U f{j{@sddlZddlZddlmZddlmZddlm Z ddl m Z ddl m Z dd lmZmZmZmZeGd d d eeZdddZeGdddeZdS)N) BaseEstimator)TransformerMixin)_regularized_covariance)_INTERPOLATION_DEFAULT)pinv)fill_doc _check_option_validate_typecopy_doccseZdZdZd-dd Zd.d d Zd d ZddZee j fddZ e dddddddddddddddddddde ddfddZ e dddddddddddddddddddde dfddZdd Zd!d"Zd#d$Zd%d&Zd'd(Zd)d*Zd+d,ZZS)/CSPa M/EEG signal decomposition using the Common Spatial Patterns (CSP). This class can be used as a supervised decomposition to estimate spatial filters for feature extraction. CSP in the context of EEG was first described in :footcite:`KolesEtAl1990`; a comprehensive tutorial on CSP can be found in :footcite:`BlankertzEtAl2008`. Multi-class solving is implemented from :footcite:`Grosse-WentrupBuss2008`. Parameters ---------- n_components : int (default 4) The number of components to decompose M/EEG signals. This number should be set by cross-validation. reg : float | str | None (default None) If not None (same as ``'empirical'``, default), allow regularization for covariance estimation. If float (between 0 and 1), shrinkage is used. For str values, ``reg`` will be passed as ``method`` to :func:`mne.compute_covariance`. log : None | bool (default None) If ``transform_into`` equals ``'average_power'`` and ``log`` is None or True, then apply a log transform to standardize features, else features are z-scored. If ``transform_into`` is ``'csp_space'``, ``log`` must be None. cov_est : 'concat' | 'epoch' (default 'concat') If ``'concat'``, covariance matrices are estimated on concatenated epochs for each class. If ``'epoch'``, covariance matrices are estimated on each epoch separately and then averaged over each class. transform_into : 'average_power' | 'csp_space' (default 'average_power') If 'average_power' then ``self.transform`` will return the average power of each spatial filter. If ``'csp_space'``, ``self.transform`` will return the data in CSP space. norm_trace : bool (default False) Normalize class covariance by its trace. Trace normalization is a step of the original CSP algorithm :footcite:`KolesEtAl1990` to eliminate magnitude variations in the EEG between individuals. It is not applied in more recent work :footcite:`BlankertzEtAl2008`, :footcite:`Grosse-WentrupBuss2008` and can have a negative impact on pattern order. cov_method_params : dict | None Parameters to pass to :func:`mne.compute_covariance`. .. versionadded:: 0.16 %(rank_none)s .. versionadded:: 0.17 component_order : 'mutual_info' | 'alternate' (default 'mutual_info') If ``'mutual_info'`` order components by decreasing mutual information (in the two-class case this uses a simplification which orders components by decreasing absolute deviation of the eigenvalues from 0.5 :footcite:`BarachantEtAl2010`). For the two-class case, ``'alternate'`` orders components by starting with the largest eigenvalue, followed by the smallest, the second-to-largest, the second-to-smallest, and so on :footcite:`BlankertzEtAl2008`. .. versionadded:: 0.21 Attributes ---------- filters_ : ndarray, shape (n_channels, n_channels) If fit, the CSP components used to decompose the data, else None. patterns_ : ndarray, shape (n_channels, n_channels) If fit, the CSP patterns used to restore M/EEG signals, else None. mean_ : ndarray, shape (n_components,) If fit, the mean squared power for each component. std_ : ndarray, shape (n_components,) If fit, the std squared power for each component. See Also -------- mne.preprocessing.Xdawn, SPoC References ---------- .. footbibliography:: Nconcat average_powerF mutual_infoc Cst|tstd||_||_||_|dks<|dks= 2.rzzcomponent_order='alternate' requires two classes, but data contains {} classes; use component_order='mutual_info' instead.Ncsg|]}t|qSr"r'dot.0rZ pick_filtersr"r# szCSP.fit..axisr)r.r'unique_classesr*rrformat_compute_covariance_matrices_decompose_covs_order_componentsTfilters_r patterns_rasarraymeanmean_stdstd_) r!r,r- n_classescovssample_weights eigen_vectors eigen_valuesixr"r3r#fits4     zCSP.fitcst|tjstdt||jdkr.td|jd|jtfdd|D}|j dkr|dj dd}|j dkr~d n|j }|rt |}n||j 8}||j }|S) a Estimate epochs sources given the CSP filters. Parameters ---------- X : array, shape (n_epochs, n_channels, n_times) The data. Returns ------- X : ndarray If self.transform_into == 'average_power' then returns the power of CSP features averaged over time and shape (n_epochs, n_sources) If self.transform_into == 'csp_space' then returns the data in CSP space and shape is (n_epochs, n_sources, n_times). r%Nz9No filters available. Please first fit CSP decomposition.csg|]}t|qSr"r/r1r3r"r#r4sz!CSP.transform..rrr5T)rr'r(rr)r> RuntimeErrorrr@rrArrBrD)r!r,rr"r3r# transforms      z CSP.transformc stj|fd|i|S)Nr-)super fit_transform)r!r,r-Z fit_params __class__r"r#rOszCSP.fit_transformZRdBu_rTza.u.@rz%3.1fzCSP%01dheadcCsddlm}|dkr t|j}t|}|d|d<W5QRX||jj |dd}|j ||||||| | || | d|| ||||||||||d S) a.Plot topographic patterns of components. The patterns explain how the measured data was generated from the neural sources (a.k.a. the forward model). Parameters ---------- %(info_not_none)s Used for fitting. If not available, consider using :func:`mne.create_info`. components : float | array of float | None The patterns to plot. If None, n_components will be shown. ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None. vmin : float | callable The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data). vmax : float | callable The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If vmin is None, but vmax is not, default np.min(data). If callable, the output equals vmax(data). cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, 'Reds' is used for all positive data, otherwise defaults to 'RdBu_r'. If 'interactive', translates to (None, True). Defaults to 'RdBu_r'. .. warning:: Interactive mode works smoothly only for a small amount of topomaps. sensors : bool | str Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., 'r+' for red plusses). If True, a circle will be used (via .add_artist). Defaults to True. colorbar : bool Plot a colorbar. scalings : dict | float | None The scalings of the channel types to be applied for plotting. If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. units : dict | str | None The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined. res : int The resolution of the topomap image (n pixels along each side). size : float Side length per topomap in inches. cbar_fmt : str String format for colorbar values. name_format : str String format for topomap values. Defaults to "CSP%%01d". show : bool Show figure if True. show_names : bool | callable If True, show channel names on top of the map. If a callable is passed, channel names will be formatted using the callable; e.g., to delete the prefix 'MEG ' from all channel names, pass the function lambda x: x.replace('MEG ', ''). If ``mask`` is not None, only significant sensors will be shown. title : str | None Title. If None (default), no title is displayed. %(mask_patterns_topomap)s %(mask_params_topomap)s %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6. %(image_interp_topomap)s average : float | None The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging. %(sphere_topomap_auto)s Returns ------- fig : instance of matplotlib.figure.Figure The figure. r EvokedArrayN?sfreqrZtmins)timesch_typevminvmaxcmapcolorbarrescbar_fmtsensorsscalingsunits time_unit time_formatsize show_namestitle mask_paramsmaskoutlinescontours image_interpshowaveragesphere) rVr'arangercpdeepcopy_unlockr?r= plot_topomap)r!info componentsr\r]r^r_rcr`rdrerarhrb name_formatrprirjrlrkrmrnrorqrrrVpatternsr"r"r# plot_patternssBc    zCSP.plot_patternscCsddlm}|dkr t|j}t|}|d|d<W5QRX||jj |dd}|j ||||||| | || | d|| |||||||||d S) aPlot topographic filters of components. The filters are used to extract discriminant neural sources from the measured data (a.k.a. the backward model). Parameters ---------- %(info_not_none)s Used for fitting. If not available, consider using :func:`mne.create_info`. components : float | array of float | None The patterns to plot. If None, n_components will be shown. ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None. vmin : float | callable The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data). vmax : float | callable The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If vmin is None, but vmax is not, defaults to np.min(data). If callable, the output equals vmax(data). cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, 'Reds' is used for all positive data, otherwise defaults to 'RdBu_r'. If 'interactive', translates to (None, True). Defaults to 'RdBu_r'. .. warning:: Interactive mode works smoothly only for a small amount of topomaps. sensors : bool | str Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., 'r+' for red plusses). If True, a circle will be used (via .add_artist). Defaults to True. colorbar : bool Plot a colorbar. scalings : dict | float | None The scalings of the channel types to be applied for plotting. If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. units : dict | str | None The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined. res : int The resolution of the topomap image (n pixels along each side). size : float Side length per topomap in inches. cbar_fmt : str String format for colorbar values. name_format : str String format for topomap values. Defaults to "CSP%%01d". show : bool Show figure if True. show_names : bool | callable If True, show channel names on top of the map. If a callable is passed, channel names will be formatted using the callable; e.g., to delete the prefix 'MEG ' from all channel names, pass the function lambda x: x.replace('MEG ', ''). If ``mask`` is not None, only significant sensors will be shown. title : str | None Title. If None (default), no title is displayed. mask : ndarray of bool, shape (n_channels, n_times) | None The channels to be marked as significant at a given time point. Indices set to `True` will be considered. Defaults to None. mask_params : dict | None Additional plotting parameters for plotting significant sensors. Default (None) equals:: dict(marker='o', markerfacecolor='w', markeredgecolor='k', linewidth=0, markersize=4) %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6. %(image_interp_topomap)s average : float | None The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging. Returns ------- fig : instance of matplotlib.figure.Figure The figure. rrUNrWrXrrYrZ)r[r\r]r^r_r`rarbrcrdrerfrgrhrirjrkrlrmrnrorprq) rsrVr'rtrrurvrwr>r=rx)r!ryrzr\r]r^r_rcr`rdrerarhrbr{rprirjrlrkrmrnrorqrVfiltersr"r"r# plot_filtersgs@h    zCSP.plot_filtersc Cs|j\}}}|jdkr|j}n|jdkr.|j}g}g}|jD]@}||||k\} } |jrh| t| } || || qsz!CSP._epoch_cov..)sumr*)r!rrrr"rr#rs   zCSP._epoch_covcCs`ddlm}t|}|dkr8||d|d\}}n t|\}}||j||}d}||fS)Nrlinalgr)scipyrr*eighr _ajd_pham_normalize_eigenvectorsr=)r!rFrGrrErIrHDr"r"r#r;s   zCSP._decompose_covsc Cs||}g}t|jdD]}d\}}t||D]b\} } tt|dd|fj| |dd|f} || tt| 7}|| | dd7}q4|d|d } | | q|S)Nr)rrrg?) rrangerzipr'r0r=rsqrtr) r!rFrGrHZ class_probasrjjZaaZbbrZprobtmpmir"r"r#_compute_mutual_infos  zCSP._compute_mutual_infocCsttj|d|d}t|jdD]P}tt|dd|fj||dd|f}|dd|ft|<q|S)Nr)r6weightsr)r'rqrrr0r=r)r!rHrFrGZmean_coviirr"r"r#r.s zCSP._normalize_eigenvectorsc Cst|j}|dkr>|dkr>||||}t|ddd}n|dkrn|dkrntt|dddd}nj|dkr|dkrt|} t| }| dt| d|ddd<| t| ddddd|ddd<|S)Nrrrg?rrr)r*r8rr'argsortabsZ empty_like) r!rFrGrHrIrrErrJir"r"r#r<8s    (zCSP._order_components) rNNrrFNNr)N)__name__ __module__ __qualname____doc__r$r.rKrMr rrOr rr}rr:rrr;rrr< __classcell__r"r"rPr#r sL & 2$ w {   r ư>c Cs|jd}tj|ddj}|j\}}t|}||d|}t|D]} d} td|D]j} t| D]Z} t| ||} t| ||}|| | f}|| |f}t|| |f|}t|| |f|}t||}t||}t||}t||}||||d}|||t |dd}||}t |||}| ||t |||d7} ddt ||}t |t|d||}t d| |g| |dgg}t||| | gddf|| | gddf<tj|dd| f|dd|ff}tj|||dfd d }t||j}tj|||dfd d }|ddd|f|dd| f<|dd|df|dd|f<t||| | gddf|| | gddf<qhqZ| |krFqqFt||d |fddd}||fS) avApproximate joint diagonalization based on Pham's algorithm. This is a direct implementation of the PHAM's AJD algorithm [1]. Parameters ---------- X : ndarray, shape (n_epochs, n_channels, n_channels) A set of covariance matrices to diagonalize. eps : float, default 1e-6 The tolerance for stopping criterion. max_iter : int, default 1000 The maximum number of iteration to reach convergence. Returns ------- V : ndarray, shape (n_channels, n_channels) The diagonalizer. D : ndarray, shape (n_epochs, n_channels, n_channels) The set of quasi diagonal matrices. References ---------- .. [1] Pham, Dinh Tuan. "Joint approximate diagonalization of positive definite Hermitian matrices." SIAM Journal on Matrix Analysis and Applications 22, no. 4 (2001): 1136-1152. rr5rg& .>g@y?rNF)orderr)rr'Z concatenater=eyerrtrArmaxZconjimagrealrr0Zc_rr)r,epsZmax_itern_epochsAZn_timesZn_mVepsilonitZdecrrrZIiZIjc1c2Zg12Zg21Zomega21Zomega12omegarZtmp1Ztmp2Zh12Zh21taurr"r"r#rIsN     " ",&  0rcs6eZdZdZd fdd ZddZfd d ZZS) SPoCaImplementation of the SPoC spatial filtering. Source Power Comodulation (SPoC) :footcite:`DahneEtAl2014` allows to extract spatial filters and patterns by using a target (continuous) variable in the decomposition process in order to give preference to components whose power correlates with the target variable. SPoC can be seen as an extension of the CSP driven by a continuous variable rather than a discrete variable. Typical applications include extraction of motor patterns using EMG power or audio patterns using sound envelope. Parameters ---------- n_components : int The number of components to decompose M/EEG signals. reg : float | str | None (default None) If not None (same as ``'empirical'``, default), allow regularization for covariance estimation. If float, shrinkage is used (0 <= shrinkage <= 1). For str options, ``reg`` will be passed to ``method`` to :func:`mne.compute_covariance`. log : None | bool (default None) If transform_into == 'average_power' and log is None or True, then applies a log transform to standardize the features, else the features are z-scored. If transform_into == 'csp_space', then log must be None. transform_into : {'average_power', 'csp_space'} If 'average_power' then self.transform will return the average power of each spatial filter. If 'csp_space' self.transform will return the data in CSP space. Defaults to 'average_power'. cov_method_params : dict | None Parameters to pass to :func:`mne.compute_covariance`. .. versionadded:: 0.16 %(rank_none)s .. versionadded:: 0.17 Attributes ---------- filters_ : ndarray, shape (n_channels, n_channels) If fit, the SPoC spatial filters, else None. patterns_ : ndarray, shape (n_channels, n_channels) If fit, the SPoC spatial patterns, else None. mean_ : ndarray, shape (n_components,) If fit, the mean squared power for each component. std_ : ndarray, shape (n_components,) If fit, the std squared power for each component. See Also -------- mne.preprocessing.Xdawn, CSP References ---------- .. footbibliography:: rNrc s8tt|j|||dd|||dt|dt|ddS)z Init of SPoC.rF)rrrrrrrr rrN)rNrr$delattr)r!rrrrr rrPr"r#r$s z SPoC.__init__csddlm}|||tt|dkr2td|tj}|| 8}|| }|j dd\}}t |||f}t |D]"\}} t| |j|j|jd||<q| d} tj ||ddtjtjfdd} || | \} } | j} | j} tt| ddd}| dd|fj} || j|_| |_|jd|jtfd d |D}|dj dd}|j dd|_|j dd|_|S) aEstimate the SPoC decomposition on epochs. Parameters ---------- X : ndarray, shape (n_epochs, n_channels, n_times) The data on which to estimate the SPoC. y : array, shape (n_epochs,) The class for each epoch. Returns ------- self : instance of SPoC Returns the modified instance. rrrz)y must have at least two distinct values.Nrr5rcsg|]}t|qSr"r/r1r3r"r#r4szSPoC.fit..)rrr.r*r'r7rZastypeZfloat64rArCrempty enumeraterrr rZnewaxisrrrrr=rr?r>rr@rBrD)r!r,r-rtargetrrrFrrCZCzZevalsZevecsrJr"r3r#rKs>       $zSPoC.fitcstt||S)a Estimate epochs sources given the SPoC filters. Parameters ---------- X : array, shape (n_epochs, n_channels, n_times) The data. Returns ------- X : ndarray If self.transform_into == 'average_power' then returns the power of CSP features averaged over time and shape (n_epochs, n_sources) If self.transform_into == 'csp_space' then returns the data in CSP space and shape is (n_epochs, n_sources, n_times). )rNrrM)r!r,rPr"r#rM*szSPoC.transform)rNNrNN)rrrrr$rKrMrr"r"rPr#rs;Br)rr)copyrunumpyr'baserZmixinrrrdefaultsrfixesrutilsr r r r r rrr"r"r"r# s      6 T