In [6]:

    

import sympy as sym
sym.init_printing()
x, y = sym.symbols('x y')
expr = 3*x**2 + sym.log(x**2 + y**2 + 1)
expr


    

    
Out[6]:





$$3 x^{2} + \log{\left (x^{2} + y^{2} + 1 \right )}$$

In [8]:

    

expr.subs({x: 17, y: 42}).evalf()
% timeit expr.subs({x: 17, y: 42}).evalf()


    

    




221 µs ± 948 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [10]:

    

import math
f = lambda x, y:  3*x**2 + math.log(x**2 + y**2 + 1)
%timeit f(17, 42)


    

    




917 ns ± 2.67 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [12]:

    

g = sym.lambdify([x, y], expr, modules=['math'])
g(17, 42)


    

    
Out[12]:





$$874.6275443904885$$

In [13]:

    

%timeit g(17, 42)


    

    




906 ns ± 1.64 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [18]:

    

import numpy as np
xarr = np.linspace(17,18,5)
h = sym.lambdify([x, y], expr)
out = h(xarr, 42)
out


    

    
Out[18]:





array([ 874.62754439,  900.31920442,  926.38590757,  952.82765322,
        979.64444076])

In [19]:

    

z = z1, z2, z3 = sym.symbols('z:3')


    

In [24]:

    

expr2 = x*y*(z1+z2+z3)
func2 = sym.lambdify([x, y, z], expr2)
func2(1,2, (3,4,5))
# Vector arguments can be done as tuples when using odeint... (see video/example)


    

    
Out[24]:





$$24$$

In [25]:

    

# How to efficiently deal with matrices without preconverting?
# Or just save as M, C, etc... What about pars? Third argument. Can it be dict or must it be tuple?
# How to efficiently save,