import helper_functions as fun from pathlib import Path, PurePath from scipy import stats from scipy.io import savemat from statsmodels.tsa.tsatools import detrend #%% sub_id = 'DiAs' proc = 'preproc' stage = '_BP_montage_HFB_raw.fif' sfreq = 100 tmin_crop = 0.200 tmax_crop = 1.5 #%% subject = hf.Subject(sub_id) datadir = subject.processing_stage_path(proc=proc) visual_populations = subject.pick_visual_chan() hfb, visual_chan = hf.load_visual_hfb(sub_id=sub_id, proc=proc, stage=stage) ts, time = hf.category_ts(hfb, visual_chan, sfreq=sfreq, tmin_crop=tmin_crop, tmax_crop=tmax_crop) #%% Detrend ts #ts = hf.substract_AERA(ts, axis=2) #%% Save time series for GC analysis ts_dict = {'data': ts, 'sfreq': sfreq, 'time': time, 'sub_id': sub_id} fname = sub_id + '_ts_visual.mat'
""" import HFB_process as hf import mne import matplotlib import matplotlib.pyplot as plt import seaborn as sns from pathlib import Path, PurePath from config import args ichan = 6 #%% Load data subject = hf.Subject() raw = subject.load_data(proc='raw_signal', stage=args.stage, preload=True, epoch=args.epoch) #%% Test narrow band envelope extraction fpath = subject.processing_stage_path(proc=args.proc) fname = args.sub_id + args.stage fpath = fpath.joinpath(fname) raw = mne.io.read_raw_fif(fpath, preload=True) raw = raw.crop(tmin=100, tmax=102) times = raw.times raw_filt = raw.copy().filter(l_freq=args.l_freq,