How can I convert a sympy expression to numpy code? For example, say I this was the code for the expression:
expression = 2 * x/y + 10 * sympy.exp(x) # Assuming that x and y are predefined from sympy.symbols
I would want to go from expression to this:
np_expression = "np.dot(2, np.dot(x, np.linalg.pinv(y))) + np.dot(10, np.exp(x))"
Note that x and y are matrices, but we can assume the shapes will match
An example with real numbers would go like this:
a = np.array([1,2],[3,4])
b = np.array([5,6],[7,8])
expression = 2 * a/b + 10 # These would be sympy symbols rather than numbers
and the result would be this:
np_expression = "np.dot(2, np.dot(5, np.linalg.pinv(9))) + 10"
>Solution :
In [1]: expr = 2 *x/y + 10 * exp(x)
In [3]: f = lambdify((x,y), expr)
In [4]: help(f)
_lambdifygenerated(x, y)
Created with lambdify. Signature:
func(x, y)
Expression:
2*x/y + 10*exp(x)
Source code:
def _lambdifygenerated(x, y):
return 2*x/y + 10*exp(x)
Which for specific inputs, array or otherwise:
In [5]: f(np.arange(1,5)[:,None], np.arange(1,4))
Out[5]:
array([[ 29.18281828, 28.18281828, 27.84948495],
[ 77.89056099, 75.89056099, 75.22389432],
[206.85536923, 203.85536923, 202.85536923],
[553.98150033, 549.98150033, 548.648167 ]])
In [6]: f(1,1)
Out[6]: 29.18281828459045
In [7]: f(2,3)
Out[7]: 75.22389432263984
In [8]: f(np.arange(1,4),np.arange(1,4))
Out[8]: array([ 29.18281828, 75.89056099, 202.85536923])
Normal array broadcasting rules apply. Note that x/y is element-wise. I’m not sure what lambdify will translate into dot and inv code.
trying your numpy code:
In [9]: np.dot(2, np.dot(2,np.linalg.pinv(3)))+10*np.exp(2)
---------------------------------------------------------------------------
LinAlgError Traceback (most recent call last)
<ipython-input-9-6cae91f0e0f8> in <module>
----> 1 np.dot(2, np.dot(2,np.linalg.pinv(3)))+10*np.exp(2)
....
LinAlgError: 0-dimensional array given. Array must be at least two-dimensional
We have to change the y into a 2d array, e.g. [[3]]:
In [10]: np.dot(2, np.dot(2,np.linalg.pinv([[3]])))+10*np.exp(2)
Out[10]: array([[75.22389432]])