# Authors: The MNE-Python contributors. # License: BSD-3-Clause # Copyright the MNE-Python contributors. from __future__ import annotations # only needed for Python ≤ 3.9 from copy import deepcopy from inspect import getfullargspec from pathlib import Path import numpy as np from ._fiff.constants import FIFF from ._fiff.meas_info import ( ContainsMixin, SetChannelsMixin, _ensure_infos_match, _read_extended_ch_info, _rename_list, read_meas_info, write_meas_info, ) from ._fiff.open import fiff_open from ._fiff.pick import _FNIRS_CH_TYPES_SPLIT, _picks_to_idx, pick_types from ._fiff.proj import ProjMixin from ._fiff.tag import read_tag from ._fiff.tree import dir_tree_find from ._fiff.write import ( end_block, start_and_end_file, start_block, write_complex_float_matrix, write_float, write_float_matrix, write_id, write_int, write_string, ) from .baseline import _check_baseline, _log_rescale, rescale from .channels.channels import InterpolationMixin, ReferenceMixin, UpdateChannelsMixin from .channels.layout import _merge_ch_data, _pair_grad_sensors from .defaults import _BORDER_DEFAULT, _EXTRAPOLATE_DEFAULT, _INTERPOLATION_DEFAULT from .filter import FilterMixin, _check_fun, detrend from .html_templates import _get_html_template from .parallel import parallel_func from .time_frequency.spectrum import Spectrum, SpectrumMixin, _validate_method from .time_frequency.tfr import AverageTFR from .utils import ( ExtendedTimeMixin, SizeMixin, _build_data_frame, _check_fname, _check_option, _check_pandas_index_arguments, _check_pandas_installed, _check_preload, _check_time_format, _convert_times, _scale_dataframe_data, _validate_type, check_fname, copy_function_doc_to_method_doc, fill_doc, logger, repr_html, sizeof_fmt, verbose, warn, ) from .viz import ( plot_evoked, plot_evoked_field, plot_evoked_image, plot_evoked_topo, plot_evoked_topomap, ) from .viz.evoked import plot_evoked_joint, plot_evoked_white from .viz.topomap import _topomap_animation _aspect_dict = { "average": FIFF.FIFFV_ASPECT_AVERAGE, "standard_error": FIFF.FIFFV_ASPECT_STD_ERR, "single_epoch": FIFF.FIFFV_ASPECT_SINGLE, "partial_average": FIFF.FIFFV_ASPECT_SUBAVERAGE, "alternating_subaverage": FIFF.FIFFV_ASPECT_ALTAVERAGE, "sample_cut_out_by_graph": FIFF.FIFFV_ASPECT_SAMPLE, "power_density_spectrum": FIFF.FIFFV_ASPECT_POWER_DENSITY, "dipole_amplitude_cuvre": FIFF.FIFFV_ASPECT_DIPOLE_WAVE, "squid_modulation_lower_bound": FIFF.FIFFV_ASPECT_IFII_LOW, "squid_modulation_upper_bound": FIFF.FIFFV_ASPECT_IFII_HIGH, "squid_gate_setting": FIFF.FIFFV_ASPECT_GATE, } _aspect_rev = {val: key for key, val in _aspect_dict.items()} @fill_doc class Evoked( ProjMixin, ContainsMixin, UpdateChannelsMixin, ReferenceMixin, SetChannelsMixin, InterpolationMixin, FilterMixin, ExtendedTimeMixin, SizeMixin, SpectrumMixin, ): """Evoked data. Parameters ---------- fname : path-like Name of evoked/average FIF file to load. If None no data is loaded. condition : int, or str Dataset ID number (int) or comment/name (str). Optional if there is only one data set in file. proj : bool, optional Apply SSP projection vectors. kind : str Either ``'average'`` or ``'standard_error'``. The type of data to read. Only used if 'condition' is a str. allow_maxshield : bool | str (default False) If True, allow loading of data that has been recorded with internal active compensation (MaxShield). Data recorded with MaxShield should generally not be loaded directly, but should first be processed using SSS/tSSS to remove the compensation signals that may also affect brain activity. Can also be ``"yes"`` to load without eliciting a warning. %(verbose)s Attributes ---------- %(info_not_none)s ch_names : list of str List of channels' names. nave : int Number of averaged epochs. kind : str Type of data, either average or standard_error. comment : str Comment on dataset. Can be the condition. data : array of shape (n_channels, n_times) Evoked response. first : int First time sample. last : int Last time sample. tmin : float The first time point in seconds. tmax : float The last time point in seconds. times : array Time vector in seconds. Goes from ``tmin`` to ``tmax``. Time interval between consecutive time samples is equal to the inverse of the sampling frequency. baseline : None | tuple of length 2 This attribute reflects whether the data has been baseline-corrected (it will be a ``tuple`` then) or not (it will be ``None``). Notes ----- Evoked objects can only contain the average of a single set of conditions. """ @verbose def __init__( self, fname, condition=None, proj=True, kind="average", allow_maxshield=False, *, verbose=None, ): _validate_type(proj, bool, "'proj'") # Read the requested data fname = _check_fname(fname=fname, must_exist=True, overwrite="read") ( self.info, self.nave, self._aspect_kind, self.comment, times, self.data, self.baseline, ) = _read_evoked(fname, condition, kind, allow_maxshield) self._set_times(times) self._raw_times = self.times.copy() self._decim = 1 self._update_first_last() self.preload = True # project and baseline correct if proj: self.apply_proj() self.filename = fname @property def filename(self) -> Path | None: """The filename of the evoked object, if it exists. :type: :class:`~pathlib.Path` | None """ return self._filename @filename.setter def filename(self, value): self._filename = Path(value) if value is not None else value @property def kind(self): """The data kind.""" return _aspect_rev[self._aspect_kind] @kind.setter def kind(self, kind): _check_option("kind", kind, list(_aspect_dict.keys())) self._aspect_kind = _aspect_dict[kind] @property def data(self): """The data matrix.""" return self._data @data.setter def data(self, data): """Set the data matrix.""" self._data = data @fill_doc def get_data(self, picks=None, units=None, tmin=None, tmax=None): """Get evoked data as 2D array. Parameters ---------- %(picks_all)s %(units)s tmin : float | None Start time of data to get in seconds. tmax : float | None End time of data to get in seconds. Returns ------- data : ndarray, shape (n_channels, n_times) A view on evoked data. Notes ----- .. versionadded:: 0.24 """ # Avoid circular import from .io.base import _get_ch_factors picks = _picks_to_idx(self.info, picks, "all", exclude=()) start, stop = self._handle_tmin_tmax(tmin, tmax) data = self.data[picks, start:stop] if units is not None: ch_factors = _get_ch_factors(self, units, picks) data *= ch_factors[:, np.newaxis] return data @verbose def apply_function( self, fun, picks=None, dtype=None, n_jobs=None, channel_wise=True, *, verbose=None, **kwargs, ): """Apply a function to a subset of channels. %(applyfun_summary_evoked)s Parameters ---------- %(fun_applyfun_evoked)s %(picks_all_data_noref)s %(dtype_applyfun)s %(n_jobs)s Ignored if ``channel_wise=False`` as the workload is split across channels. %(channel_wise_applyfun)s .. versionadded:: 1.6 %(verbose)s %(kwargs_fun)s Returns ------- self : instance of Evoked The evoked object with transformed data. """ _check_preload(self, "evoked.apply_function") picks = _picks_to_idx(self.info, picks, exclude=(), with_ref_meg=False) if not callable(fun): raise ValueError("fun needs to be a function") data_in = self._data if dtype is not None and dtype != self._data.dtype: self._data = self._data.astype(dtype) args = getfullargspec(fun).args + getfullargspec(fun).kwonlyargs if channel_wise is False: if ("ch_idx" in args) or ("ch_name" in args): raise ValueError( "apply_function cannot access ch_idx or ch_name " "when channel_wise=False" ) if "ch_idx" in args: logger.info("apply_function requested to access ch_idx") if "ch_name" in args: logger.info("apply_function requested to access ch_name") # check the dimension of the incoming evoked data _check_option("evoked.ndim", self._data.ndim, [2]) if channel_wise: parallel, p_fun, n_jobs = parallel_func(_check_fun, n_jobs) if n_jobs == 1: # modify data inplace to save memory for ch_idx in picks: if "ch_idx" in args: kwargs.update(ch_idx=ch_idx) if "ch_name" in args: kwargs.update(ch_name=self.info["ch_names"][ch_idx]) self._data[ch_idx, :] = _check_fun( fun, data_in[ch_idx, :], **kwargs ) else: # use parallel function data_picks_new = parallel( p_fun( fun, data_in[ch_idx, :], **kwargs, **{ k: v for k, v in [ ("ch_name", self.info["ch_names"][ch_idx]), ("ch_idx", ch_idx), ] if k in args }, ) for ch_idx in picks ) for run_idx, ch_idx in enumerate(picks): self._data[ch_idx, :] = data_picks_new[run_idx] else: self._data[picks, :] = _check_fun(fun, data_in[picks, :], **kwargs) return self @verbose def apply_baseline(self, baseline=(None, 0), *, verbose=None): """Baseline correct evoked data. Parameters ---------- %(baseline_evoked)s Defaults to ``(None, 0)``, i.e. beginning of the the data until time point zero. %(verbose)s Returns ------- evoked : instance of Evoked The baseline-corrected Evoked object. Notes ----- Baseline correction can be done multiple times. .. versionadded:: 0.13.0 """ baseline = _check_baseline(baseline, times=self.times, sfreq=self.info["sfreq"]) if self.baseline is not None and baseline is None: raise ValueError( "The data has already been baseline-corrected. " "Cannot remove existing baseline correction." ) elif baseline is None: # Do not rescale logger.info(_log_rescale(None)) else: # Actually baseline correct the data. Logging happens in rescale(). self.data = rescale(self.data, self.times, baseline, copy=False) self.baseline = baseline return self @verbose def save(self, fname, *, overwrite=False, verbose=None): """Save evoked data to a file. Parameters ---------- fname : path-like The name of the file, which should end with ``-ave.fif(.gz)`` or ``_ave.fif(.gz)``. %(overwrite)s %(verbose)s Notes ----- To write multiple conditions into a single file, use `mne.write_evokeds`. .. versionchanged:: 0.23 Information on baseline correction will be stored with the data, and will be restored when reading again via `mne.read_evokeds`. """ write_evokeds(fname, self, overwrite=overwrite) @verbose def export(self, fname, fmt="auto", *, overwrite=False, verbose=None): """Export Evoked to external formats. %(export_fmt_support_evoked)s %(export_warning)s Parameters ---------- %(fname_export_params)s %(export_fmt_params_evoked)s %(overwrite)s %(verbose)s Notes ----- .. versionadded:: 1.1 %(export_warning_note_evoked)s """ from .export import export_evokeds export_evokeds(fname, self, fmt, overwrite=overwrite, verbose=verbose) def __repr__(self): # noqa: D105 max_comment_length = 1000 if len(self.comment) > max_comment_length: comment = self.comment[:max_comment_length] comment += "..." else: comment = self.comment s = f"'{comment}' ({self.kind}, N={self.nave})" s += f", {self.times[0]:0.5g} – {self.times[-1]:0.5g} s" s += ", baseline " if self.baseline is None: s += "off" else: s += f"{self.baseline[0]:g} – {self.baseline[1]:g} s" if self.baseline != _check_baseline( self.baseline, times=self.times, sfreq=self.info["sfreq"], on_baseline_outside_data="adjust", ): s += " (baseline period was cropped after baseline correction)" s += f", {self.data.shape[0]} ch" s += f", ~{sizeof_fmt(self._size)}" return f"" @repr_html def _repr_html_(self): t = _get_html_template("repr", "evoked.html.jinja") t = t.render( inst=self, filenames=( [Path(self.filename).name] if getattr(self, "filename", None) is not None else None ), ) return t @property def ch_names(self): """Channel names.""" return self.info["ch_names"] @copy_function_doc_to_method_doc(plot_evoked) def plot( self, picks=None, exclude="bads", unit=True, show=True, ylim=None, xlim="tight", proj=False, hline=None, units=None, scalings=None, titles=None, axes=None, gfp=False, window_title=None, spatial_colors="auto", zorder="unsorted", selectable=True, noise_cov=None, time_unit="s", sphere=None, *, highlight=None, verbose=None, ): return plot_evoked( self, picks=picks, exclude=exclude, unit=unit, show=show, ylim=ylim, proj=proj, xlim=xlim, hline=hline, units=units, scalings=scalings, titles=titles, axes=axes, gfp=gfp, window_title=window_title, spatial_colors=spatial_colors, zorder=zorder, selectable=selectable, noise_cov=noise_cov, time_unit=time_unit, sphere=sphere, highlight=highlight, verbose=verbose, ) @copy_function_doc_to_method_doc(plot_evoked_image) def plot_image( self, picks=None, exclude="bads", unit=True, show=True, clim=None, xlim="tight", proj=False, units=None, scalings=None, titles=None, axes=None, cmap="RdBu_r", colorbar=True, mask=None, mask_style=None, mask_cmap="Greys", mask_alpha=0.25, time_unit="s", show_names=None, group_by=None, sphere=None, ): return plot_evoked_image( self, picks=picks, exclude=exclude, unit=unit, show=show, clim=clim, xlim=xlim, proj=proj, units=units, scalings=scalings, titles=titles, axes=axes, cmap=cmap, colorbar=colorbar, mask=mask, mask_style=mask_style, mask_cmap=mask_cmap, mask_alpha=mask_alpha, time_unit=time_unit, show_names=show_names, group_by=group_by, sphere=sphere, ) @copy_function_doc_to_method_doc(plot_evoked_topo) def plot_topo( self, layout=None, layout_scale=0.945, color=None, border="none", ylim=None, scalings=None, title=None, proj=False, vline=(0.0,), fig_background=None, merge_grads=False, legend=True, axes=None, background_color="w", noise_cov=None, exclude="bads", show=True, ): """. Notes ----- .. versionadded:: 0.10.0 """ return plot_evoked_topo( self, layout=layout, layout_scale=layout_scale, color=color, border=border, ylim=ylim, scalings=scalings, title=title, proj=proj, vline=vline, fig_background=fig_background, merge_grads=merge_grads, legend=legend, axes=axes, background_color=background_color, noise_cov=noise_cov, exclude=exclude, show=show, ) @copy_function_doc_to_method_doc(plot_evoked_topomap) def plot_topomap( self, times="auto", *, average=None, ch_type=None, scalings=None, proj=False, sensors=True, show_names=False, mask=None, mask_params=None, contours=6, outlines="head", sphere=None, image_interp=_INTERPOLATION_DEFAULT, extrapolate=_EXTRAPOLATE_DEFAULT, border=_BORDER_DEFAULT, res=64, size=1, cmap=None, vlim=(None, None), cnorm=None, colorbar=True, cbar_fmt="%3.1f", units=None, axes=None, time_unit="s", time_format=None, nrows=1, ncols="auto", show=True, ): return plot_evoked_topomap( self, times=times, ch_type=ch_type, vlim=vlim, cmap=cmap, cnorm=cnorm, sensors=sensors, colorbar=colorbar, scalings=scalings, units=units, res=res, size=size, cbar_fmt=cbar_fmt, time_unit=time_unit, time_format=time_format, proj=proj, show=show, show_names=show_names, mask=mask, mask_params=mask_params, outlines=outlines, contours=contours, image_interp=image_interp, average=average, axes=axes, extrapolate=extrapolate, sphere=sphere, border=border, nrows=nrows, ncols=ncols, ) @copy_function_doc_to_method_doc(plot_evoked_field) def plot_field( self, surf_maps, time=None, time_label="t = %0.0f ms", n_jobs=None, fig=None, vmax=None, n_contours=21, *, show_density=True, alpha=None, interpolation="nearest", interaction="terrain", time_viewer="auto", verbose=None, ): return plot_evoked_field( self, surf_maps, time=time, time_label=time_label, n_jobs=n_jobs, fig=fig, vmax=vmax, n_contours=n_contours, show_density=show_density, alpha=alpha, interpolation=interpolation, interaction=interaction, time_viewer=time_viewer, verbose=verbose, ) @copy_function_doc_to_method_doc(plot_evoked_white) def plot_white( self, noise_cov, show=True, rank=None, time_unit="s", sphere=None, axes=None, *, spatial_colors="auto", verbose=None, ): return plot_evoked_white( self, noise_cov=noise_cov, rank=rank, show=show, time_unit=time_unit, sphere=sphere, axes=axes, spatial_colors=spatial_colors, verbose=verbose, ) @copy_function_doc_to_method_doc(plot_evoked_joint) def plot_joint( self, times="peaks", title="", picks=None, exclude="bads", show=True, ts_args=None, topomap_args=None, ): return plot_evoked_joint( self, times=times, title=title, picks=picks, exclude=exclude, show=show, ts_args=ts_args, topomap_args=topomap_args, ) @fill_doc def animate_topomap( self, ch_type=None, times=None, frame_rate=None, butterfly=False, blit=True, show=True, time_unit="s", sphere=None, *, image_interp=_INTERPOLATION_DEFAULT, extrapolate=_EXTRAPOLATE_DEFAULT, vmin=None, vmax=None, verbose=None, ): """Make animation of evoked data as topomap timeseries. The animation can be paused/resumed with left mouse button. Left and right arrow keys can be used to move backward or forward in time. Parameters ---------- ch_type : str | None Channel type to plot. Accepted data types: 'mag', 'grad', 'eeg', 'hbo', 'hbr', 'fnirs_cw_amplitude', 'fnirs_fd_ac_amplitude', 'fnirs_fd_phase', and 'fnirs_od'. If None, first available channel type from the above list is used. Defaults to None. times : array of float | None The time points to plot. If None, 10 evenly spaced samples are calculated over the evoked time series. Defaults to None. frame_rate : int | None Frame rate for the animation in Hz. If None, frame rate = sfreq / 10. Defaults to None. butterfly : bool Whether to plot the data as butterfly plot under the topomap. Defaults to False. blit : bool Whether to use blit to optimize drawing. In general, it is recommended to use blit in combination with ``show=True``. If you intend to save the animation it is better to disable blit. Defaults to True. show : bool Whether to show the animation. Defaults to True. time_unit : str The units for the time axis, can be "ms" (default in 0.16) or "s" (will become the default in 0.17). .. versionadded:: 0.16 %(sphere_topomap_auto)s %(image_interp_topomap)s %(extrapolate_topomap)s .. versionadded:: 0.22 %(vmin_vmax_topomap)s .. versionadded:: 1.1.0 %(verbose)s Returns ------- fig : instance of matplotlib.figure.Figure The figure. anim : instance of matplotlib.animation.FuncAnimation Animation of the topomap. Notes ----- .. versionadded:: 0.12.0 """ return _topomap_animation( self, ch_type=ch_type, times=times, frame_rate=frame_rate, butterfly=butterfly, blit=blit, show=show, time_unit=time_unit, sphere=sphere, image_interp=image_interp, extrapolate=extrapolate, vmin=vmin, vmax=vmax, verbose=verbose, ) def as_type(self, ch_type="grad", mode="fast"): """Compute virtual evoked using interpolated fields. .. Warning:: Using virtual evoked to compute inverse can yield unexpected results. The virtual channels have ``'_v'`` appended at the end of the names to emphasize that the data contained in them are interpolated. Parameters ---------- ch_type : str The destination channel type. It can be 'mag' or 'grad'. mode : str Either ``'accurate'`` or ``'fast'``, determines the quality of the Legendre polynomial expansion used. ``'fast'`` should be sufficient for most applications. Returns ------- evoked : instance of mne.Evoked The transformed evoked object containing only virtual channels. Notes ----- This method returns a copy and does not modify the data it operates on. It also returns an EvokedArray instance. .. versionadded:: 0.9.0 """ from .forward import _as_meg_type_inst return _as_meg_type_inst(self, ch_type=ch_type, mode=mode) @fill_doc def detrend(self, order=1, picks=None): """Detrend data. This function operates in-place. Parameters ---------- order : int Either 0 or 1, the order of the detrending. 0 is a constant (DC) detrend, 1 is a linear detrend. %(picks_good_data)s Returns ------- evoked : instance of Evoked The detrended evoked object. """ picks = _picks_to_idx(self.info, picks) self.data[picks] = detrend(self.data[picks], order, axis=-1) return self def copy(self): """Copy the instance of evoked. Returns ------- evoked : instance of Evoked A copy of the object. """ evoked = deepcopy(self) return evoked def __neg__(self): """Negate channel responses. Returns ------- evoked_neg : instance of Evoked The Evoked instance with channel data negated and '-' prepended to the comment. """ out = self.copy() out.data *= -1 if out.comment is not None and " + " in out.comment: out.comment = f"({out.comment})" # multiple conditions in evoked out.comment = f'- {out.comment or "unknown"}' return out def get_peak( self, ch_type=None, tmin=None, tmax=None, mode="abs", time_as_index=False, merge_grads=False, return_amplitude=False, *, strict=True, ): """Get location and latency of peak amplitude. Parameters ---------- ch_type : str | None The channel type to use. Defaults to None. If more than one channel type is present in the data, this value **must** be provided. tmin : float | None The minimum point in time to be considered for peak getting. If None (default), the beginning of the data is used. tmax : float | None The maximum point in time to be considered for peak getting. If None (default), the end of the data is used. mode : 'pos' | 'neg' | 'abs' How to deal with the sign of the data. If 'pos' only positive values will be considered. If 'neg' only negative values will be considered. If 'abs' absolute values will be considered. Defaults to 'abs'. time_as_index : bool Whether to return the time index instead of the latency in seconds. merge_grads : bool If True, compute peak from merged gradiometer data. return_amplitude : bool If True, return also the amplitude at the maximum response. .. versionadded:: 0.16 strict : bool If True, raise an error if values are all positive when detecting a minimum (mode='neg'), or all negative when detecting a maximum (mode='pos'). Defaults to True. .. versionadded:: 1.7 Returns ------- ch_name : str The channel exhibiting the maximum response. latency : float | int The time point of the maximum response, either latency in seconds or index. amplitude : float The amplitude of the maximum response. Only returned if return_amplitude is True. .. versionadded:: 0.16 """ # noqa: E501 supported = ( "mag", "grad", "eeg", "seeg", "dbs", "ecog", "misc", "None", ) + _FNIRS_CH_TYPES_SPLIT types_used = self.get_channel_types(unique=True, only_data_chs=True) _check_option("ch_type", str(ch_type), supported) if ch_type is not None and ch_type not in types_used: raise ValueError( f'Channel type "{ch_type}" not found in this evoked object.' ) elif len(types_used) > 1 and ch_type is None: raise RuntimeError( 'Multiple data channel types found. Please pass the "ch_type" ' "parameter." ) if merge_grads: if ch_type != "grad": raise ValueError('Channel type must be "grad" for merge_grads') elif mode == "neg": raise ValueError( "Negative mode (mode=neg) does not make " "sense with merge_grads=True" ) meg = eeg = misc = seeg = dbs = ecog = fnirs = False picks = None if ch_type in ("mag", "grad"): meg = ch_type elif ch_type == "eeg": eeg = True elif ch_type == "misc": misc = True elif ch_type == "seeg": seeg = True elif ch_type == "dbs": dbs = True elif ch_type == "ecog": ecog = True elif ch_type in _FNIRS_CH_TYPES_SPLIT: fnirs = ch_type if ch_type is not None: if merge_grads: picks = _pair_grad_sensors(self.info, topomap_coords=False) else: picks = pick_types( self.info, meg=meg, eeg=eeg, misc=misc, seeg=seeg, ecog=ecog, ref_meg=False, fnirs=fnirs, dbs=dbs, ) data = self.data ch_names = self.ch_names if picks is not None: data = data[picks] ch_names = [ch_names[k] for k in picks] if merge_grads: data, _ = _merge_ch_data(data, ch_type, []) ch_names = [ch_name[:-1] + "X" for ch_name in ch_names[::2]] ch_idx, time_idx, max_amp = _get_peak( data, self.times, tmin, tmax, mode, strict=strict, ) out = (ch_names[ch_idx], time_idx if time_as_index else self.times[time_idx]) if return_amplitude: out += (max_amp,) return out @verbose def compute_psd( self, method="multitaper", fmin=0, fmax=np.inf, tmin=None, tmax=None, picks=None, proj=False, remove_dc=True, exclude=(), *, n_jobs=1, verbose=None, **method_kw, ): """Perform spectral analysis on sensor data. Parameters ---------- %(method_psd)s Default is ``'multitaper'``. %(fmin_fmax_psd)s %(tmin_tmax_psd)s %(picks_good_data_noref)s %(proj_psd)s %(remove_dc)s %(exclude_psd)s %(n_jobs)s %(verbose)s %(method_kw_psd)s Returns ------- spectrum : instance of Spectrum The spectral representation of the data. Notes ----- .. versionadded:: 1.2 References ---------- .. footbibliography:: """ method = _validate_method(method, type(self).__name__) self._set_legacy_nfft_default(tmin, tmax, method, method_kw) return Spectrum( self, method=method, fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, picks=picks, exclude=exclude, proj=proj, remove_dc=remove_dc, reject_by_annotation=False, n_jobs=n_jobs, verbose=verbose, **method_kw, ) @verbose def compute_tfr( self, method, freqs, *, tmin=None, tmax=None, picks=None, proj=False, output="power", decim=1, n_jobs=None, verbose=None, **method_kw, ): """Compute a time-frequency representation of evoked data. Parameters ---------- %(method_tfr)s %(freqs_tfr)s %(tmin_tmax_psd)s %(picks_good_data_noref)s %(proj_psd)s %(output_compute_tfr)s %(decim_tfr)s %(n_jobs)s %(verbose)s %(method_kw_tfr)s Returns ------- tfr : instance of AverageTFR The time-frequency-resolved power estimates of the data. Notes ----- .. versionadded:: 1.7 References ---------- .. footbibliography:: """ _check_option("output", output, ("power", "phase", "complex")) method_kw["output"] = output return AverageTFR( inst=self, method=method, freqs=freqs, tmin=tmin, tmax=tmax, picks=picks, proj=proj, decim=decim, n_jobs=n_jobs, verbose=verbose, **method_kw, ) @verbose def plot_psd( self, fmin=0, fmax=np.inf, tmin=None, tmax=None, picks=None, proj=False, *, method="auto", average=False, dB=True, estimate="power", xscale="linear", area_mode="std", area_alpha=0.33, color="black", line_alpha=None, spatial_colors=True, sphere=None, exclude="bads", ax=None, show=True, n_jobs=1, verbose=None, **method_kw, ): """%(plot_psd_doc)s. Parameters ---------- %(fmin_fmax_psd)s %(tmin_tmax_psd)s %(picks_good_data_noref)s %(proj_psd)s %(method_plot_psd_auto)s %(average_plot_psd)s %(dB_plot_psd)s %(estimate_plot_psd)s %(xscale_plot_psd)s %(area_mode_plot_psd)s %(area_alpha_plot_psd)s %(color_plot_psd)s %(line_alpha_plot_psd)s %(spatial_colors_psd)s %(sphere_topomap_auto)s .. versionadded:: 0.22.0 exclude : list of str | 'bads' Channels names to exclude from being shown. If 'bads', the bad channels are excluded. Pass an empty list to plot all channels (including channels marked "bad", if any). .. versionadded:: 0.24.0 %(ax_plot_psd)s %(show)s %(n_jobs)s %(verbose)s %(method_kw_psd)s Returns ------- fig : instance of Figure Figure with frequency spectra of the data channels. Notes ----- %(notes_plot_psd_meth)s """ return super().plot_psd( fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, picks=picks, proj=proj, reject_by_annotation=False, method=method, average=average, dB=dB, estimate=estimate, xscale=xscale, area_mode=area_mode, area_alpha=area_alpha, color=color, line_alpha=line_alpha, spatial_colors=spatial_colors, sphere=sphere, exclude=exclude, ax=ax, show=show, n_jobs=n_jobs, verbose=verbose, **method_kw, ) @verbose def to_data_frame( self, picks=None, index=None, scalings=None, copy=True, long_format=False, time_format=None, *, verbose=None, ): """Export data in tabular structure as a pandas DataFrame. Channels 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 ---------- %(picks_all)s %(index_df_evk)s Defaults to ``None``. %(scalings_df)s %(copy_df)s %(long_format_df_raw)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"] 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 picks = _picks_to_idx(self.info, picks, "all", exclude=()) data = self.data[picks, :] times = self.times data = data.T if copy: data = data.copy() data = _scale_dataframe_data(self, data, picks, scalings) # prepare extra columns / multiindex mindex = list() times = _convert_times(times, time_format, self.info["meas_date"]) mindex.append(("time", times)) # build DataFrame df = _build_data_frame( self, data, picks, long_format, mindex, index, default_index=["time"] ) return df @fill_doc class EvokedArray(Evoked): """Evoked object from numpy array. Parameters ---------- data : array of shape (n_channels, n_times) The channels' evoked response. See notes for proper units of measure. %(info_not_none)s Consider using :func:`mne.create_info` to populate this structure. tmin : float Start time before event. Defaults to 0. comment : str Comment on dataset. Can be the condition. Defaults to ''. nave : int Number of averaged epochs. Defaults to 1. kind : str Type of data, either average or standard_error. Defaults to 'average'. %(baseline_evoked)s Defaults to ``None``, i.e. no baseline correction. .. versionadded:: 0.23 %(verbose)s See Also -------- EpochsArray, io.RawArray, create_info Notes ----- Proper units of measure: * V: eeg, eog, seeg, dbs, emg, ecg, bio, ecog * T: mag * T/m: grad * M: hbo, hbr * Am: dipole * AU: misc """ @verbose def __init__( self, data, info, tmin=0.0, comment="", nave=1, kind="average", baseline=None, *, verbose=None, ): dtype = np.complex128 if np.iscomplexobj(data) else np.float64 data = np.asanyarray(data, dtype=dtype) if data.ndim != 2: raise ValueError( "Data must be a 2D array of shape (n_channels, n_samples), got shape " f"{data.shape}" ) if len(info["ch_names"]) != np.shape(data)[0]: raise ValueError( f"Info ({len(info['ch_names'])}) and data ({np.shape(data)[0]}) must " "have same number of channels." ) self.data = data self.first = int(round(tmin * info["sfreq"])) self.last = self.first + np.shape(data)[-1] - 1 self._set_times( np.arange(self.first, self.last + 1, dtype=np.float64) / info["sfreq"] ) self._raw_times = self.times.copy() self._decim = 1 self.info = info.copy() # do not modify original info self.nave = nave self.kind = kind self.comment = comment self.picks = None self.preload = True self._projector = None _validate_type(self.kind, "str", "kind") if self.kind not in _aspect_dict: raise ValueError( f'unknown kind "{self.kind}", should be "average" or "standard_error"' ) self._aspect_kind = _aspect_dict[self.kind] self.baseline = baseline if self.baseline is not None: # omit log msg if not baselining self.apply_baseline(self.baseline) self._filename = None def _get_entries(fid, evoked_node, allow_maxshield=False): """Get all evoked entries.""" comments = list() aspect_kinds = list() for ev in evoked_node: for k in range(ev["nent"]): my_kind = ev["directory"][k].kind pos = ev["directory"][k].pos if my_kind == FIFF.FIFF_COMMENT: tag = read_tag(fid, pos) comments.append(tag.data) my_aspect = _get_aspect(ev, allow_maxshield)[0] for k in range(my_aspect["nent"]): my_kind = my_aspect["directory"][k].kind pos = my_aspect["directory"][k].pos if my_kind == FIFF.FIFF_ASPECT_KIND: tag = read_tag(fid, pos) aspect_kinds.append(int(tag.data.item())) comments = np.atleast_1d(comments) aspect_kinds = np.atleast_1d(aspect_kinds) if len(comments) != len(aspect_kinds) or len(comments) == 0: fid.close() raise ValueError("Dataset names in FIF file could not be found.") t = [_aspect_rev[a] for a in aspect_kinds] t = ['"' + c + '" (' + tt + ")" for tt, c in zip(t, comments)] t = "\n".join(t) return comments, aspect_kinds, t def _get_aspect(evoked, allow_maxshield): """Get Evoked data aspect.""" from .io.base import _check_maxshield is_maxshield = False aspect = dir_tree_find(evoked, FIFF.FIFFB_ASPECT) if len(aspect) == 0: _check_maxshield(allow_maxshield) aspect = dir_tree_find(evoked, FIFF.FIFFB_IAS_ASPECT) is_maxshield = True if len(aspect) > 1: logger.info("Multiple data aspects found. Taking first one.") return aspect[0], is_maxshield def _get_evoked_node(fname): """Get info in evoked file.""" f, tree, _ = fiff_open(fname) with f as fid: _, meas = read_meas_info(fid, tree, verbose=False) evoked_node = dir_tree_find(meas, FIFF.FIFFB_EVOKED) return evoked_node def _check_evokeds_ch_names_times(all_evoked): evoked = all_evoked[0] ch_names = evoked.ch_names for ii, ev in enumerate(all_evoked[1:]): if ev.ch_names != ch_names: if set(ev.ch_names) != set(ch_names): raise ValueError(f"{evoked} and {ev} do not contain the same channels.") else: warn("Order of channels differs, reordering channels ...") ev = ev.copy() ev.reorder_channels(ch_names) all_evoked[ii + 1] = ev if not np.max(np.abs(ev.times - evoked.times)) < 1e-7: raise ValueError(f"{evoked} and {ev} do not contain the same time instants") return all_evoked def combine_evoked(all_evoked, weights): """Merge evoked data by weighted addition or subtraction. Each `~mne.Evoked` in ``all_evoked`` should have the same channels and the same time instants. Subtraction can be performed by passing ``weights=[1, -1]``. .. Warning:: Other than cases like simple subtraction mentioned above (where all weights are ``-1`` or ``1``), if you provide numeric weights instead of using ``'equal'`` or ``'nave'``, the resulting `~mne.Evoked` object's ``.nave`` attribute (which is used to scale noise covariance when applying the inverse operator) may not be suitable for inverse imaging. Parameters ---------- all_evoked : list of Evoked The evoked datasets. weights : list of float | ``'equal'`` | ``'nave'`` The weights to apply to the data of each evoked instance, or a string describing the weighting strategy to apply: ``'nave'`` computes sum-to-one weights proportional to each object's ``nave`` attribute; ``'equal'`` weights each `~mne.Evoked` by ``1 / len(all_evoked)``. Returns ------- evoked : Evoked The new evoked data. Notes ----- .. versionadded:: 0.9.0 """ naves = np.array([evk.nave for evk in all_evoked], float) if isinstance(weights, str): _check_option("weights", weights, ["nave", "equal"]) if weights == "nave": weights = naves / naves.sum() else: weights = np.ones_like(naves) / len(naves) else: weights = np.array(weights, float) if weights.ndim != 1 or weights.size != len(all_evoked): raise ValueError("weights must be the same size as all_evoked") # cf. https://en.wikipedia.org/wiki/Weighted_arithmetic_mean, section on # "weighted sample variance". The variance of a weighted sample mean is: # # σ² = w₁² σ₁² + w₂² σ₂² + ... + wₙ² σₙ² # # We estimate the variance of each evoked instance as 1 / nave to get: # # σ² = w₁² / nave₁ + w₂² / nave₂ + ... + wₙ² / naveₙ # # And our resulting nave is the reciprocal of this: new_nave = 1.0 / np.sum(weights**2 / naves) # This general formula is equivalent to formulae in Matti's manual # (pp 128-129), where: # new_nave = sum(naves) when weights='nave' and # new_nave = 1. / sum(1. / naves) when weights are all 1. all_evoked = _check_evokeds_ch_names_times(all_evoked) evoked = all_evoked[0].copy() # use union of bad channels bads = list(set(b for e in all_evoked for b in e.info["bads"])) evoked.info["bads"] = bads evoked.data = sum(w * e.data for w, e in zip(weights, all_evoked)) evoked.nave = new_nave comment = "" for idx, (w, e) in enumerate(zip(weights, all_evoked)): # pick sign sign = "" if w >= 0 else "-" # format weight weight = "" if np.isclose(abs(w), 1.0) else f"{abs(w):0.3f}" # format multiplier multiplier = " × " if weight else "" # format comment if e.comment is not None and " + " in e.comment: # multiple conditions this_comment = f"({e.comment})" else: this_comment = f'{e.comment or "unknown"}' # assemble everything if idx == 0: comment += f"{sign}{weight}{multiplier}{this_comment}" else: comment += f' {sign or "+"} {weight}{multiplier}{this_comment}' # special-case: combine_evoked([e1, -e2], [1, -1]) evoked.comment = comment.replace(" - - ", " + ") return evoked @verbose def read_evokeds( fname, condition=None, baseline=None, kind="average", proj=True, allow_maxshield=False, verbose=None, ) -> list[Evoked] | Evoked: """Read evoked dataset(s). Parameters ---------- fname : path-like The filename, which should end with ``-ave.fif`` or ``-ave.fif.gz``. condition : int or str | list of int or str | None The index or list of indices of the evoked dataset to read. FIF files can contain multiple datasets. If None, all datasets are returned as a list. %(baseline_evoked)s If ``None`` (default), do not apply baseline correction. .. note:: Note that if the read `~mne.Evoked` objects have already been baseline-corrected, the data retrieved from disk will **always** be baseline-corrected (in fact, only the baseline-corrected version of the data will be saved, so there is no way to undo this procedure). Only **after** the data has been loaded, a custom (additional) baseline correction **may** be optionally applied by passing a tuple here. Passing ``None`` will **not** remove an existing baseline correction, but merely omit the optional, additional baseline correction. kind : str Either ``'average'`` or ``'standard_error'``, the type of data to read. proj : bool If False, available projectors won't be applied to the data. allow_maxshield : bool | str (default False) If True, allow loading of data that has been recorded with internal active compensation (MaxShield). Data recorded with MaxShield should generally not be loaded directly, but should first be processed using SSS/tSSS to remove the compensation signals that may also affect brain activity. Can also be ``"yes"`` to load without eliciting a warning. %(verbose)s Returns ------- evoked : Evoked or list of Evoked The evoked dataset(s); one `~mne.Evoked` if ``condition`` is an integer or string; or a list of `~mne.Evoked` if ``condition`` is ``None`` or a list. See Also -------- write_evokeds Notes ----- .. versionchanged:: 0.23 If the read `~mne.Evoked` objects had been baseline-corrected before saving, this will be reflected in their ``baseline`` attribute after reading. """ fname = _check_fname(fname, overwrite="read", must_exist=True) check_fname(fname, "evoked", ("-ave.fif", "-ave.fif.gz", "_ave.fif", "_ave.fif.gz")) logger.info(f"Reading {fname} ...") return_list = True if condition is None: evoked_node = _get_evoked_node(fname) condition = range(len(evoked_node)) elif not isinstance(condition, list): condition = [condition] return_list = False out = [] for c in condition: evoked = Evoked( fname, c, kind=kind, proj=proj, allow_maxshield=allow_maxshield, verbose=verbose, ) if baseline is None and evoked.baseline is None: logger.info(_log_rescale(None)) elif baseline is None and evoked.baseline is not None: # Don't touch an existing baseline bmin, bmax = evoked.baseline logger.info( f"Loaded Evoked data is baseline-corrected " f"(baseline: [{bmin:g}, {bmax:g}] s)" ) else: evoked.apply_baseline(baseline) out.append(evoked) return out if return_list else out[0] def _read_evoked(fname, condition=None, kind="average", allow_maxshield=False): """Read evoked data from a FIF file.""" if fname is None: raise ValueError("No evoked filename specified") f, tree, _ = fiff_open(fname) with f as fid: # Read the measurement info info, meas = read_meas_info(fid, tree, clean_bads=True) # Locate the data of interest processed = dir_tree_find(meas, FIFF.FIFFB_PROCESSED_DATA) if len(processed) == 0: raise ValueError("Could not find processed data") evoked_node = dir_tree_find(meas, FIFF.FIFFB_EVOKED) if len(evoked_node) == 0: raise ValueError("Could not find evoked data") # find string-based entry if isinstance(condition, str): if kind not in _aspect_dict.keys(): raise ValueError('kind must be "average" or "standard_error"') comments, aspect_kinds, t = _get_entries(fid, evoked_node, allow_maxshield) goods = np.isin(comments, [condition]) & np.isin( aspect_kinds, [_aspect_dict[kind]] ) found_cond = np.where(goods)[0] if len(found_cond) != 1: raise ValueError( f'condition "{condition}" ({kind}) not found, out of found ' f"datasets:\n{t}" ) condition = found_cond[0] elif condition is None: if len(evoked_node) > 1: _, _, conditions = _get_entries(fid, evoked_node, allow_maxshield) raise TypeError( "Evoked file has more than one condition, the condition parameters " f"must be specified from:\n{conditions}" ) else: condition = 0 if condition >= len(evoked_node) or condition < 0: raise ValueError("Data set selector out of range") my_evoked = evoked_node[condition] # Identify the aspects with info._unlock(): my_aspect, info["maxshield"] = _get_aspect(my_evoked, allow_maxshield) # Now find the data in the evoked block nchan = 0 sfreq = -1 chs = [] baseline = bmin = bmax = None comment = last = first = first_time = nsamp = None for k in range(my_evoked["nent"]): my_kind = my_evoked["directory"][k].kind pos = my_evoked["directory"][k].pos if my_kind == FIFF.FIFF_COMMENT: tag = read_tag(fid, pos) comment = tag.data elif my_kind == FIFF.FIFF_FIRST_SAMPLE: tag = read_tag(fid, pos) first = int(tag.data.item()) elif my_kind == FIFF.FIFF_LAST_SAMPLE: tag = read_tag(fid, pos) last = int(tag.data.item()) elif my_kind == FIFF.FIFF_NCHAN: tag = read_tag(fid, pos) nchan = int(tag.data.item()) elif my_kind == FIFF.FIFF_SFREQ: tag = read_tag(fid, pos) sfreq = float(tag.data.item()) elif my_kind == FIFF.FIFF_CH_INFO: tag = read_tag(fid, pos) chs.append(tag.data) elif my_kind == FIFF.FIFF_FIRST_TIME: tag = read_tag(fid, pos) first_time = float(tag.data.item()) elif my_kind == FIFF.FIFF_NO_SAMPLES: tag = read_tag(fid, pos) nsamp = int(tag.data.item()) elif my_kind == FIFF.FIFF_MNE_BASELINE_MIN: tag = read_tag(fid, pos) bmin = float(tag.data.item()) elif my_kind == FIFF.FIFF_MNE_BASELINE_MAX: tag = read_tag(fid, pos) bmax = float(tag.data.item()) if comment is None: comment = "No comment" if bmin is not None or bmax is not None: # None's should've been replaced with floats assert bmin is not None and bmax is not None baseline = (bmin, bmax) # Local channel information? if nchan > 0: if chs is None: raise ValueError( "Local channel information was not found when it was expected." ) if len(chs) != nchan: raise ValueError( "Number of channels and number of " "channel definitions are different" ) ch_names_mapping = _read_extended_ch_info(chs, my_evoked, fid) info["chs"] = chs info["bads"][:] = _rename_list(info["bads"], ch_names_mapping) logger.info( f" Found channel information in evoked data. nchan = {nchan}" ) if sfreq > 0: info["sfreq"] = sfreq # Read the data in the aspect block nave = 1 epoch = [] for k in range(my_aspect["nent"]): kind = my_aspect["directory"][k].kind pos = my_aspect["directory"][k].pos if kind == FIFF.FIFF_COMMENT: tag = read_tag(fid, pos) comment = tag.data elif kind == FIFF.FIFF_ASPECT_KIND: tag = read_tag(fid, pos) aspect_kind = int(tag.data.item()) elif kind == FIFF.FIFF_NAVE: tag = read_tag(fid, pos) nave = int(tag.data.item()) elif kind == FIFF.FIFF_EPOCH: tag = read_tag(fid, pos) epoch.append(tag) nepoch = len(epoch) if nepoch != 1 and nepoch != info["nchan"]: raise ValueError( "Number of epoch tags is unreasonable " f"(nepoch = {nepoch} nchan = {info['nchan']})" ) if nepoch == 1: # Only one epoch data = epoch[0].data # May need a transpose if the number of channels is one if data.shape[1] == 1 and info["nchan"] == 1: data = data.T else: # Put the old style epochs together data = np.concatenate([e.data[None, :] for e in epoch], axis=0) if np.isrealobj(data): data = data.astype(np.float64) else: data = data.astype(np.complex128) if first_time is not None and nsamp is not None: times = first_time + np.arange(nsamp) / info["sfreq"] elif first is not None: nsamp = last - first + 1 times = np.arange(first, last + 1) / info["sfreq"] else: raise RuntimeError("Could not read time parameters") del first, last if nsamp is not None and data.shape[1] != nsamp: raise ValueError( f"Incorrect number of samples ({data.shape[1]} instead of {nsamp})" ) logger.info(" Found the data of interest:") logger.info( f" t = {1000 * times[0]:10.2f} ... {1000 * times[-1]:10.2f} ms (" f"{comment})" ) if info["comps"] is not None: logger.info( f" {len(info['comps'])} CTF compensation matrices available" ) logger.info(f" nave = {nave} - aspect type = {aspect_kind}") # Calibrate cals = np.array( [ info["chs"][k]["cal"] * info["chs"][k].get("scale", 1.0) for k in range(info["nchan"]) ] ) data *= cals[:, np.newaxis] return info, nave, aspect_kind, comment, times, data, baseline @verbose def write_evokeds(fname, evoked, *, on_mismatch="raise", overwrite=False, verbose=None): """Write an evoked dataset to a file. Parameters ---------- fname : path-like The file name, which should end with ``-ave.fif`` or ``-ave.fif.gz``. evoked : Evoked instance, or list of Evoked instances The evoked dataset, or list of evoked datasets, to save in one file. Note that the measurement info from the first evoked instance is used, so be sure that information matches. %(on_mismatch_info)s %(overwrite)s .. versionadded:: 1.0 %(verbose)s .. versionadded:: 0.24 See Also -------- read_evokeds Notes ----- .. versionchanged:: 0.23 Information on baseline correction will be stored with each individual `~mne.Evoked` object, and will be restored when reading the data again via `mne.read_evokeds`. """ _write_evokeds(fname, evoked, on_mismatch=on_mismatch, overwrite=overwrite) def _write_evokeds(fname, evoked, check=True, *, on_mismatch="raise", overwrite=False): """Write evoked data.""" from .dipole import DipoleFixed # avoid circular import fname = _check_fname(fname=fname, overwrite=overwrite) if check: check_fname( fname, "evoked", ("-ave.fif", "-ave.fif.gz", "_ave.fif", "_ave.fif.gz") ) if not isinstance(evoked, list | tuple): evoked = [evoked] warned = False # Create the file and save the essentials with start_and_end_file(fname) as fid: start_block(fid, FIFF.FIFFB_MEAS) write_id(fid, FIFF.FIFF_BLOCK_ID) if evoked[0].info["meas_id"] is not None: write_id(fid, FIFF.FIFF_PARENT_BLOCK_ID, evoked[0].info["meas_id"]) # Write measurement info write_meas_info(fid, evoked[0].info) # One or more evoked data sets start_block(fid, FIFF.FIFFB_PROCESSED_DATA) for ei, e in enumerate(evoked): if ei: _ensure_infos_match( info1=evoked[0].info, info2=e.info, name=f"evoked[{ei}]", on_mismatch=on_mismatch, ) start_block(fid, FIFF.FIFFB_EVOKED) # Comment is optional if e.comment is not None and len(e.comment) > 0: write_string(fid, FIFF.FIFF_COMMENT, e.comment) # First time, num. samples, first and last sample write_float(fid, FIFF.FIFF_FIRST_TIME, e.times[0]) write_int(fid, FIFF.FIFF_NO_SAMPLES, len(e.times)) write_int(fid, FIFF.FIFF_FIRST_SAMPLE, e.first) write_int(fid, FIFF.FIFF_LAST_SAMPLE, e.last) # Baseline if not isinstance(e, DipoleFixed) and e.baseline is not None: bmin, bmax = e.baseline write_float(fid, FIFF.FIFF_MNE_BASELINE_MIN, bmin) write_float(fid, FIFF.FIFF_MNE_BASELINE_MAX, bmax) # The evoked data itself if e.info.get("maxshield"): aspect = FIFF.FIFFB_IAS_ASPECT else: aspect = FIFF.FIFFB_ASPECT start_block(fid, aspect) write_int(fid, FIFF.FIFF_ASPECT_KIND, e._aspect_kind) # convert nave to integer to comply with FIFF spec nave_int = int(round(e.nave)) if nave_int != e.nave and not warned: warn( 'converting "nave" to integer before saving evoked; this ' "can have a minor effect on the scale of source " 'estimates that are computed using "nave".' ) warned = True write_int(fid, FIFF.FIFF_NAVE, nave_int) del nave_int decal = np.zeros((e.info["nchan"], 1)) for k in range(e.info["nchan"]): decal[k] = 1.0 / ( e.info["chs"][k]["cal"] * e.info["chs"][k].get("scale", 1.0) ) if np.iscomplexobj(e.data): write_function = write_complex_float_matrix else: write_function = write_float_matrix write_function(fid, FIFF.FIFF_EPOCH, decal * e.data) end_block(fid, aspect) end_block(fid, FIFF.FIFFB_EVOKED) end_block(fid, FIFF.FIFFB_PROCESSED_DATA) end_block(fid, FIFF.FIFFB_MEAS) def _get_peak(data, times, tmin=None, tmax=None, mode="abs", *, strict=True): """Get feature-index and time of maximum signal from 2D array. Note. This is a 'getter', not a 'finder'. For non-evoked type data and continuous signals, please use proper peak detection algorithms. Parameters ---------- data : instance of numpy.ndarray (n_locations, n_times) The data, either evoked in sensor or source space. times : instance of numpy.ndarray (n_times) The times in seconds. tmin : float | None The minimum point in time to be considered for peak getting. tmax : float | None The maximum point in time to be considered for peak getting. mode : {'pos', 'neg', 'abs'} How to deal with the sign of the data. If 'pos' only positive values will be considered. If 'neg' only negative values will be considered. If 'abs' absolute values will be considered. Defaults to 'abs'. strict : bool If True, raise an error if values are all positive when detecting a minimum (mode='neg'), or all negative when detecting a maximum (mode='pos'). Defaults to True. Returns ------- max_loc : int The index of the feature with the maximum value. max_time : int The time point of the maximum response, index. max_amp : float Amplitude of the maximum response. """ _check_option("mode", mode, ["abs", "neg", "pos"]) if tmin is None: tmin = times[0] if tmax is None: tmax = times[-1] if tmin < times.min() or tmax > times.max(): if tmin < times.min(): param_name = "tmin" param_val = tmin else: param_name = "tmax" param_val = tmax raise ValueError( f"{param_name} ({param_val}) is out of bounds. It must be " f"between {times.min()} and {times.max()}" ) elif tmin > tmax: raise ValueError(f"tmin ({tmin}) must be <= tmax ({tmax})") time_win = (times >= tmin) & (times <= tmax) mask = np.ones_like(data).astype(bool) mask[:, time_win] = False maxfun = np.argmax if mode == "pos": if strict and not np.any(data[~mask] > 0): raise ValueError( "No positive values encountered. Cannot operate in pos mode." ) elif mode == "neg": if strict and not np.any(data[~mask] < 0): raise ValueError( "No negative values encountered. Cannot operate in neg mode." ) maxfun = np.argmin masked_index = np.ma.array(np.abs(data) if mode == "abs" else data, mask=mask) max_loc, max_time = np.unravel_index(maxfun(masked_index), data.shape) return max_loc, max_time, data[max_loc, max_time]