Using Sympy for Analytical Maths

Saturday, August 25th 2018

This is an example of how i would use sympy to evaluate a set of analytical questions, for example:

Find a[0,12,24,30,99]\forall a \in [0,12,24,30,99] and b=100b=100:

abαx3sin(2x)βexdx\int_a^b \frac{\alpha x^{3} - \sin(2x)}{\sqrt{\beta e^x}} dx

Where α=2.341\alpha = 2.341, β=ex\beta = e^x

First we need to import the library (and start the printing for nice easy to read stuff):

import sympy as sp

Now create the eqn above so we can evaluate it

a, b, alpha, beta, x = sp.symbols('a b \\alpha \\beta x')
expr = (alpha*x**3 - sp.sin(2*x)) * (1/sp.sqrt(beta*sp.E**x))

1βex(αx3sin(2x))\frac{1}{\sqrt{\beta e^{x}}} \left(\alpha x^{3} - \sin{\left (2 x \right )}\right)

This like the integrand we hav above. Now theres a few things we can do, if we know α\alpha and β\beta, which we do, we can sub them in. then we will have a more simple expression:

expr2 = expr.subs({alpha: 2.341, beta:sp.E**x})

1e2x(2.341x3sin(2x))\frac{1}{\sqrt{e^{2 x}}} \left(2.341 x^{3} - \sin{\left (2 x \right )}\right)

If we wanted we could now evaluate this using the .subs() command again (this time with a value for x such as .subs(x,3)), but we're asked to evalue the integral:

integral = sp.integrate(expr2, (x,a,b))

2.341a3e2a+7.023a2e2a+14.046ae2a2.341b3e2b7.023b2e2b14.046be2b+0.2e2bsin(2b)+0.4e2bcos(2b)14.046e2b0.2e2asin(2a)0.4e2acos(2a)+14.046e2a\frac{2.341 a^{3}}{\sqrt{e^{2 a}}} + \frac{7.023 a^{2}}{\sqrt{e^{2 a}}} + \frac{14.046 a}{\sqrt{e^{2 a}}} - \frac{2.341 b^{3}}{\sqrt{e^{2 b}}} - \frac{7.023 b^{2}}{\sqrt{e^{2 b}}} - \frac{14.046 b}{\sqrt{e^{2 b}}} + \frac{0.2}{\sqrt{e^{2 b}}} \sin{\left (2 b \right )} + \frac{0.4}{\sqrt{e^{2 b}}} \cos{\left (2 b \right )} - \frac{14.046}{\sqrt{e^{2 b}}} - \frac{0.2}{\sqrt{e^{2 a}}} \sin{\left (2 a \right )} - \frac{0.4}{\sqrt{e^{2 a}}} \cos{\left (2 a \right )} + \frac{14.046}{\sqrt{e^{2 a}}}

Now we need to calculate all the values of this expression for every combination of a[0,12,24,30,99]a \in [0,12,24,30,99] and b=100b=100. We can do this by starting with lambdifying the expression into a callable function:

my_callable = sp.lambdify((a, b), integral)
my_callable(1,2) # this will sub in a=1 and b=2 to the above.


Now we just loop over the varable s for a and we can print out the values:

for a in [0,12,24,30,99]:
    print("a =",a)
a = 0

a = 12

a = 24

a = 30

a = 99

This decreasing sequence is expected as were taking essentially taking area slices of the below plot, moving further left each time as a starting point.

%matplotlib inline
import matplotlib.pyplot as plt
X = [x for x in range(0,101)]
expr2_callable = sp.lambdify(x, expr2)
plt.plot(X,[expr2_callable(x) for in_x in X])
[<matplotlib.lines.Line2D at 0x24a166e8668>]


And we're done!