I have some issue using sympy.lambdify. I have a rather simple symbolic expression involving only square root, sine and cosine and some large numbers (which get generated by other parts of the program not shown here). Lambdify does work for single floats, but not for numpy arrays. However, these would be very helpful for plotting later.

The error I get is

AttributeError: 'float' object has no attribute 'sqrt'

Here is a mwe. Note that expr1 works just fine whereas expr2 does not. Any help to fix the issue would be well appreciated.

import sympy
import numpy

x = sympy.symbols('x', real=True)

expr1 = -sympy.sqrt(4*sympy.sin(3*x/4)**2 - 2*sympy.cos(3*x/83) + 5*sympy.cos(2*x/3)**2 + 2)
expr2 = -sympy.sqrt(2.14881349445107e+30*sympy.sin(209178661335919*x/10000000000000)**2 + 13456000000000000000000000000*sympy.cos(209178661335919*x/10000000000000)**2 - 1.40793126300373e+29*sympy.cos(209178661335919*x/10000000000000) + 4.73607234789273e+30)

func1 = sympy.lambdify(x, expr1, modules='numpy')
func2 = sympy.lambdify(x, expr2, modules='numpy')

array = numpy.arange(2)
print(func1(array))
print(func2(array[0]))
print(func2(array[1]))  #works fine until here
print(func2(array))     #fails

python 3.7.3
numpy 1.16.3
sympy 1.14

EDIT:
I cannot directly modify expr2. It just appears here in this form to provide a mwe. However, in the real code it get generated as eigenvalue of a matrix and takes rather long to calculate.

eigenvalues = Matrix.eigenvals()
expr2 = list(eigenvalues.keys())[0]

1 Answers

1
smichr On Best Solutions

Try to apply nfloat to the expression before passing it to lambdify: expr2 = sympy.nfloat(expr1).