gp_neurobio/code/read_baseline_data.py
2018-11-15 14:33:11 +01:00

48 lines
1.5 KiB
Python

import nixio as nix
import os
import numpy as np
from IPython import embed
def read_baseline_eod(dataset):
base = dataset.split(os.path.sep)[-1] + ".nix"
nix_file = nix.File.open(os.path.join(dataset, base), nix.FileMode.ReadOnly)
b = nix_file.blocks[0]
if 'BaselineActivity_1' in b.tags:
t = b.tags["BaselineActivity_1"]
elif "BaselineActivity_2" in b.tags:
t = b.tags["BaselineActivity_2"]
else:
f.close()
return [],[]
eod_da = b.data_arrays["LocalEOD-1"]
eod = t.retrieve_data("LocalEOD-1")[:]
time = np.asarray(eod_da.dimensions[0].axis(len(eod)))
nix_file.close()
return time, eod
def read_baseline_spikes(dataset):
base = dataset.split(os.path.sep)[-1] + ".nix"
nix_file = nix.File.open(os.path.join(dataset, base), nix.FileMode.ReadOnly)
b = nix_file.blocks[0]
if 'BaselineActivity_1' in b.tags:
t = b.tags["BaselineActivity_1"]
elif "BaselineActivity_2" in b.tags:
t = b.tags["BaselineActivity_2"]
else:
f.close()
return [],[]
spikes_da = b.data_arrays["Spikes-1"]
spike_times = spikes_da[:spikes_da.shape[0]-5000]
baseline_spikes = spike_times[(spike_times > t.position[0]) & (spike_times < (t.position[0] + t.extent[0]))]
nix_file.close()
return baseline_spikes
if __name__ == "__main__":
data_dir = "../data"
dataset = "2018-11-09-ad-invivo-1"
time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))