function [chantypes,senstype_mapping]=cosmo_meeg_chantype(ds,varargin)
% return channel types and optionally a feature mask matching a type
%
% [chantypes,senstype_mapping]=cosmo_meeg_chantype(ds)
%
% Inputs:
% ds dataset struct for MEEG dataset
%
% Output:
% chantypes 1xN cell with type of each channel in ds, where
% N is the number of channels.
% senstype_mapping struct with keys the unique chantypes, and values
% the sensor (acquisition) type
%
% Example:
% % This example requires FieldTrip
% cosmo_skip_test_if_no_external('fieldtrip');
% %
% % generate synthetic dataset with meg_planar and meg_axial channels
% % as found in the neuromag306 system
% ds=cosmo_synthetic_dataset('type','meeg','sens','neuromag306_all',...
% 'size','big','nchunks',1,'ntargets',1);
% [chantypes,senstypes]=cosmo_meeg_chantype(ds);
% cosmo_disp(chantypes,'edgeitems',2);
% %|| { 'meg_axial' 'meg_planar' ... 'meg_planar' 'meg_planar' }@1x306
% cosmo_disp(senstypes,'strlen',inf);
% %|| .meg_axial
% %|| 'neuromag306alt_mag'
% %|| .meg_planar
% %|| 'neuromag306alt_planar'
% %
% % filter the dataset to only contain the planar channels:
% %
% % see which features have a matching channel
% chan_indices=find(cosmo_match(chantypes,'meg_planar'));
% planar_msk=cosmo_match(ds.fa.chan,chan_indices);
% % slice and prune dataset along feature dimension
% ds_planar=cosmo_slice(ds,planar_msk,2);
% ds_planar=cosmo_dim_prune(ds_planar);
% % the output dataset has only the 204 planar channels left
% cosmo_disp(ds_planar.a.fdim,'edgeitems',2);
% %|| .labels
% %|| { 'chan'
% %|| 'time' }
% %|| .values
% %|| { { 'MEG0112' 'MEG0113' ... 'MEG2642' 'MEG2643' }@1x204
% %|| [ -0.2 -0.15 ... 0.05 0.1 ]@1x7 }
% %
% cosmo_disp(ds_planar.fa.chan);
% %|| [ 1 2 3 ... 202 203 204 ]@1x1428
%
% # For CoSMoMVPA's copyright information and license terms, #
% # see the COPYING file distributed with CoSMoMVPA. #
