What is the best way to convert a SymPy matrix to a numpy array/matrix

Question

I am not sure if the approach I've been using in sympy to convert a MutableDenseMatrix to a numpy.array or numpy.matrix is a good current practice.

I have a symbolic matrix like:

g = sympy.Matrix( [[   x,  2*x,  3*x,  4*x,  5*x,  6*x,  7*x,  8*x,   9*x,  10*x],                    [x**2, x**3, x**4, x**5, x**6, x**7, x**8, x**9, x**10, x**11]] ) 

and I am converting to a numpy.array doing:

g_func = lambda val: numpy.array( g.subs( {x:val} ).tolist(), dtype=float ) 

where I get an array for a given value of x.

Is there a better built-in solution in SymPy to do that?

Thank you!

Answer 1

This looks like the most straightforward:

np.array(g).astype(np.float64) 

If you skip the astype method, numpy will create a matrix of type 'object', which won't work with common array operations.

Answer 2

This answer is based on the advices from Krastanov and asmeurer. This little snippet uses sympy.lambdify:

from sympy import lambdify from sympy.abc import x, y  g = sympy.Matrix([[   x,  2*x,  3*x,  4*x,  5*x,  6*x,  7*x,  8*x,   9*x,  10*x],                   [y**2, y**3, y**4, y**5, y**6, y**7, y**8, y**9, y**10, y**11]]) s = (x, y) g_func = lambdify(s, g, modules='numpy') 

where g is your expression containing all symbols grouped in s.

If modules='numpy' is used the output of function g_func will be a np.ndarray object:

g_func(2, 3) #array([[     2,      4,      6,      8,     10,     12,     14,     16,       18,     20], #       [     9,     27,     81,    243,    729,   2187,   6561,  19683,    59049, 177147]])  g_func(2, y) #array([[2, 4, 6, 8, 10, 12, 14, 16, 18, 20], #       [y**2, y**3, y**4, y**5, y**6, y**7, y**8, y**9, y**10, y**11]], dtype=object) 

If modules='sympy' the output is a sympy.Matrix object.

g_func = lambdify(vars, g, modules='sympy') g_func(2, 3) #Matrix([[2,  4,  6,   8,  10,   12,   14,    16,    18,     20], #        [9, 27, 81, 243, 729, 2187, 6561, 19683, 59049, 177147]])  g_func(2, y) #Matrix([[   2,    4,    6,    8,   10,   12,   14,   16,    18,    20], #        [y**2, y**3, y**4, y**5, y**6, y**7, y**8, y**9, y**10, y**11]])