Thanks for sharing. One thing I would suggest, to avoid mixing SymPy and
NumPy functions, is to only import sympy and numpy as
import sympy as sym
import numpy as np
and then use sym.cos or np.cos. Otherwise, one cell has SymPy cos and
another has NumPy cos, and it can be very confusing, since you cannot use
SymPy functions on NumPy arrays or NumPy functions on SymPy expressions.
You can also use sympy.lambdify to convert a SymPy expression into a NumPy
function for use with matplotlib, like
x = sympy.Symbol('x')
expr = sympy.cos(x) + 1
f = lambdify(x, expr)
t = np.linspace(-10, 10, 100)
plt.plot(t, f(t))
That way you don't have to rewrite the expression if you already have it in
SymPy.
Aaron Meurer
On Thu, May 4, 2017 at 1:46 AM, chu-ching huang <[email protected]>
wrote:
> As title, sympy package had been introduced in these lectures, especially
> for Multiple-variable functions. If interested, welcome to visit the site:
>
> https://github.com/cchuang2009/2016-1/tree/master/calculus
>
> cch
>
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