hyperspy.misc.model_tools module
- hyperspy.misc.model_tools._calculate_covariance(target_signal, coefficients, component_data, residual=None, lazy=False)
Calculate covariance matrix after having performed Linear Regression.
- Parameters:
target_signal (array-like, shape (N,) or (M, N)) – The signal array to be fit to.
coefficients (array-like, shape C or (M, C)) – The fitted coefficients.
component_data (array-like, shape N or (C, N)) – The component data.
residual (array-like, shape (0,) or (M,)) – The residual sum of squares, optional. Calculated if None.
lazy (bool) – Whether the signal is lazy.
Notes
Explanation of the array shapes in HyperSpy terms: N : flattened signal shape M : flattened navigation shape C : number of components
See https://stats.stackexchange.com/questions/62470 for more info on the algorithm
- hyperspy.misc.model_tools._is_iter(val)
Checks if value is a list or tuple.
- hyperspy.misc.model_tools._iter_join(val)
Joins values of iterable parameters for the fancy view, unless it equals None, then blank
- hyperspy.misc.model_tools._non_iter(val)
Returns formatted string for a value unless it equals None, then blank
- class hyperspy.misc.model_tools.current_component_values(component, only_free=False, only_active=False)
Bases:
objectConvenience class that makes use of __repr__ methods for nice printing in the notebook of the properties of parameters of a component.
- Parameters:
component (hyperspy component instance) –
only_free (bool, default False) – If True: Only include the free parameters in the view
only_active (bool, default False) – If True: Helper for current_model_values. Only include active components in the view. Always shows values if used on an individual component.