os.mkdir(_RESULTS)
##############################################################################
# Get root path
##############################################################################
coh_sig_file = f'thr_{metric}_at_{at}_surr.nc'
wt = None
##############################################################################
# Load the supertensor and convert to adjacency matrix
##############################################################################
net = temporal_network(coh_file=f'{metric}_at_{at}.nc',
coh_sig_file=coh_sig_file,
wt=wt,
date=sessions[s_id],
trial_type=[1],
behavioral_response=[1])
net.convert_to_adjacency()
# If the metric is pec take the absolute value of weigths only
if metric == "pec":
net.super_tensor.values = np.abs(net.super_tensor.values)
##############################################################################
# 1. Strength
##############################################################################
degree = []
for f in tqdm(range(net.A.sizes["freqs"])):
import os
from tqdm import tqdm
import numpy as np
import xarray as xr
from config import sessions
from GDa.util import average_stages
from GDa.temporal_network import temporal_network
coh_file = 'coh_at_cue.nc'
coh_sig_file = 'thr_coh_at_cue_surr.nc'
data = []
for s_id in tqdm(sessions):
net = temporal_network(coh_file=coh_file,
coh_sig_file=coh_sig_file,
wt=None,
date=s_id,
trial_type=[1],
behavioral_response=[1])
# Average if needed
out = average_stages(net.super_tensor, 1)
# To save memory
del net
# Convert to format required by the MI workflow
data += [out.isel(roi=[r]).mean("trials") for r in range(len(out['roi']))]
# Concatenate channels
data = xr.concat(data, dim="roi")
# Get unique rois
urois, counts = np.unique(data.roi.data, return_counts=True)
# Get unique rois that has at leats 10 channels
urois = urois[counts >= 10]
##################################################################
# Loading temporal network
##################################################################
_ROOT = os.path.expanduser("~/storage1/projects/GrayData-Analysis")
# Path in which to save coherence data
_RESULTS = os.path.join("Results", "lucy", "141017", "session01")
coh_sig_file = "coh_k_0.3_multitaper_at_cue_surr.nc"
wt = None
net = temporal_network(
coh_file="coh_k_0.3_multitaper_at_cue.nc",
coh_sig_file=coh_sig_file,
wt=wt,
date="141017",
trial_type=[1],
behavioral_response=[1],
)
##################################################################
# Compute static coherence networks
##################################################################
# Stage masks
net.create_stage_masks(flatten=True)
coh = []
# Save attributes
attrs = net.super_tensor.attrs
# Stack trials and times
# Path in which to save coherence data
_RESULTS = os.path.join("Results", "lucy", session, "session01")
coh_sig_file = None
if bool(surr) is False:
coh_file = f'{metric}_at_cue.nc'
if bool(thr):
coh_sig_file = f'thr_{metric}_at_cue_surr.nc'
else:
coh_file = f'{metric}_at_cue_surr.nc'
wt = None
net = temporal_network(
coh_file=coh_file,
coh_sig_file=coh_sig_file,
wt=wt,
date=session,
trial_type=[1],
behavioral_response=[1],
)
# Masks for each stage
net.create_stage_masks(flatten=True)
# Stack trials
FC = net.super_tensor.stack(obs=("trials", "times")).data
##############################################################################
# Compute meta-connectivity
##############################################################################
n_edges = net.super_tensor.sizes["roi"]
n_freqs = net.super_tensor.sizes["freqs"]
###############################################################################
# Path in which to save burst stats data
###############################################################################
path_st = os.path.join(_ROOT, f"Results/lucy/{session}/session01")
path_st = os.path.join(path_st, f"bs_stats_k_{_KS}_numba_{mode}.nc")
###############################################################################
# Instantiate temp net
###############################################################################
# Instantiating a temporal network object without thresholding the data
net = temporal_network(coh_file=_COH_FILE,
coh_sig_file=_COH_FILE_SIG,
date=session,
trial_type=[1],
behavioral_response=[1],
q=None,
relative=False)
###############################################################################
# Compute burstness statistics for different thresholds
###############################################################################
bs_stats = np.zeros((2, net.super_tensor.sizes["freqs"],
net.super_tensor.shape[0], len(stages), 4))
# Set to one all values about siginificance level
net.super_tensor.values = (net.super_tensor.values > 0)
# coh = net.super_tensor
# Wheter to compute the burst stats for sequences of siliences