# -*- coding: utf-8 -*-
"""
Created on Sun Dec 26 13:12:14 2021
@author: Loïc
"""
#%% Packages importations
import csv
import os
import pandas as pd
import numpy as np
from scipy.signal import butter, filtfilt
#%% Functions process
def import_csv(csv_name, folder, separator, useless=True):
"""
Parameters
----------
csv_name : STRING
Name of the csv file that needs to be imported
folder : STRING, optional
Path to the folder containing csv file.
The default is data_folder (see Parameters section).
separator : STRING
Separator for folders.
useless : BOOLEAN, optional
Does the file contains useless infos after the table ?
If True, remove those infos
Returns
-------
data : LIST
List containing all rows of the csv file.
Exclusion of last lines if they don't end with .wav.
"""
data = []
# read data csv
with open(folder + separator + csv_name, newline="") as csv_file:
lines = csv.reader(csv_file, delimiter=',')
for row in lines:
data = data + [row]
csv_file.close()
# clean csv (last lines contain useless info)
n = 0
while useless:
# in case wrong use of the function
if n == len(data):
useless = False
n = 0
raise Exception("First column contains no audio file name")
# exit the loop when audio is found
if data[-n][0].endswith(".wav"):
useless = False
# search audio in next line
else:
n += 1
data = data[0:-n]
return data
def get_csv(csv_folder, slash="\\"):
"""
Parameters
----------
csv_folder : STRING
Path to a folder containing ONLY csv file.
slash : STRING
Separator for folders.
Returns
-------
data_20_21 : DATAFRAME
Contains the inforamtion inside the .CSVs.
sorted_names : LIST
Names corresponding to audio files in the first column of the .CSVs.
"""
csv_names = [a for a in os.listdir(csv_folder) if a.endswith('.csv')]
# import data
data = import_csv(csv_names[0], csv_folder, separator=slash)
for i in range(1,len(csv_names)):
data = data + import_csv(csv_names[i], csv_folder, separator=slash)[1:]
data_frame = pd.DataFrame(data=data[:][1:], columns=data[:][0])
# change dtype for selected columns
for i in range(3,17):
data_frame.iloc[:,i] = data_frame.iloc[:,i].astype(int)
# fetch audio files names
audio_names = np.copy([])
for filename in data_frame["Fichier Audio"]:
if filename.endswith(".wav"):
audio_names = np.append(audio_names, filename.replace('/', slash))
# sort audio files names
# We want to sort it by year
years = np.unique([path[4:8] for path in audio_names])
sorted_names = np.array([], dtype='<U49')
# and inside each year, sort it by date.
for year in years:
idx_to_sort = np.where(np.array([path[4:8] for path in audio_names]) == year)[0]
temp_path = np.copy(audio_names[idx_to_sort])
temp_path.sort()
sorted_names = np.append(temp_path, sorted_names)
return data_frame, sorted_names
def butter_pass_filter(data, cutoff, fs, order=1, mode='high'):
"""
Parameters
----------
data : NUMPY ARRAY
Audio signal (1D array)
cutoff : INT or FLOAT
Frequency limit for the filter.
fs : INT
Sample rate of the audio given as 'data'
order : INT, optional
Order of the highpass filter. The default is 1.
mode : STRING, optional
Mode of the filter (higpass or low pass). Must be 'high' or 'low'
Returns
-------
y : NUMPY ARRAY
Filtered signal.
"""
normal_cutoff = cutoff / (fs/2)
# Get the filter coefficients
b, a = butter(order, normal_cutoff, btype=mode, analog=False)
y = filtfilt(b, a, data)
return y
def TeagerKaiser_operator(audio):
"""
Parameters
----------
audio : NUMPY ARRAY
Audio signal (1D array).
Returns
-------
tk_signal : NUMPY ARRAY
Signal energy computed with teager kaiser operator (1D array).
"""
# Define Teager-Kaiser operator
tk_signal = np.empty(audio.shape)
# Formula : TK = x(t)**2 - x(t-1)*x(t+1)
tk_signal[1:(audio.shape[0]-1)] = (audio[1:(audio.shape[0]-1)]**2) - \
(audio[0:(audio.shape[0]-2)]*audio[2:(audio.shape[0])])
tk_signal[0] = 0 # set first and last value to 0 (neutral)
tk_signal[-1] = 0
return tk_signal
#%% Functions plots
def get_category(samples, names, data_frame, category='acoustic'):
"""
Parameters
----------
samples : LIST OF STRINGS.
Origin of each click,
should be like ['SCW1807_20200711_083600_extract0.npy']).
names : LIST OF STRINGS
Names of each recording,
(should be like ['11072020/SCW1807_20200711_082400.wav']).
data_frame : PANDAS DATAFRAME
Dataframe containing all csv infos.
category : TYPE, optional
Category to extratc from dataframe. The default is 'acoustic'.
Should be 'acoustic', 'fishing_net', 'behavior', 'beacon', 'date',
'number', 'net' or 'none'
Returns
-------
TYPE
DESCRIPTION.
"""
use_samples = np.array([file[:23] for file in samples])
use_names = np.array([name.split('/')[-1][-27:-4] for name in names])
cat_list = np.zeros(use_samples.shape, dtype="object")
if category == 'acoustic':
start, stop = 3, 10
elif category == 'fishing_net':
start, stop = 10, 13
elif category == 'behavior':
start, stop = 13, 16
elif category == 'none':
return np.array(['none']*len(use_samples))
for i, file in enumerate(use_samples):
idx_ind = np.where(file == use_names)[0][0]
if category == 'beacon':
cat_list[i] = data_frame.iloc[idx_ind, 17]
elif category == 'date':
cat_list[i] = data_frame.iloc[idx_ind, 1]
elif category == 'number':
cat_list[i] = data_frame.iloc[idx_ind, 18]
elif category == 'net':
cat_list[i] = data_frame.iloc[idx_ind,19]
else:
column = np.argmax(data_frame.iloc[idx_ind, start:stop])+start
cat_list[i] = data_frame.columns[column]
if category == 'beacon':
NC = np.where(get_category(samples, names, data_frame, category='acoustic')
== 'AV')[0]
cat_list[NC] = 'NC'
return cat_list