CODEWITHSUNDEEP
pythonnumpymatplotlibplotsympy
Reyjhonny
Reyjhonny

Reputation: 43

How to convert sympy function to plot with matplotlib?

I have certain function, for example sin(b*x), with sympy I get derivative and antiderivative expressions, but I need to plot these 3 functions in matplotlib. My problem is I can't convert correctly functions to numpy in order to plot in matplotlib. I have followed the documentation in sympy page with lambify function but it doesn't work. http://docs.sympy.org/latest/modules/utilities/lambdify.html

I have this code:

from sympy import Symbol, diff, integrate, sin, cos, Function
from sympy.utilities.lambdify import lambdify, implemented_function
from sympy.abc import x

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons

def signal(b,x):
    return sin(b*x)

def derivative(b,x):
    yprime = diff(signal(b,x), x)   
    return yprime

def antiderivative(b,x):
    anti = integrate(signal(b,x), x)
    return anti

b = 5

evalfunc = lambdify((b,x), signal(b,x), modules=['numpy'])
evalderiv = lambdify((b,x), derivative(b,x), modules=['numpy'])
evalantideriv = lambdify((b,x), antiderivative(b,x), modules=['numpy'])

axis_color = 'lightgoldenrodyellow'
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
fig.subplots_adjust(left=0.25, bottom=0.25)
t = np.arange(-10, 10, 0.001)

[line] = ax.plot(t, evalfunc(b,t), linewidth=2, color='red')
[line2] = ax.plot(t, evalderiv(b,t), linewidth=2, color='blue')
[line3] = ax.plot(t, evalantideriv(b,t), linewidth=2, color='blue')
ax.set_xlim([-10, 10])
ax.set_ylim([-5, 5])

ax.grid()
plt.show()

It fails in ax.plot ValueError: sequence too large; cannot be greater than 32

Upvotes: 1

Views: 2287

Answers (1)

laolux
laolux

Reputation: 1565

Your code is not quite a minimal working example, but it requires only minimal changes to work.

You need to declare your b as real symbol before the derivation. You set it as b=5 before the numerical evaluation.

See:

from sympy import Symbol, diff, integrate, sin, cos, Function
from sympy.utilities.lambdify import lambdify, implemented_function
from sympy.abc import x

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons

def signal(b,x):
    return sin(b*x)

def derivative(b,x):
    yprime = diff(signal(b,x), x)   
    return yprime

def antiderivative(b,x):
    anti = integrate(signal(b,x), x)
    return anti

b = Symbol('b', real=True)

evalfunc = lambdify((b,x), signal(b,x), modules=['numpy'])
evalderiv = lambdify((b,x), derivative(b,x), modules=['numpy'])
evalantideriv = lambdify((b,x), antiderivative(b,x), modules=['numpy'])

axis_color = 'lightgoldenrodyellow'
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
fig.subplots_adjust(left=0.25, bottom=0.25)
t = np.arange(-10, 10, 0.001)

b = 5

[line] = ax.plot(t, evalfunc(b,t), linewidth=2, color='red')
[line2] = ax.plot(t, evalderiv(b,t), linewidth=2, color='blue')
[line3] = ax.plot(t, evalantideriv(b,t), linewidth=2, color='blue')
ax.set_xlim([-10, 10])
ax.set_ylim([-5, 5])

ax.grid()
plt.show()

Upvotes: 2

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