lorenzoriano

import numpy as np
from math import pi, log
import pylab
from scipy import fft, ifft
from scipy.optimize import curve_fit

i = 10000
x = np.linspace(0, 3.5 * pi, i)
y = (0.3*np.sin(x) + np.sin(1.3 * x) + 0.9 * np.sin(4.2 * x) + 0.06 *
    np.random.randn(i))

lorenzoriano

template <typename T>
std::vector<T> linspace(T a, T b, size_t N) {
    T h = (b - a) / static_cast<T>(N-1);
    std::vector<T> xs(N);
    typename std::vector<T>::iterator x;
    T val;
    for (x = xs.begin(), val = a; x != xs.end(); ++x, val += h)
        *x = val;
    return xs;
}