import pandas
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

data_raw = pandas.read_csv("proctatinium_data.csv")
data_raw = data_raw.astype(float)


def func(t, l):
    return 32 * np.exp(-1 * l * t)

times = data_raw.iloc[:,0]
count = data_raw.iloc[:,1]

x_data = np.array(times)
y_data = np.array(count)

trialX = np.linspace(x_data[0], x_data[-1], 1000)

data = []

for x in range(10000):
    y_noise = np.random.normal(0, 1, size = len(y_data))
    Y = y_data + y_noise
    popt, pcov = curve_fit(func, x_data, Y)
    data.append(popt[0])

plt.hist(data, bins = 100)
print (np.mean(data))