Simpson's Rule Integration Negative Area

Question

I am having a problem when using simpson's rule from scipy.integrate library. The Area calculated sometimes is negative even if all the numbers are positive and the values on the x-axis are increasing from left to right. For example:

from scipy.integrate import simps

x = [0.0, 99.0, 100.0, 299.0, 400.0, 600.0, 1700.0, 3299.0, 3300.0, 3399.0, 3400.0, 3599.0, 3699.0, 3900.0,
    4000.0, 4300.0, 4400.0, 4900.0, 5000.0, 5100.0, 5300.0, 5500.0, 5700.0, 5900.0, 6100.0, 6300.0, 6600.0,
    6900.0, 7200.0, 7600.0, 7799.0, 8000.0, 8400.0, 8900.0, 9400.0, 10000.0, 10600.0, 11300.0, 11699.0,
    11700.0, 11799.0]

y = [3399.68, 3399.68, 3309.76, 3309.76, 3274.95, 3234.34, 3203.88, 3203.88, 3843.5,
     3843.5,  4893.57, 4893.57, 4893.57, 4847.16, 4764.49, 4867.46, 4921.13, 4886.32,
     4761.59, 4731.13, 4689.07, 4649.91, 4610.75, 4578.84, 4545.48, 4515.02, 4475.86,
     4438.15, 4403.34, 4364.18, 4364.18, 4327.92, 4291.66, 4258.31, 4226.4,  4188.69,
     4152.43, 4120.52, 4120.52, 3747.77, 3747.77]

area = simps(y,x)

The result returned by simps(y,x) is -226271544.06562585. Why is it negative? This happens only in some cases while in other cases it works fine. For example:

x = [0.0, 100.0, 101.0, 200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 1300.0, 3300.0, 3400.0, 3600.0, 3700.0,
    5100.0, 5200.0, 5400.0, 5600.0, 5800.0, 6000.0, 6200.0, 6400.0, 6600.0, 6900.0, 7200.0, 7500.0, 7900.0,
    8299.0, 8400.0, 8900.0, 9400.0, 10000.0, 10600.0, 11200.0, 11900.0, 12600.0, 13500.0, 14300.0, 15300.0,
    16400.0, 16499.0, 17500.0, 18900.0, 20100.0, 20999.0, 21000.0, 21099.0]

y = [2813.73, 2813.73, 3200.98, 3309.76, 3356.17, 3296.71, 3243.04, 3243.04, 3198.08, 3161.82, 3488.16,
     4929.83, 4897.92, 4897.92, 4763.04, 4726.78, 4680.37, 4638.31, 4597.69, 4561.44, 4525.18, 4494.72,
     4464.26, 4426.55, 4388.84, 4354.03, 4316.32, 4316.32, 4275.71, 4239.45, 4203.19, 4171.28, 4136.47,
     4104.57, 4074.11, 4042.2, 4011.74, 3979.83, 3949.38, 3918.92, 3918.92, 3887.01, 3855.1, 3824.64,
     3824.64,3605.64, 3605.64]

area = simps(y,x)

The area in this case is positive 83849670.99112588.

What is the reason of this?

Answer 1

The problem is how simpson works, it makes an estimate of the best possible quadratic function, with some data like yours, in which there is an almost vertical zone, the operation is wrong.

import numpy as np
from scipy.integrate import simps, trapz
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

def func(x, a, b, c):
    return a + b * x + c * x ** 2

x = np.array([0.0, 99.0, 100.0, 299.0, 400.0, 600.0, 1700.0, 3299.0, 3300.0, 3399.0, 3400.0, 3599.0, 3699.0, 3900.0,
    4000.0, 4300.0, 4400.0, 4900.0, 5000.0, 5100.0, 5300.0, 5500.0, 5700.0, 5900.0, 6100.0, 6300.0, 6600.0,
    6900.0, 7200.0, 7600.0, 7799.0, 8000.0, 8400.0, 8900.0, 9400.0, 10000.0, 10600.0, 11300.0, 11699.0,
    11700.0, 11799.0])

y = np.array([3399.68, 3399.68, 3309.76, 3309.76, 3274.95, 3234.34, 3203.88, 3203.88, 3843.5,
     3843.5,  4893.57, 4893.57, 4893.57, 4847.16, 4764.49, 4867.46, 4921.13, 4886.32,
     4761.59, 4731.13, 4689.07, 4649.91, 4610.75, 4578.84, 4545.48, 4515.02, 4475.86,
     4438.15, 4403.34, 4364.18, 4364.18, 4327.92, 4291.66, 4258.31, 4226.4,  4188.69,
     4152.43, 4120.52, 4120.52, 3747.77, 3747.77])

for i in range(3,len(x)):
    popt, _ = curve_fit(func, x[i-3:i], y[i-3:i])
    xnew = np.linspace(x[i-3], x[i-1], 100)
    plt.plot(xnew, func(xnew, *popt), 'k-')

plt.plot(x, y)
plt.show()

Answer 2

Your samples have a very strong variation and x are not equally spaced. Could it be something like Runge's phenomenon? trapz would be more accurate ?