Problem with Numpy differentiating and Scipy integration in Python

Question

I have problem with integration an differentiation. I've tried to plot the differentitaion of x^2

plt.plot(0.5*(x[:-1]+x[1:]), np.diff(x**2, 1)/np.diff(x, 1))

And it worked. After I wanted to find the integral of x^2 times differentiation of x^2:

x = np.linspace(0, 1, 1000)     
result = spi.simps(x**2 * np.diff(x**2/np.diff(x,1)), x)
print(result)

But i got a ValueError: operands could not be broadcast together with shapes (1000,) (999,)

And I've also tried instead of x = np.linspace(0, 1, 1000) use x = 0.5*(x[:-1]+x[1:]), but no chance.

Any help would be appreciated.

cutelittlegnome · Accepted Answer · 2018-12-19 18:50:33Z

2

x has a size of 1000 samples, the diff version only has 999 due to boundary differences.

You are actually aware of that fact when you do 0.5*(x[:-1]+x[1:]). Same applies when you do x**2 * np.diff(x**2/np.diff(x,1)). Perhaps what you want to do is:

x_half = 0.5*(x[:-1]+x[1:])
result = spi.simps(x_half **2 * np.diff(x**2), x_half )

edited Dec 19, 2018 at 18:50

cutelittlegnome

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answered Dec 19, 2018 at 11:04

Matthieu Brucher

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2 Comments

cutelittlegnome Over a year ago

I've tried it , and it gave me the same error: ValueError: operands could not be broadcast together with shapes (1000,) (999,)

Matthieu Brucher Over a year ago

Oh yes, last argument should probably also be x_half.

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