A Calculus course for Informatics engineering class

[chu-ching huang]

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

[Aaron Meurer]

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

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