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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
The trapezoidal rule generates a numerical approximation to the 1d integral:
$$ I(a,b) = \int_a^b f(x) dx $$by dividing the interval $[a,b]$ into $N$ subdivisions of length $h$:
$$ h = (b-a)/N $$Note that this means the function will be evaluated at $N+1$ points on $[a,b]$. The main idea of the trapezoidal rule is that the function is approximated by a straight line between each of these points.
Write a function trapz(f, a, b, N) that performs trapezoidal rule on the function f over the interval $[a,b]$ with N subdivisions (N+1 points).
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def trapz(f, a, b, N):
"""Integrate the function f(x) over the range [a,b] with N points."""
h = (b-a)/N
k = np.arange(1,N)
I = h * (0.5*f(a)+0.5*f(b)+np.sum(f(a+k*h)))
return I
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f = lambda x: x**2
g = lambda x: np.sin(x)
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I = trapz(f, 0, 1, 1000)
assert np.allclose(I, 0.33333349999999995)
J = trapz(g, 0, np.pi, 1000)
assert np.allclose(J, 1.9999983550656628)
Now use scipy.integrate.quad to integrate the f and g functions and see how the result compares with your trapz function. Print the results and errors.
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If, errf = integrate.quad(f,0,1)
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print("Integral:",If)
print("Error:",errf)
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Ig, errg = integrate.quad(g,0,np.pi)
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print("Integral:",Ig)
print("Error:",errg)
Results are closer to the actual values using the scipy.integrate.quad function rather than the trapz function
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assert True # leave this cell to grade the previous one