downsampled.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May  9 16:32:55 2022

@author: genzel
"""
import os, sys, json
import numpy as np
import pandas as pd
import mdaio 
from scipy import signal
from scipy.signal import butter, lfilter
from timeit import default_timer as timer
from scipy.signal import butter, sosfilt, sosfreqz

def butter_bandpass(lowcut, highcut, fs, order=5):
        nyq = 0.5 * fs
        low = lowcut / nyq
        high = highcut / nyq
        sos = butter(order, [low, high], analog=False, btype='band', output='sos')
        return sos

def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
        sos = butter_bandpass(lowcut, highcut, fs, order=order)
        y = sosfilt(sos, data)
        return y
    
directory = input("Enter the directory: ")
print(directory)
#directory = '/media/genzel/data/spikesorting/Rat/rat2/Rat_Hm_Ephys_Rat2_389237_20200915_postsleep.mountainsort/'
import pathlib



def main():
    
    for filename in os.listdir(directory):
        f = os.path.join(directory, filename)
        # checking if it is a file
        extension = f[-4:]
        check = f[-14:]
        if os.path.isfile(f):
            print(f)
            start = timer()
            if extension == '.mda' and check != 'timestamps.mda':
                
                studyday = filename[25:33]
                postsleep = mdaio.readmda(f)
                postsleep = postsleep[:,:108000000*4]
                postsleep = np.transpose(postsleep).astype(int)
                recording = pd.DataFrame()
                for j in range(4):
                    fe = 30000
                    f_nyq = fe/2
                    fc1 = 1
                    fc2 = 300
                    recording1 = butter_bandpass_filter(postsleep[:, j], fc1, fc2, fe, order=6)
                    q=10
                    down1 = signal.decimate(recording1,q)
                    q=5
                    down1 = signal.decimate(down1,q)
                    ch1 = pd.DataFrame(down1)
                    recording = pd.concat([recording,ch1], axis=1)
                recording.columns = ['wavech1','wavech2','wavech3','wavech4']
                recording = recording.to_numpy()    
                recording = recording.transpose()
                            
                if not os.path.exists(directory+'/StudyDay'+str(studyday)+'/'):
                    os.makedirs(directory+'/StudyDay'+str(studyday)+'/')
                mdaio.writemda16i(recording,directory+'/StudyDay'+str(studyday)+'/'+filename);
            end = timer()  
            print(f'elapsed time: {end - start}')


    
if __name__ == '__main__':
    main()