Supplemental material, Pacheco et al., “Joint Profile Characteristics of Long-Latency Transient Evoked and Distortion Otoacoustic Emissions,” AJA, https://doi.org/10.1044/2022_AJA-21-00182

Supplemental Material S2. Python script used in the current study to fit each gain function with the model equation.

import numpy as np
from scipy.optimize import curve_fit
import xlrd

# Main fit function #
def func(A, G0, Act, alpha):
    return (G0/((1 + (A/Act))**alpha))

def p_to_dB(p):
    return 20*np.log10(p/20)

def dB_to_p(I):
    return 20*(10**(I/20))

def main(n):
    
    path = 'file_location'
 
    data = xlrd.open_workbook(path+'gain_function_data.xlsx')
    sheet = data.sheet_by_index(3)
    x, y = [], []
    for i in range(16,26):
        if type(sheet.cell_value(i,n))==float:
            x.append(sheet.cell_value(i,0))
            y.append(sheet.cell_value(i,n))
            
    popt, pcov = curve_fit(func, x, y, bounds=((dB_to_p(-50), dB_to_p(20), 0),(dB_to_p(-10), dB_to_p(65), 5)))
       
    y1 = func(x, *popt)
       
    # R^2 value for the curve fitting
    residuals = y - y1
    ss_res = np.sum(residuals**2)
    ss_tot = np.sum((y - np.mean(y))**2)
    r_squared = 1 - (ss_res / ss_tot)
    
    print("R_squared value is: "+str(r_squared))
    
    Peak_str = p_to_dB(popt[0]) # Peak strength in dB
    Comp_thresh = p_to_dB(popt[1]) # Compression threshold in dB
    
#    print("Peak strength, G0 = "+str(Peak_str)+" dB")
#    print("Compression slope, alpha = "+str(popt[2]))
#    print("Compression Threshold, Act = "+str(Comp_thresh)+" dB")
    
    F = open(path+"\\out.txt", 'a')
    F.write("\n"+str(Peak_str)+"   "+str(Comp_thresh)+"   "+str(popt[2])+"   "+str(r_squared))
    F.close()
    

if __name__ == "__main__":
    path = "file_location"
    file = open(path+"\\out.txt", 'w')
    file.write("Peak_strength   Compression_Threshold   alpha   R_squared")
    file.close()
    
    for i in range(16,21):
        main(i+1)