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,