# Authors: Alexandre Gramfort # Matti Hämäläinen # Martin Luessi # Mads Jensen # # License: BSD-3-Clause import contextlib import copy import os.path as op from types import GeneratorType import numpy as np from .baseline import rescale from .cov import Covariance from .evoked import _get_peak from .filter import resample from .fixes import _safe_svd from ._freesurfer import (_import_nibabel, _get_mri_info_data, _get_atlas_values, read_freesurfer_lut) from .io.constants import FIFF from .io.pick import pick_types from .surface import (read_surface, _get_ico_surface, mesh_edges, _project_onto_surface) from .source_space import (_ensure_src, _get_morph_src_reordering, _ensure_src_subject, SourceSpaces, _get_src_nn, _check_volume_labels) from .transforms import _get_trans, apply_trans from .utils import (get_subjects_dir, _check_subject, logger, verbose, _pl, _time_mask, warn, copy_function_doc_to_method_doc, fill_doc, _check_option, _validate_type, _check_src_normal, _check_stc_units, _check_pandas_installed, _check_pandas_index_arguments, _convert_times, _ensure_int, _build_data_frame, _check_time_format, _path_like, sizeof_fmt, object_size, _check_fname, _import_h5io_funcs) from .viz import (plot_source_estimates, plot_vector_source_estimates, plot_volume_source_estimates) from .io.base import TimeMixin from .io.meas_info import Info 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). """ filename with open(filename, 'wb') as fid: # write start time in ms fid.write(np.array(1000 * tmin, dtype='>f4').tobytes()) # write sampling rate in ms fid.write(np.array(1000 * tstep, dtype='>f4').tobytes()) # write number of vertices fid.write(np.array(vertices.shape[0], dtype='>u4').tobytes()) # write the vertex indices fid.write(np.array(vertices, dtype='>u4').tobytes()) # write the number of timepts fid.write(np.array(data.shape[1], dtype='>u4').tobytes()) # write the data fid.write(np.array(data.T, dtype='>f4').tobytes()) 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.tobytes()) 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)) with open(filename, 'wb') as fid: # write 2 zero bytes fid.write(np.zeros((2), dtype=np.uint8).tobytes()) # 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').tobytes()) 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') # expand `~` without checking whether the file actually exists – we'll # take care of that later, as it's complicated by the different suffixes # STC files can have fname = _check_fname(fname=fname, overwrite='read', must_exist=False) # make sure corresponding file(s) can be found ftype = None if op.exists(fname): if fname.endswith(('-vl.stc', '-vol.stc', '-vl.w', '-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') kwargs['vertices'] = [kwargs['vertices']] 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': read_hdf5, _ = _import_h5io_funcs() kwargs = read_hdf5(fname + '.h5', title='mnepython') ftype = kwargs.pop('src_type', 'surface') if isinstance(kwargs['vertices'], np.ndarray): kwargs['vertices'] = [kwargs['vertices']] 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'])) if ftype in ('volume', 'discrete'): klass = VolVectorSourceEstimate elif ftype == 'mixed': klass = MixedVectorSourceEstimate else: assert ftype == 'surface' klass = VectorSourceEstimate if kwargs['data'].ndim < 3: klass = klass._scalar_class 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 == '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 = MixedVectorSourceEstimate if vector else MixedSourceEstimate else: raise ValueError('vertices has to be either a list with one or more ' 'arrays or an array') # Rotate back for vector source estimates if vector: n_vertices = sum(len(v) for v in vertices) assert data.shape[0] in (n_vertices, n_vertices * 3) if len(data) == n_vertices: assert src_type == 'surface' # should only be possible for this assert source_nn.shape == (n_vertices, 3) data = data[:, np.newaxis] * source_nn[:, :, np.newaxis] else: data = data.reshape((-1, 3, data.shape[-1])) assert source_nn.shape in ((n_vertices, 3, 3), (n_vertices * 3, 3)) # This will be an identity transform for volumes, but let's keep # the code simple and general and just do the matrix mult data = np.matmul( np.transpose(source_nn.reshape(n_vertices, 3, 3), axes=[0, 2, 1]), data) 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(TimeMixin): _data_ndim = 2 @verbose def __init__(self, data, vertices, tmin, tstep, subject=None, verbose=None): # noqa: D102 assert hasattr(self, '_data_ndim'), self.__class__.__name__ assert hasattr(self, '_src_type'), self.__class__.__name__ assert hasattr(self, '_src_count'), 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 (%s) and sens_data (%s) have invalid ' 'dimensions' % (kernel.shape, sens_data.shape)) if sens_data.ndim != 2: raise ValueError('The sensor data must have 2 dimensions, got ' '%s' % (sens_data.ndim,)) _validate_type(vertices, list, 'vertices') if self._src_count is not None: if len(vertices) != self._src_count: raise ValueError('vertices must be a list with %d entries, ' 'got %s' % (self._src_count, len(vertices))) vertices = [np.array(v, np.int64) for v in vertices] # makes copy if any(np.any(np.diff(v) <= 0) for v in vertices): raise ValueError('Vertices must be ordered in increasing order.') n_src = sum([len(v) for v in vertices]) # safeguard the user against doing something silly if data is not None: if data.ndim not in (self._data_ndim, self._data_ndim - 1): raise ValueError('Data (shape %s) must have %s dimensions for ' '%s' % (data.shape, self._data_ndim, self.__class__.__name__)) if data.shape[0] != n_src: raise ValueError( f'Number of vertices ({n_src}) and stc.data.shape[0] ' f'({data.shape[0]}) must match') if self._data_ndim == 3: if data.shape[1] != 3: raise ValueError( 'Data for VectorSourceEstimate must have ' 'shape[1] == 3, got shape %s' % (data.shape,)) if data.ndim == self._data_ndim - 1: # allow upbroadcasting data = data[..., np.newaxis] self._data = data self._tmin = tmin self._tstep = tstep self.vertices = vertices self._kernel = kernel self._sens_data = sens_data self._kernel_removed = False self._times = None self._update_times() self.subject = _check_subject(None, subject, raise_error=False) def __repr__(self): # noqa: D105 s = "%d vertices" % (sum(len(v) for v in self.vertices),) 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,) sz = sum(object_size(x) for x in (self.vertices + [self.data])) s += f", ~{sizeof_fmt(sz)}" return "<%s | %s>" % (type(self).__name__, s) @fill_doc 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 ---------- %(get_peak_parameters)s 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 if self._n_vertices == 0: raise RuntimeError('Cannot find peaks with no vertices') vert_idx, time_idx, _ = _get_peak( stc.data, self.times, tmin, tmax, mode) if not vert_as_index: vert_idx = np.concatenate(self.vertices)[vert_idx] if not time_as_index: time_idx = self.times[time_idx] return vert_idx, time_idx @verbose def extract_label_time_course(self, labels, src, mode='auto', 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_eltc)s %(src_eltc)s %(mode_eltc)s %(allow_empty_eltc)s %(verbose)s Returns ------- %(label_tc_el_returns)s See Also -------- extract_label_time_course : Extract time courses for multiple STCs. Notes ----- %(eltc_mode_notes)s """ return extract_label_time_course( self, labels, src, mode=mode, return_generator=False, allow_empty=allow_empty, verbose=verbose) @verbose def apply_baseline(self, baseline=(None, 0), *, verbose=None): """Baseline correct source estimate data. Parameters ---------- %(baseline_stc)s Defaults to ``(None, 0)``, i.e. beginning of the the data until time point zero. %(verbose)s Returns ------- stc : instance of SourceEstimate The baseline-corrected source estimate object. Notes ----- Baseline correction can be done multiple times. """ self.data = rescale(self.data, self.times, baseline, copy=False) return self @verbose def save(self, fname, ftype='h5', *, overwrite=False, verbose=None): """Save the full source estimate to an HDF5 file. Parameters ---------- fname : str The file name to write the source estimate to, should end in '-stc.h5'. ftype : str File format to use. Currently, the only allowed values is "h5". %(overwrite)s .. versionadded:: 1.0 %(verbose)s """ fname = _check_fname(fname=fname, overwrite=True) # check below if ftype != 'h5': raise ValueError('%s objects can only be written as HDF5 files.' % (self.__class__.__name__,)) _, write_hdf5 = _import_h5io_funcs() if not fname.endswith('.h5'): fname += '-stc.h5' fname = _check_fname(fname=fname, overwrite=overwrite) 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) @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='auto', subjects_dir=None, figure=None, views='auto', colorbar=True, clim='auto', cortex="classic", size=800, background="black", foreground=None, initial_time=None, time_unit='s', backend='auto', spacing='oct6', title=None, show_traces='auto', src=None, volume_options=1., view_layout='vertical', add_data_kwargs=None, brain_kwargs=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, show_traces=show_traces, src=src, volume_options=volume_options, view_layout=view_layout, add_data_kwargs=add_data_kwargs, brain_kwargs=brain_kwargs, verbose=verbose) return brain @property def sfreq(self): """Sample rate of the data.""" return 1. / self.tstep @property def _n_vertices(self): return sum(len(v) for v in self.vertices) 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 Returns ------- stc : instance of SourceEstimate The cropped source estimate. """ 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=None, verbose=None): """Resample data. If appropriate, an anti-aliasing filter is applied before resampling. See :ref:`resampling-and-decimating` for more information. 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 : str | tuple Window to use in resampling. See :func:`scipy.signal.resample`. %(n_jobs)s %(verbose)s Returns ------- stc : instance of SourceEstimate The resampled source estimate. 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) n_verts = sum(len(v) for v in self.vertices) 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. Returns ------- stc : instance of SourceEstimate A copy of the source estimate. """ 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 @verbose def to_data_frame(self, index=None, scalings=None, long_format=False, time_format=None, *, verbose=None): """Export data in tabular structure as a pandas DataFrame. Vertices are converted to columns in the DataFrame. By default, an additional column "time" is added, unless ``index='time'`` (in which case time values form the DataFrame's index). Parameters ---------- %(index_df_evk)s Defaults to ``None``. %(scalings_df)s %(long_format_df_stc)s %(time_format_df)s .. versionadded:: 0.20 %(verbose)s Returns ------- %(df_return)s """ # check pandas once here, instead of in each private utils function pd = _check_pandas_installed() # noqa # arg checking valid_index_args = ['time', 'subject'] valid_time_formats = ['ms', 'timedelta'] index = _check_pandas_index_arguments(index, valid_index_args) time_format = _check_time_format(time_format, valid_time_formats) # get data data = self.data.T times = self.times # prepare extra columns / multiindex mindex = list() default_index = ['time'] if self.subject is not None: default_index = ['subject', 'time'] mindex.append(('subject', np.repeat(self.subject, data.shape[0]))) times = _convert_times(self, times, time_format) mindex.append(('time', times)) # triage surface vs volume source estimates col_names = list() kinds = ['VOL'] * len(self.vertices) if isinstance(self, (_BaseSurfaceSourceEstimate, _BaseMixedSourceEstimate)): kinds[:2] = ['LH', 'RH'] for ii, (kind, vertno) in enumerate(zip(kinds, self.vertices)): col_names.extend(['{}_{}'.format(kind, vert) for vert in vertno]) # build DataFrame df = _build_data_frame(self, data, None, long_format, mindex, index, default_index=default_index, col_names=col_names, col_kind='source') return df 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 of array, shape (2,) Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere. %(tmin)s %(tstep)s %(subject_optional)s %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. vertices : list of array, shape (2,) Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere. data : array The data in source space. shape : tuple The shape of the data. A tuple of int (n_dipoles, n_times). """ _src_type = 'surface' _src_count = 2 @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 from .label import Label, BiHemiLabel _validate_type(label, (Label, BiHemiLabel), 'label') 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)] else: assert label.hemi == 'rh' rh_vert, values = self._hemilabel_stc(label) vertices = [np.array([], int), rh_vert] 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)s %(verbose)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 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. %(get_peak_parameters)s 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. """ _check_option('hemi', hemi, ('lh', 'rh', None)) vertex_offset = 0 if hemi is not None: if hemi == 'lh': data = self.lh_data vertices = [self.lh_vertno, []] else: vertex_offset = len(self.vertices[0]) data = self.rh_data vertices = [[], self.rh_vertno] meth = self.__class__( data, vertices, self.tmin, self.tstep).get_peak else: meth = super().get_peak out = meth(tmin=tmin, tmax=tmax, mode=mode, vert_as_index=vert_as_index, time_as_index=time_as_index) if vertex_offset and vert_as_index: out = (out[0] + vertex_offset, out[1]) return out @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. When it is a single array, the left hemisphere is stored in data[:len(vertices[0])] and the right hemisphere is stored in data[-len(vertices[1]):]. When data is a tuple, it contains 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 ``np.dot(kernel, sens_data)``. vertices : list of array, shape (2,) Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere. %(tmin)s %(tstep)s %(subject_optional)s %(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 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 surface source estimates. VolSourceEstimate : A container for volume 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', *, overwrite=False, verbose=None): """Save the source estimates to a file. Parameters ---------- fname : str 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 : str File format to use. Allowed values are "stc" (default), "w", and "h5". The "w" format only supports a single time point. %(overwrite)s .. versionadded:: 1.0 %(verbose)s """ fname = _check_fname(fname=fname, overwrite=True) # checked below _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': if np.iscomplexobj(self.data): raise ValueError("Cannot save complex-valued STC data in " "FIFF format; please set ftype='h5' to save " "in HDF5 format instead, or cast the data to " "real numbers before saving.") logger.info('Writing STC to disk...') fname_l = _check_fname(fname + '-lh.stc', overwrite=overwrite) fname_r = _check_fname(fname + '-rh.stc', overwrite=overwrite) _write_stc(fname_l, tmin=self.tmin, tstep=self.tstep, vertices=self.lh_vertno, data=lh_data) _write_stc(fname_r, 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)...') fname_l = _check_fname(fname + '-lh.w', overwrite=overwrite) fname_r = _check_fname(fname + '-rh.w', overwrite=overwrite) _write_w(fname_l, vertices=self.lh_vertno, data=lh_data[:, 0]) _write_w(fname_r, vertices=self.rh_vertno, data=rh_data[:, 0]) elif ftype == 'h5': super().save(fname, overwrite=overwrite) logger.info('[done]') @verbose def estimate_snr(self, info, fwd, cov, verbose=None): r"""Compute time-varying SNR in the source space. This function should only be used with source estimates with units nanoAmperes (i.e., MNE-like solutions, *not* dSPM or sLORETA). See also :footcite:`GoldenholzEtAl2009`. .. warning:: This function currently only works properly for fixed orientation. Parameters ---------- %(info_not_none)s fwd : instance of Forward The forward solution used to create the source estimate. cov : instance of Covariance The noise covariance used to estimate the resting cortical activations. Should be an evoked covariance, not empty room. %(verbose)s Returns ------- snr_stc : instance of SourceEstimate The source estimate with the SNR computed. Notes ----- We define the SNR in decibels for each source location at each time point as: .. math:: {\rm SNR} = 10\log_10[\frac{a^2}{N}\sum_k\frac{b_k^2}{s_k^2}] where :math:`\\b_k` is the signal on sensor :math:`k` provided by the forward model for a source with unit amplitude, :math:`a` is the source amplitude, :math:`N` is the number of sensors, and :math:`s_k^2` is the noise variance on sensor :math:`k`. References ---------- .. footbibliography:: """ from .forward import convert_forward_solution, Forward from .minimum_norm.inverse import _prepare_forward _validate_type(fwd, Forward, 'fwd') _validate_type(info, Info, 'info') _validate_type(cov, Covariance, 'cov') _check_stc_units(self) if (self.data >= 0).all(): warn('This STC appears to be from free orientation, currently SNR' ' function is valid only for fixed orientation') fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=False) # G is gain matrix [ch x src], cov is noise covariance [ch x ch] G, _, _, _, _, _, _, cov, _ = _prepare_forward( fwd, info, cov, fixed=True, loose=0, rank=None, pca=False, use_cps=True, exp=None, limit_depth_chs=False, combine_xyz='fro', allow_fixed_depth=False, limit=None) G = G['sol']['data'] n_channels = cov['dim'] # number of sensors/channels b_k2 = (G * G).T s_k2 = np.diag(cov['data']) scaling = (1 / n_channels) * np.sum(b_k2 / s_k2, axis=1, keepdims=True) snr_stc = self.copy() snr_stc._data[:] = 10 * np.log10((self.data * self.data) * scaling) return snr_stc @fill_doc 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 :footcite:`LarsonLee2013`. .. 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 : str | 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)s 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. 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). See Also -------- mne.Label.center_of_mass mne.vertex_to_mni References ---------- .. footbibliography:: """ 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) 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) def _get_src_normals(self, src, use_cps): normals = np.vstack([_get_src_nn(s, use_cps, v) for s, v in zip(src, self.vertices)]) return normals @fill_doc def project(self, directions, src=None, use_cps=True): """Project the data for each vertex in a given direction. Parameters ---------- directions : ndarray, shape (n_vertices, 3) | str Can be: - ``'normal'`` Project onto the source space normals. - ``'pca'`` SVD will be used to project onto the direction of maximal power for each source. - :class:`~numpy.ndarray`, shape (n_vertices, 3) Projection directions for each source. src : instance of SourceSpaces | None The source spaces corresponding to the source estimate. Not used when ``directions`` is an array, optional when ``directions='pca'``. %(use_cps)s Should be the same value that was used when the forward model was computed (typically True). Returns ------- stc : instance of SourceEstimate The projected source estimate. directions : ndarray, shape (n_vertices, 3) The directions that were computed (or just used). Notes ----- When using SVD, there is a sign ambiguity for the direction of maximal power. When ``src is None``, the direction is chosen that makes the resulting time waveform sum positive (i.e., have positive amplitudes). When ``src`` is provided, the directions are flipped in the direction of the source normals, i.e., outward from cortex for surface source spaces and in the +Z / superior direction for volume source spaces. .. versionadded:: 0.21 """ _validate_type(directions, (str, np.ndarray), 'directions') _validate_type(src, (None, SourceSpaces), 'src') if isinstance(directions, str): _check_option('directions', directions, ('normal', 'pca'), extra='when str') if directions == 'normal': if src is None: raise ValueError( 'If directions="normal", src cannot be None') _check_src_normal('normal', src) directions = self._get_src_normals(src, use_cps) else: assert directions == 'pca' x = self.data if not np.isrealobj(self.data): _check_option('stc.data.dtype', self.data.dtype, (np.complex64, np.complex128)) dtype = \ np.float32 if x.dtype == np.complex64 else np.float64 x = x.view(dtype) assert x.shape[-1] == 2 * self.data.shape[-1] u, _, v = np.linalg.svd(x, full_matrices=False) directions = u[:, :, 0] # The sign is arbitrary, so let's flip it in the direction that # makes the resulting time series the most positive: if src is None: signs = np.sum(v[:, 0].real, axis=1, keepdims=True) else: normals = self._get_src_normals(src, use_cps) signs = np.sum(directions * normals, axis=1, keepdims=True) assert signs.shape == (self.data.shape[0], 1) signs = np.sign(signs) signs[signs == 0] = 1. directions *= signs _check_option( 'directions.shape', directions.shape, [(self.data.shape[0], 3)]) data_norm = np.matmul(directions[:, np.newaxis], self.data)[:, 0] stc = self._scalar_class( data_norm, self.vertices, self.tmin, self.tstep, self.subject) return stc, directions @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='auto', subjects_dir=None, figure=None, views='lateral', colorbar=True, clim='auto', cortex='classic', size=800, background='black', foreground=None, initial_time=None, time_unit='s', show_traces='auto', src=None, volume_options=1., view_layout='vertical', add_data_kwargs=None, brain_kwargs=None, verbose=None): # 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, show_traces=show_traces, src=src, volume_options=volume_options, view_layout=view_layout, add_data_kwargs=add_data_kwargs, brain_kwargs=brain_kwargs, verbose=verbose) class _BaseVolSourceEstimate(_BaseSourceEstimate): _src_type = 'volume' _src_count = None @copy_function_doc_to_method_doc(plot_source_estimates) def plot_3d(self, subject=None, surface='white', hemi='both', colormap='auto', time_label='auto', smoothing_steps=10, transparent=True, alpha=0.1, time_viewer='auto', subjects_dir=None, figure=None, views='axial', colorbar=True, clim='auto', cortex="classic", size=800, background="black", foreground=None, initial_time=None, time_unit='s', backend='auto', spacing='oct6', title=None, show_traces='auto', src=None, volume_options=1., view_layout='vertical', add_data_kwargs=None, brain_kwargs=None, verbose=None): return super().plot( subject=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, show_traces=show_traces, src=src, volume_options=volume_options, view_layout=view_layout, add_data_kwargs=add_data_kwargs, brain_kwargs=brain_kwargs, verbose=verbose) @copy_function_doc_to_method_doc(plot_volume_source_estimates) def plot(self, src, subject=None, subjects_dir=None, mode='stat_map', bg_img='T1.mgz', 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) # Override here to provide the volume-specific options @verbose def extract_label_time_course(self, labels, src, mode='auto', allow_empty=False, *, mri_resolution=True, 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_eltc)s %(src_eltc)s %(mode_eltc)s %(allow_empty_eltc)s %(mri_resolution_eltc)s %(verbose)s Returns ------- %(label_tc_el_returns)s See Also -------- extract_label_time_course : Extract time courses for multiple STCs. Notes ----- %(eltc_mode_notes)s """ return extract_label_time_course( self, labels, src, mode=mode, return_generator=False, allow_empty=allow_empty, mri_resolution=mri_resolution, verbose=verbose) @verbose def in_label(self, label, mri, src, *, verbose=None): """Get a source estimate object restricted to a label. SourceEstimate contains the time course of activation of all sources inside the label. Parameters ---------- label : str | int The label to use. Can be the name of a label if using a standard FreeSurfer atlas, or an integer value to extract from the ``mri``. mri : str Path to the atlas to use. src : instance of SourceSpaces The volumetric source space. It must be a single, whole-brain volume. %(verbose)s Returns ------- stc : VolSourceEstimate | VolVectorSourceEstimate The source estimate restricted to the given label. Notes ----- .. versionadded:: 0.21.0 """ if len(self.vertices) != 1: raise RuntimeError('This method can only be used with whole-brain ' 'volume source spaces') _validate_type(label, (str, 'int-like'), 'label') if isinstance(label, str): volume_label = [label] else: volume_label = {'Volume ID %s' % (label): _ensure_int(label)} label = _volume_labels(src, (mri, volume_label), mri_resolution=False) assert len(label) == 1 label = label[0] vertices = label.vertices keep = np.in1d(self.vertices[0], label.vertices) values, vertices = self.data[keep], [self.vertices[0][keep]] label_stc = self.__class__(values, vertices=vertices, tmin=self.tmin, tstep=self.tstep, subject=self.subject) return label_stc @verbose def save_as_volume(self, fname, src, dest='mri', mri_resolution=False, format='nifti1', *, overwrite=False, verbose=None): """Save a volume source estimate in a NIfTI file. Parameters ---------- fname : str 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 %(overwrite)s .. versionadded:: 1.0 %(verbose)s .. versionadded:: 1.0 Returns ------- img : instance Nifti1Image The image object. Notes ----- .. versionadded:: 0.9.0 """ import nibabel as nib _validate_type(fname, 'path-like', 'fname') fname = _check_fname(fname=fname, overwrite=overwrite) 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 : instance of SourceSpaces The source spaces (should all be of type volume, or part of a mixed source space). 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) @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 ``np.dot(kernel, sens_data)``. %(vertices_volume)s %(tmin)s %(tstep)s %(subject_optional)s %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. %(vertices_volume)s 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 -------- SourceEstimate : A container for surface source estimates. VectorSourceEstimate : A container for vector surface source estimates. VolVectorSourceEstimate : A container for volume vector source estimates. MixedSourceEstimate : A container for mixed surface + volume source estimates. Notes ----- .. versionadded:: 0.9.0 """ @verbose def save(self, fname, ftype='stc', *, overwrite=False, verbose=None): """Save the source estimates to a file. Parameters ---------- fname : str The stem of the file name. The stem is extended with "-vl.stc" or "-vl.w". ftype : str File format to use. Allowed values are "stc" (default), "w", and "h5". The "w" format only supports a single time point. %(overwrite)s .. versionadded:: 1.0 %(verbose)s """ # check overwrite individually below fname = _check_fname(fname=fname, overwrite=True) # checked below _check_option('ftype', ftype, ['stc', 'w', 'h5']) if ftype != 'h5' and len(self.vertices) != 1: raise ValueError('Can only write to .stc or .w if a single volume ' 'source space was used, use .h5 instead') if ftype != 'h5' and self.data.dtype == 'complex': raise ValueError('Can only write non-complex data to .stc or .w' ', use .h5 instead') if ftype == 'stc': logger.info('Writing STC to disk...') if not fname.endswith(('-vl.stc', '-vol.stc')): fname += '-vl.stc' fname = _check_fname(fname, overwrite=overwrite) _write_stc(fname, tmin=self.tmin, tstep=self.tstep, vertices=self.vertices[0], data=self.data) elif ftype == 'w': logger.info('Writing STC to disk (w format)...') if not fname.endswith(('-vl.w', '-vol.w')): fname += '-vl.w' fname = _check_fname(fname, overwrite=overwrite) _write_w(fname, vertices=self.vertices[0], data=self.data) elif ftype == 'h5': super().save(fname, 'h5', overwrite=overwrite) logger.info('[done]') @fill_doc class VolVectorSourceEstimate(_BaseVolSourceEstimate, _BaseVectorSourceEstimate): """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_volume)s %(tmin)s %(tstep)s %(subject_optional)s %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. %(vertices_volume)s 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 -------- SourceEstimate : A container for surface source estimates. VectorSourceEstimate : A container for vector surface source estimates. VolSourceEstimate : A container for volume source estimates. MixedSourceEstimate : A container for mixed surface + volume source estimates. Notes ----- .. versionadded:: 0.9.0 """ _scalar_class = VolSourceEstimate # defaults differ: hemi='both', views='axial' @copy_function_doc_to_method_doc(plot_vector_source_estimates) def plot_3d(self, subject=None, hemi='both', 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='auto', subjects_dir=None, figure=None, views='axial', colorbar=True, clim='auto', cortex='classic', size=800, background='black', foreground=None, initial_time=None, time_unit='s', show_traces='auto', src=None, volume_options=1., view_layout='vertical', add_data_kwargs=None, brain_kwargs=None, verbose=None): # noqa: D102 return _BaseVectorSourceEstimate.plot( 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, show_traces=show_traces, src=src, volume_options=volume_options, view_layout=view_layout, add_data_kwargs=add_data_kwargs, brain_kwargs=brain_kwargs, verbose=verbose) @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 : list of array, shape (2,) Vertex numbers corresponding to the data. The first element of the list contains vertices of left hemisphere and the second element contains vertices of right hemisphere. %(tmin)s %(tstep)s %(subject_optional)s %(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). 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. Notes ----- .. versionadded:: 0.15 """ _scalar_class = SourceEstimate ############################################################################### # Mixed source estimate (two cortical surfs plus other stuff) class _BaseMixedSourceEstimate(_BaseSourceEstimate): _src_type = 'mixed' _src_count = None @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.') super().__init__(data, vertices=vertices, tmin=tmin, tstep=tstep, subject=subject, verbose=verbose) @property def _n_surf_vert(self): return sum(len(v) for v in self.vertices[:2]) def surface(self): """Return the cortical surface source estimate. Returns ------- stc : instance of SourceEstimate or VectorSourceEstimate The surface source estimate. """ if self._data_ndim == 3: klass = VectorSourceEstimate else: klass = SourceEstimate return klass( self.data[:self._n_surf_vert], self.vertices[:2], self.tmin, self.tstep, self.subject) def volume(self): """Return the volume surface source estimate. Returns ------- stc : instance of VolSourceEstimate or VolVectorSourceEstimate The volume source estimate. """ if self._data_ndim == 3: klass = VolVectorSourceEstimate else: klass = VolSourceEstimate return klass( self.data[self._n_surf_vert:], self.vertices[2:], self.tmin, self.tstep, self.subject) @fill_doc class MixedSourceEstimate(_BaseMixedSourceEstimate): """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 ``np.dot(kernel, sens_data)``. vertices : list of array Vertex numbers corresponding to the data. The list contains arrays with one array per source space. %(tmin)s %(tstep)s %(subject_optional)s %(verbose)s Attributes ---------- subject : str | None The subject name. times : array of shape (n_times,) The time vector. vertices : list of array Vertex numbers corresponding to the data. The list contains arrays with one array per 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 -------- SourceEstimate : A container for surface source estimates. VectorSourceEstimate : A container for vector surface source estimates. VolSourceEstimate : A container for volume source estimates. VolVectorSourceEstimate : A container for Volume vector source estimates. Notes ----- .. versionadded:: 0.9.0 """ @fill_doc class MixedVectorSourceEstimate(_BaseVectorSourceEstimate, _BaseMixedSourceEstimate): """Container for volume source estimates. Parameters ---------- data : array, 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 : list of array, shape (n_src,) Vertex numbers corresponding to the data. %(tmin)s %(tstep)s %(subject_optional)s %(verbose)s Attributes ---------- subject : str | None The subject name. times : array, 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). See Also -------- MixedSourceEstimate : A container for mixed surface + volume source estimates. Notes ----- .. versionadded:: 0.21.0 """ _scalar_class = MixedSourceEstimate ############################################################################### # 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_adjacency_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) adjacency = _get_adjacency_from_edges(edges, n_times) return adjacency def _spatio_temporal_src_adjacency_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. adjacency 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)]) adjacency = spatio_temporal_tris_adjacency(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) != adjacency.shape[0] / n_times: raise ValueError('Used vertices do not match adjacency 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 adjacency will have holes.\n' 'Consider using distance-based adjacency or ' 'morphing data to all source space vertices.' % missing) masks = np.tile(masks, n_times) masks = np.where(masks)[0] adjacency = adjacency.tocsr() adjacency = adjacency[masks] adjacency = adjacency[:, masks] # return to original format adjacency = adjacency.tocoo() return adjacency @verbose def spatio_temporal_src_adjacency(src, n_times, dist=None, verbose=None): """Compute adjacency 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 ------- adjacency : ~scipy.sparse.coo_matrix The adjacency 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 adjacency 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) adjacency = _spatio_temporal_src_adjacency_vol(src, n_times) elif dist is not None: # use distances computed and saved in the source space file adjacency = spatio_temporal_dist_adjacency(src, n_times, dist) else: adjacency = _spatio_temporal_src_adjacency_surf(src, n_times) return adjacency @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_adjacency. """ a = _get_ico_tris(grade, None, False) tris = np.concatenate((a, a + (np.max(a) + 1))) return tris @verbose def spatio_temporal_tris_adjacency(tris, n_times, remap_vertices=False, verbose=None): """Compute adjacency 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 ------- adjacency : ~scipy.sparse.coo_matrix The adjacency 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. """ from scipy import sparse if remap_vertices: logger.info('Reassigning vertex indices.') tris = np.searchsorted(np.unique(tris), tris) edges = mesh_edges(tris) edges = (edges + sparse.eye(edges.shape[0], format='csr')).tocoo() return _get_adjacency_from_edges(edges, n_times) @verbose def spatio_temporal_dist_adjacency(src, n_times, dist, verbose=None): """Compute adjacency 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 ------- adjacency : ~scipy.sparse.coo_matrix The adjacency 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. """ from scipy.sparse import block_diag as sparse_block_diag if src[0]['dist'] is None: raise RuntimeError('src must have distances included, consider using ' 'setup_source_space with add_dist=True') blocks = [s['dist'][s['vertno'], :][:, s['vertno']] for s in src] # Ensure we keep explicit zeros; deal with changes in SciPy for block in blocks: if isinstance(block, np.ndarray): block[block == 0] = -np.inf else: block.data[block.data == 0] == -1 edges = sparse_block_diag(blocks) 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_adjacency_from_edges(edges, n_times) @verbose def spatial_src_adjacency(src, dist=None, verbose=None): """Compute adjacency 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 ------- adjacency : ~scipy.sparse.coo_matrix The adjacency matrix describing the spatial graph structure. """ return spatio_temporal_src_adjacency(src, 1, dist) @verbose def spatial_tris_adjacency(tris, remap_vertices=False, verbose=None): """Compute adjacency 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 ------- adjacency : ~scipy.sparse.coo_matrix The adjacency matrix describing the spatial graph structure. """ return spatio_temporal_tris_adjacency(tris, 1, remap_vertices) @verbose def spatial_dist_adjacency(src, dist, verbose=None): """Compute adjacency 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 ------- adjacency : ~scipy.sparse.coo_matrix The adjacency matrix describing the spatial graph structure. """ return spatio_temporal_dist_adjacency(src, 1, dist) @verbose def spatial_inter_hemi_adjacency(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 ------- adjacency : ~scipy.sparse.coo_matrix The adjacency matrix describing the spatial graph structure. Typically this should be combined (addititively) with another existing intra-hemispheric adjacency matrix, e.g. computed using geodesic distances. """ from scipy import sparse from scipy.spatial.distance import cdist src = _ensure_src(src, kind='surface') adj = cdist(src[0]['rr'][src[0]['vertno']], src[1]['rr'][src[1]['vertno']]) adj = sparse.csr_matrix(adj <= dist, dtype=int) empties = [sparse.csr_matrix((nv, nv), dtype=int) for nv in adj.shape] adj = sparse.vstack([sparse.hstack([empties[0], adj]), sparse.hstack([adj.T, empties[1]])]) return adj @verbose def _get_adjacency_from_edges(edges, n_times, verbose=None): """Given edges sparse matrix, create adjacency matrix.""" from scipy.sparse import coo_matrix n_vertices = edges.shape[0] logger.info("-- number of adjacent vertices : %d" % n_vertices) nnz = edges.col.size aux = n_vertices * np.tile(np.arange(n_times)[:, None], (1, nnz)) 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.int64) adjacency = coo_matrix((data, (row, col)), shape=(n_times * n_vertices,) * 2) return adjacency @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 = _safe_svd(data, full_matrices=False) # determine sign-flip sign = np.sign(np.dot(U[:, 0], flip)) # use average power in label for scaling scale = np.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 _check_stc_src(stc, src): if stc is not None and src is not None: _check_subject( src._subject, stc.subject, raise_error=False, first_kind='source space subject', second_kind='stc.subject') 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)) def _prepare_label_extraction(stc, labels, src, mode, allow_empty, use_sparse): """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 # given by the number of ROIs of the cortical parcellation plus the number # of vol src space. # If stc=None (i.e. no activation time courses provided) and mode='mean', # only computes vertex indices and label_flip will be list of None. from scipy import sparse from .label import label_sign_flip, Label, BiHemiLabel # if source estimate provided in stc, get vertices from source space and # check that they are the same as in the stcs _check_stc_src(stc, src) vertno = [s['vertno'] for s in src] if stc is None else stc.vertices nvert = [len(vn) for vn in vertno] # initialization label_flip = list() label_vertidx = list() bad_labels = list() for li, label in enumerate(labels): subject = label['subject'] if use_sparse else label.subject # stc and src can each be None _check_subject( subject, getattr(stc, 'subject', None), raise_error=False, first_kind='label.subject', second_kind='stc.subject') _check_subject( subject, getattr(src, '_subject', None), raise_error=False, first_kind='label.subject', second_kind='source space subject') if use_sparse: assert isinstance(label, dict) vertidx = label['csr'] # This can happen if some labels aren't present in the space if vertidx.shape[0] == 0: bad_labels.append(label['name']) vertidx = None # Efficiency shortcut: use linearity early to avoid redundant # calculations elif mode == 'mean': vertidx = sparse.csr_matrix(vertidx.mean(axis=0)) label_vertidx.append(vertidx) label_flip.append(None) continue # standard case _validate_type(label, (Label, BiHemiLabel), 'labels[%d]' % (li,)) 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: bad_labels.append(label.name) 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) if len(bad_labels): msg = ('source space does not contain any vertices for %d label%s:\n%s' % (len(bad_labels), _pl(bad_labels), bad_labels)) if not allow_empty: raise ValueError(msg) else: msg += '\nAssigning all-zero time series.' if allow_empty == 'ignore': logger.info(msg) else: warn(msg) return label_vertidx, label_flip def _vol_src_rr(src): return apply_trans( src[0]['src_mri_t'], np.array( [d.ravel(order='F') for d in np.meshgrid( *(np.arange(s) for s in src[0]['shape']), indexing='ij')], float).T) def _volume_labels(src, labels, mri_resolution): # This will create Label objects that should do the right thing for our # given volumetric source space when used with extract_label_time_course from .label import Label assert src.kind == 'volume' subject = src._subject extra = ' when using a volume source space' _import_nibabel('use volume atlas labels') _validate_type(labels, ('path-like', list, tuple), 'labels' + extra) if _path_like(labels): mri = labels infer_labels = True else: if len(labels) != 2: raise ValueError('labels, if list or tuple, must have length 2, ' 'got %s' % (len(labels),)) mri, labels = labels infer_labels = False _validate_type(mri, 'path-like', 'labels[0]' + extra) logger.info('Reading atlas %s' % (mri,)) vol_info = _get_mri_info_data(str(mri), data=True) atlas_data = vol_info['data'] atlas_values = np.unique(atlas_data) if atlas_values.dtype.kind == 'f': # MGZ will be 'i' atlas_values = atlas_values[np.isfinite(atlas_values)] if not (atlas_values == np.round(atlas_values)).all(): raise RuntimeError('Non-integer values present in atlas, cannot ' 'labelize') atlas_values = np.round(atlas_values).astype(np.int64) if infer_labels: labels = { k: v for k, v in read_freesurfer_lut()[0].items() if v in atlas_values} labels = _check_volume_labels(labels, mri, name='labels[1]') assert isinstance(labels, dict) del atlas_values vox_mri_t = vol_info['vox_mri_t'] want = src[0].get('vox_mri_t', None) if want is None: raise RuntimeError( 'Cannot use volumetric atlas if no mri was supplied during ' 'source space creation') vox_mri_t, want = vox_mri_t['trans'], want['trans'] if not np.allclose(vox_mri_t, want, atol=1e-6): raise RuntimeError( 'atlas vox_mri_t does not match that used to create the source ' 'space') src_shape = tuple(src[0]['mri_' + k] for k in ('width', 'height', 'depth')) atlas_shape = atlas_data.shape if atlas_shape != src_shape: raise RuntimeError('atlas shape %s does not match source space MRI ' 'shape %s' % (atlas_shape, src_shape)) atlas_data = atlas_data.ravel(order='F') if mri_resolution: # Upsample then just index out_labels = list() nnz = 0 interp = src[0]['interpolator'] # should be guaranteed by size checks above and our src interp code assert interp.shape[0] == np.prod(src_shape) assert interp.shape == (atlas_data.size, len(src[0]['rr'])) interp = interp[:, src[0]['vertno']] for k, v in labels.items(): mask = atlas_data == v csr = interp[mask] out_labels.append(dict(csr=csr, name=k, subject=subject)) nnz += csr.shape[0] > 0 else: # Use nearest values vertno = src[0]['vertno'] rr = _vol_src_rr(src) del src src_values = _get_atlas_values(vol_info, rr[vertno]) vertices = [vertno[src_values == val] for val in labels.values()] out_labels = [Label(v, hemi='lh', name=val, subject=subject) for v, val in zip(vertices, labels.keys())] nnz = sum(len(v) != 0 for v in vertices) logger.info('%d/%d atlas regions had at least one vertex ' 'in the source space' % (nnz, len(out_labels))) return out_labels def _get_default_label_modes(): return sorted(_label_funcs.keys()) + ['auto'] def _get_allowed_label_modes(stc): if isinstance(stc, (_BaseVolSourceEstimate, _BaseVectorSourceEstimate)): return ('mean', 'max', 'auto') else: return _get_default_label_modes() def _gen_extract_label_time_course(stcs, labels, src, *, mode='mean', allow_empty=False, mri_resolution=True, verbose=None): # loop through source estimates and extract time series from scipy import sparse if src is None and mode in ['mean', 'max']: kind = 'surface' else: _validate_type(src, SourceSpaces) kind = src.kind _check_option('mode', mode, _get_default_label_modes()) if kind in ('surface', 'mixed'): if not isinstance(labels, list): labels = [labels] use_sparse = False else: labels = _volume_labels(src, labels, mri_resolution) use_sparse = bool(mri_resolution) n_mode = len(labels) # how many processed with the given mode n_mean = len(src[2:]) if kind == 'mixed' else 0 n_labels = n_mode + n_mean vertno = func = None for si, stc in enumerate(stcs): _validate_type(stc, _BaseSourceEstimate, 'stcs[%d]' % (si,), 'source estimate') _check_option( 'mode', mode, _get_allowed_label_modes(stc), 'when using a vector and/or volume source estimate') if isinstance(stc, (_BaseVolSourceEstimate, _BaseVectorSourceEstimate)): mode = 'mean' if mode == 'auto' else mode else: mode = 'mean_flip' if mode == 'auto' else mode if vertno is None: vertno = copy.deepcopy(stc.vertices) # avoid keeping a ref nvert = np.array([len(v) for v in vertno]) label_vertidx, src_flip = _prepare_label_extraction( stc, labels, src, mode, allow_empty, use_sparse) func = _label_funcs[mode] # make sure the stc is compatible with the source space if len(vertno) != len(stc.vertices): raise ValueError('stc not compatible with source space') for vn, svn in zip(vertno, stc.vertices): if len(vn) != len(svn): raise ValueError('stc not compatible with source space. ' 'stc has %s time series but there are %s ' 'vertices in source space. Ensure you used ' 'src from the forward or inverse operator, ' 'as forward computation can exclude vertices.' % (len(svn), len(vn))) if not np.array_equal(svn, vn): raise ValueError('stc not compatible with source space') 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: if isinstance(vertidx, sparse.csr_matrix): assert mri_resolution assert vertidx.shape[1] == stc.data.shape[0] this_data = np.reshape(stc.data, (stc.data.shape[0], -1)) this_data = vertidx @ this_data this_data.shape = \ (this_data.shape[0],) + stc.data.shape[1:] else: this_data = stc.data[vertidx] label_tc[i] = func(flip, this_data) # extract label time series for the vol src space (only mean supported) offset = nvert[:-n_mean].sum() # effectively :2 or :0 for i, nv in enumerate(nvert[2:]): if nv != 0: v2 = offset + nv label_tc[n_mode + i] = np.mean(stc.data[offset:v2], axis=0) offset = v2 # this is a generator! yield label_tc @verbose def extract_label_time_course(stcs, labels, src, mode='auto', allow_empty=False, return_generator=False, *, mri_resolution=True, 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_eltc)s %(src_eltc)s %(mode_eltc)s %(allow_empty_eltc)s return_generator : bool If True, a generator instead of a list is returned. %(mri_resolution_eltc)s %(verbose)s Returns ------- %(label_tc_el_returns)s Notes ----- %(eltc_mode_notes)s 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']`` or ``inv['src']`` to ensure the source space is the one actually used by the inverse to compute the source time courses. """ # convert inputs to lists if not isinstance(stcs, (list, tuple, GeneratorType)): stcs = [stcs] return_several = False return_generator = False else: return_several = True label_tc = _gen_extract_label_time_course( stcs, labels, src, mode=mode, allow_empty=allow_empty, mri_resolution=mri_resolution) 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 @verbose def stc_near_sensors(evoked, trans, subject, distance=0.01, mode='sum', project=True, subjects_dir=None, src=None, picks=None, surface='pial', verbose=None): """Create a STC from ECoG, sEEG and DBS sensor data. Parameters ---------- evoked : instance of Evoked The evoked data. Must contain ECoG, sEEG or DBS channels. %(trans)s .. versionchanged:: 0.19 Support for 'fsaverage' argument. subject : str The subject name. distance : float Distance (m) defining the activation "ball" of the sensor. mode : str Can be "sum" to do a linear sum of weights, "weighted" to make this a weighted sum, "nearest" to use only the weight of the nearest sensor, or "single" to do a distance-weight of the nearest sensor. Default is "sum". See Notes. .. versionchanged:: 0.24 Added "weighted" option. project : bool If True, project the sensors to the nearest ``'pial`` surface vertex before computing distances. Only used when doing a surface projection. %(subjects_dir)s src : instance of SourceSpaces The source space. .. warning:: If a surface source space is used, make sure that ``surface='pial'`` was used during construction, or that you set ``surface='pial'`` here. %(picks_base)s good sEEG, ECoG, and DBS channels. .. versionadded:: 0.24 surface : str | None The surface to use if ``src=None``. Default is the pial surface. If None, the source space surface will be used. .. versionadded:: 0.24.1 %(verbose)s Returns ------- stc : instance of SourceEstimate The surface source estimate. If src is None, a surface source estimate will be produced, and the number of vertices will equal the number of pial-surface vertices that were close enough to the sensors to take on a non-zero volue. If src is not None, a surface, volume, or mixed source estimate will be produced (depending on the kind of source space passed) and the vertices will match those of src (i.e., there may be me many all-zero values in stc.data). Notes ----- For surface projections, this function projects the ECoG sensors to the pial surface (if ``project``), then the activation at each pial surface vertex is given by the mode: - ``'sum'`` Activation is the sum across each sensor weighted by the fractional ``distance`` from each sensor. A sensor with zero distance gets weight 1 and a sensor at ``distance`` meters away (or larger) gets weight 0. If ``distance`` is less than half the distance between any two sensors, this will be the same as ``'single'``. - ``'single'`` Same as ``'sum'`` except that only the nearest sensor is used, rather than summing across sensors within the ``distance`` radius. As ``'nearest'`` for vertices with distance zero to the projected sensor. - ``'nearest'`` The value is given by the value of the nearest sensor, up to a ``distance`` (beyond which it is zero). - ``'weighted'`` The value is given by the same as ``sum`` but the total weight for each vertex is 1. (i.e., it's a weighted sum based on proximity). If creating a Volume STC, ``src`` must be passed in, and this function will project sEEG and DBS sensors to nearby surrounding vertices. Then the activation at each volume vertex is given by the mode in the same way as ECoG surface projections. .. versionadded:: 0.22 """ from scipy.spatial.distance import cdist, pdist from .evoked import Evoked _validate_type(evoked, Evoked, 'evoked') _validate_type(mode, str, 'mode') _validate_type(src, (None, SourceSpaces), 'src') _check_option('mode', mode, ('sum', 'single', 'nearest', 'weighted')) # create a copy of Evoked using ecog, seeg and dbs if picks is None: picks = pick_types(evoked.info, ecog=True, seeg=True, dbs=True) evoked = evoked.copy().pick(picks) frames = set(evoked.info['chs'][pick]['coord_frame'] for pick in picks) if not frames == {FIFF.FIFFV_COORD_HEAD}: raise RuntimeError('Channels must be in the head coordinate frame, ' f'got {sorted(frames)}') # get channel positions that will be used to pinpoint where # in the Source space we will use the evoked data pos = evoked._get_channel_positions() # remove nan channels nan_inds = np.where(np.isnan(pos).any(axis=1))[0] nan_chs = [evoked.ch_names[idx] for idx in nan_inds] if len(nan_chs): evoked.drop_channels(nan_chs) pos = [pos[idx] for idx in range(len(pos)) if idx not in nan_inds] # coord_frame transformation from native mne "head" to MRI coord_frame trans, _ = _get_trans(trans, 'head', 'mri', allow_none=True) # convert head positions -> coord_frame MRI pos = apply_trans(trans, pos) subject = _check_subject(None, subject, raise_error=False) subjects_dir = get_subjects_dir(subjects_dir, raise_error=True) if surface is not None: surf_rr = [read_surface(op.join(subjects_dir, subject, 'surf', f'{hemi}.{surface}'))[0] / 1000. for hemi in ('lh', 'rh')] if src is None: # fake a full surface one _validate_type(surface, str, 'surface', 'when src is None') src = SourceSpaces([ dict(rr=rr, vertno=np.arange(len(rr)), type='surf', coord_frame=FIFF.FIFFV_COORD_MRI) for rr in surf_rr]) rrs = np.concatenate([s_rr[s['vertno']] for s_rr, s in zip(surf_rr, src)]) keep_all = False else: if surface is None: rrs = np.concatenate([s['rr'][s['vertno']] for s in src]) if src[0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD: rrs = apply_trans(trans, rrs) else: rrs = np.concatenate([s_rr[s['vertno']] for s_rr, s in zip(surf_rr, src)]) keep_all = True # ensure it's a usable one klass = dict( surface=SourceEstimate, volume=VolSourceEstimate, mixed=MixedSourceEstimate, ) _check_option('src.kind', src.kind, sorted(klass.keys())) klass = klass[src.kind] # projection will only occur with surfaces logger.info( f'Projecting data from {len(pos)} sensor{_pl(pos)} onto {len(rrs)} ' f'{src.kind} vertices: {mode} mode') if project and src.kind == 'surface': logger.info(' Projecting sensors onto surface') pos = _project_onto_surface(pos, dict(rr=rrs), project_rrs=True, method='nearest')[2] min_dist = pdist(pos).min() * 1000 logger.info( f' Minimum {"projected " if project else ""}intra-sensor distance: ' f'{min_dist:0.1f} mm') # compute pairwise distance between source space points and sensors dists = cdist(rrs, pos) assert dists.shape == (len(rrs), len(pos)) # only consider vertices within our "epsilon-ball" # characterized by distance kwarg vertices = np.where((dists <= distance).any(-1))[0] logger.info(f' {len(vertices)} / {len(rrs)} non-zero vertices') w = np.maximum(1. - dists[vertices] / distance, 0) # now we triage based on mode if mode in ('single', 'nearest'): range_ = np.arange(w.shape[0]) idx = np.argmax(w, axis=1) vals = w[range_, idx] if mode == 'single' else 1. w.fill(0) w[range_, idx] = vals elif mode == 'weighted': norms = w.sum(-1, keepdims=True) norms[norms == 0] = 1. w /= norms missing = np.where(~np.any(w, axis=0))[0] if len(missing): warn(f'Channel{_pl(missing)} missing in STC: ' f'{", ".join(evoked.ch_names[mi] for mi in missing)}') nz_data = w @ evoked.data if keep_all: data = np.zeros( (sum(len(s['vertno']) for s in src), len(evoked.times)), dtype=nz_data.dtype) data[vertices] = nz_data vertices = [s['vertno'].copy() for s in src] else: assert src.kind == 'surface' data = nz_data offset = len(src[0]['vertno']) vertices = [vertices[vertices < offset], vertices[vertices >= offset] - offset] return klass(data, vertices, evoked.times[0], 1. / evoked.info['sfreq'], subject=subject, verbose=verbose)