plotting exponential and polynomial function together in python

Question

I want to plot the following y and y' functions together on the same axes in python. The x-axis values should go from -10 to 10 in 0.1 intervals:

what I tried: I tried plotting just y' (labelled y_p) and I am getting errors.

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(-10,10,0.1)

A=1
B=0.1
C=0.1
y_p = (A*np.exp((-x**2)/2))(1+(B*((2*(np.sqrt(2))*(x**3))-(3*(np.sqrt(2))*x))/((np.sqrt(6)))))
plt.plot(x,y_p)

but this generates error:

TypeError                                 Traceback (most recent call last)
<ipython-input-71-c184f15d17c7> in <module>
      7 B=0.1
      8 C=0.1
----> 9 y_p = (A*np.exp((-x**2)/2))(1+(B*((2*(np.sqrt(2))*(x**3))-(3*(np.sqrt(2))*x))/((np.sqrt(6)))))
     10 plt.plot(x,y_p)

TypeError: 'numpy.ndarray' object is not callable

I am sure there is a better way to do this. I'm quite new to python, so any help is truly appreciated!

^ applies a binary operation. To use the power function, the synthax is **. Example x²: x**2 — Julien Sorin
– Julien Sorin, Commented Sep 21, 2021 at 8:45
thanks @Julien..i have corrected that bit now but still getting error in calling the y_p function..please check my edit in my post — Jerome
– Jerome, Commented Sep 21, 2021 at 8:53
you have a missing * sign in y_p, right before (1+B*...) — Hadar
– Hadar, Commented Sep 21, 2021 at 8:58

Ivan · Accepted Answer · 2021-09-21 08:59:11Z

2

The appropriate approach would be to take it a step at a time:

You could start by defining y1 as a lambda:

y1 = lambda x: (2*np.sqrt(2)*x**3 - 3*np.sqrt(2)*x)/np.sqrt(6)

Then implement your formula:

y_p = A * np.exp(-x**2/2)*(1 + B*y1(x))

This way you have less chances of getting a typo.

answered Sep 21, 2021 at 8:59

Ivan

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1 Comment

Julien Sorin Over a year ago

Prettier answer than mine

Julien Sorin · Accepted Answer · 2021-09-21 08:56:28Z

1

You cannot multiply implicitly (without using *) in python.

Here is your code after the correction:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(-10, 10, 0.1)

A = 1
B = 0.1
C = 0.1
y_p = (A * np.exp((-x ** 2) / 2)) * (1 + (B * ((2 * (np.sqrt(2)) * (x ** 3)) - (3 * (np.sqrt(2)) * x)) / (np.sqrt(6))))
plt.plot(x, y_p)
plt.show()

answered Sep 21, 2021 at 8:56

Julien Sorin

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