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