def get_tfr(fif_file, cfg, tfr, n_jobs=1): raw, events = pu.load_raw(fif_file) p = qc.parse_path(fif_file) fname = p.name outpath = p.dir export_dir = '%s/plot_%s' % (outpath, fname) qc.make_dirs(export_dir) # set channels of interest picks = pu.channel_names_to_index(raw, cfg.CHANNEL_PICKS) spchannels = pu.channel_names_to_index(raw, cfg.SP_CHANNELS) if max(picks) > len(raw.info['ch_names']): msg = 'ERROR: "picks" has a channel index %d while there are only %d channels.' %\ (max(picks), len(raw.info['ch_names'])) raise RuntimeError(msg) # Apply filters pu.preprocess(raw, spatial=cfg.SP_FILTER, spatial_ch=spchannels, spectral=cfg.TP_FILTER, spectral_ch=picks, notch=cfg.NOTCH_FILTER, notch_ch=picks, multiplier=cfg.MULTIPLIER, n_jobs=n_jobs) # MNE TFR functions do not support Raw instances yet, so convert to Epoch if cfg.EVENT_START is None: raw._data[0][0] = 1 events = np.array([[0, 0, 1]]) classes = None else: classes = {'START':cfg.EVENT_START} tmax = (raw._data.shape[1] - 1) / raw.info['sfreq'] epochs_all = mne.Epochs(raw, events, classes, tmin=0, tmax=tmax, picks=picks, baseline=None, preload=True) print('\n>> Processing %s' % fif_file) freqs = cfg.FREQ_RANGE # define frequencies of interest n_cycles = freqs / 2. # different number of cycle per frequency power = tfr(epochs_all, freqs=freqs, n_cycles=n_cycles, use_fft=False, return_itc=False, decim=1, n_jobs=n_jobs) if cfg.EXPORT_MATLAB is True: # export all channels to MATLAB mout = '%s/%s-%s.mat' % (export_dir, fname, cfg.SP_FILTER) scipy.io.savemat(mout, {'tfr':power.data, 'chs':power.ch_names, 'events':events, 'sfreq':raw.info['sfreq'], 'freqs':cfg.FREQ_RANGE}) if cfg.EXPORT_PNG is True: # Plot power of each channel for ch in np.arange(len(picks)): ch_name = raw.ch_names[picks[ch]] title = 'Channel %s' % (ch_name) # mode= None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent' fig = power.plot([ch], baseline=cfg.BS_TIMES, mode=cfg.BS_MODE, show=False, colorbar=True, title=title, vmin=cfg.VMIN, vmax=cfg.VMAX, dB=False) fout = '%s/%s-%s-%s.png' % (export_dir, fname, cfg.SP_FILTER, ch_name) fig.savefig(fout) print('Exported %s' % fout) print('Finished !')
def get_tfr(cfg, recursive=False, n_jobs=1): ''' @params: tfr_type: 'multitaper' or 'morlet' recursive: if True, load raw files in sub-dirs recursively export_path: path to save plots n_jobs: number of cores to run in parallel ''' cfg = check_cfg(cfg) tfr_type = cfg.TFR_TYPE export_path = cfg.EXPORT_PATH t_buffer = cfg.T_BUFFER if tfr_type == 'multitaper': tfr = mne.time_frequency.tfr_multitaper elif tfr_type == 'morlet': tfr = mne.time_frequency.tfr_morlet elif tfr_type == 'butter': butter_order = 4 # TODO: parameterize tfr = lfilter elif tfr_type == 'fir': raise NotImplementedError else: raise ValueError('Wrong TFR type %s' % tfr_type) n_jobs = cfg.N_JOBS if n_jobs is None: n_jobs = mp.cpu_count() if hasattr(cfg, 'DATA_DIRS'): if export_path is None: raise ValueError('For multiple directories, cfg.EXPORT_PATH cannot be None') else: outpath = export_path # custom event file if hasattr(cfg, 'EVENT_FILE') and cfg.EVENT_FILE is not None: events = mne.read_events(cfg.EVENT_FILE) file_prefix = 'grandavg' # load and merge files from all directories flist = [] for ddir in cfg.DATA_DIRS: ddir = ddir.replace('\\', '/') if ddir[-1] != '/': ddir += '/' for f in qc.get_file_list(ddir, fullpath=True, recursive=recursive): if qc.parse_path(f).ext in ['fif', 'bdf', 'gdf']: flist.append(f) raw, events = pu.load_multi(flist) else: print('Loading', cfg.DATA_FILE) raw, events = pu.load_raw(cfg.DATA_FILE) # custom events if hasattr(cfg, 'EVENT_FILE') and cfg.EVENT_FILE is not None: events = mne.read_events(cfg.EVENT_FILE) if export_path is None: [outpath, file_prefix, _] = qc.parse_path_list(cfg.DATA_FILE) else: outpath = export_path # re-referencing if cfg.REREFERENCE is not None: pu.rereference(raw, cfg.REREFERENCE[1], cfg.REREFERENCE[0]) sfreq = raw.info['sfreq'] # set channels of interest picks = pu.channel_names_to_index(raw, cfg.CHANNEL_PICKS) spchannels = pu.channel_names_to_index(raw, cfg.SP_CHANNELS) if max(picks) > len(raw.info['ch_names']): msg = 'ERROR: "picks" has a channel index %d while there are only %d channels.' %\ (max(picks), len(raw.info['ch_names'])) raise RuntimeError(msg) # Apply filters pu.preprocess(raw, spatial=cfg.SP_FILTER, spatial_ch=spchannels, spectral=cfg.TP_FILTER, spectral_ch=picks, notch=cfg.NOTCH_FILTER, notch_ch=picks, multiplier=cfg.MULTIPLIER, n_jobs=n_jobs) # Read epochs classes = {} for t in cfg.TRIGGERS: if t in set(events[:, -1]): if hasattr(cfg, 'tdef'): classes[cfg.tdef.by_value[t]] = t else: classes[str(t)] = t if len(classes) == 0: raise ValueError('No desired event was found from the data.') try: tmin = cfg.EPOCH[0] tmin_buffer = tmin - t_buffer raw_tmax = raw._data.shape[1] / sfreq - 0.1 if cfg.EPOCH[1] is None: if cfg.POWER_AVERAGED: raise ValueError('EPOCH value cannot have None for grand averaged TFR') else: if len(cfg.TRIGGERS) > 1: raise ValueError('If the end time of EPOCH is None, only a single event can be defined.') t_ref = events[np.where(events[:,2] == list(cfg.TRIGGERS)[0])[0][0], 0] / sfreq tmax = raw_tmax - t_ref - t_buffer else: tmax = cfg.EPOCH[1] tmax_buffer = tmax + t_buffer if tmax_buffer > raw_tmax: raise ValueError('Epoch length with buffer (%.3f) is larger than signal length (%.3f)' % (tmax_buffer, raw_tmax)) #print('Epoch tmin = %.1f, tmax = %.1f, raw length = %.1f' % (tmin, tmax, raw_tmax)) epochs_all = mne.Epochs(raw, events, classes, tmin=tmin_buffer, tmax=tmax_buffer, proj=False, picks=picks, baseline=None, preload=True) if epochs_all.drop_log_stats() > 0: print('\n** Bad epochs found. Dropping into a Python shell.') print(epochs_all.drop_log) print('tmin = %.1f, tmax = %.1f, tmin_buffer = %.1f, tmax_buffer = %.1f, raw length = %.1f' % \ (tmin, tmax, tmin_buffer, tmax_buffer, raw._data.shape[1] / sfreq)) print('\nType exit to continue.\n') pdb.set_trace() except: print('\n*** (tfr_export) ERROR OCCURRED WHILE EPOCHING ***') traceback.print_exc() print('tmin = %.1f, tmax = %.1f, tmin_buffer = %.1f, tmax_buffer = %.1f, raw length = %.1f' % \ (tmin, tmax, tmin_buffer, tmax_buffer, raw._data.shape[1] / sfreq)) pdb.set_trace() power = {} for evname in classes: #export_dir = '%s/plot_%s' % (outpath, evname) export_dir = outpath qc.make_dirs(export_dir) print('\n>> Processing %s' % evname) freqs = cfg.FREQ_RANGE # define frequencies of interest n_cycles = freqs / 2. # different number of cycle per frequency if cfg.POWER_AVERAGED: # grand-average TFR epochs = epochs_all[evname][:] if len(epochs) == 0: print('No %s epochs. Skipping.' % evname) continue if tfr_type == 'butter': b, a = butter_bandpass(cfg.FREQ_RANGE[0], cfg.FREQ_RANGE[-1], sfreq, order=butter_order) tfr_filtered = lfilter(b, a, epochs, axis=2) tfr_hilbert = hilbert(tfr_filtered) tfr_power = abs(tfr_hilbert) tfr_data = np.mean(tfr_power, axis=0) elif tfr_type == 'fir': raise NotImplementedError else: power[evname] = tfr(epochs, freqs=freqs, n_cycles=n_cycles, use_fft=False, return_itc=False, decim=1, n_jobs=n_jobs) power[evname] = power[evname].crop(tmin=tmin, tmax=tmax) tfr_data = power[evname].data if cfg.EXPORT_MATLAB is True: # export all channels to MATLAB mout = '%s/%s-%s-%s.mat' % (export_dir, file_prefix, cfg.SP_FILTER, evname) scipy.io.savemat(mout, {'tfr':tfr_data, 'chs':epochs.ch_names, 'events':events, 'sfreq':sfreq, 'epochs':cfg.EPOCH, 'freqs':cfg.FREQ_RANGE}) print('Exported %s' % mout) if cfg.EXPORT_PNG is True: # Inspect power for each channel for ch in np.arange(len(picks)): chname = raw.ch_names[picks[ch]] title = 'Peri-event %s - Channel %s' % (evname, chname) # mode= None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent' fig = power[evname].plot([ch], baseline=cfg.BS_TIMES, mode=cfg.BS_MODE, show=False, colorbar=True, title=title, vmin=cfg.VMIN, vmax=cfg.VMAX, dB=False) fout = '%s/%s-%s-%s-%s.png' % (export_dir, file_prefix, cfg.SP_FILTER, evname, chname) fig.savefig(fout) fig.clf() print('Exported to %s' % fout) else: # TFR per event for ep in range(len(epochs_all[evname])): epochs = epochs_all[evname][ep] if len(epochs) == 0: print('No %s epochs. Skipping.' % evname) continue power[evname] = tfr(epochs, freqs=freqs, n_cycles=n_cycles, use_fft=False, return_itc=False, decim=1, n_jobs=n_jobs) power[evname] = power[evname].crop(tmin=tmin, tmax=tmax) if cfg.EXPORT_MATLAB is True: # export all channels to MATLAB mout = '%s/%s-%s-%s-ep%02d.mat' % (export_dir, file_prefix, cfg.SP_FILTER, evname, ep + 1) scipy.io.savemat(mout, {'tfr':power[evname].data, 'chs':power[evname].ch_names, 'events':events, 'sfreq':sfreq, 'tmin':tmin, 'tmax':tmax, 'freqs':cfg.FREQ_RANGE}) print('Exported %s' % mout) if cfg.EXPORT_PNG is True: # Inspect power for each channel for ch in np.arange(len(picks)): chname = raw.ch_names[picks[ch]] title = 'Peri-event %s - Channel %s, Trial %d' % (evname, chname, ep + 1) # mode= None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent' fig = power[evname].plot([ch], baseline=cfg.BS_TIMES, mode=cfg.BS_MODE, show=False, colorbar=True, title=title, vmin=cfg.VMIN, vmax=cfg.VMAX, dB=False) fout = '%s/%s-%s-%s-%s-ep%02d.png' % (export_dir, file_prefix, cfg.SP_FILTER, evname, chname, ep + 1) fig.savefig(fout) fig.clf() print('Exported %s' % fout) if hasattr(cfg, 'POWER_DIFF'): export_dir = '%s/diff' % outpath qc.make_dirs(export_dir) labels = classes.keys() df = power[labels[0]] - power[labels[1]] df.data = np.log(np.abs(df.data)) # Inspect power diff for each channel for ch in np.arange(len(picks)): chname = raw.ch_names[picks[ch]] title = 'Peri-event %s-%s - Channel %s' % (labels[0], labels[1], chname) # mode= None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent' fig = df.plot([ch], baseline=cfg.BS_TIMES, mode=cfg.BS_MODE, show=False, colorbar=True, title=title, vmin=3.0, vmax=-3.0, dB=False) fout = '%s/%s-%s-diff-%s-%s-%s.jpg' % (export_dir, file_prefix, cfg.SP_FILTER, labels[0], labels[1], chname) print('Exporting to %s' % fout) fig.savefig(fout) fig.clf() print('Finished !')