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run_fra_pipeline_cruella.py
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run_fra_pipeline_cruella.py
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import numpy as np
import h5py
import tdt
import pickle
import pandas as pd
from helpers.cleandata2 import clean_data
import scipy.io as sio
import scipy
from helpers.plotfrafromspikes import run_fra
from helpers.plotpsthfromspikes import run_psth
from helpers.getspiketimes import get_spike_times
from scipy.signal import ellip, bilinear, zpk2ss, ss2zpk
# from helpers.ellipfunc import ellip_filter_design
from helpers.filterfuncs import filtfilthd
def print_hi(name):
# Use a breakpoint in the code line below to debug your script.
print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint.
def highpass_filter(file_path, file_name, tank, output_folder):
# Add paths and load the MATLAB .mat file
# file_path = 'D:/Data/F1815_Cruella/weekfebruary282022/F1815_Cruella/'
# file_name = 'Recording_Session_Date_01-Mar-2022_Time_15-56-19.mat'
mat_data = scipy.io.loadmat(file_path + file_name)
block = mat_data['recBlock']
#
# # Extract relevant data from the MATLAB .mat file
# tank = 'D:/Electrophysiological Data/F1815_Cruella/'
# block = 'Block1-10'
#concatenate tank and block
full_nd_path = tank + block[0]
try:
data = tdt.read_block(full_nd_path)
except:
print('error reading block')
print(full_nd_path)
return block
# params = data.streams['info']
fStim = 24414.0625*2
fs = 24414.0625
StartSamples = mat_data['StartSamples'].flatten()
sTimes = StartSamples / fStim # seconds
# Define epoch timings and filter parameters
sT = sTimes[:, np.newaxis] - 0.2
sT = np.hstack((sT, sT + 1)) # epoch so total duration is 1s
sT = (sT * fs).astype(int) # samples
f = np.where(sT[:, 0] > 0)[0] # check first index is not negative
sT = sT[f, :]
f = np.where(sT[:, 1] < data.streams['BB_2'].data.shape[1])[0] # check last index is not larger than data
sT = sT[f, :]
sTseconds = sT / fs
# Define filter
f_order = 10
filterWindow = [500, 3000] # Filter cutoff frequencies
freq_range = np.array([300, 5000]) / (fs / 2)
# b, a = ellip(6, 0.1, 40, [300, 5000] / (fs / 2))
Wp = np.array([300, 5000]) / (fs / 2)
# Design the bandpass filter using ellip
b, a = ellip(6, 0.1, 40, Wp, btype='band')
streams = ['BB_2', 'BB_3', 'BB_4', 'BB_5']
for i2 in range(4):
traces = []
for ss in range(sT.shape[0]-1):
# Epoch and filter
dat = data.streams[streams[i2]].data[:, sT[ss, 0]:sT[ss, 1]]
traces_ss = [scipy.signal.filtfilt(b, a, dat[cc, :]) for cc in range(16)]
traces.append(np.vstack(traces_ss))
#save as matlab file as well
# sio.savemat(output_folder + f'HPf{block[0]}{i2 + 1}.mat', {'traces': traces})
#remove all inhomogeneous data
drop_list = []
for ii in range(len(traces)):
#get dimensions
dims = traces[ii].shape[1]
#if not the same shape as the first one, remove it
if dims != traces[0].shape[1]:
drop_list.append(ii)
traces = np.delete(traces, drop_list, axis=0)
fname = f'HPf{block[0]}{i2 + 1}.h5'
with h5py.File(output_folder + fname, 'w') as hf:
hf.create_dataset('traces', data=traces, compression='gzip', compression_opts=9)
return block
def clean_data_pipeline(output_folder, block, side = 'right'):
fname = f'{output_folder}HPf{block[0]}1.h5'
fname2 = f'{output_folder}HPf{block[0]}2.h5'
if side == 'right':
fname = f'{output_folder}HPf{block[0]}1.h5'
fname2 = f'{output_folder}HPf{block[0]}2.h5'
elif side == 'left':
fname = f'{output_folder}HPf{block[0]}3.h5'
fname2 = f'{output_folder}HPf{block[0]}4.h5'
# Access the traces from the loaded data
try:
h = h5py.File(fname2, 'r')
hh = h5py.File(fname, 'r')
traces_h = h['traces']
traces_hh = hh['traces']
except:
print('error reading file')
print(fname)
return block
cleaned_data = []
print('Cleaning data...')
try:
for ii in range(len(traces_hh)):
print('at trial ' + str(ii))
to_clean = np.vstack((traces_h[ii], traces_hh[ii]))
cleaned_data.append(clean_data(0, to_clean))
except:
print('error cleaning data, probably shitty data')
print(fname)
return block
fs = 24414.065
nChan = 32
spikes = []
# spikes = np.empty((nChan, len(traces_hh)))
#spikes needs to be a nChan by nTrials cell array
# spikes= np.empty((nChan, len(traces_hh)))
for cc in range(nChan):
spikes_in_chan = []
for ss in range(len(traces_hh)):
# test = cleaned_data[ss]
# test2=test[0][:,cc]
# print('getting spike times for trial ' + str(ss) + ' channel ' + str(cc) )
#get the spike times -0.1s to 0.1s around the stim
#assuming each trial is 1s long, so 0.2s around the stim
start = int((0.1)*fs) # 0.1s before stim
end = int((0.8*fs)) # 0.1s after stim
t, wv = get_spike_times(cleaned_data[ss][0][cc,start:end])
# t, wv = get_spike_times(cleaned_data[ss][0][cc,:])
spikes_in_chan.append(t)
# spikes[cc,:] = spikes_in_chan
spikes.append(spikes_in_chan)
# Save the spikes data in the same format as MATLAB
spikes_data = np.array(spikes, dtype=object)
#check spikes is not empty
if spikes_data.size == 0:
print('No spikes found!')
print('saving spikes' + block[0] + side + '.h5')
#save as pickle file
with open(output_folder + 'spikes' + block[0] + side + '.pkl', 'wb') as f:
pickle.dump(spikes_data, f)
# #save to output folder
# with h5py.File(output_folder + 'spikes' + block[0] + side + '.h5', 'w') as f:
# dset = f.create_dataset('spikes', data=spikes_data, dtype=h5py.special_dtype(vlen=np.dtype('float64')))
# with h5py.File('spikes'+block[0]+side+'.h5', 'w') as f:
# dset = f.create_dataset('spikes', data=spikes, dtype=h5py.special_dtype(vlen=np.dtype('float64')))
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
# file_name = 'Recording_Session_Date_25-Jan-2023_Time_12-26-44.mat'
tank = 'E:\Electrophysiological_Data\F1815_Cruella\FRAS//'
output_folder = 'E:\Electrophysiological_Data\F1815_Cruella\FRAS\output_filtered//before13072022//'
file_path = 'D:\Data\F1815_Cruella\FRAS/before13072022/'
#get a lsit of all the files in the directory
import os
files = os.listdir(file_path)
#exclude all files that don't end with .mat
files = [file for file in files if file.endswith('.mat')]
# files = files[9:13]
#only the right side good for zola
for file in files:
print(file)
# mat_data = scipy.io.loadmat(file_path + file)
# block = mat_data['recBlock']
# # #
# block = highpass_filter(file_path, file, tank, output_folder)
# #
# # # print(block)
# clean_data_pipeline(output_folder, block, side = 'left')
# clean_data_pipeline(output_folder, block, side = 'right')
run_fra('right', file_path, file, output_folder, animal='F1815')
run_fra('left', file_path, file, output_folder, animal='F1815')
# run_psth('left', file_path, file, output_folder, animal='F1815')
# run_psth('right', file_path, file, output_folder, animal='F1815')
# run_fra('left', file_path, file, output_folder)
# clean_data_pipeline(output_folder, block, side = 'left')
# See PyCharm help at https://www.jetbrains.com/help/pycharm/