generate_simulate_meas_cost_optimize_ngspice

import os
import ipdb # ipdb.set_trace()
import numpy as np
import matplotlib.pyplot as plt
import re
from my_optimization import *


def generate_netlist(R2):
    print("Generating netlist")
    cirfile="generated_netlist.net"
    f = open(cirfile,"w")
    f.write("* Generated Netlist \n")
    f.write("V1 N001 0 10 \n")
    f.write("R1 N001 out 10k \n")
    f.write("R2 out 0 %f \n" %R2)
    f.write(".tran 0.1ns 1ns \n")
    f.write(".meas TRAN vsaida FIND V(out) at=1n \n")
    f.write(".end \n")
    f.close()


def meas_result():
    print("Reading log file")
    logfile="generated_netlist.log"
    with open(logfile) as f:
        lines = f.readlines()
    f.close()

    for l in lines:
        #print(l)
        if 'vsaida' in l:
            # match =re.search(r"=(.*) at",l)  # LTSPICE
            match =re.search(r"=  (.*)",l)  # ngspice
            vout = float(match.group(1))
            return vout

def simulate():
    print("Simulating")
    os.system('ngspice -b generated_netlist.net > generated_netlist.log')

def cost_function(x):
    print("Evaluating cost function")
    #generate, simulate and read log
    generate_netlist(x)
    simulate()
    voltage = meas_result()

    #calculate cost
    cost = abs(5-voltage)
    return cost



# OPTIMIZATION

size_x = 1
bound_i = (100,20000)
bounds = []
for i in range (0, size_x):
      bounds.append(bound_i)

x0 = np.array([3000])

maxiter = 100
step = 1000
args = []


res = my_hillclimbing(cost_function, x0, bounds, maxiter, step, args)
print("Final x: [",)
print("%f" % res.x[0],)
for i in range(1,len(res.x)):
    print(", %f" % res.x[i],)
    print("]\n",)
    #print res.x
print("Final cost: %f \n" % res.cost)