将额外的参数传递给broyden1

Question

Im trying to execute scipy broyden1 function with extra parameters (called "data" in the example), here is the code:

data = [radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis, ellipsoid_semiminor_axis, srange]
target_xyz = broyden1(Pixlinexyx_2Bsolved, start_xyz, args=data)

def Pixlinexyx_2Bsolved(target, *data):
    radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis, ellipsoid_semiminor_axis, srange = data

    print target
    print radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis, ellipsoid_semiminor_axis, srange

Pixlinexyx_2Bsolved is the function whose root I want to find.

start_xyz is initial guess of the solution:

start_xyz = [4543557.208584103, 1097477.4119051248, 4176990.636060918]

And data is this list containing a lot of numbers, that will be used inside the Pixlinexyx_2Bsolved function:

data = [0.056666, [5147114.2523595653, 1584731.770061729, 4715875.3525346108], [5162.8213179936156, -365.24378919717839, -5497.6237250296626], 6378144.0430000005, 6356758.789000001, 850681.12442702544]

When I call the function broyden1 (as in the second line of example code) I get the next error:

target_xyz = broyden1(Pixlinexyx_2Bsolved, start_xyz, args=data)
  File "<string>", line 5, in broyden1
TypeError: __init__() got an unexpected keyword argument 'args'

What I'm doing wrong?

Now, seeing the documentation of fsolve , it seems to be able to get extra args in the callable func... Here is a similar question as mine.

Answer 1

There is a similar question at scipy's issue-tracker including a solution using python's functools -module (here: PEP 309 -- Partial Function Application ).

Small example based on the above link and the original problem from the docs :

import numpy as np
import scipy.optimize

""" No external data """
def F(x):
   return np.cos(x) + x[::-1] - [1, 2, 3, 4]
x = scipy.optimize.broyden1(F, [1,1,1,1], f_tol=1e-14)
print(x)

""" External data """
from functools import partial
def G(data, x):
    return np.cos(x) + x[::-1] - data
data = [1,2,3,4]
G_partial = partial(G, data)
x = scipy.optimize.broyden1(G_partial, [1,1,1,1], f_tol=1e-14)
print(x)

Out

[ 4.04674914  3.91158389  2.71791677  1.61756251]
[ 4.04674914  3.91158389  2.71791677  1.61756251]