问题
I am trying to make lambdify understand to expect more than one type of input using the modules keyword argument. According to the source code of lambdify (http://docs.sympy.org/dev/_modules/sympy/utilities/lambdify.html), this can be done by using lists of the arguments, but i am not able to do so.
import sympy
from sympy import lambdify
x,y=sympy.symbols('x y')
from sympy.parsing.sympy_parser import parse_expr
func=lambdify(x,parse_expr(exp(x)),modules=["numpy","sympy"])
func(array([3,4]))
gives
array([ 20.08553692, 54.59815003])
but when i try
func(y)
i get an
Attribute error:exp
What am i doing wrong here? Shouldn't func accept both numpy and sympy types? Any help appreciated!!
回答1:
The modules don't dispatch or anything like that. The way that lambdify works is that it creates
lambda x: exp(x)
where exp
comes from the namespace of the module(s) you chose. lambdify(x, exp(x), ['numpy', 'sympy'])
is roughly equivalent to
from sympy import *
from numpy import *
# Various name replacements for differences in numpy naming conventions, like
# asin = arcsin
return lambda x: exp(x)
If you want to provide a custom function that dispatches, you can use something like Saullo Castro's example. You can also use this with lambdify by providing a dict, like
import numpy as np
import sympy
def myexp(x):
if isinstance(x, np.ndarray):
return np.exp(x)
else:
return sympy.exp(x)
func = lambdify(x, exp(x), [{'exp': myexp}, 'numpy'])
This gives
>>> func(np.array([1, 2]))
array([ 2.71828183, 7.3890561 ])
>>> func(sympy.Symbol('y'))
exp(y)
回答2:
The documentation says that the modules
argument will give more priority to the modules appearing first, which in this case is "numpy"
. Thefore, if the two modules have the same function it will always take the first one.
A good approach would be:
import numpy as np
def func(x):
if isinstance(x, np.ndarray):
return np.exp(x)
else:
return sympy.exp(x)
来源:https://stackoverflow.com/questions/25495375/more-than-one-module-for-lambdify-in-sympy