Gradient descent on spice netlist

from __future__ import print_function
from numpy import *
import subprocess
import re
from scipy.optimize import minimize, show_options
import os

with open('nand.net', 'r') as f:
    source = f.read()

TMP_NAME = 'tmp.net'

def test(p, v=False):
    if v:
        print (' '.join('{0: 13.7f}'.format(p[i]) for i in range(len(p))), end='')
    if any(p < 0):
        a, b, c, d = nan, nan, nan, nan
    else:
        with open(TMP_NAME, 'w+') as f:
            f.write(source.format(**locals()))
            f.flush()
        p = subprocess.Popen(['ngspice', TMP_NAME], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        out = p.communicate(input='exit')[0]
        try:
            a = re.findall(r'tplh_b1af *?=  ([\d\.\-e]*)', out)[0]
            b = re.findall(r'tphl_b1ar *?=  ([\d\.\-e]*)', out)[0]
            c = re.findall(r'tphl_a1br *?=  ([\d\.\-e]*)', out)[0]
            d = re.findall(r'tplh_a1bf *?=  ([\d\.\-e]*)', out)[0]
        except:
            a, b, c, d = nan, nan, nan, nan
    t = array([a, b, c, d], dtype=float128)
    if v:
        print (' ', t)
    return t

a1, b1, _, _ = test([1])
def cost(p, v=False):
    return sum((test(p, v)[2:] - 2)**2)

if __name__ == '__main__':
    x0 = array([2], dtype=float128)
    res = minimize(cost, x0, args=(True,), method='Nelder-Mead', options=dict(maxiter=160))
    print (res)
    os.remove(TMP_NAME)
#5 2 nand