Fitting a logistic curve

logistic.py

from scipy.optimize import curve_fit

def logistic(x, x0, L, k):
    return L / (1.0 + np.exp(-k*(x - x0)))

def resample(X, y) -> np.array:
    X = np.array(X)
    y = np.array(y)
    bounds = (0, [1.0, np.max(y), 1000])
    popt, pcov = curve_fit(logistic, X, y, bounds=bounds)
    return [logistic(x=x, x0=popt[0], L=popt[1], k=popt[2]) for x in X]