# Authors: Alexandre Gramfort # Matti Hamalainen # Martin Luessi # Mads Jensen # # License: BSD (3-clause) import contextlib import copy import os.path as op import numpy as np from scipy import linalg, sparse from scipy.sparse import coo_matrix, block_diag as sparse_block_diag from .filter import resample from .fixes import einsum from .evoked import _get_peak from .surface import read_surface, _get_ico_surface, mesh_edges from .source_space import (_ensure_src, _get_morph_src_reordering, _ensure_src_subject, SourceSpaces) from .utils import (get_subjects_dir, _check_subject, logger, verbose, _time_mask, warn as warn_, copy_function_doc_to_method_doc, fill_doc, _check_option, _validate_type, _check_src_normal) from .viz import (plot_source_estimates, plot_vector_source_estimates, plot_volume_source_estimates) from .io.base import ToDataFrameMixin, TimeMixin from .externals.h5io import read_hdf5, write_hdf5 def _read_stc(filename): """Aux Function.""" with open(filename, 'rb') as fid: buf = fid.read() stc = dict() offset = 0 num_bytes = 4 # read tmin in ms stc['tmin'] = float(np.frombuffer(buf, dtype=">f4", count=1, offset=offset)) stc['tmin'] /= 1000.0 offset += num_bytes # read sampling rate in ms stc['tstep'] = float(np.frombuffer(buf, dtype=">f4", count=1, offset=offset)) stc['tstep'] /= 1000.0 offset += num_bytes # read number of vertices/sources vertices_n = int(np.frombuffer(buf, dtype=">u4", count=1, offset=offset)) offset += num_bytes # read the source vector stc['vertices'] = np.frombuffer(buf, dtype=">u4", count=vertices_n, offset=offset) offset += num_bytes * vertices_n # read the number of timepts data_n = int(np.frombuffer(buf, dtype=">u4", count=1, offset=offset)) offset += num_bytes if (vertices_n and # vertices_n can be 0 (empty stc) ((len(buf) // 4 - 4 - vertices_n) % (data_n * vertices_n)) != 0): raise ValueError('incorrect stc file size') # read the data matrix stc['data'] = np.frombuffer(buf, dtype=">f4", count=vertices_n * data_n, offset=offset) stc['data'] = stc['data'].reshape([data_n, vertices_n]).T return stc def _write_stc(filename, tmin, tstep, vertices, data): """Write an STC file. Parameters ---------- filename : string The name of the STC file. tmin : float The first time point of the data in seconds. tstep : float Time between frames in seconds. vertices : array of integers Vertex indices (0 based). data : 2D array The data matrix (nvert * ntime). """ fid = open(filename, 'wb') # write start time in ms fid.write(np.array(1000 * tmin, dtype='>f4').tostring()) # write sampling rate in ms fid.write(np.array(1000 * tstep, dtype='>f4').tostring()) # write number of vertices fid.write(np.array(vertices.shape[0], dtype='>u4').tostring()) # write the vertex indices fid.write(np.array(vertices, dtype='>u4').tostring()) # write the number of timepts fid.write(np.array(data.shape[1], dtype='>u4').tostring()) # # write the data # fid.write(np.array(data.T, dtype='>f4').tostring()) # close the file fid.close() def _read_3(fid): """Read 3 byte integer from file.""" data = np.fromfile(fid, dtype=np.uint8, count=3).astype(np.int32) out = np.left_shift(data[0], 16) + np.left_shift(data[1], 8) + data[2] return out def _read_w(filename): """Read a w file. w files contain activations or source reconstructions for a single time point. Parameters ---------- filename : string The name of the w file. Returns ------- data: dict The w structure. It has the following keys: vertices vertex indices (0 based) data The data matrix (nvert long) """ with open(filename, 'rb', buffering=0) as fid: # buffering=0 for np bug # skip first 2 bytes fid.read(2) # read number of vertices/sources (3 byte integer) vertices_n = int(_read_3(fid)) vertices = np.zeros((vertices_n), dtype=np.int32) data = np.zeros((vertices_n), dtype=np.float32) # read the vertices and data for i in range(vertices_n): vertices[i] = _read_3(fid) data[i] = np.fromfile(fid, dtype='>f4', count=1)[0] w = dict() w['vertices'] = vertices w['data'] = data return w def _write_3(fid, val): """Write 3 byte integer to file.""" f_bytes = np.zeros((3), dtype=np.uint8) f_bytes[0] = (val >> 16) & 255 f_bytes[1] = (val >> 8) & 255 f_bytes[2] = val & 255 fid.write(f_bytes.tostring()) def _write_w(filename, vertices, data): """Write a w file. w files contain activations or source reconstructions for a single time point. Parameters ---------- filename: string The name of the w file. vertices: array of int Vertex indices (0 based). data: 1D array The data array (nvert). """ assert (len(vertices) == len(data)) fid = open(filename, 'wb') # write 2 zero bytes fid.write(np.zeros((2), dtype=np.uint8).tostring()) # write number of vertices/sources (3 byte integer) vertices_n = len(vertices) _write_3(fid, vertices_n) # write the vertices and data for i in range(vertices_n): _write_3(fid, vertices[i]) # XXX: without float() endianness is wrong, not sure why fid.write(np.array(float(data[i]), dtype='>f4').tostring()) # close the file fid.close() def read_source_estimate(fname, subject=None): """Read a source estimate object. Parameters ---------- fname : str Path to (a) source-estimate file(s). subject : str | None Name of the subject the source estimate(s) is (are) from. It is good practice to set this attribute to avoid combining incompatible labels and SourceEstimates (e.g., ones from other subjects). Note that due to file specification limitations, the subject name isn't saved to or loaded from files written to disk. Returns ------- stc : SourceEstimate | VectorSourceEstimate | VolSourceEstimate | MixedSourceEstimate The source estimate object loaded from file. Notes ----- - for volume source estimates, ``fname`` should provide the path to a single file named '*-vl.stc` or '*-vol.stc' - for surface source estimates, ``fname`` should either provide the path to the file corresponding to a single hemisphere ('*-lh.stc', '*-rh.stc') or only specify the asterisk part in these patterns. In any case, the function expects files for both hemisphere with names following this pattern. - for vector surface source estimates, only HDF5 files are supported. - for mixed source estimates, only HDF5 files are supported. - for single time point .w files, ``fname`` should follow the same pattern as for surface estimates, except that files are named '*-lh.w' and '*-rh.w'. """ # noqa: E501 fname_arg = fname _validate_type(fname, 'path-like', 'fname') fname = str(fname) # make sure corresponding file(s) can be found ftype = None if op.exists(fname): if fname.endswith('-vl.stc') or fname.endswith('-vol.stc') or \ fname.endswith('-vl.w') or fname.endswith('-vol.w'): ftype = 'volume' elif fname.endswith('.stc'): ftype = 'surface' if fname.endswith(('-lh.stc', '-rh.stc')): fname = fname[:-7] else: err = ("Invalid .stc filename: %r; needs to end with " "hemisphere tag ('...-lh.stc' or '...-rh.stc')" % fname) raise IOError(err) elif fname.endswith('.w'): ftype = 'w' if fname.endswith(('-lh.w', '-rh.w')): fname = fname[:-5] else: err = ("Invalid .w filename: %r; needs to end with " "hemisphere tag ('...-lh.w' or '...-rh.w')" % fname) raise IOError(err) elif fname.endswith('.h5'): ftype = 'h5' fname = fname[:-3] else: raise RuntimeError('Unknown extension for file %s' % fname_arg) if ftype != 'volume': stc_exist = [op.exists(f) for f in [fname + '-rh.stc', fname + '-lh.stc']] w_exist = [op.exists(f) for f in [fname + '-rh.w', fname + '-lh.w']] if all(stc_exist) and ftype != 'w': ftype = 'surface' elif all(w_exist): ftype = 'w' elif op.exists(fname + '.h5'): ftype = 'h5' elif op.exists(fname + '-stc.h5'): ftype = 'h5' fname += '-stc' elif any(stc_exist) or any(w_exist): raise IOError("Hemisphere missing for %r" % fname_arg) else: raise IOError("SourceEstimate File(s) not found for: %r" % fname_arg) # read the files if ftype == 'volume': # volume source space if fname.endswith('.stc'): kwargs = _read_stc(fname) elif fname.endswith('.w'): kwargs = _read_w(fname) kwargs['data'] = kwargs['data'][:, np.newaxis] kwargs['tmin'] = 0.0 kwargs['tstep'] = 0.0 else: raise IOError('Volume source estimate must end with .stc or .w') elif ftype == 'surface': # stc file with surface source spaces lh = _read_stc(fname + '-lh.stc') rh = _read_stc(fname + '-rh.stc') assert lh['tmin'] == rh['tmin'] assert lh['tstep'] == rh['tstep'] kwargs = lh.copy() kwargs['data'] = np.r_[lh['data'], rh['data']] kwargs['vertices'] = [lh['vertices'], rh['vertices']] elif ftype == 'w': # w file with surface source spaces lh = _read_w(fname + '-lh.w') rh = _read_w(fname + '-rh.w') kwargs = lh.copy() kwargs['data'] = np.atleast_2d(np.r_[lh['data'], rh['data']]).T kwargs['vertices'] = [lh['vertices'], rh['vertices']] # w files only have a single time point kwargs['tmin'] = 0.0 kwargs['tstep'] = 1.0 ftype = 'surface' elif ftype == 'h5': kwargs = read_hdf5(fname + '.h5', title='mnepython') ftype = kwargs.pop('src_type', 'surface') if ftype != 'volume': # Make sure the vertices are ordered vertices = kwargs['vertices'] if any(np.any(np.diff(v.astype(int)) <= 0) for v in vertices): sidx = [np.argsort(verts) for verts in vertices] vertices = [verts[idx] for verts, idx in zip(vertices, sidx)] data = kwargs['data'][np.r_[sidx[0], len(sidx[0]) + sidx[1]]] kwargs['vertices'] = vertices kwargs['data'] = data if 'subject' not in kwargs: kwargs['subject'] = subject if subject is not None and subject != kwargs['subject']: raise RuntimeError('provided subject name "%s" does not match ' 'subject name from the file "%s' % (subject, kwargs['subject'])) vector = kwargs['data'].ndim == 3 if ftype in ('volume', 'discrete'): klass = VolVectorSourceEstimate if vector else VolSourceEstimate elif ftype == 'mixed': if vector: # XXX we should really support this at some point raise NotImplementedError('Vector mixed source estimates not yet ' 'supported') klass = MixedSourceEstimate else: assert ftype == 'surface' klass = VectorSourceEstimate if vector else SourceEstimate return klass(**kwargs) def _get_src_type(src, vertices, warn_text=None): src_type = None if src is None: if warn_text is None: warn_("src should not be None for a robust guess of stc type.") else: warn_(warn_text) if isinstance(vertices, list) and len(vertices) == 2: src_type = 'surface' elif isinstance(vertices, np.ndarray) or isinstance(vertices, list) \ and len(vertices) == 1: src_type = 'volume' elif isinstance(vertices, list) and len(vertices) > 2: src_type = 'mixed' else: src_type = src.kind assert src_type in ('surface', 'volume', 'mixed', 'discrete') return src_type def _make_stc(data, vertices, src_type=None, tmin=None, tstep=None, subject=None, vector=False, source_nn=None, warn_text=None): """Generate a surface, vector-surface, volume or mixed source estimate.""" def guess_src_type(): return _get_src_type(src=None, vertices=vertices, warn_text=warn_text) src_type = guess_src_type() if src_type is None else src_type if vector and src_type == 'mixed': # XXX this should be supported someday raise NotImplementedError( 'Vector source estimates for mixed source spaces are not supported' ) if vector and src_type == 'surface' and source_nn is None: raise RuntimeError('No source vectors supplied.') # infer Klass from src_type if src_type == 'surface': Klass = VectorSourceEstimate if vector else SourceEstimate elif src_type in ('volume', 'discrete'): Klass = VolVectorSourceEstimate if vector else VolSourceEstimate elif src_type == 'mixed': Klass = MixedSourceEstimate else: raise ValueError('vertices has to be either a list with one or more ' 'arrays or an array') # massage the data if src_type == 'surface' and vector: n_vertices = len(vertices[0]) + len(vertices[1]) data = np.matmul( np.transpose(source_nn.reshape(n_vertices, 3, 3), axes=[0, 2, 1]), data.reshape(n_vertices, 3, -1) ) elif src_type in ('volume', 'discrete') and vector: data = data.reshape((-1, 3, data.shape[-1])) else: pass # noqa return Klass( data=data, vertices=vertices, tmin=tmin, tstep=tstep, subject=subject ) def _verify_source_estimate_compat(a, b): """Make sure two SourceEstimates are compatible for arith. operations.""" compat = False if type(a) != type(b): raise ValueError('Cannot combine %s and %s.' % (type(a), type(b))) if len(a.vertices) == len(b.vertices): if all(np.array_equal(av, vv) for av, vv in zip(a.vertices, b.vertices)): compat = True if not compat: raise ValueError('Cannot combine source estimates that do not have ' 'the same vertices. Consider using stc.expand().') if a.subject != b.subject: raise ValueError('source estimates do not have the same subject ' 'names, %r and %r' % (a.subject, b.subject)) class _BaseSourceEstimate(ToDataFrameMixin, TimeMixin): """Base class for all source estimates. Parameters ---------- data : array, shape (n_dipoles, n_times) | tuple, shape (2,) The data in source space. The data can either be a single array or a tuple with two arrays: "kernel" shape (n_vertices, n_sensors) and "sens_data" shape (n_sensors, n_times). In this case, the source space data corresponds to "numpy.dot(kernel, sens_data)". vertices : array | list of array Vertex numbers corresponding to the data. tmin : float Time point of the first sample in data. tstep : float Time step between successive samples in data. subject : str | None The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors. %(verbose)s Attributes ---------- subject : str | None The subject name. times : array, shape (n_times,) The time vector. vertices : array | list of array of shape (n_dipoles,) The indices of the dipoles in the different source spaces. Can be an array if there is only one source space (e.g., for volumes). data : array of shape (n_dipoles, n_times) The data in source space. shape : tuple The shape of the data. A tuple of int (n_dipoles, n_times). """ @verbose def __init__(self, data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None): # noqa: D102 assert hasattr(self, '_data_ndim'), self.__class__.__name__ assert hasattr(self, '_src_type'), self.__class__.__name__ kernel, sens_data = None, None if isinstance(data, tuple): if len(data) != 2: raise ValueError('If data is a tuple it has to be length 2') kernel, sens_data = data data = None if kernel.shape[1] != sens_data.shape[0]: raise ValueError('kernel and sens_data have invalid ' 'dimensions') if sens_data.ndim != 2: raise ValueError('The sensor data must have 2 dimensions, got ' '%s' % (sens_data.ndim,)) if isinstance(vertices, list): vertices = [np.asarray(v, int) for v in vertices] if any(np.any(np.diff(v.astype(int)) <= 0) for v in vertices): raise ValueError('Vertices must be ordered in increasing ' 'order.') n_src = sum([len(v) for v in vertices]) if len(vertices) == 1: vertices = vertices[0] elif isinstance(vertices, np.ndarray): n_src = len(vertices) else: raise ValueError('Vertices must be a list or numpy array') # safeguard the user against doing something silly if data is not None: if data.shape[0] != n_src: raise ValueError('Number of vertices (%i) and stc.shape[0] ' '(%i) must match' % (n_src, data.shape[0])) if data.ndim == self._data_ndim - 1: # allow upbroadcasting data = data[..., np.newaxis] if data.ndim != self._data_ndim: raise ValueError('Data (shape %s) must have %s dimensions for ' '%s' % (data.shape, self._data_ndim, self.__class__.__name__)) self._data = data self._tmin = tmin self._tstep = tstep self.vertices = vertices self.verbose = verbose self._kernel = kernel self._sens_data = sens_data self._kernel_removed = False self._times = None self._update_times() self.subject = _check_subject(None, subject, False) def __repr__(self): # noqa: D105 s = "%d vertices" % (sum(len(v) for v in self._vertices_list),) if self.subject is not None: s += ", subject : %s" % self.subject s += ", tmin : %s (ms)" % (1e3 * self.tmin) s += ", tmax : %s (ms)" % (1e3 * self.times[-1]) s += ", tstep : %s (ms)" % (1e3 * self.tstep) s += ", data shape : %s" % (self.shape,) return "<%s | %s>" % (type(self).__name__, s) @property def _vertices_list(self): return self.vertices @verbose def save(self, fname, ftype='h5', verbose=None): """Save the full source estimate to an HDF5 file. Parameters ---------- fname : string The file name to write the source estimate to, should end in '-stc.h5'. ftype : string File format to use. Currently, the only allowed values is "h5". %(verbose_meth)s """ _validate_type(fname, 'path-like', 'fname') fname = str(fname) if ftype != 'h5': raise ValueError('%s objects can only be written as HDF5 files.' % (self.__class__.__name__,)) if not fname.endswith('.h5'): fname += '-stc.h5' write_hdf5(fname, dict(vertices=self.vertices, data=self.data, tmin=self.tmin, tstep=self.tstep, subject=self.subject, src_type=self._src_type), title='mnepython', overwrite=True) @property def sfreq(self): """Sample rate of the data.""" return 1. / self.tstep def _remove_kernel_sens_data_(self): """Remove kernel and sensor space data and compute self._data.""" if self._kernel is not None or self._sens_data is not None: self._kernel_removed = True self._data = np.dot(self._kernel, self._sens_data) self._kernel = None self._sens_data = None @fill_doc def crop(self, tmin=None, tmax=None, include_tmax=True): """Restrict SourceEstimate to a time interval. Parameters ---------- tmin : float | None The first time point in seconds. If None the first present is used. tmax : float | None The last time point in seconds. If None the last present is used. %(include_tmax)s """ mask = _time_mask(self.times, tmin, tmax, sfreq=self.sfreq, include_tmax=include_tmax) self.tmin = self.times[np.where(mask)[0][0]] if self._kernel is not None and self._sens_data is not None: self._sens_data = self._sens_data[..., mask] else: self.data = self.data[..., mask] return self # return self for chaining methods @verbose def resample(self, sfreq, npad='auto', window='boxcar', n_jobs=1, verbose=None): """Resample data. Parameters ---------- sfreq : float New sample rate to use. npad : int | str Amount to pad the start and end of the data. Can also be "auto" to use a padding that will result in a power-of-two size (can be much faster). window : string or tuple Window to use in resampling. See scipy.signal.resample. %(n_jobs)s %(verbose_meth)s Notes ----- For some data, it may be more accurate to use npad=0 to reduce artifacts. This is dataset dependent -- check your data! Note that the sample rate of the original data is inferred from tstep. """ # resampling in sensor instead of source space gives a somewhat # different result, so we don't allow it self._remove_kernel_sens_data_() o_sfreq = 1.0 / self.tstep data = self.data if data.dtype == np.float32: data = data.astype(np.float64) self.data = resample(data, sfreq, o_sfreq, npad, n_jobs=n_jobs) # adjust indirectly affected variables self.tstep = 1.0 / sfreq return self @property def data(self): """Numpy array of source estimate data.""" if self._data is None: # compute the solution the first time the data is accessed and # remove the kernel and sensor data self._remove_kernel_sens_data_() return self._data @data.setter def data(self, value): value = np.asarray(value) if self._data is not None and value.ndim != self._data.ndim: raise ValueError('Data array should have %d dimensions.' % self._data.ndim) # vertices can be a single number, so cast to ndarray if isinstance(self.vertices, list): n_verts = sum([len(v) for v in self.vertices]) elif isinstance(self.vertices, np.ndarray): n_verts = len(self.vertices) else: raise ValueError('Vertices must be a list or numpy array') if value.shape[0] != n_verts: raise ValueError('The first dimension of the data array must ' 'match the number of vertices (%d != %d)' % (value.shape[0], n_verts)) self._data = value self._update_times() @property def shape(self): """Shape of the data.""" if self._data is not None: return self._data.shape return (self._kernel.shape[0], self._sens_data.shape[1]) @property def tmin(self): """The first timestamp.""" return self._tmin @tmin.setter def tmin(self, value): self._tmin = float(value) self._update_times() @property def tstep(self): """The change in time between two consecutive samples (1 / sfreq).""" return self._tstep @tstep.setter def tstep(self, value): if value <= 0: raise ValueError('.tstep must be greater than 0.') self._tstep = float(value) self._update_times() @property def times(self): """A timestamp for each sample.""" return self._times @times.setter def times(self, value): raise ValueError('You cannot write to the .times attribute directly. ' 'This property automatically updates whenever ' '.tmin, .tstep or .data changes.') def _update_times(self): """Update the times attribute after changing tmin, tmax, or tstep.""" self._times = self.tmin + (self.tstep * np.arange(self.shape[-1])) self._times.flags.writeable = False def __add__(self, a): """Add source estimates.""" stc = self.copy() stc += a return stc def __iadd__(self, a): # noqa: D105 self._remove_kernel_sens_data_() if isinstance(a, _BaseSourceEstimate): _verify_source_estimate_compat(self, a) self.data += a.data else: self.data += a return self def mean(self): """Make a summary stc file with mean over time points. Returns ------- stc : SourceEstimate | VectorSourceEstimate The modified stc. """ out = self.sum() out /= len(self.times) return out def sum(self): """Make a summary stc file with sum over time points. Returns ------- stc : SourceEstimate | VectorSourceEstimate The modified stc. """ data = self.data tmax = self.tmin + self.tstep * data.shape[-1] tmin = (self.tmin + tmax) / 2. tstep = tmax - self.tmin sum_stc = self.__class__(self.data.sum(axis=-1, keepdims=True), vertices=self.vertices, tmin=tmin, tstep=tstep, subject=self.subject) return sum_stc def __sub__(self, a): """Subtract source estimates.""" stc = self.copy() stc -= a return stc def __isub__(self, a): # noqa: D105 self._remove_kernel_sens_data_() if isinstance(a, _BaseSourceEstimate): _verify_source_estimate_compat(self, a) self.data -= a.data else: self.data -= a return self def __truediv__(self, a): # noqa: D105 return self.__div__(a) def __div__(self, a): # noqa: D105 """Divide source estimates.""" stc = self.copy() stc /= a return stc def __itruediv__(self, a): # noqa: D105 return self.__idiv__(a) def __idiv__(self, a): # noqa: D105 self._remove_kernel_sens_data_() if isinstance(a, _BaseSourceEstimate): _verify_source_estimate_compat(self, a) self.data /= a.data else: self.data /= a return self def __mul__(self, a): """Multiply source estimates.""" stc = self.copy() stc *= a return stc def __imul__(self, a): # noqa: D105 self._remove_kernel_sens_data_() if isinstance(a, _BaseSourceEstimate): _verify_source_estimate_compat(self, a) self.data *= a.data else: self.data *= a return self def __pow__(self, a): # noqa: D105 stc = self.copy() stc **= a return stc def __ipow__(self, a): # noqa: D105 self._remove_kernel_sens_data_() self.data **= a return self def __radd__(self, a): # noqa: D105 return self + a def __rsub__(self, a): # noqa: D105 return self - a def __rmul__(self, a): # noqa: D105 return self * a def __rdiv__(self, a): # noqa: D105 return self / a def __neg__(self): # noqa: D105 """Negate the source estimate.""" stc = self.copy() stc._remove_kernel_sens_data_() stc.data *= -1 return stc def __pos__(self): # noqa: D105 return self def __abs__(self): """Compute the absolute value of the data. Returns ------- stc : instance of _BaseSourceEstimate A version of the source estimate, where the data attribute is set to abs(self.data). """ stc = self.copy() stc._remove_kernel_sens_data_() stc._data = abs(stc._data) return stc def sqrt(self): """Take the square root. Returns ------- stc : instance of SourceEstimate A copy of the SourceEstimate with sqrt(data). """ return self ** (0.5) def copy(self): """Return copy of source estimate instance.""" return copy.deepcopy(self) def bin(self, width, tstart=None, tstop=None, func=np.mean): """Return a source estimate object with data summarized over time bins. Time bins of ``width`` seconds. This method is intended for visualization only. No filter is applied to the data before binning, making the method inappropriate as a tool for downsampling data. Parameters ---------- width : scalar Width of the individual bins in seconds. tstart : scalar | None Time point where the first bin starts. The default is the first time point of the stc. tstop : scalar | None Last possible time point contained in a bin (if the last bin would be shorter than width it is dropped). The default is the last time point of the stc. func : callable Function that is applied to summarize the data. Needs to accept a numpy.array as first input and an ``axis`` keyword argument. Returns ------- stc : SourceEstimate | VectorSourceEstimate The binned source estimate. """ if tstart is None: tstart = self.tmin if tstop is None: tstop = self.times[-1] times = np.arange(tstart, tstop + self.tstep, width) nt = len(times) - 1 data = np.empty(self.shape[:-1] + (nt,), dtype=self.data.dtype) for i in range(nt): idx = (self.times >= times[i]) & (self.times < times[i + 1]) data[..., i] = func(self.data[..., idx], axis=-1) tmin = times[0] + width / 2. stc = self.copy() stc._data = data stc.tmin = tmin stc.tstep = width return stc def transform_data(self, func, idx=None, tmin_idx=None, tmax_idx=None): """Get data after a linear (time) transform has been applied. The transform is applied to each source time course independently. Parameters ---------- func : callable The transform to be applied, including parameters (see, e.g., :func:`functools.partial`). The first parameter of the function is the input data. The first return value is the transformed data, remaining outputs are ignored. The first dimension of the transformed data has to be the same as the first dimension of the input data. idx : array | None Indicices of source time courses for which to compute transform. If None, all time courses are used. tmin_idx : int | None Index of first time point to include. If None, the index of the first time point is used. tmax_idx : int | None Index of the first time point not to include. If None, time points up to (and including) the last time point are included. Returns ------- data_t : ndarray The transformed data. Notes ----- Applying transforms can be significantly faster if the SourceEstimate object was created using "(kernel, sens_data)", for the "data" parameter as the transform is applied in sensor space. Inverse methods, e.g., "apply_inverse_epochs", or "apply_lcmv_epochs" do this automatically (if possible). """ if idx is None: # use all time courses by default idx = slice(None, None) if self._kernel is None and self._sens_data is None: if self._kernel_removed: warn_('Performance can be improved by not accessing the data ' 'attribute before calling this method.') # transform source space data directly data_t = func(self.data[idx, ..., tmin_idx:tmax_idx]) if isinstance(data_t, tuple): # use only first return value data_t = data_t[0] else: # apply transform in sensor space sens_data_t = func(self._sens_data[:, tmin_idx:tmax_idx]) if isinstance(sens_data_t, tuple): # use only first return value sens_data_t = sens_data_t[0] # apply inverse data_shape = sens_data_t.shape if len(data_shape) > 2: # flatten the last dimensions sens_data_t = sens_data_t.reshape(data_shape[0], np.prod(data_shape[1:])) data_t = np.dot(self._kernel[idx, :], sens_data_t) # restore original shape if necessary if len(data_shape) > 2: data_t = data_t.reshape(data_t.shape[0], *data_shape[1:]) return data_t def transform(self, func, idx=None, tmin=None, tmax=None, copy=False): """Apply linear transform. The transform is applied to each source time course independently. Parameters ---------- func : callable The transform to be applied, including parameters (see, e.g., :func:`functools.partial`). The first parameter of the function is the input data. The first two dimensions of the transformed data should be (i) vertices and (ii) time. See Notes for details. idx : array | None Indices of source time courses for which to compute transform. If None, all time courses are used. tmin : float | int | None First time point to include (ms). If None, self.tmin is used. tmax : float | int | None Last time point to include (ms). If None, self.tmax is used. copy : bool If True, return a new instance of SourceEstimate instead of modifying the input inplace. Returns ------- stcs : SourceEstimate | VectorSourceEstimate | list The transformed stc or, in the case of transforms which yield N-dimensional output (where N > 2), a list of stcs. For a list, copy must be True. Notes ----- Transforms which yield 3D output (e.g. time-frequency transforms) are valid, so long as the first two dimensions are vertices and time. In this case, the copy parameter must be True and a list of SourceEstimates, rather than a single instance of SourceEstimate, will be returned, one for each index of the 3rd dimension of the transformed data. In the case of transforms yielding 2D output (e.g. filtering), the user has the option of modifying the input inplace (copy = False) or returning a new instance of SourceEstimate (copy = True) with the transformed data. Applying transforms can be significantly faster if the SourceEstimate object was created using "(kernel, sens_data)", for the "data" parameter as the transform is applied in sensor space. Inverse methods, e.g., "apply_inverse_epochs", or "apply_lcmv_epochs" do this automatically (if possible). """ # min and max data indices to include times = 1000. * self.times t_idx = np.where(_time_mask(times, tmin, tmax, sfreq=self.sfreq))[0] if tmin is None: tmin_idx = None else: tmin_idx = t_idx[0] if tmax is None: tmax_idx = None else: # +1, because upper boundary needs to include the last sample tmax_idx = t_idx[-1] + 1 data_t = self.transform_data(func, idx=idx, tmin_idx=tmin_idx, tmax_idx=tmax_idx) # account for change in n_vertices if idx is not None: idx_lh = idx[idx < len(self.lh_vertno)] idx_rh = idx[idx >= len(self.lh_vertno)] - len(self.lh_vertno) verts_lh = self.lh_vertno[idx_lh] verts_rh = self.rh_vertno[idx_rh] else: verts_lh = self.lh_vertno verts_rh = self.rh_vertno verts = [verts_lh, verts_rh] tmin_idx = 0 if tmin_idx is None else tmin_idx tmin = self.times[tmin_idx] if data_t.ndim > 2: # return list of stcs if transformed data has dimensionality > 2 if copy: stcs = [SourceEstimate(data_t[:, :, a], verts, tmin, self.tstep, self.subject) for a in range(data_t.shape[-1])] else: raise ValueError('copy must be True if transformed data has ' 'more than 2 dimensions') else: # return new or overwritten stc stcs = self if not copy else self.copy() stcs.vertices = verts stcs.data = data_t stcs.tmin = tmin return stcs def _center_of_mass(vertices, values, hemi, surf, subject, subjects_dir, restrict_vertices): """Find the center of mass on a surface.""" if (values == 0).all() or (values < 0).any(): raise ValueError('All values must be non-negative and at least one ' 'must be non-zero, cannot compute COM') subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) surf = read_surface(op.join(subjects_dir, subject, 'surf', hemi + '.' + surf)) if restrict_vertices is True: restrict_vertices = vertices elif restrict_vertices is False: restrict_vertices = np.arange(surf[0].shape[0]) elif isinstance(restrict_vertices, SourceSpaces): idx = 1 if restrict_vertices.kind == 'surface' and hemi == 'rh' else 0 restrict_vertices = restrict_vertices[idx]['vertno'] else: restrict_vertices = np.array(restrict_vertices, int) pos = surf[0][vertices, :].T c_o_m = np.sum(pos * values, axis=1) / np.sum(values) vertex = np.argmin(np.sqrt(np.mean((surf[0][restrict_vertices, :] - c_o_m) ** 2, axis=1))) vertex = restrict_vertices[vertex] return vertex @fill_doc class _BaseSurfaceSourceEstimate(_BaseSourceEstimate): """Abstract base class for surface source estimates. Parameters ---------- data : array The data in source space. vertices : list, shape (2,) Vertex numbers corresponding to the data. tmin : scalar Time point of the first sample in data. tstep : scalar Time step between successive samples in data. subject : str | None The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors. %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. vertices : list of array, shape (2,) The indices of the dipoles in the left and right source space. data : array The data in source space. shape : tuple The shape of the data. A tuple of int (n_dipoles, n_times). """ _data_ndim = 2 _src_type = 'surface' @verbose def __init__(self, data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None): # noqa: D102 if not (isinstance(vertices, list) and len(vertices) == 2): raise ValueError('Vertices must be a list containing two ' 'numpy arrays, got type %s (%s)' % (type(vertices), vertices)) _BaseSourceEstimate.__init__(self, data, vertices=vertices, tmin=tmin, tstep=tstep, subject=subject, verbose=verbose) @property def lh_data(self): """Left hemisphere data.""" return self.data[:len(self.lh_vertno)] @property def rh_data(self): """Right hemisphere data.""" return self.data[len(self.lh_vertno):] @property def lh_vertno(self): """Left hemisphere vertno.""" return self.vertices[0] @property def rh_vertno(self): """Right hemisphere vertno.""" return self.vertices[1] def _hemilabel_stc(self, label): if label.hemi == 'lh': stc_vertices = self.vertices[0] else: stc_vertices = self.vertices[1] # find index of the Label's vertices idx = np.nonzero(np.in1d(stc_vertices, label.vertices))[0] # find output vertices vertices = stc_vertices[idx] # find data if label.hemi == 'rh': values = self.data[idx + len(self.vertices[0])] else: values = self.data[idx] return vertices, values def in_label(self, label): """Get a source estimate object restricted to a label. SourceEstimate contains the time course of activation of all sources inside the label. Parameters ---------- label : Label | BiHemiLabel The label (as created for example by mne.read_label). If the label does not match any sources in the SourceEstimate, a ValueError is raised. Returns ------- stc : SourceEstimate | VectorSourceEstimate The source estimate restricted to the given label. """ # make sure label and stc are compatible if label.subject is not None and self.subject is not None \ and label.subject != self.subject: raise RuntimeError('label and stc must have same subject names, ' 'currently "%s" and "%s"' % (label.subject, self.subject)) if label.hemi == 'both': lh_vert, lh_val = self._hemilabel_stc(label.lh) rh_vert, rh_val = self._hemilabel_stc(label.rh) vertices = [lh_vert, rh_vert] values = np.vstack((lh_val, rh_val)) elif label.hemi == 'lh': lh_vert, values = self._hemilabel_stc(label) vertices = [lh_vert, np.array([], int)] elif label.hemi == 'rh': rh_vert, values = self._hemilabel_stc(label) vertices = [np.array([], int), rh_vert] else: raise TypeError("Expected Label or BiHemiLabel; got %r" % label) if sum([len(v) for v in vertices]) == 0: raise ValueError('No vertices match the label in the stc file') label_stc = self.__class__(values, vertices=vertices, tmin=self.tmin, tstep=self.tstep, subject=self.subject) return label_stc def expand(self, vertices): """Expand SourceEstimate to include more vertices. This will add rows to stc.data (zero-filled) and modify stc.vertices to include all vertices in stc.vertices and the input vertices. Parameters ---------- vertices : list of array New vertices to add. Can also contain old values. Returns ------- stc : SourceEstimate | VectorSourceEstimate The modified stc (note: method operates inplace). """ if not isinstance(vertices, list): raise TypeError('vertices must be a list') if not len(self.vertices) == len(vertices): raise ValueError('vertices must have the same length as ' 'stc.vertices') # can no longer use kernel and sensor data self._remove_kernel_sens_data_() inserters = list() offsets = [0] for vi, (v_old, v_new) in enumerate(zip(self.vertices, vertices)): v_new = np.setdiff1d(v_new, v_old) inds = np.searchsorted(v_old, v_new) # newer numpy might overwrite inds after np.insert, copy here inserters += [inds.copy()] offsets += [len(v_old)] self.vertices[vi] = np.insert(v_old, inds, v_new) inds = [ii + offset for ii, offset in zip(inserters, offsets[:-1])] inds = np.concatenate(inds) new_data = np.zeros((len(inds),) + self.data.shape[1:]) self.data = np.insert(self.data, inds, new_data, axis=0) return self @verbose def to_original_src(self, src_orig, subject_orig=None, subjects_dir=None, verbose=None): """Get a source estimate from morphed source to the original subject. Parameters ---------- src_orig : instance of SourceSpaces The original source spaces that were morphed to the current subject. subject_orig : str | None The original subject. For most source spaces this shouldn't need to be provided, since it is stored in the source space itself. subjects_dir : string, or None Path to SUBJECTS_DIR if it is not set in the environment. %(verbose_meth)s Returns ------- stc : SourceEstimate | VectorSourceEstimate The transformed source estimate. See Also -------- morph_source_spaces Notes ----- .. versionadded:: 0.10.0 """ if self.subject is None: raise ValueError('stc.subject must be set') src_orig = _ensure_src(src_orig, kind='surface') subject_orig = _ensure_src_subject(src_orig, subject_orig) data_idx, vertices = _get_morph_src_reordering( self.vertices, src_orig, subject_orig, self.subject, subjects_dir) return self.__class__(self._data[data_idx], vertices, self.tmin, self.tstep, subject_orig) @fill_doc class SourceEstimate(_BaseSurfaceSourceEstimate): """Container for surface source estimates. Parameters ---------- data : array of shape (n_dipoles, n_times) | tuple, shape (2,) The data in source space. The data can either be a single array or a tuple with two arrays: "kernel" shape (n_vertices, n_sensors) and "sens_data" shape (n_sensors, n_times). In this case, the source space data corresponds to "numpy.dot(kernel, sens_data)". vertices : list of shape (2,) Vertex numbers corresponding to the data. tmin : scalar Time point of the first sample in data. tstep : scalar Time step between successive samples in data. subject : str | None The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors. %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. vertices : list of shape (2,) The indices of the dipoles in the left and right source space. data : array of shape (n_dipoles, n_times) The data in source space. shape : tuple The shape of the data. A tuple of int (n_dipoles, n_times). See Also -------- VectorSourceEstimate : A container for vector source estimates. VolSourceEstimate : A container for volume source estimates. MixedSourceEstimate : A container for mixed surface + volume source estimates. """ @verbose def save(self, fname, ftype='stc', verbose=None): """Save the source estimates to a file. Parameters ---------- fname : string The stem of the file name. The file names used for surface source spaces are obtained by adding "-lh.stc" and "-rh.stc" (or "-lh.w" and "-rh.w") to the stem provided, for the left and the right hemisphere, respectively. ftype : string File format to use. Allowed values are "stc" (default), "w", and "h5". The "w" format only supports a single time point. %(verbose_meth)s """ _validate_type(fname, 'path-like', 'fname') fname = str(fname) _check_option('ftype', ftype, ['stc', 'w', 'h5']) lh_data = self.data[:len(self.lh_vertno)] rh_data = self.data[-len(self.rh_vertno):] if ftype == 'stc': logger.info('Writing STC to disk...') _write_stc(fname + '-lh.stc', tmin=self.tmin, tstep=self.tstep, vertices=self.lh_vertno, data=lh_data) _write_stc(fname + '-rh.stc', tmin=self.tmin, tstep=self.tstep, vertices=self.rh_vertno, data=rh_data) elif ftype == 'w': if self.shape[1] != 1: raise ValueError('w files can only contain a single time ' 'point') logger.info('Writing STC to disk (w format)...') _write_w(fname + '-lh.w', vertices=self.lh_vertno, data=lh_data[:, 0]) _write_w(fname + '-rh.w', vertices=self.rh_vertno, data=rh_data[:, 0]) elif ftype == 'h5': super().save(fname) logger.info('[done]') @copy_function_doc_to_method_doc(plot_source_estimates) def plot(self, subject=None, surface='inflated', hemi='lh', colormap='auto', time_label='auto', smoothing_steps=10, transparent=True, alpha=1.0, time_viewer=False, subjects_dir=None, figure=None, views='lat', colorbar=True, clim='auto', cortex="classic", size=800, background="black", foreground="white", initial_time=None, time_unit='s', backend='auto', spacing='oct6', title=None, verbose=None): brain = plot_source_estimates( self, subject, surface=surface, hemi=hemi, colormap=colormap, time_label=time_label, smoothing_steps=smoothing_steps, transparent=transparent, alpha=alpha, time_viewer=time_viewer, subjects_dir=subjects_dir, figure=figure, views=views, colorbar=colorbar, clim=clim, cortex=cortex, size=size, background=background, foreground=foreground, initial_time=initial_time, time_unit=time_unit, backend=backend, spacing=spacing, title=title, verbose=verbose) return brain @verbose def extract_label_time_course(self, labels, src, mode='mean_flip', allow_empty=False, verbose=None): """Extract label time courses for lists of labels. This function will extract one time course for each label. The way the time courses are extracted depends on the mode parameter. Parameters ---------- labels : Label | BiHemiLabel | list of Label or BiHemiLabel The labels for which to extract the time courses. src : list Source spaces for left and right hemisphere. mode : str Extraction mode, see explanation below. allow_empty : bool Instead of emitting an error, return all-zero time course for labels that do not have any vertices in the source estimate. %(verbose_meth)s Returns ------- label_tc : array, shape=(n_labels, n_times) Extracted time course for each label. See Also -------- extract_label_time_course : extract time courses for multiple STCs Notes ----- Valid values for mode are: - 'mean' Average within each label. - 'mean_flip' Average within each label with sign flip depending on source orientation. - 'pca_flip' Apply an SVD to the time courses within each label and use the scaled and sign-flipped first right-singular vector as the label time course. The scaling is performed such that the power of the label time course is the same as the average per-vertex time course power within the label. The sign of the resulting time course is adjusted by multiplying it with "sign(dot(u, flip))" where u is the first left-singular vector, and flip is a sing-flip vector based on the vertex normals. This procedure assures that the phase does not randomly change by 180 degrees from one stc to the next. - 'max' Max value within each label. """ label_tc = extract_label_time_course( self, labels, src, mode=mode, return_generator=False, allow_empty=allow_empty, verbose=verbose) return label_tc def get_peak(self, hemi=None, tmin=None, tmax=None, mode='abs', vert_as_index=False, time_as_index=False): """Get location and latency of peak amplitude. Parameters ---------- hemi : {'lh', 'rh', None} The hemi to be considered. If None, the entire source space is considered. tmin : float | None The minimum point in time to be considered for peak getting. tmax : float | None The maximum point in time to be considered for peak getting. mode : {'pos', 'neg', 'abs'} How to deal with the sign of the data. If 'pos' only positive values will be considered. If 'neg' only negative values will be considered. If 'abs' absolute values will be considered. Defaults to 'abs'. vert_as_index : bool whether to return the vertex index instead of of its ID. Defaults to False. time_as_index : bool Whether to return the time index instead of the latency. Defaults to False. Returns ------- pos : int The vertex exhibiting the maximum response, either ID or index. latency : float | int The time point of the maximum response, either latency in seconds or index. """ data = {'lh': self.lh_data, 'rh': self.rh_data, None: self.data}[hemi] vertno = {'lh': self.lh_vertno, 'rh': self.rh_vertno, None: np.concatenate(self.vertices)}[hemi] vert_idx, time_idx, _ = _get_peak(data, self.times, tmin, tmax, mode) return (vert_idx if vert_as_index else vertno[vert_idx], time_idx if time_as_index else self.times[time_idx]) def center_of_mass(self, subject=None, hemi=None, restrict_vertices=False, subjects_dir=None, surf='sphere'): """Compute the center of mass of activity. This function computes the spatial center of mass on the surface as well as the temporal center of mass as in [1]_. .. note:: All activity must occur in a single hemisphere, otherwise an error is raised. The "mass" of each point in space for computing the spatial center of mass is computed by summing across time, and vice-versa for each point in time in computing the temporal center of mass. This is useful for quantifying spatio-temporal cluster locations, especially when combined with :func:`mne.vertex_to_mni`. Parameters ---------- subject : string | None The subject the stc is defined for. hemi : int, or None Calculate the center of mass for the left (0) or right (1) hemisphere. If None, one of the hemispheres must be all zeroes, and the center of mass will be calculated for the other hemisphere (useful for getting COM for clusters). restrict_vertices : bool | array of int | instance of SourceSpaces If True, returned vertex will be one from stc. Otherwise, it could be any vertex from surf. If an array of int, the returned vertex will come from that array. If instance of SourceSpaces (as of 0.13), the returned vertex will be from the given source space. For most accuruate estimates, do not restrict vertices. subjects_dir : str, or None Path to the SUBJECTS_DIR. If None, the path is obtained by using the environment variable SUBJECTS_DIR. surf : str The surface to use for Euclidean distance center of mass finding. The default here is "sphere", which finds the center of mass on the spherical surface to help avoid potential issues with cortical folding. See Also -------- mne.Label.center_of_mass mne.vertex_to_mni Returns ------- vertex : int Vertex of the spatial center of mass for the inferred hemisphere, with each vertex weighted by the sum of the stc across time. For a boolean stc, then, this would be weighted purely by the duration each vertex was active. hemi : int Hemisphere the vertex was taken from. t : float Time of the temporal center of mass (weighted by the sum across source vertices). References ---------- .. [1] Larson and Lee, "The cortical dynamics underlying effective switching of auditory spatial attention", NeuroImage 2012. """ if not isinstance(surf, str): raise TypeError('surf must be a string, got %s' % (type(surf),)) subject = _check_subject(self.subject, subject) if np.any(self.data < 0): raise ValueError('Cannot compute COM with negative values') values = np.sum(self.data, axis=1) # sum across time vert_inds = [np.arange(len(self.vertices[0])), np.arange(len(self.vertices[1])) + len(self.vertices[0])] if hemi is None: hemi = np.where(np.array([np.sum(values[vi]) for vi in vert_inds]))[0] if not len(hemi) == 1: raise ValueError('Could not infer hemisphere') hemi = hemi[0] _check_option('hemi', hemi, [0, 1]) vertices = self.vertices[hemi] values = values[vert_inds[hemi]] # left or right del vert_inds vertex = _center_of_mass( vertices, values, hemi=['lh', 'rh'][hemi], surf=surf, subject=subject, subjects_dir=subjects_dir, restrict_vertices=restrict_vertices) # do time center of mass by using the values across space masses = np.sum(self.data, axis=0).astype(float) t_ind = np.sum(masses * np.arange(self.shape[1])) / np.sum(masses) t = self.tmin + self.tstep * t_ind return vertex, hemi, t class _BaseVectorSourceEstimate(_BaseSourceEstimate): _data_ndim = 3 @verbose def __init__(self, data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None): # noqa: D102 assert hasattr(self, '_scalar_class') super().__init__(data, vertices, tmin, tstep, subject, verbose) if self._data is not None and self._data.shape[1] != 3: raise ValueError('Data for VectorSourceEstimate must have second ' 'dimension of length 3, got length %s' % (self._data.shape[1],)) def magnitude(self): """Compute magnitude of activity without directionality. Returns ------- stc : instance of SourceEstimate The source estimate without directionality information. """ data_mag = np.linalg.norm(self.data, axis=1) return self._scalar_class( data_mag, self.vertices, self.tmin, self.tstep, self.subject, self.verbose) def normal(self, src): """Compute activity orthogonal to the cortex. Parameters ---------- src : instance of SourceSpaces The source space for which this source estimate is specified. Returns ------- stc : instance of SourceEstimate The source estimate only retaining the activity orthogonal to the cortex. """ _check_src_normal('normal', src) normals = np.vstack([s['nn'][v] for s, v in zip(src, self._vertices_list)]) data_norm = einsum('ijk,ij->ik', self.data, normals) return self._scalar_class( data_norm, self.vertices, self.tmin, self.tstep, self.subject, self.verbose) class _BaseVolSourceEstimate(_BaseSourceEstimate): _data_ndim = 2 _src_type = 'volume' @verbose def __init__(self, data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None): # noqa: D102 if not (isinstance(vertices, np.ndarray) or isinstance(vertices, list)): raise ValueError('Vertices must be a numpy array or a list of ' 'arrays') _BaseSourceEstimate.__init__(self, data, vertices=vertices, tmin=tmin, tstep=tstep, subject=subject, verbose=verbose) @property def _vertices_list(self): return [self.vertices] @copy_function_doc_to_method_doc(plot_volume_source_estimates) def plot(self, src, subject=None, subjects_dir=None, mode='stat_map', bg_img=None, colorbar=True, colormap='auto', clim='auto', transparent='auto', show=True, initial_time=None, initial_pos=None, verbose=None): data = self.magnitude() if self._data_ndim == 3 else self return plot_volume_source_estimates( data, src=src, subject=subject, subjects_dir=subjects_dir, mode=mode, bg_img=bg_img, colorbar=colorbar, colormap=colormap, clim=clim, transparent=transparent, show=show, initial_time=initial_time, initial_pos=initial_pos, verbose=verbose) def save_as_volume(self, fname, src, dest='mri', mri_resolution=False, format='nifti1'): """Save a volume source estimate in a NIfTI file. Parameters ---------- fname : string The name of the generated nifti file. src : list The list of source spaces (should all be of type volume). dest : 'mri' | 'surf' If 'mri' the volume is defined in the coordinate system of the original T1 image. If 'surf' the coordinate system of the FreeSurfer surface is used (Surface RAS). mri_resolution: bool It True the image is saved in MRI resolution. WARNING: if you have many time points the file produced can be huge. format : str Either 'nifti1' (default) or 'nifti2'. .. versionadded:: 0.17 Returns ------- img : instance Nifti1Image The image object. Notes ----- .. versionadded:: 0.9.0 """ import nibabel as nib _validate_type(fname, 'path-like', 'fname') fname = str(fname) img = self.as_volume(src, dest=dest, mri_resolution=mri_resolution, format=format) nib.save(img, fname) def as_volume(self, src, dest='mri', mri_resolution=False, format='nifti1'): """Export volume source estimate as a nifti object. Parameters ---------- src : list The list of source spaces (should all be of type volume). dest : 'mri' | 'surf' If 'mri' the volume is defined in the coordinate system of the original T1 image. If 'surf' the coordinate system of the FreeSurfer surface is used (Surface RAS). mri_resolution: bool It True the image is saved in MRI resolution. WARNING: if you have many time points the file produced can be huge. format : str Either 'nifti1' (default) or 'nifti2'. Returns ------- img : instance of Nifti1Image The image object. Notes ----- .. versionadded:: 0.9.0 """ from .morph import _interpolate_data data = self.magnitude() if self._data_ndim == 3 else self return _interpolate_data(data, src, mri_resolution=mri_resolution, mri_space=True, output=format) def get_peak(self, tmin=None, tmax=None, mode='abs', vert_as_index=False, time_as_index=False): """Get location and latency of peak amplitude. Parameters ---------- tmin : float | None The minimum point in time to be considered for peak getting. tmax : float | None The maximum point in time to be considered for peak getting. mode : {'pos', 'neg', 'abs'} How to deal with the sign of the data. If 'pos' only positive values will be considered. If 'neg' only negative values will be considered. If 'abs' absolute values will be considered. Defaults to 'abs'. vert_as_index : bool whether to return the vertex index instead of of its ID. Defaults to False. time_as_index : bool Whether to return the time index instead of the latency. Defaults to False. Returns ------- pos : int The vertex exhibiting the maximum response, either ID or index. latency : float The latency in seconds. """ stc = self.magnitude() if self._data_ndim == 3 else self vert_idx, time_idx, _ = _get_peak(stc.data, self.times, tmin, tmax, mode) return (vert_idx if vert_as_index else self.vertices[vert_idx], time_idx if time_as_index else self.times[time_idx]) @fill_doc class VolSourceEstimate(_BaseVolSourceEstimate): """Container for volume source estimates. Parameters ---------- data : array of shape (n_dipoles, n_times) | tuple, shape (2,) The data in source space. The data can either be a single array or a tuple with two arrays: "kernel" shape (n_vertices, n_sensors) and "sens_data" shape (n_sensors, n_times). In this case, the source space data corresponds to "numpy.dot(kernel, sens_data)". vertices : array Vertex numbers corresponding to the data. tmin : scalar Time point of the first sample in data. tstep : scalar Time step between successive samples in data. subject : str | None The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors. %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. vertices : array of shape (n_dipoles,) The indices of the dipoles in the source space. data : array of shape (n_dipoles, n_times) The data in source space. shape : tuple The shape of the data. A tuple of int (n_dipoles, n_times). Notes ----- .. versionadded:: 0.9.0 See Also -------- SourceEstimate : A container for surface source estimates. VolVectorSourceEstimate : A container for volume vector source estimates. MixedSourceEstimate : A container for mixed surface + volume source estimates. """ @verbose def save(self, fname, ftype='stc', verbose=None): """Save the source estimates to a file. Parameters ---------- fname : string The stem of the file name. The stem is extended with "-vl.stc" or "-vl.w". ftype : string File format to use. Allowed values are "stc" (default), "w", and "h5". The "w" format only supports a single time point. %(verbose_meth)s """ _validate_type(fname, 'path-like', 'fname') fname = str(fname) _check_option('ftype', ftype, ['stc', 'w', 'h5']) if ftype == 'stc': logger.info('Writing STC to disk...') if not (fname.endswith('-vl.stc') or fname.endswith('-vol.stc')): fname += '-vl.stc' _write_stc(fname, tmin=self.tmin, tstep=self.tstep, vertices=self.vertices, data=self.data) elif ftype == 'w': logger.info('Writing STC to disk (w format)...') if not (fname.endswith('-vl.w') or fname.endswith('-vol.w')): fname += '-vl.w' _write_w(fname, vertices=self.vertices, data=self.data) elif ftype == 'h5': super().save(fname, 'h5') logger.info('[done]') @fill_doc class VolVectorSourceEstimate(_BaseVectorSourceEstimate, _BaseVolSourceEstimate): """Container for volume source estimates. Parameters ---------- data : array of shape (n_dipoles, 3, n_times) The data in source space. Each dipole contains three vectors that denote the dipole strength in X, Y and Z directions over time. vertices : array Vertex numbers corresponding to the data. tmin : scalar Time point of the first sample in data. tstep : scalar Time step between successive samples in data. subject : str | None The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors. %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. vertices : array of shape (n_dipoles,) The indices of the dipoles in the source space. data : array of shape (n_dipoles, n_times) The data in source space. shape : tuple The shape of the data. A tuple of int (n_dipoles, n_times). Notes ----- .. versionadded:: 0.9.0 See Also -------- SourceEstimate : A container for surface source estimates. VectorSourceEstimate : A container for vector source estimates. MixedSourceEstimate : A container for mixed surface + volume source estimates. """ _data_ndim = 3 _scalar_class = VolSourceEstimate @fill_doc class VectorSourceEstimate(_BaseVectorSourceEstimate, _BaseSurfaceSourceEstimate): """Container for vector surface source estimates. For each vertex, the magnitude of the current is defined in the X, Y and Z directions. Parameters ---------- data : array of shape (n_dipoles, 3, n_times) The data in source space. Each dipole contains three vectors that denote the dipole strength in X, Y and Z directions over time. vertices : array | list of shape (2,) Vertex numbers corresponding to the data. tmin : float Time point of the first sample in data. tstep : float Time step between successive samples in data. subject : str | None The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors. %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. shape : tuple The shape of the data. A tuple of int (n_dipoles, n_times). Notes ----- .. versionadded:: 0.15 See Also -------- SourceEstimate : A container for surface source estimates. VolSourceEstimate : A container for volume source estimates. MixedSourceEstimate : A container for mixed surface + volume source estimates. """ _data_ndim = 3 _scalar_class = SourceEstimate @copy_function_doc_to_method_doc(plot_vector_source_estimates) def plot(self, subject=None, hemi='lh', colormap='hot', time_label='auto', smoothing_steps=10, transparent=True, brain_alpha=0.4, overlay_alpha=None, vector_alpha=1.0, scale_factor=None, time_viewer=False, subjects_dir=None, figure=None, views='lat', colorbar=True, clim='auto', cortex='classic', size=800, background='black', foreground='white', initial_time=None, time_unit='s'): # noqa: D102 return plot_vector_source_estimates( self, subject=subject, hemi=hemi, colormap=colormap, time_label=time_label, smoothing_steps=smoothing_steps, transparent=transparent, brain_alpha=brain_alpha, overlay_alpha=overlay_alpha, vector_alpha=vector_alpha, scale_factor=scale_factor, time_viewer=time_viewer, subjects_dir=subjects_dir, figure=figure, views=views, colorbar=colorbar, clim=clim, cortex=cortex, size=size, background=background, foreground=foreground, initial_time=initial_time, time_unit=time_unit ) @fill_doc class MixedSourceEstimate(_BaseSourceEstimate): """Container for mixed surface and volume source estimates. Parameters ---------- data : array of shape (n_dipoles, n_times) | tuple, shape (2,) The data in source space. The data can either be a single array or a tuple with two arrays: "kernel" shape (n_vertices, n_sensors) and "sens_data" shape (n_sensors, n_times). In this case, the source space data corresponds to "numpy.dot(kernel, sens_data)". vertices : list of array Vertex numbers corresponding to the data. tmin : scalar Time point of the first sample in data. tstep : scalar Time step between successive samples in data. subject : str | None The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors. %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. vertices : list of array The indices of the dipoles in each source space. data : array of shape (n_dipoles, n_times) The data in source space. shape : tuple The shape of the data. A tuple of int (n_dipoles, n_times). Notes ----- .. versionadded:: 0.9.0 See Also -------- SourceEstimate : A container for surface source estimates. VectorSourceEstimate : A container for vector source estimates. VolSourceEstimate : A container for volume source estimates. VolVectorSourceEstimate : A container for Volume vector source estimates. """ _data_ndim = 2 _src_type = 'mixed' @verbose def __init__(self, data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None): # noqa: D102 if not isinstance(vertices, list) or len(vertices) < 2: raise ValueError('Vertices must be a list of numpy arrays with ' 'one array per source space.') _BaseSourceEstimate.__init__(self, data, vertices=vertices, tmin=tmin, tstep=tstep, subject=subject, verbose=verbose) def plot_surface(self, src, subject=None, surface='inflated', hemi='lh', colormap='auto', time_label='time=%02.f ms', smoothing_steps=10, transparent=None, alpha=1.0, time_viewer=False, config_opts=None, subjects_dir=None, figure=None, views='lat', colorbar=True, clim='auto'): """Plot surface source estimates with PySurfer. Note: PySurfer currently needs the SUBJECTS_DIR environment variable, which will automatically be set by this function. Plotting multiple SourceEstimates with different values for subjects_dir will cause PySurfer to use the wrong FreeSurfer surfaces when using methods of the returned Brain object. It is therefore recommended to set the SUBJECTS_DIR environment variable or always use the same value for subjects_dir (within the same Python session). Parameters ---------- src : SourceSpaces The source spaces to plot. subject : str | None The subject name corresponding to FreeSurfer environment variable SUBJECT. If None stc.subject will be used. If that is None, the environment will be used. surface : str The type of surface (inflated, white etc.). hemi : str, 'lh' | 'rh' | 'split' | 'both' The hemisphere to display. Using 'both' or 'split' requires PySurfer version 0.4 or above. colormap : str | np.ndarray of float, shape(n_colors, 3 | 4) Name of colormap to use. See `plot_source_estimates`. time_label : str How to print info about the time instant visualized. smoothing_steps : int The amount of smoothing. transparent : bool | None If True, use a linear transparency between fmin and fmid. None will choose automatically based on colormap type. alpha : float Alpha value to apply globally to the overlay. time_viewer : bool Display time viewer GUI. config_opts : dict Keyword arguments for Brain initialization. See pysurfer.viz.Brain. subjects_dir : str The path to the FreeSurfer subjects reconstructions. It corresponds to FreeSurfer environment variable SUBJECTS_DIR. figure : instance of mayavi.mlab.Figure | None If None, the last figure will be cleaned and a new figure will be created. views : str | list View to use. See `surfer.Brain`. colorbar : bool If True, display colorbar on scene. clim : str | dict Colorbar properties specification. See `plot_source_estimates`. Returns ------- brain : instance of surfer.Brain A instance of `surfer.Brain` from PySurfer. """ # extract surface source spaces surf = _ensure_src(src, kind='surface') # extract surface source estimate data = self.data[:surf[0]['nuse'] + surf[1]['nuse']] vertices = [s['vertno'] for s in surf] stc = SourceEstimate(data, vertices, self.tmin, self.tstep, self.subject, self.verbose) return plot_source_estimates(stc, subject, surface=surface, hemi=hemi, colormap=colormap, time_label=time_label, smoothing_steps=smoothing_steps, transparent=transparent, alpha=alpha, time_viewer=time_viewer, config_opts=config_opts, subjects_dir=subjects_dir, figure=figure, views=views, colorbar=colorbar, clim=clim) ############################################################################### # Morphing def _get_vol_mask(src): """Get the volume source space mask.""" assert len(src) == 1 # not a mixed source space shape = src[0]['shape'][::-1] mask = np.zeros(shape, bool) mask.flat[src[0]['vertno']] = True return mask def _spatio_temporal_src_connectivity_vol(src, n_times): from sklearn.feature_extraction import grid_to_graph mask = _get_vol_mask(src) edges = grid_to_graph(*mask.shape, mask=mask) connectivity = _get_connectivity_from_edges(edges, n_times) return connectivity def _spatio_temporal_src_connectivity_surf(src, n_times): if src[0]['use_tris'] is None: # XXX It would be nice to support non oct source spaces too... raise RuntimeError("The source space does not appear to be an ico " "surface. Connectivity cannot be extracted from" " non-ico source spaces.") used_verts = [np.unique(s['use_tris']) for s in src] offs = np.cumsum([0] + [len(u_v) for u_v in used_verts])[:-1] tris = np.concatenate([np.searchsorted(u_v, s['use_tris']) + off for u_v, s, off in zip(used_verts, src, offs)]) connectivity = spatio_temporal_tris_connectivity(tris, n_times) # deal with source space only using a subset of vertices masks = [np.in1d(u, s['vertno']) for s, u in zip(src, used_verts)] if sum(u.size for u in used_verts) != connectivity.shape[0] / n_times: raise ValueError('Used vertices do not match connectivity shape') if [np.sum(m) for m in masks] != [len(s['vertno']) for s in src]: raise ValueError('Vertex mask does not match number of vertices') masks = np.concatenate(masks) missing = 100 * float(len(masks) - np.sum(masks)) / len(masks) if missing: warn_('%0.1f%% of original source space vertices have been' ' omitted, tri-based connectivity will have holes.\n' 'Consider using distance-based connectivity or ' 'morphing data to all source space vertices.' % missing) masks = np.tile(masks, n_times) masks = np.where(masks)[0] connectivity = connectivity.tocsr() connectivity = connectivity[masks] connectivity = connectivity[:, masks] # return to original format connectivity = connectivity.tocoo() return connectivity @verbose def spatio_temporal_src_connectivity(src, n_times, dist=None, verbose=None): """Compute connectivity for a source space activation over time. Parameters ---------- src : instance of SourceSpaces The source space. It can be a surface source space or a volume source space. n_times : int Number of time instants. dist : float, or None Maximal geodesic distance (in m) between vertices in the source space to consider neighbors. If None, immediate neighbors are extracted from an ico surface. %(verbose)s Returns ------- connectivity : ~scipy.sparse.coo_matrix The connectivity matrix describing the spatio-temporal graph structure. If N is the number of vertices in the source space, the N first nodes in the graph are the vertices are time 1, the nodes from 2 to 2N are the vertices during time 2, etc. """ # XXX we should compute connectivity for each source space and then # use scipy.sparse.block_diag to concatenate them if src[0]['type'] == 'vol': if dist is not None: raise ValueError('dist must be None for a volume ' 'source space. Got %s.' % dist) connectivity = _spatio_temporal_src_connectivity_vol(src, n_times) elif dist is not None: # use distances computed and saved in the source space file connectivity = spatio_temporal_dist_connectivity(src, n_times, dist) else: connectivity = _spatio_temporal_src_connectivity_surf(src, n_times) return connectivity @verbose def grade_to_tris(grade, verbose=None): """Get tris defined for a certain grade. Parameters ---------- grade : int Grade of an icosahedral mesh. %(verbose)s Returns ------- tris : list 2-element list containing Nx3 arrays of tris, suitable for use in spatio_temporal_tris_connectivity. """ a = _get_ico_tris(grade, None, False) tris = np.concatenate((a, a + (np.max(a) + 1))) return tris @verbose def spatio_temporal_tris_connectivity(tris, n_times, remap_vertices=False, verbose=None): """Compute connectivity from triangles and time instants. Parameters ---------- tris : array N x 3 array defining triangles. n_times : int Number of time points remap_vertices : bool Reassign vertex indices based on unique values. Useful to process a subset of triangles. Defaults to False. %(verbose)s Returns ------- connectivity : ~scipy.sparse.coo_matrix The connectivity matrix describing the spatio-temporal graph structure. If N is the number of vertices in the source space, the N first nodes in the graph are the vertices are time 1, the nodes from 2 to 2N are the vertices during time 2, etc. """ if remap_vertices: logger.info('Reassigning vertex indices.') tris = np.searchsorted(np.unique(tris), tris) edges = mesh_edges(tris).tocoo() return _get_connectivity_from_edges(edges, n_times) @verbose def spatio_temporal_dist_connectivity(src, n_times, dist, verbose=None): """Compute connectivity from distances in a source space and time instants. Parameters ---------- src : instance of SourceSpaces The source space must have distances between vertices computed, such that src['dist'] exists and is useful. This can be obtained with a call to :func:`mne.setup_source_space` with the ``add_dist=True`` option. n_times : int Number of time points dist : float Maximal geodesic distance (in m) between vertices in the source space to consider neighbors. %(verbose)s Returns ------- connectivity : ~scipy.sparse.coo_matrix The connectivity matrix describing the spatio-temporal graph structure. If N is the number of vertices in the source space, the N first nodes in the graph are the vertices are time 1, the nodes from 2 to 2N are the vertices during time 2, etc. """ if src[0]['dist'] is None: raise RuntimeError('src must have distances included, consider using ' 'setup_source_space with add_dist=True') edges = sparse_block_diag([s['dist'][s['vertno'], :][:, s['vertno']] for s in src]) edges.data[:] = np.less_equal(edges.data, dist) # clean it up and put it in coo format edges = edges.tocsr() edges.eliminate_zeros() edges = edges.tocoo() return _get_connectivity_from_edges(edges, n_times) @verbose def spatial_src_connectivity(src, dist=None, verbose=None): """Compute connectivity for a source space activation. Parameters ---------- src : instance of SourceSpaces The source space. It can be a surface source space or a volume source space. dist : float, or None Maximal geodesic distance (in m) between vertices in the source space to consider neighbors. If None, immediate neighbors are extracted from an ico surface. %(verbose)s Returns ------- connectivity : ~scipy.sparse.coo_matrix The connectivity matrix describing the spatial graph structure. """ return spatio_temporal_src_connectivity(src, 1, dist) @verbose def spatial_tris_connectivity(tris, remap_vertices=False, verbose=None): """Compute connectivity from triangles. Parameters ---------- tris : array N x 3 array defining triangles. remap_vertices : bool Reassign vertex indices based on unique values. Useful to process a subset of triangles. Defaults to False. %(verbose)s Returns ------- connectivity : ~scipy.sparse.coo_matrix The connectivity matrix describing the spatial graph structure. """ return spatio_temporal_tris_connectivity(tris, 1, remap_vertices) @verbose def spatial_dist_connectivity(src, dist, verbose=None): """Compute connectivity from distances in a source space. Parameters ---------- src : instance of SourceSpaces The source space must have distances between vertices computed, such that src['dist'] exists and is useful. This can be obtained with a call to :func:`mne.setup_source_space` with the ``add_dist=True`` option. dist : float Maximal geodesic distance (in m) between vertices in the source space to consider neighbors. %(verbose)s Returns ------- connectivity : ~scipy.sparse.coo_matrix The connectivity matrix describing the spatial graph structure. """ return spatio_temporal_dist_connectivity(src, 1, dist) @verbose def spatial_inter_hemi_connectivity(src, dist, verbose=None): """Get vertices on each hemisphere that are close to the other hemisphere. Parameters ---------- src : instance of SourceSpaces The source space. Must be surface type. dist : float Maximal Euclidean distance (in m) between vertices in one hemisphere compared to the other to consider neighbors. %(verbose)s Returns ------- connectivity : ~scipy.sparse.coo_matrix The connectivity matrix describing the spatial graph structure. Typically this should be combined (addititively) with another existing intra-hemispheric connectivity matrix, e.g. computed using geodesic distances. """ from scipy.spatial.distance import cdist src = _ensure_src(src, kind='surface') conn = cdist(src[0]['rr'][src[0]['vertno']], src[1]['rr'][src[1]['vertno']]) conn = sparse.csr_matrix(conn <= dist, dtype=int) empties = [sparse.csr_matrix((nv, nv), dtype=int) for nv in conn.shape] conn = sparse.vstack([sparse.hstack([empties[0], conn]), sparse.hstack([conn.T, empties[1]])]) return conn @verbose def _get_connectivity_from_edges(edges, n_times, verbose=None): """Given edges sparse matrix, create connectivity matrix.""" n_vertices = edges.shape[0] logger.info("-- number of connected vertices : %d" % n_vertices) nnz = edges.col.size aux = n_vertices * np.arange(n_times)[:, None] * np.ones((1, nnz), np.int) col = (edges.col[None, :] + aux).ravel() row = (edges.row[None, :] + aux).ravel() if n_times > 1: # add temporal edges o = (n_vertices * np.arange(n_times - 1)[:, None] + np.arange(n_vertices)[None, :]).ravel() d = (n_vertices * np.arange(1, n_times)[:, None] + np.arange(n_vertices)[None, :]).ravel() row = np.concatenate((row, o, d)) col = np.concatenate((col, d, o)) data = np.ones(edges.data.size * n_times + 2 * n_vertices * (n_times - 1), dtype=np.int) connectivity = coo_matrix((data, (row, col)), shape=(n_times * n_vertices,) * 2) return connectivity @verbose def _get_ico_tris(grade, verbose=None, return_surf=False): """Get triangles for ico surface.""" ico = _get_ico_surface(grade) if not return_surf: return ico['tris'] else: return ico def _pca_flip(flip, data): U, s, V = linalg.svd(data, full_matrices=False) # determine sign-flip sign = np.sign(np.dot(U[:, 0], flip)) # use average power in label for scaling scale = linalg.norm(s) / np.sqrt(len(data)) return sign * scale * V[0] _label_funcs = { 'mean': lambda flip, data: np.mean(data, axis=0), 'mean_flip': lambda flip, data: np.mean(flip * data, axis=0), 'max': lambda flip, data: np.max(np.abs(data), axis=0), 'pca_flip': _pca_flip, } @contextlib.contextmanager def _temporary_vertices(src, vertices): orig_vertices = [s['vertno'] for s in src] for s, v in zip(src, vertices): s['vertno'] = v try: yield finally: for s, v in zip(src, orig_vertices): s['vertno'] = v def _prepare_label_extraction(stc, labels, src, mode, allow_empty): """Prepare indices and flips for extract_label_time_course.""" # if src is a mixed src space, the first 2 src spaces are surf type and # the other ones are vol type. For mixed source space n_labels will be the # given by the number of ROIs of the cortical parcellation plus the number # of vol src space from .label import label_sign_flip # get vertices from source space, they have to be the same as in the stcs vertno = stc.vertices nvert = [len(vn) for vn in vertno] # do the initialization label_vertidx = list() label_flip = list() for s, v, hemi in zip(src, stc.vertices, ('left', 'right')): n_missing = (~np.in1d(v, s['vertno'])).sum() if n_missing: raise ValueError('%d/%d %s hemisphere stc vertices missing from ' 'the source space, likely mismatch' % (n_missing, len(v), hemi)) for label in labels: if label.hemi == 'both': # handle BiHemiLabel sub_labels = [label.lh, label.rh] else: sub_labels = [label] this_vertidx = list() for slabel in sub_labels: if slabel.hemi == 'lh': this_vertices = np.intersect1d(vertno[0], slabel.vertices) vertidx = np.searchsorted(vertno[0], this_vertices) elif slabel.hemi == 'rh': this_vertices = np.intersect1d(vertno[1], slabel.vertices) vertidx = nvert[0] + np.searchsorted(vertno[1], this_vertices) else: raise ValueError('label %s has invalid hemi' % label.name) this_vertidx.append(vertidx) # convert it to an array this_vertidx = np.concatenate(this_vertidx) this_flip = None if len(this_vertidx) == 0: msg = ('source space does not contain any vertices for label %s' % label.name) if not allow_empty: raise ValueError(msg) else: warn_(msg + '. Assigning all-zero time series to label.') this_vertidx = None # to later check if label is empty elif mode not in ('mean', 'max'): # mode-dependent initialization # label_sign_flip uses two properties: # # - src[ii]['nn'] # - src[ii]['vertno'] # # So if we override vertno with the stc vertices, it will pick # the correct normals. with _temporary_vertices(src, stc.vertices): this_flip = label_sign_flip(label, src[:2])[:, None] label_vertidx.append(this_vertidx) label_flip.append(this_flip) return label_vertidx, label_flip def _gen_extract_label_time_course(stcs, labels, src, mode='mean', allow_empty=False, verbose=None): # loop through source estimates and extract time series _check_option('mode', mode, sorted(_label_funcs.keys())) func = _label_funcs[mode] if len(src) > 2: if src[0]['type'] != 'surf' or src[1]['type'] != 'surf': raise ValueError('The first 2 source spaces have to be surf type') if any(np.any(s['type'] != 'vol') for s in src[2:]): raise ValueError('source spaces have to be of vol type') n_aparc = len(labels) n_aseg = len(src[2:]) n_labels = n_aparc + n_aseg else: n_labels = len(labels) vertno = None for stc in stcs: if vertno is None: vertno = copy.deepcopy(stc.vertices) nvert = [len(v) for v in vertno] label_vertidx, src_flip = _prepare_label_extraction( stc, labels, src, mode, allow_empty) # make sure the stc is compatible with the source space for i in range(len(vertno)): if len(stc.vertices[i]) != nvert[i]: raise ValueError('stc not compatible with source space. ' 'stc has %s time series but there are %s ' 'vertices in source space' % (len(stc.vertices[i]), nvert[i])) if any(np.any(svn != vn) for svn, vn in zip(stc.vertices, vertno)): raise ValueError('stc not compatible with source space') if sum(nvert) != stc.shape[0]: raise ValueError('stc not compatible with source space. ' 'stc has %s vertices but the source space ' 'has %s vertices' % (stc.shape[0], sum(nvert))) logger.info('Extracting time courses for %d labels (mode: %s)' % (n_labels, mode)) # do the extraction label_tc = np.zeros((n_labels, stc.data.shape[1]), dtype=stc.data.dtype) for i, (vertidx, flip) in enumerate(zip(label_vertidx, src_flip)): if vertidx is not None: label_tc[i] = func(flip, stc.data[vertidx, :]) # extract label time series for the vol src space if len(src) > 2: v1 = nvert[0] + nvert[1] for i, nv in enumerate(nvert[2:]): v2 = v1 + nv v = range(v1, v2) if nv != 0: label_tc[n_aparc + i] = np.mean(stc.data[v, :], axis=0) v1 = v2 # this is a generator! yield label_tc @verbose def extract_label_time_course(stcs, labels, src, mode='mean_flip', allow_empty=False, return_generator=False, verbose=None): """Extract label time course for lists of labels and source estimates. This function will extract one time course for each label and source estimate. The way the time courses are extracted depends on the mode parameter (see Notes). Parameters ---------- stcs : SourceEstimate | list (or generator) of SourceEstimate The source estimates from which to extract the time course. labels : Label | BiHemiLabel | list of Label or BiHemiLabel The labels for which to extract the time course. src : list Source spaces for left and right hemisphere. mode : str Extraction mode, see explanation above. allow_empty : bool Instead of emitting an error, return all-zero time courses for labels that do not have any vertices in the source estimate. return_generator : bool If True, a generator instead of a list is returned. %(verbose)s Returns ------- label_tc : array | list (or generator) of array, shape (n_labels, n_times) Extracted time course for each label and source estimate. Notes ----- Valid values for mode are: ``'mean'`` Average within each label. ``'mean_flip'`` Average within each label with sign flip depending on source orientation. ``'pca_flip'`` Apply an SVD to the time courses within each label and use the scaled and sign-flipped first right-singular vector as the label time course. The scaling is performed such that the power of the label time course is the same as the average per-vertex time course power within the label. The sign of the resulting time course is adjusted by multiplying it with "sign(dot(u, flip))" where u is the first left-singular vector, and flip is a sing-flip vector based on the vertex normals. This procedure assures that the phase does not randomly change by 180 degrees from one stc to the next. ``'max'`` Max value within each label. If encountering a ``ValueError`` due to mismatch between number of source points in the subject source space and computed ``stc`` object set ``src`` argument to ``fwd['src']`` to ensure the source space is compatible between forward and inverse routines. """ # convert inputs to lists if isinstance(stcs, SourceEstimate): stcs = [stcs] return_several = False return_generator = False else: return_several = True if not isinstance(labels, list): labels = [labels] label_tc = _gen_extract_label_time_course(stcs, labels, src, mode=mode, allow_empty=allow_empty) if not return_generator: # do the extraction and return a list label_tc = list(label_tc) if not return_several: # input was a single SoureEstimate, return single array label_tc = label_tc[0] return label_tc