What is the best way to create a Sympy equation, do something like take the derivative, and then plot the results of that equation?
I have my symbolic equation, but can't figure out how to make an array of values for plotting. Here's my code:
from sympy import symbols
import matplotlib.pyplot as mpl
t = symbols('t')
x = 0.05*t + 0.2/((t - 5)**2 + 2)
nums = []
for i in range(1000):
nums.append(t)
t += 0.02
plotted = [x for t in nums]
mpl.plot(plotted)
mpl.ylabel("Speed")
mpl.show()
In my case I just calculated the derivative of that equation, and now I want to plot the speed x
, so this is fairly simplified.
You can use numpy.linspace()
to create the values of the x axis ( x_vals
in the code below) and lambdify()
.
from sympy import symbols
from numpy import linspace
from sympy import lambdify
import matplotlib.pyplot as mpl
t = symbols('t')
x = 0.05*t + 0.2/((t - 5)**2 + 2)
lam_x = lambdify(t, x, modules=['numpy'])
x_vals = linspace(0, 10, 100)
y_vals = lam_x(x_vals)
mpl.plot(x_vals, y_vals)
mpl.ylabel("Speed")
mpl.show()
(improvements suggested by asmeurer and MaxNoe )
Alternatively, you can use sympy's plot()
:
from sympy import symbols
from sympy import plot
t = symbols('t')
x = 0.05*t + 0.2/((t - 5)**2 + 2)
plot(x, (t, 0, 10), ylabel='Speed')
You can use directly the plotting functions of SymPy :
from sympy import symbols
from sympy.plotting import plot as symplot
t = symbols('t')
x = 0.05*t + 0.2/((t - 5)**2 + 2)
symplot(x)
Most of the time it uses matplotlib as a backend.
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