Model1D#

class hyperspy.models.model1d.Model1D(signal1D, dictionary=None)#

Bases: BaseModel

Model and data fitting for one dimensional signals.

A model is constructed as a linear combination of components1D that are added to the model using append() or extend(). There are many predifined components available in the components1D module. If needed, new components can be created easily using the Expression component or by using the code of existing components as a template.

Once defined, the model can be fitted to the data using fit() or multifit(). Once the optimizer reaches the convergence criteria or the maximum number of iterations the new value of the component parameters are stored in the components.

It is possible to access the components in the model by their name or by the index in the model. An example is given at the end of this docstring.

Methods

`fit_component`(component[, signal_range, ...])	Fit the given component in the given signal range.
`enable_adjust_position`([components, ...])	Allow changing the x position of component by dragging a vertical line that is plotted in the signal model figure
`disable_adjust_position`()	Disable the interactive adjust position feature
`plot`([plot_components, plot_residual])	Plot the current spectrum to the screen and a map with a cursor to explore the SI.
`set_signal_range`(args, *kwargs)	Use only the selected spectral range defined in its own units in the fitting routine.
`remove_signal_range`(args, *kwargs)	Removes the data in the given range from the data range that will be used by the fitting rountine.
`reset_signal_range`()	Resets the data range
`add_signal_range`(args, *kwargs)	Adds the data in the given range from the data range that will be used by the fitting rountine.

See also

hyperspy.model.BaseModel, hyperspy.models.model2d.Model2D

Examples

In the following example we create a histogram from a normal distribution and fit it with a gaussian component. It demonstrates how to create a model from a Signal1D instance, add components to it, adjust the value of the parameters of the components, fit the model to the data and access the components in the model.

>>> s = hs.signals.Signal1D(
...    np.random.normal(scale=2, size=10000)).get_histogram()
>>> g = hs.model.components1D.Gaussian()
>>> m = s.create_model()
>>> m.append(g)
>>> m.print_current_values()
Model1D:  histogram
CurrentComponentValues: Gaussian
Active: True
Parameter Name |    Free |      Value |        Std |        Min |        Max | Linear
============== | ======= | ========== | ========== | ========== | ========== | ======
             A |    True |        1.0 |       None |        0.0 |       None |   True
        centre |    True |        0.0 |       None |       None |       None |  False
         sigma |    True |        1.0 |       None |        0.0 |       None |  False
>>> g.centre.value = 3
>>> m.print_current_values()
Model1D:  histogram
CurrentComponentValues: Gaussian
Active: True
Parameter Name |    Free |      Value |        Std |        Min |        Max | Linear
============== | ======= | ========== | ========== | ========== | ========== | ======
             A |    True |        1.0 |       None |        0.0 |       None |   True
        centre |    True |        3.0 |       None |       None |       None |  False
         sigma |    True |        1.0 |       None |        0.0 |       None |  False
>>> g.sigma.value
1.0
>>> m.fit() 
>>> g.sigma.value 
1.9779042300856682
>>> m[0].sigma.value 
1.9779042300856682
>>> m["Gaussian"].centre.value 
-0.072121936813224569

add_signal_range(*args, **kwargs)#

Adds the data in the given range from the data range that will be used by the fitting rountine.

Parameters:

x1, x2None or float

See also

set_signal_range, reset_signal_range, remove_signal_range

append(thing)#

Add component to Model.

Parameters:

thingComponent: The component to add to the model.

disable_adjust_position()#

Disable the interactive adjust position feature

See also

enable_adjust_position

disable_plot_components()#: Disable interactive adjustment of the position of the components that have a well defined position. Use after plot().

enable_adjust_position(components=None, fix_them=True, show_label=True)#

Allow changing the x position of component by dragging a vertical line that is plotted in the signal model figure

Parameters:

componentsNone, list of Component: If None, the position of all the active components of the model that has a well defined x position with a value in the axis range will get a position adjustment line. Otherwise the feature is added only to the given components. The components can be specified by name, index or themselves.
fix_thembool, default True: If True the position parameter of the components will be temporarily fixed until adjust position is disable. This can be useful to iteratively adjust the component positions and fit the model.
show_labelbool, default True: If True, a label showing the component name is added to the plot next to the vertical line.

See also

disable_adjust_position

enable_plot_components()#: Enable interactive adjustment of the position of the components that have a well defined position. Use after plot().

fit_component(component, signal_range='interactive', estimate_parameters=True, fit_independent=False, only_current=True, display=True, toolkit=None, **kwargs)#

Fit the given component in the given signal range.

This method is useful to obtain starting parameters for the components. Any keyword arguments are passed to the fit method.

Parameters:

componentComponent: The component must be in the model, otherwise an exception is raised. The component can be specified by name, index or itself.
signal_rangestr, tuple of None: If 'interactive' the signal range is selected using the span selector on the spectrum plot. The signal range can also be manually specified by passing a tuple of floats (left, right). If None the current signal range is used. Note that ROIs can be used in place of a tuple.
estimate_parametersbool, default True: If True will check if the component has an estimate_parameters function, and use it to estimate the parameters in the component.
fit_independentbool, default False: If True, all other components are disabled. If False, all other component paramemeters are fixed.
displaybool: If True, display the user interface widgets. If False, return the widgets container in a dictionary, usually for customisation or testing.
toolkitstr, iterable of str or None: If None (default), all available widgets are displayed or returned. If string, only the widgets of the selected toolkit are displayed if available. If an interable of toolkit strings, the widgets of all listed toolkits are displayed or returned.
**kwargsdict: All extra keyword arguments are passed to the py:meth:~hyperspy.model.BaseModel.fit or py:meth:~hyperspy.model.BaseModel.multifit method, depending if only_current is True or False.

Examples

Signal range set interactivly

>>> s = hs.signals.Signal1D([0, 1, 2, 4, 8, 4, 2, 1, 0])
>>> m = s.create_model()
>>> g1 = hs.model.components1D.Gaussian()
>>> m.append(g1)
>>> m.fit_component(g1) 

Signal range set through direct input

>>> m.fit_component(g1, signal_range=(1, 7))

plot(plot_components=False, plot_residual=False, **kwargs)#

Plot the current spectrum to the screen and a map with a cursor to explore the SI.

Parameters:

plot_componentsbool: If True, add a line per component to the signal figure.
plot_residualbool: If True, add a residual line (Signal - Model) to the signal figure.
**kwargsdict: All extra keyword arguements are passed to plot()

remove(things)#

Remove component from model.

Examples

>>> s = hs.signals.Signal1D(np.empty(1))
>>> m = s.create_model()
>>> g1 = hs.model.components1D.Gaussian()
>>> g2 = hs.model.components1D.Gaussian()
>>> m.extend([g1, g2])

You could remove g1 like this

>>> m.remove(g1)

Or like this:

>>> m.remove(0)

remove_signal_range(*args, **kwargs)#

Removes the data in the given range from the data range that will be used by the fitting rountine.

Parameters:

x1, x2None or float

See also

set_signal_range, add_signal_range, reset_signal_range
hyperspy.model.BaseModel.set_signal_range_from_mask

reset_signal_range()#

Resets the data range

See also

set_signal_range, add_signal_range, remove_signal_range

set_signal_range(*args, **kwargs)#

Use only the selected spectral range defined in its own units in the fitting routine.

Parameters:

x1, x2None or float

See also

add_signal_range, remove_signal_range, reset_signal_range
hyperspy.model.BaseModel.set_signal_range_from_mask