I'm experimenting with sympy and I've hit upon an issue I can't work out.
Using scipy I can write an expression and evaluate it for an array of x values as follows:
import scipy xvals = scipy.arange(-100,100,0.1) f = lambda x: x**2 f(xvals)
Using sympy I can write the same expression as follows:
import sympy x = sympy.symbols('x') g = x**2
I can evaluate this expression for a single value by doing the following:
g.evalf(subs={x:10})
However I can't work out how to evaluate it for an array of x values, like I did with scipy. How would I do this?
To evaluate a numerical expression into a floating point number, use evalf . SymPy can evaluate floating point expressions to arbitrary precision. By default, 15 digits of precision are used, but you can pass any number as the argument to evalf .
Basics. Exact SymPy expressions can be converted to floating-point approximations (decimal numbers) using either the . evalf() method or the N() function.
Numpy arrays don't play well with sympy objects (they're best if you use built-in numerical types with numpy). You should try sympy arrays instead, which will support something like . subs . Look at recent sympy posts that also use lambdify .
First of all, at the moment SymPy does not guarantee support for numpy arrays which is what you want in this case. Check this bug report http://code.google.com/p/sympy/issues/detail?id=537
Second, If you want to evaluate something numerically for many values SymPy is not the best choice (it is a symbolic library after all). Use numpy and scipy.
However, a valid reason to evaluate something numerically will be that deriving the expression to be evaluated was hard so you derive it in SymPy and then evaluate it in NumPy/SciPy/C/Fortran. To translate an expression to numpy just use
from sympy.utilities.lambdify import lambdify func = lambdify(x, big_expression_containing_x,'numpy') # returns a numpy-ready function numpy_array_of_results = func(numpy_array_of_arguments)
Check the docstring of lambdify for more details. Be aware that lambdify still has some issues and may need a rewrite.
And just as a side note, if you want to evaluate the expressions really many times, you can use the codegen/autowrap module from sympy in order to create fortran or C code that is wrapped and callable from python.
EDIT: An updates list of ways to do numerics in SymPy can be found on the wiki https://github.com/sympy/sympy/wiki/Philosophy-of-Numerics-and-Code-Generation-in-SymPy
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