import numpy as np
from scipy.optimize import curve_fit


# Define the function to fit
def func(x, a, b, c):
    return a * np.exp(-b * x) + c


# Load the data
data = np.array(
    [
        [0, 0],
        [20, 0],
        [40, 1],
        [60, 2],
        [80, 3],
        [100, 5],
        [120, 6],
        [140, 7],
        [160, 9],
        [180, 11],
        [200, 13],
        [220, 15],
        [240, 17],
        [260, 19],
        [280, 21],
        [300, 23],
        [320, 26],
        [340, 28],
        [360, 31],
        [380, 34],
        [400, 36],
        [420, 39],
        [440, 42],
        [460, 45],
        [480, 48],
        [500, 52],
        [520, 55],
        [540, 58],
        [560, 62],
        [580, 65],
        [600, 69],
        [620, 72],
        [640, 76],
        [660, 80],
        [680, 84],
        [700, 88],
        [720, 92],
        [740, 96],
        [760, 100],
        [780, 104],
        [800, 108],
        [820, 112],
        [840, 117],
        [860, 121],
        [880, 126],
        [900, 130],
        [920, 135],
        [940, 140],
        [960, 145],
        [980, 150],
        [1000, 154],
    ]
)

# Separate the data into x and y arrays
xdata = data[:, 0]
ydata = data[:, 1]

# Fit the curve to the data
popt, pcov = curve_fit(func, xdata, ydata)

# Print the fitted parameters
print(popt)