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pythonperformancesympyprocessing-efficiencylambdify

Python 3: Sympy: Include list information to optimize lambdify


I use lambdify to compile an expression which is a function of certain parameters. Each parameter has N points. So I need to evaluate the expression N times. The following shows a simplified example on how this is done.

import numpy as np
from sympy.parsing.sympy_parser import parse_expr
from sympy.utilities.lambdify import lambdify, implemented_function
from sympy import S, Symbol
from sympy.utilities.autowrap import ufuncify


def CreateMagneticFieldsList(dataToSave,equationString,DSList):

    expression  = S(equationString)
    numOfElements = len(dataToSave["MagneticFields"])

    #initialize the magnetic field output array
    magFieldsArray    = np.empty(numOfElements)
    magFieldsArray[:] = np.NaN

    lam_f = lambdify(tuple(DSList),expression,modules='numpy')
    try:
        for i in range(numOfElements):
            replacementList = np.zeros(len(DSList))


            for j in range(len(DSList)):
                replacementList[j] = dataToSave[DSList[j]][i]

            try:
                val = np.double(lam_f(*replacementList))

            except:
                val = np.nan
            magFieldsArray[i] = val
    except:
        print("Error while evaluating the magnetic field expression")
    return magFieldsArray


list={"MagneticFields":list(range(10000)), "Chx":list(range(10000))}

out=CreateMagneticFieldsList(list,"MagneticFields*5+Chx",["MagneticFields","Chx"])

print(out)

Is there a way to optimize this call further? Specifically, I mean is there a way to make lambdify include that I'm calculating for a list of points, so that the loop evalulation can be optimized?


Solution

  • Thanks to @asmeurer, he gave the idea on how to do it.

    Since lambdify is compiled using numpy, then one could simply pass the lists as arguments! The following is a working example

    #!/usr/bin/python3
    
    import numpy as np
    from sympy.parsing.sympy_parser import parse_expr
    from sympy.utilities.lambdify import lambdify, implemented_function
    from sympy import S, Symbol
    from sympy.utilities.autowrap import ufuncify
    
    
    def CreateMagneticFieldsListOpt(dataToSave,equationString,DSList):
    
        expression  = S(equationString)
        numOfElements = len(dataToSave["MagneticFields"])
    
        #initialize the magnetic field output array
        magFieldsArray    = np.empty(numOfElements)
        magFieldsArray[:] = np.NaN
    
        lam_f = lambdify(tuple(DSList),expression,modules='numpy')
        replacementList = [None]*len(DSList)
    
        for j in range(len(DSList)):
            replacementList[j] = np.array(dataToSave[DSList[j]])
        print(replacementList)
    
        magFieldsArray = np.double(lam_f(*replacementList))
    
    
        return magFieldsArray
    
    list={"MagneticFields":[1,2,3,4,5],"ChX":[2,4,6,8,10]}
    
    out=CreateMagneticFieldsListOpt(list,"MagneticFields*5+ChX",["MagneticFields","ChX"])
    
    print(out)