes values of the resulting (calculated) dataset.
As a simple example, consider a polynomial defined by its (constant)
coefficients. The model will evaluate the polynomial for the values,
and the result will be a aspecd.dataset.CalculatedDataset
object
containing the values of the evaluated model in its data, and the
variables as its axes values.
Models can be seen as abstraction to simulations in some regard. In this respect, they will play a central role in conjunction with fitting models to data by adjusting their respective parameters, a quite general approach in science and particularly in spectroscopy.
A bit of terminology¶
 parameters :
constant parameters (sometimes termed coefficients) characterising the model
Example: In case of a polynomial, the coefficients would be the parameters of the model.
 variables :
values to evaluate the model for
Example: In case of a polynomial, the x values the model is evaluated for would be the variables, with the y values being the corresponding depending values dictated by the model and its parameters.
Models provided within this module¶
Besides providing the basis for models for the ASpecD framework, this module comes with a (growing) number of generalpurpose models useful for basically all kinds of spectroscopic data.
Here is a list as a first overview. For details, see the detailed documentation of each of the classes, readily accessible by the link.
Primitive models¶
Primitive models are mainly used to create test datasets that can be operated on afterwards. The particular strength and beauty of wrapping essential oneliners of code with a fullfledged model class is twofold: These classes return ASpecD datasets, and you can work completely in context of recipedriven data analysis, requiring no actual programming skills.
If nothing else, these primitive models can serve as a way to create
datasets with fixed data dimensions. Those datasets may be used as templates
for more advanced models, by using the aspecd.model.Model.from_dataset()
method.
Having that said, here you go with a list of primitive models:

Dataset consisting entirely of zeros (in N dimensions)

Dataset consisting entirely of ones (in N dimensions)
Mathematical models¶
Besides the primitive models listed above, there is a growing number of mathematical models implementing comparably simple mathematical equations that are often used. Packages derived from the ASpecD framework may well define more specific models as well.

Polynomial (of arbitrary degree/order, depending on the number of coefficients)

Generalised Gaussian where amplitude, position, and width can be set explicitly. Hence, this is usually not identical to the probability density function (PDF) of a normally distributed random variable.
aspecd.model.NormalisedGaussian
Normalised Gaussian with an integral of one, identical to the probability density function (PDF) of a normally distributed random variable.

Generalised Lorentzian where amplitude, position, and width can be set explicitly. Hence, this is usually not identical to the probability density function (PDF) of the Cauchy distribution.
aspecd.model.NormalisedLorentzian
Normalised Lorentzian with an integral of one, identical to the probability density function (PDF) of the Cauchy distribution.

Sine wave with adjustable amplitude, frequency, and phase.

Exponential function with adjustable prefactor and rate.
Composite models consisting of a sum of individual models¶
Often you encounter situations where a model consists of a (weighted) sum of individual models. A simple example would be a damped oscillation. Or think of a spectral line consisting of several overlapping individual lines (Lorentzian or Gaussian).
All this can be easily set up using the aspecd.model.CompositeModel
class that lets you conveniently specify a list of models, their individual
parameters, and optional weights.
Family of curves¶
Systematically varying one parameter at a time for a given model is key to understanding the impact this parameter has. Therefore, automatically creating a family of curves with one parameter varied is quite convenient.
To achieve this, use the class aspecd.model.FamilyOfCurves
that will
take the name of a model (needs to be the name of an existing model class)
and create a family of curves for this model, adding the name of the
parameter as quantity to the additional axis.
Writing your own models¶
All models should inherit from the aspecd.model.Model
class.
Furthermore, they should conform to a series of requirements:
Parameters are stored in the
aspecd.model.Model.parameters
dict.Note that this is a
dict
. In the simplest case, you may name the corresponding key “coefficients”, as in case of a polynomial. In other cases, there are common names for parameters, such as “mu” and “sigma” for a Gaussian. Whether the keys should be named this way or describe the actual meaning of the parameter is partly a matter of personal taste. Use whatever is more common in the given context, but tend to be descriptive. Usually, implementing mathematical equations by simply naming every variable according to the mathematical notation is a bad idea, as the programmer will not know what these variables represent.Models create calculated datasets of class
aspecd.dataset.CalculatedDataset
.The data of these datasets need to have dimensions corresponding to the variables set for the model. Think of the variables as being the axes values of the resulting dataset.
The
_origdata
property of the dataset is automatically set accordingly (see below for details). This is crucially important to have the resulting dataset work as expected, including undo and redo functionality within the ASpecD framework. Remember: A calculated dataset is a regular dataset, and you can perform all the tasks with you would do with other datasets, including processing, analysis and alike.Model creation takes place entirely in the nonpublic
_perform_task
method of the model.This method gets called from
aspecd.model.Model.create()
, but not before some background checks have been performed, including preparing the metadata of theaspecd.dataset.CalculatedDataset
object returned byaspecd.model.Model.create()
.After calling out to
_perform_task
, the axes of theaspecd.dataset.CalculatedDataset
object returned byaspecd.model.Model.create()
are set accordingly, i.e. fitting to the shape of the data.
On the other hand, a series of things will be automatically taken care of for you:
Metadata of the resulting
aspecd.dataset.CalculatedDataset
object are automatically set, includingtype
(set to the full class name of the model) andparameters
(copied over from the parameters attribute of the model).Axes of the resulting
aspecd.dataset.CalculatedDataset
object are automatically adjusted according to the size and content of theaspecd.model.Model.variables
attribute.In case you used
aspecd.model.Model.from_dataset()
, the axes from the dataset will be copied over from there.The
_origdata
property of the dataset is automatically set accordingly. This is crucially important to have the resulting dataset work as expected, including undo and redo functionality within the ASpecD framework.
Make sure your models do not raise errors such as ZeroDivisionError
depending on the parameters set. Use the aspecd.utils.not_zero()
function where appropriate. This is particularly important in light of using
models in the context of automated fitting.
Module documentation¶

class
aspecd.model.
Model
¶ Bases:
object
Base class for numerical models.
Models are defined by (constant) parameters and variables the model is evaluated for. The variables can be thought of as the axes values of the resulting (calculated) dataset.
As a simple example, consider a polynomial defined by its (constant) coefficients. The model will evaluate the polynomial for the values, and the result will be a
aspecd.dataset.CalculatedDataset
object containing the values of the evaluated model in its data, and the variables as its axes values.Models can be seen as abstraction to simulations in some regard. In this respect, they will play a central role in conjunction with fitting models to data by adjusting their respective parameters, a quite general approach in science and particularly in spectroscopy.

variables
¶ values to evaluate the model for
Usually
numpy.ndarray
arrays, one for each variableThe variables will become the values of the respective axes.
 Type
Changed in version 0.3: New attribute
description
Changed in version 0.3: New nonpublic method
_sanitise_parameters()

create
()¶ Create dataset containing the evaluated model as data
The actual model creation should be implemented within the nonpublic method
_perform_task()
. Furthermore, you should make sure your model will be evaluated for the values given inaspecd.model.Model.values
and the resulting dataset having set the axes appropriately.Furthermore, don’t forget to set the
_origdata
property of the dataset, usually simply by copying thedata
property over there after it has been filled with content. This is crucially important to have the resulting dataset work as expected, including undo and redo functionality within the ASpecD framework. Remember: A calculated dataset is a regular dataset, and you can perform all the tasks with you would do with other datasets, including processing, analysis and alike. Returns
dataset – Calculated dataset containing the evaluated model as data
 Return type
 Raises
aspecd.exceptions.MissingParameterError – Raised if either parameters or values are not set

from_dataset
(dataset=None)¶ Obtain crucial information from an existing dataset.
Often, models should be calculated for the same values as an existing dataset. Therefore, you can set the
aspecd.model.Model.values
property from a given dataset.If you get the variables from an existing dataset, the calculated dataset containing the evaluated model will have the same axes settings. Thus, it is pretty convenient to get a model with identical axes, including quantity etcetera. This helps a lot with plotting both, an (experimental) dataset and the model, in one plot.
 Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset to obtain crucial information for building the model from Raises
aspecd.exceptions.MissingDatasetError – Raised if no dataset is provided


class
aspecd.model.
CompositeModel
¶ Bases:
aspecd.model.Model
Composite model consisting of a weighted contributions of individual models.
Individual models can either be added up (default) or multiplied, depending on which operators are provided. Both situations occur frequently. If you would like to describe a spectrum as sum of Gaussian or Lorentzian lines, you need to add the individual contributions. If you would like to model a damped oscillation, you would need to multiply the exponential decay onto the oscillation.

models
¶ Names of the models the composite model consists of
Each name needs to be the name of an existing model class.
 Type

parameters
¶ Constant parameters characterising each individual model
For the parameters that can (and need to) be set, consult the documentation of each of the respective model classes specified in the
models
attribute. Type

operators
¶ Operators to be used for the individual models.
Addition (“+”, “add”, “plus”) and multiplication (“*”, “multiply”, “times”) are supported.
Note that one operator less than models needs to be provided.
Default: add
 Type
 Raises
IndexError – Raised if number of models, parameter sets, operators, and weights are incompatible
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to describe your data with a model consisting of two Lorentzian line shapes. Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your model: kind: model type: Zeros properties: parameters: shape: 1001 range: [0, 20] result: dummy  kind: model type: CompositeModel from_dataset: dummy properties: models:  Lorentzian  Lorentzian parameters:  position: 3  position: 5 result: multiple_lorentzians
Note that you need to provide parameters for each of the individual models, even if the class for a model would work without explicitly providing parameters.
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.While adding up the contributions of the individual components works well for describing spectra, sometimes you need to multiply contributions. Suppose you would want to create a damped oscillation consisting of a sine and an exponential. Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your model: kind: model type: Zeros properties: parameters: shape: 1001 range: [0, 20] result: dummy  kind: model type: CompositeModel from_dataset: dummy properties: models:  Sine  Exponential parameters:  frequency: 1 phase: 1.57  rate: 1 operators:  multiply result: damped_oscillation
Again, you need to provide parameters for each of the individual models, even if the class for a model would work without explicitly providing parameters.
New in version 0.3.


class
aspecd.model.
FamilyOfCurves
¶ Bases:
aspecd.model.Model
Create a family of curves for a model, varying a single parameter.
Systematically varying one parameter at a time for a given model is key to understanding the impact this parameter has. Therefore, automatically creating a family of curves with one parameter varied is quite convenient.
This class will take the name of a model (needs to be the name of an existing model class) and create a family of curves for this model, adding the name of the parameter as quantity to the additional axis.

model
¶ Name of the model the family of curves should be calculated for
Needs to be the name of an existing model class.
 Type

vary
¶ Name and values of the parameter to be varied
 parameter
str
Name of the parameter that should be varied
 values
list
Values of the parameter to be varied
 Type
 parameter
 Raises
ValueError – Raised if no model is provided
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to create a family of curves of a Gaussian with varying the width. Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your family of curves: kind: model type: Zeros properties: parameters: shape: 1001 range: [0, 20] result: dummy  kind: model type: FamilyOfCurves from_dataset: dummy properties: model: Gaussian vary: parameter: width values: [1., 1.5, 2., 2.5, 3] result: gaussian_with_varied_width
This would create a 2D dataset with a Gaussian with standard values for amplitude and position and the value for the width varied as given.
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.If you would like to control additional parameters of the Gaussian, you can do that as well:
 kind: model type: FamilyOfCurves from_dataset: dummy properties: model: Gaussian parameters: amplitude: 3. position: 1 vary: parameter: width values: [1., 1.5, 2., 2.5, 3] result: gaussian_with_varied_width
Note that if you provide a value for the parameter to be varied in the list of parameters, it will be silently overwritten by the values provided with
vary
.New in version 0.3.


class
aspecd.model.
Zeros
¶ Bases:
aspecd.model.Model
Zeros of given shape.
One of the most primitive models: zeros in N dimensions.
This model is quite helpful for creating test datasets, e.g. with added noise (of different colour). Basically, it can be thought of as a wrapper for
numpy.zeros()
. Its particular strength is that using this model, creating test datasets becomes straightforward in context of recipedriven data analysis.
parameters
¶ All parameters necessary for this step.
 shape
list
shape of the data
Have in mind that ND datasets get huge very fast. Therefore, it is not the best idea to create an 3D dataset with zeros with 2**12 elements along each dimension.
 range
list
range of each of the axes
Useful if you want to specify the axes values as well.
If the data are multidimensional, one range for each axis needs to be provided.
 Type
 shape
 Raises
aspecd.exceptions.MissingParameterError – Raised if no shape is given
IndexError – Raised if elements in shape and range are incompatible
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Creating a dataset consisting of 2**10 zeros is quite simple:
 kind: model type: Zeros properties: parameters: shape: 1024 result: 1d_zeros
Of course, you are not limited to 1D datasets, and you can easily create ND datasets as well:
 kind: model type: Zeros properties: parameters: shape: [1024, 256, 256] result: 3d_zeros
Please have in mind that the memory of your computer is usually limited and that ND datasets become huge very fast. Hence, creating a 3D array with 2**10 elements along each dimension is most probably not the best idea.
Suppose you not only want to create a dataset with a given shape, but set the axes values (i.e., their range) as well:
 kind: model type: Zeros properties: parameters: shape: 1024 range: [35, 42] result: 1d_zeros
This would create a 1D dataset with 1024 values, with the axes values spanning a range from 35 to 42. Of course, the same can be done with ND datasets.
Now, let’s assume that you would want to play around with the different types of (coloured) noise. Therefore, you would want to first create a dataset and afterwards add noise to it:
 kind: model type: Zeros properties: parameters: shape: 8192 result: 1d_zeros  kind: processing type: Noise properties: parameters: normalise: True
This would create a dataset consisting of 2**14 zeros and add pink (1/f) noise to it that is normalised (has an amplitude of 1). To check that the noise is really 1/f noise, you may look at its power density. See
aspecd.analysis.PowerDensitySpectrum
for details, including how to even plot both, the power density spectrum and a linear fit together in one figure.New in version 0.3.


class
aspecd.model.
Ones
¶ Bases:
aspecd.model.Model
Ones of given shape.
One of the most primitive models: ones in N dimensions.
This model is quite helpful for creating test datasets, e.g. with added noise (of different colour). Basically, it can be thought of as a wrapper for
numpy.ones()
. Its particular strength is that using this model, creating test datasets becomes straightforward in context of recipedriven data analysis.
parameters
¶ All parameters necessary for this step.
 shape
list
shape of the data
Have in mind that ND datasets get huge very fast. Therefore, it is not the best idea to create an 3D dataset with ones with 2**12 elements along each dimension.
 range
list
range of each of the axes
Useful if you want to specify the axes values as well.
If the data are multidimensional, one range for each axis needs to be provided.
 Type
 shape
 Raises
aspecd.exceptions.MissingParameterError – Raised if no shape is given
IndexError – Raised if elements in shape and range are incompatible
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Creating a dataset consisting of 2**10 ones is quite simple:
 kind: model type: Ones properties: parameters: shape: 1024 result: 1d_ones
Of course, you are not limited to 1D datasets, and you can easily create ND datasets as well:
 kind: model type: Ones properties: parameters: shape: [1024, 256, 256] result: 3d_ones
Please have in mind that the memory of your computer is usually limited and that ND datasets become huge very fast. Hence, creating a 3D array with 2**10 elements along each dimension is most probably not the best idea.
Suppose you not only want to create a dataset with a given shape, but set the axes values (i.e., their range) as well:
 kind: model type: Ones properties: parameters: shape: 1024 range: [35, 42] result: 1d_zeros
This would create a 1D dataset with 1024 values, with the axes values spanning a range from 35 to 42. Of course, the same can be done with ND datasets.
Now, let’s assume that you would want to play around with the different types of (coloured) noise. Therefore, you would want to first create a dataset and afterwards add noise to it:
 kind: model type: Ones properties: parameters: shape: 8192 result: 1d_ones  kind: processing type: Noise properties: parameters: normalise: True
This would create a dataset consisting of 2**14 ones and add pink (1/f) noise to it that is normalised (has an amplitude of 1). To check that the noise is really 1/f noise, you may look at its power density. See
aspecd.analysis.PowerDensitySpectrum
for details, including how to even plot both, the power density spectrum and a linear fit together in one figure.New in version 0.3.


class
aspecd.model.
Polynomial
¶ Bases:
aspecd.model.Model
Polynomial.
Evaluate a polynomial with given coefficients for the data provided in
aspecd.model.Model.variables
.Note
As the new
numpy.polynomial
package is used, particularly thenumpy.polynomial.polynomial.Polynomial
class, the coefficients are given in increasing order, with the first element corresponding to x**0.Furthermore, the coefficients are assumed to be provided in the unscaled data domain (by using the
numpy.polynomial.polynomial.Polynomial.convert()
method).
parameters
¶ All parameters necessary for this step.
 coefficients
list
coefficients of the polynomial to be evaluated
The number of coefficients determines the order (degree) of the polynomial. The coefficients have to be given in increasing order (see note above). Furthermore, you need to provide the coefficients in the unscaled data domain (using the
numpy.polynomial.polynomial.Polynomial.convert()
method).
 Type
 coefficients
 Raises
aspecd.exceptions.MissingParameterError – Raised if no coefficients are given
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to create a Polynomial of first order with a slope of 42 and an intercept of 3. Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your Polynomial: kind: model type: Zeros properties: parameters: shape: 1001 range: [5, 5] result: dummy  kind: model type: Polynomial from_dataset: dummy properties: parameters: coefficients: [3, 42] result: polynomial
Note that the coefficients are given in increasing order of the exponent, here intercept first, followed by the slope.
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.New in version 0.3.


class
aspecd.model.
Gaussian
¶ Bases:
aspecd.model.Model
Generalised Gaussian.
Creates a Gaussian function or Gaussian, with its characteristic symmetric “bell curve” shape.
The underlying mathematical equation may be written as follows:
\[f(x) = a \exp\left(\frac{(xb)^2}{2c^2}\right)\]with \(a\) being the amplitude, \(b\) the position, and \(c\) the width of the Gaussian.
Important
Note that this is a generalised Gaussian where you can set amplitude, position, and width independently. Hence, it is not normalised to an integral of one, and therefore not to be confused with the the probability density function (PDF) of a normally distributed random variable. If you are interested in this, see the
aspecd.model.NormalisedGaussian
class.
parameters
¶ All parameters necessary for this step.
 amplitude
float
Amplitude or height of the Gaussian
Default: 1
 position
float
Position (of the maximum) of the Gaussian
Default: 0
 width
float
Width of the Gaussian
The full width at half maximum (FWHM) is related to the width b by: \(2 \sqrt{2 \log(2)} b\).
Default: 1
 Type
 amplitude
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to create a Gaussian with standard values (amplitude=1, position=0, width=1). Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your Gaussian: kind: model type: Zeros properties: parameters: shape: 1001 range: [5, 5] result: dummy  kind: model type: Gaussian from_dataset: dummy result: gaussian
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.Of course, you can control all three parameters (amplitude, position, width) explicitly:
 kind: model type: Gaussian properties: parameters: amplitude: 5 position: 1.5 width: 0.5 from_dataset: dummy result: gaussian
This would create a Gaussian with an amplitude (height) of 5, situated at a value of 1.5 at the x axis, and with a width of 0.5.
New in version 0.3.


class
aspecd.model.
NormalisedGaussian
¶ Bases:
aspecd.model.Model
Normalised Gaussian.
Creates a Gaussian function or Gaussian, with its characteristic symmetric “bell curve” shape, normalised to an integral of one. Thus, it is the probability density function (PDF) of a normally distributed random variable.
The underlying mathematical equation may be written as follows:
\[f(x) = \frac{1}{\sigma\sqrt{2\pi}} \exp\left(\frac{(x\mu)^2}{ 2\sigma^2}\right)\]with \(\mu\) being the position and \(\sigma\) the width of the Gaussian, and \(\sigma^2\) the variance.
Note
This class creates a normalised Gaussian, equivalent to the PDF of a normally distributed random variable. If you are interested in a Gaussian where you can set all three parameters (amplitude, position, width) independently, see the
aspecd.model.Gaussian
class.
parameters
¶ All parameters necessary for this step.
 position
float
Position (of the maximum) of the Gaussian
For a normally distributed random variable \(x\), the position is identical to its expected value \(E(x)\) or mean \(\mu\). Other names include first moment and average.
Default: 0
 width
float
Width of the Gaussian
The full width at half maximum (FWHM) is related to the width \(\sigma\) by: \(2 \sqrt{2 \log(2)} \sigma\). The squared value of the width is better known the variance \(\sigma^2\).
Default: 1
 Type
 position
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to create a normalised Gaussian with standard values (position=0, width=1). Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your normalised Gaussian: kind: model type: Zeros properties: parameters: shape: 1001 range: [5, 5] result: dummy  kind: model type: NormalisedGaussian from_dataset: dummy result: normalised_gaussian
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.Of course, you can control position and width explicitly:
 kind: model type: NormalisedGaussian properties: parameters: position: 1.5 width: 0.5 from_dataset: dummy result: normalised_gaussian
This would create a normalised Gaussian with its maximum situated at a value of 1.5 at the x axis, and with a width of 0.5.
New in version 0.3.


class
aspecd.model.
Lorentzian
¶ Bases:
aspecd.model.Model
Generalised Lorentzian.
Creates a Lorentzian function or Lorentzian often used in spectroscopy, as the line shape of a purely lifetimebroadened spectral line is identical to such a Lorentzian.
The underlying mathematical equation may be written as follows:
\[f(x) = a \left[\frac{c^2}{(xb)^2 + c^2}\right]\]with \(a\) being the amplitude, \(b\) the position, and \(c\) the width of the Lorentzian.
Important
Note that this is a generalised Lorentzian where you can set amplitude, position, and width independently. Hence, it is not normalised to an integral of one, and therefore not to be confused with the the probability density function (PDF) of the Cauchy distribution. If you are interested in this, see the
aspecd.model.NormalisedLorentzian
class.
parameters
¶ All parameters necessary for this step.
 amplitude
float
Amplitude or height of the Lorentzian
Default: 1
 position
float
Position (of the maximum) of the Lorentzian
Default: 0
 width
float
Width of the Lorentzian
The full width at half maximum (FWHM) is related to the width \(b\) by: \(2b\).
Default: 1
 Type
 amplitude
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to create a Lorentzian with standard values (amplitude=1, position=0, width=1). Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your Lorentzian: kind: model type: Zeros properties: parameters: shape: 1001 range: [5, 5] result: dummy  kind: model type: Lorentzian from_dataset: dummy result: lorentzian
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.Of course, you can control all three parameters (amplitude, position, width) explicitly:
 kind: model type: Lorentzian properties: parameters: amplitude: 5 position: 1.5 width: 0.5 from_dataset: dummy result: lorentzian
This would create a Lorentzian with an amplitude (height) of 5, situated at a value of 1.5 at the x axis, and with a width of 0.5.
New in version 0.3.


class
aspecd.model.
NormalisedLorentzian
¶ Bases:
aspecd.model.Model
Normalised Lorentzian.
Creates a normalised Lorentzian function or Lorentzian with an integral of one, i.e. the probability density function (PDF) of the Cauchy distribution.
The underlying mathematical equation may be written as follows:
\[f(x) = \frac{1}{\pi b} \left[\frac{c^2}{(xb)^2 + c^2}\right]\]with \(b\) being the position and \(c\) the width of the Lorentzian.
Note
This class creates a normalised Lorentzian, equivalent to the PDF of the Cauchy distribution. If you are interested in a Lorentzian where you can set all three parameters (amplitude, position, width) independently, see the
aspecd.model.Lorentzian
class.
parameters
¶ All parameters necessary for this step.
 position
float
Position (of the maximum) of the Lorentzian
Default: 0
 width
float
Width of the Lorentzian
The full width at half maximum (FWHM) is related to the width \(b\) by: \(2b\).
Default: 1
 Type
 position
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to create a normalised Lorentzian with standard values (position=0, width=1). Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your Lorentzian: kind: model type: Zeros properties: parameters: shape: 1001 range: [5, 5] result: dummy  kind: model type: NormalisedLorentzian from_dataset: dummy result: normalised_lorentzian
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.Of course, you can control position and width explicitly:
 kind: model type: NormalisedLorentzian properties: parameters: position: 1.5 width: 0.5 from_dataset: dummy result: normalised_lorentzian
This would create a normalised Lorentzian with its maximum situated at a value of 1.5 at the x axis, and with a width of 0.5.
New in version 0.3.


class
aspecd.model.
Sine
¶ Bases:
aspecd.model.Model
Sine wave.
Creates a sine function with given amplitude, frequency, and phase.
The underlying mathematical equation may be written as follows:
\[f(x) = a \sin(fx + \phi)\]with \(a\) being the amplitude, \(f\) the frequency, and \(\phi\) the phase of the sine.

parameters
¶ All parameters necessary for this step.
 amplitude
float
Amplitude of the sine.
Note that the real amplitude (maxmin) is twice the value given here. Nevertheless, calling this factor “amplitude” seems to be common.
Default: 1
 frequency
float
Frequency of the sine (in radians).
Default: 1
 phase
float
Phase (i.e., shift) of the sine (in radians).
Setting the phase to \(\pi/2\) would result in a cosine.
Default: 0
 Type
 amplitude
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to create a sine with standard values (amplitude=1, frequency=1, shift=0). Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your sine: kind: model type: Zeros properties: parameters: shape: 1001 range: [5, 5] result: dummy  kind: model type: Sine from_dataset: dummy result: sine
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.Of course, you can control all three parameters (amplitude, frequency, and shift) explicitly:
 kind: model type: Sine properties: parameters: amplitude: 42 frequency: 4.2 shift: 1.57 from_dataset: dummy result: sine
This would create a sine with an amplitude of 42 (the actual amplitude, defined as maxmin, would be twice this value), a frequency of 4.2 and a shift of about pi/2.
New in version 0.3.


class
aspecd.model.
Exponential
¶ Bases:
aspecd.model.Model
Exponential function.
Creates an exponential with given prefactor and rate.
The underlying mathematical equation may be written as follows:
\[f(x) = a \exp(bx)\]with \(a\) being the prefactor and \(b\) the rate of the exponential.

parameters
¶ All parameters necessary for this step.
 Type
Note
In case of modelling exponential decays, the rate constant will become negative. This rate constant (decay rate) is the inverse of the lifetime. Lifetime and halflife are related by a factor of ln(2).
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you would want to create an exponential with standard values (prefactor=1, rate=1). Starting from scratch, you need to create a dummy dataset (using, e.g.,
aspecd.model.Zeros
) of given length and axes range. Based on that you can create your exponential: kind: model type: Zeros properties: parameters: shape: 1001 range: [5, 5] result: dummy  kind: model type: Exponential from_dataset: dummy result: exponential
Of course, if you start with an existing dataset (e.g., loaded from some real data), you could use the label to this dataset directly in
from_dataset
, without needing to create a dummy dataset first.Of course, you can control all parameters (prefactor, rate) explicitly:
 kind: model type: Exponential properties: parameters: prefactor: 42 rate: 4.2 from_dataset: dummy result: exponential
This would create an exponential with a prefactor of 42 (i.e. the intercept) and a rate of 4.2.
New in version 0.3.
