def test_4synthlines_with_mngaussians_atone():
x = np.linspace(5800, 5803, num=1000)
y1 = (gaussian(x, -0.8, 5801.1, 0.2) + gaussian(x, -0.4, 5802.1, 0.2)) + \
np.random.normal(0, 0.02, x.shape[0]) + 1.0
y2 = (gaussian(x, -0.3, 5801.7, 0.2) + gaussian(x, -0.2, 5802.8, 0.2)) + \
np.random.normal(0, 0.01, x.shape[0]) + 1.0
y = y1 * y2
m_params = [-0.6, 5801, 0.1, -0.3, 5802.3, 0.1]
n_params = [-0.4, 5801.4, 0.1, -0.2, 5802.8, 0.1]
mod, out, init = lmfit_mngauss(x, y, m_params, n_params)
#mod, out, init = lmfit_mngauss(x,y, m_params)
plt.plot(x, y, label="y")
plt.plot(x, init, 'k--', label="init fit")
print(out.fit_report(min_correl=0.5))
plt.plot(x, out.best_fit, 'r-', label="best fit")
ax = plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
plt.legend(loc=0)
plt.show()
def test_4synthlines_with_mngaussians():
x = np.linspace(5800, 5803, num=1000)
y = (gaussian(x, -0.8, 5801.1, 0.2) + gaussian(x, -0.6, 5802.1, 0.2)) * \
(gaussian(x, -0.3, 5801.7, 0.2) + gaussian(x, -0.2, 5802.8, 0.2)) + \
np.random.normal(0, 0.01, x.shape[0])
#params = [-0.9, 5801, 0.1, -0.4, 5802, 0.1,-0.4, 5801.5, 0.1, -0.1, 5803, 0.1]
m_params = [-0.9, 5801.05, 0.1, -0.7, 5802, 0.1]
n_params = [-0.4, 5801.6, 0.1, -0.1, 5803, 0.1]
mod, out, init = lmfit_mngauss(x,y, m_params, n_params)
#mod, out, init = lmfit_mngauss(x,y, m_params)
plt.plot(x, y)
plt.plot(x, init, 'k--')
print(out.fit_report(min_correl=0.5))
plt.plot(x, out.best_fit, 'r-', label="best fit")
plt.legend()
plt.show()
