hyperspy.models.model1d module

class hyperspy.models.model1d.ComponentFit(model, component, signal_range=None, estimate_parameters=True, fit_independent=False, only_current=True, iterpath='flyback', **kwargs)

Bases: hyperspy.signal_tools.SpanSelectorInSignal1D

gui(display=True, toolkit=None, **kwargs)

Display or return interactive GUI element if available.

Parameters
  • display (bool) – If True, display the user interface widgets. If False, return the widgets container in a dictionary, usually for customisation or testing.

  • toolkit (str, iterable of strings 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.

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

Bases: hyperspy.model.BaseModel

Model and data fitting for one dimensional signals.

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

signal

It contains the data to fit.

Type

Signal1D instance

chisq

Chi-squared of the signal (or np.nan if not yet fit)

Type

A Signal of floats

dof

Degrees of freedom of the signal (0 if not yet fit)

Type

A Signal of integers

red_chisq

Reduced chi-squared.

Type

Signal instance

components

The components of the model are attributes of this class. This provides a convinient way to access the model components when working in IPython as it enables tab completion.

Type

ModelComponents instance

extend()

Append multiple components to the model.

as_signal()

Generate a Signal1D instance (possible multidimensional) from the model.

store_current_values()

Store the value of the parameters at the current position.

fetch_stored_values()

fetch stored values of the parameters.

update_plot()

Force a plot update. (In most cases the plot should update automatically.)

set_signal_range, remove_signal range, reset_signal_range,
add signal_range.

Customize the signal range to fit.

fit, multifit

Fit the model to the data at the current position or the full dataset.

save_parameters2file, load_parameters_from_file

Save/load the parameter values to/from a file.

enable_plot_components, disable_plot_components

Plot each component separately. (Use after plot.)

set_current_values_to()

Set the current value of all the parameters of the given component as the value for all the dataset.

export_results()

Save the value of the parameters in separate files.

plot_results()

Plot the value of all parameters at all positions.

print_current_values()

Print the value of the parameters at the current position.

enable_adjust_position, disable_adjust_position

Enable/disable interactive adjustment of the position of the components that have a well defined position. (Use after plot).

set_parameters_not_free, set_parameters_free

Fit the free status of several components and parameters at once.

set_parameters_value()

Set the value of a parameter in components in a model to a specified value.

as_dictionary()

Exports the model to a dictionary that can be saved in a file.

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()
Components  Parameter       Value
Gaussian
            sigma   1.000000
            A       1.000000
            centre  0.000000
>>> g.centre.value = 3
>>> m.print_current_values()
Components  Parameter       Value
Gaussian
            sigma   1.000000
            A       1.000000
            centre  3.000000
>>> 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_in_pixels(i1=None, i2=None)

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

Parameters

i2 (i1,) –

_check_analytical_jacobian()

Check all components have analytical gradients.

If they do, return True and an empty string. If they do not, return False and an error message.

_parse_signal_range_values(x1=None, x2=None)

Parse signal range values to be used by the set_signal_range, add_signal_range and remove_signal_range and return sorted indices

_poisson_likelihood_function(param, y, weights=None)

Returns the likelihood function of the model for the given data and parameters

_remove_signal_range_in_pixels(i1=None, i2=None)

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

Parameters

i2 (i1,) –

_set_signal_range_in_pixels(i1=None, i2=None)

Use only the selected spectral range in the fitting routine.

Parameters
  • i1 (Int) –

  • i2 (Int) –

Notes

To use the full energy range call the function without arguments.

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

x2 (x1,) –

append(thing)

Add component to Model.

Parameters

thing (Component instance.) –

disable_adjust_position()

Disables the interactive adjust position feature

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
  • components ({None, list of components}) – 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_them (bool) – 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_label (bool, optional) – If True, a label showing the component name is added to the plot next to the vertical line.

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

Fit just 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
  • component (component instance) – The component must be in the model, otherwise an exception is raised. The component can be specified by name, index or itself.

  • signal_range ({'interactive', (left_value, right_value), 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. If None the current signal range is used. Note that ROIs can be used in place of a tuple.

  • estimate_parameters (bool, 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_independent (bool, default False) – If True, all other components are disabled. If False, all other component paramemeters are fixed.

  • display (bool) – If True, display the user interface widgets. If False, return the widgets container in a dictionary, usually for customisation or testing.

  • toolkit (str, iterable of strings 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.

  • **kwargs (dict) – 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, **kwargs)

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

Parameters
  • plot_components (bool) – If True, add a line per component to the signal figure.

  • **kwargs (dict) – 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()
>>> g = hs.model.components1D.Gaussian()
>>> m.append(g)

You could remove g like this

>>> m.remove(g)

Like this:

>>> m.remove("Gaussian")

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

x2 (x1,) –

reset_signal_range()

Resets the data range

set_convolution_axis()

Creates an axis to use to generate the data of the model in the precise scale to obtain the correct axis and origin after convolution with the lowloss spectrum.

set_signal_range(*args, **kwargs)

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

Parameters

x2 (x1,) –