hyperspy.learn.mva module¶

class hyperspy.learn.mva.LearningResults¶

Bases: object

bss_algorithm = None¶

bss_factors = None¶

bss_loadings = None¶

centre = None¶

crop_decomposition_dimension(n, compute=False)¶

Crop the score matrix up to the given number. It is mainly useful to save memory and reduce the storage size

Parameters

n (int) – Number of components to keep.
compute (bool) – If the decomposition results are lazy, also compute the results. Default is False.

decomposition_algorithm = None¶

explained_variance = None¶

explained_variance_ratio = None¶

factors = None¶

load(filename)¶

Load the results of a previous decomposition and: demixing analysis from a file.

Parameters: filename (string) –

loadings = None¶

mean = None¶

navigation_mask = None¶

original_shape = None¶

output_dimension = None¶

poissonian_noise_normalized = None¶

save(filename, overwrite=None)¶

Save the result of the decomposition and demixing analysis :param filename: :type filename: string :param overwrite: If True(False) overwrite(don’t overwrite) the file if it exists.

If None (default) ask what to do if file exists.

signal_mask = None¶

summary()¶: Prints a summary of the decomposition and demixing parameters to the stdout

unfolded = None¶

unmixing_matrix = None¶

class hyperspy.learn.mva.MVA¶

Bases: object

Multivariate analysis capabilities for the Signal1D class.

blind_source_separation(number_of_components=None, algorithm='sklearn_fastica', diff_order=1, diff_axes=None, factors=None, comp_list=None, mask=None, on_loadings=False, pretreatment=None, compute=False, **kwargs)¶

Blind source separation (BSS) on the result on the decomposition.

Available algorithms: FastICA, JADE, CuBICA, and TDSEP

Parameters

number_of_components (int) – number of principal components to pass to the BSS algorithm
algorithm ({FastICA, JADE, CuBICA, TDSEP}) –
diff_order (int) – Sometimes it is convenient to perform the BSS on the derivative of the signal. If diff_order is 0, the signal is not differentiated.
diff_axes (None or list of ints or strings) – If None, when diff_order is greater than 1 and signal_dimension (navigation_dimension) when on_loadings is False (True) is greater than 1, the differences are calculated across all signal (navigation) axes. Otherwise the axes can be specified in a list.
factors (Signal or numpy array.) – Factors to decompose. If None, the BSS is performed on the factors of a previous decomposition. If a Signal instance the navigation dimension must be 1 and the size greater than 1.
comp_list (boolen numpy array) –

choose the components to use by the boolean list. It permits
to choose non contiguous components.
mask (bool numpy array or Signal instance.) – If not None, the signal locations marked as True are masked. The mask shape must be equal to the signal shape (navigation shape) when on_loadings is False (True).
on_loadings (bool) – If True, perform the BSS on the loadings of a previous decomposition. If False, performs it on the factors.
pretreatment (dict) –
compute (bool) – If the decomposition results are lazy, compute the BSS components so that they are not lazy. Default is False.
**kwargs (extra key word arguments) – Any keyword arguments are passed to the BSS algorithm.
documentation is here, with more arguments that can be passed as **kwargs (FastICA) –
http (//scikit-learn.org/stable/modules/generated/sklearn.decomposition.FastICA.html) –

decomposition(normalize_poissonian_noise=False, algorithm='svd', output_dimension=None, centre=None, auto_transpose=True, navigation_mask=None, signal_mask=None, var_array=None, var_func=None, polyfit=None, reproject=None, return_info=False, **kwargs)¶

Decomposition with a choice of algorithms

The results are stored in self.learning_results

Parameters

normalize_poissonian_noise (bool) – If True, scale the SI to normalize Poissonian noise
algorithm ('svd' | 'fast_svd' | 'mlpca' | 'fast_mlpca' | 'nmf' |) – ‘sparse_pca’ | ‘mini_batch_sparse_pca’ | ‘RPCA_GoDec’ | ‘ORPCA’
output_dimension (None or int) – number of components to keep/calculate
centre (None | 'variables' | 'trials') – If None no centring is applied. If ‘variable’ the centring will be performed in the variable axis. If ‘trials’, the centring will be performed in the ‘trials’ axis. It only has effect when using the svd or fast_svd algorithms
auto_transpose (bool) – If True, automatically transposes the data to boost performance. Only has effect when using the svd of fast_svd algorithms.
navigation_mask (boolean numpy array) – The navigation locations marked as True are not used in the decompostion.
signal_mask (boolean numpy array) – The signal locations marked as True are not used in the decomposition.
var_array (numpy array) – Array of variance for the maximum likelihood PCA algorithm
var_func (function or numpy array) – If function, it will apply it to the dataset to obtain the var_array. Alternatively, it can a an array with the coefficients of a polynomial.
reproject (None | signal | navigation | both) – If not None, the results of the decomposition will be projected in the selected masked area.
return_info (bool, default False) – The result of the decomposition is stored internally. However, some algorithms generate some extra information that is not stored. If True (the default is False) return any extra information if available

Returns

(X, E) – If ‘algorithm’ == ‘RPCA_GoDec’ or ‘ORPCA’ and ‘return_info’ is True, returns the low-rank (X) and sparse (E) matrices from robust PCA.

Return type

(numpy array, numpy array)

See also

plot_decomposition_factors(), plot_decomposition_loadings(), plot_lev()

get_bss_model(components=None)¶

Return the spectrum generated with the selected number of independent components

Parameters: components (None, int, or list of ints) – if None, rebuilds SI from all components if int, rebuilds SI from components in range 0-given int if list of ints, rebuilds SI from only components in given list
Returns
Return type: Signal instance

get_decomposition_model(components=None)¶

Return the spectrum generated with the selected number of principal components

Parameters: components (None, int, or list of ints) – if None, rebuilds SI from all components if int, rebuilds SI from components in range 0-given int if list of ints, rebuilds SI from only components in given list
Returns
Return type: Signal instance

get_explained_variance_ratio()¶

Return the explained variation ratio of the PCA components as a Signal1D.

Returns

s (Signal1D) – Explained variation ratio.
See Also
———
plot_explained_variance_ration, decomposition,
get_decomposition_loadings,
get_decomposition_factors.

normalize_bss_components(target='factors', function=<function sum>)¶

Normalize BSS components.

Parameters

target ({"factors", "loadings"}) –
function (numpy universal function, optional, default np.sum) – Each target component is divided by the output of function(target). function must return a scalar when operating on numpy arrays and must have an axis.

normalize_decomposition_components(target='factors', function=<function sum>)¶

Normalize decomposition components.

Parameters

target ({"factors", "loadings"}) –
function (numpy universal function, optional, default np.sum) – Each target component is divided by the output of function(target). function must return a scalar when operating on numpy arrays and must have an axis.

normalize_poissonian_noise(navigation_mask=None, signal_mask=None)¶

Scales the SI following Surf. Interface Anal. 2004; 36: 203–212 to “normalize” the poissonian data for decomposition analysis

Parameters

navigation_mask (boolen numpy array) –
signal_mask (boolen numpy array) –

plot_cumulative_explained_variance_ratio(n=50)¶

Plot the principal components explained variance up to the given number

Parameters: n (int) –

plot_explained_variance_ratio(n=30, log=True, threshold=0, hline='auto', xaxis_type='index', xaxis_labeling=None, signal_fmt=None, noise_fmt=None, fig=None, ax=None, **kwargs)¶

Plot the decomposition explained variance ratio vs index number (Scree Plot).

Parameters

n (int or None) – Number of components to plot. If None, all components will be plot
log (bool) – If True, the y axis uses a log scale.
threshold (float or int) – Threshold used to determine how many components should be highlighted as signal (as opposed to noise). If a float (between 0 and 1), threshold will be interpreted as a cutoff value, defining the variance at which to draw a line showing the cutoff between signal and noise; the number of signal components will be automatically determined by the cutoff value. If an int, threshold is interpreted as the number of components to highlight as signal (and no cutoff line will be drawn)
hline ({'auto', True, False}) – Whether or not to draw a horizontal line illustrating the variance cutoff for signal/noise determination. Default is to draw the line at the value given in threshold (if it is a float) and not draw in the case threshold is an int, or not given. If True, (and threshold is an int), the line will be drawn through the last component defined as signal. If False, the line will not be drawn in any circumstance.
xaxis_type ({'index', 'number'}) – Determines the type of labeling applied to the x-axis. If 'index', axis will be labeled starting at 0 (i.e. “pythonic index” labeling); if 'number', it will start at 1 (number labeling).
xaxis_labeling ({'ordinal', 'cardinal', None}) – Determines the format of the x-axis tick labels. If 'ordinal', “1st, 2nd, …” will be used; if 'cardinal', “1, 2, …” will be used. If None, an appropriate default will be selected.
signal_fmt (dict) – Dictionary of matplotlib formatting values for the signal components
noise_fmt (dict) – Dictionary of matplotlib formatting values for the noise components
fig (matplotlib figure or None) – If None, a default figure will be created, otherwise will plot into fig
ax (matplotlib ax (subplot) or None) – If None, a default ax will be created, otherwise will plot into ax
**kwargs – remaining keyword arguments are passed to matplotlib.figure()

Example

To generate a scree plot with customized symbols for signal vs. noise components and a modified cutoff threshold value:

>>> s = hs.load("some_spectrum_image")
>>> s.decomposition()
>>> s.plot_explained_variance_ratio(n=40,
>>>                                 threshold=0.005,
>>>                                 signal_fmt={'marker': 'v',
>>>                                             's': 150,
>>>                                             'c': 'pink'}
>>>                                 noise_fmt={'marker': '*',
>>>                                             's': 200,
>>>                                             'c': 'green'})

Returns: ax
Return type: matplotlib.axes