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Simplex_Fit.py
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Simplex_Fit.py
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# -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
import PSim
import pickle
import csv
import simplex
import time
fit_type = 'global'
load_time = time.strftime('%Y.%m.%d.%H.%M')
filename = "simplex_{}_output_{}.log".format(fit_type, load_time)
with open("pickled_data.p", "r") as file:
pickled_data = pickle.load(file)
powers = pickled_data['powers']
xdata = pickled_data['allxdata']
ydata = pickled_data['allydata']
xarray = pickled_data['xarray']
yarrays = pickled_data['yarrays']
averages = pickled_data['averages']
period = 50 # ns
with open("pickled_data_250.p", "r") as file:
pickled_data_250 = pickle.load(file)
powers_250 = pickled_data_250['powers']
xdata_250 = pickled_data_250['allxdata']
ydata_250 = pickled_data_250['allydata']
xarray_250 = pickled_data_250['xarray']
yarrays_250 = pickled_data_250['yarrays']
averages_250 = pickled_data_250['averages']
period_250 = 1.0 / 250000.0 / 1e-9 # ns
ssn = 0
def scalar_min(data):
xdata, ydata, scaled_ysim = data[0]
xdata_250, ydata_250, scaled_ysim_250 = data[1]
err_20 = 0
err_250 = 0
num_points = 0
if (fit_type is 'global') or (fit_type is 20): # 20 MHz data
for dat, sim in zip(ydata, scaled_ysim):
for x, d, s in zip(xdata, dat, sim):
try:
if s > 0:
log_s = np.log(s)
else:
log_s = 0
log_d = np.log(d)
error = (log_s - log_d)
# error = np.log(error)
err_20 += error*error
num_points = num_points + 1
except:
err_20 += 8e20
err_20 = err_20 / num_points
num_points = 0
if (fit_type is 'global') or (fit_type is 250): # 250 kHz data
for dat, sim in zip(ydata_250[:-1], scaled_ysim_250[:-1]): # Exclude the lowest noisy power
for x, d, s in zip(xdata_250, dat, sim):
try:
if s > 0:
log_s = np.log(s)
else:
log_s = 0
log_d = np.log(d)
error = (log_s - log_d)
# error = np.log(error)
if x >= -0.25 and x <= 120:
err_250 += error*error
num_points = num_points + 1
except:
err_250 += 8e20
err_250 = err_250 / num_points
if fit_type is 'global':
err = np.sqrt(err_250*err_20)
elif fit_type is 20:
err = err_20
elif fit_type is 250:
err = err_250
else:
err = 6e20
if np.isnan(err):
err = 7e20
fitness = err * 100
return fitness
def evaluate(p):
global ssn
dummy_x = np.zeros(10)
dummy_y = np.zeros([10, 10])
data = [[dummy_x, dummy_y, dummy_y], [dummy_x, dummy_y, dummy_y]]
if fit_type is 'global' or fit_type is 20: # 20 MHz data
sim = PSim.DecaySim(reprate=20000000, tolerance=0.005, step=5e-12)
sim.trap = p[0]
sim.EHdecay = p[1] * sim.step
sim.Etrap = p[2] * sim.step
sim.FHloss = p[3] * sim.step
sim.Gdecay = p[4] * sim.step
sim.G2decay = p[5] * sim.step
sim.G3decay = p[6] * sim.step
sim.GHdecay = p[7] * sim.step
sim.Gescape = p[8] * sim.step
sim.Gform = p[9] * sim.step * 0
sim.G3loss = p[10] * sim.step
sim.scalar = p[11]
for power in powers:
sim.addPower(power)
sim.runSim()
interp_signals = []
for this_run in sim.signal:
interp_this = np.interp(xarray, sim.xdata, this_run)
interp_signals.append(interp_this)
interp_signals = np.array(interp_signals)
data[0] = [xarray, yarrays, interp_signals]
if fit_type is 'global' or fit_type is 250: # 250 kHz data
sim_250 = PSim.DecaySim(reprate=250000, tolerance=0.005, step=5e-12)
sim_250.trap = p[0]
sim_250.EHdecay = p[1] * sim_250.step
sim_250.Etrap = p[2] * sim_250.step
sim_250.FHloss = p[3] * sim_250.step
sim_250.Gdecay = p[4] * sim_250.step
sim_250.G2decay = p[5] * sim_250.step
sim_250.G3decay = p[6] * sim_250.step
sim_250.GHdecay = p[7] * sim_250.step
sim_250.Gescape = p[8] * sim_250.step
sim_250.Gform = p[9] * sim_250.step * 0
sim_250.G3loss = p[10] * sim_250.step
sim_250.scalar = p[11]
for power in powers_250:
sim_250.addPower(power)
sim_250.runSim()
interp_signals_250 = []
for this_run in sim_250.signal:
interp_this = np.interp(xarray_250, sim_250.xdata, this_run)
interp_signals_250.append(interp_this)
interp_signals_250 = np.array(interp_signals_250)
data[1] = [xarray_250, yarrays_250, interp_signals_250]
fitness = scalar_min(data=data)
for param in p:
if param < 0:
fitness = fitness * 100000
with open(filename, 'a') as log_file:
writer = csv.writer(log_file, dialect="excel-tab")
row = [ssn, '{:.4e}'.format(fitness)]
for var in p:
row.append('{:.4e}'.format(var))
writer.writerow(row)
ssn = ssn + 1
return fitness
def main():
try:
logname = 'best_{}.log'.format(fit_type)
with open(logname, 'rb') as best_file:
reader = csv.reader(best_file, dialect='excel-tab')
p0 = []
for val in reader.next():
p0.append(np.float(val))
p0 = np.array(p0)
ranges = p0*0.02
s = simplex.Simplex(evaluate, p0, ranges)
p, err, iter = s.minimize(epsilon=0.00001, maxiters=2000,
monitor=0)
with open(logname, 'a') as save_file:
writer = csv.writer(save_file, dialect='excel-tab')
writer.writerow(p)
writer.writerow([ssn, err])
except Exception as e:
print(e)
if __name__ == '__main__':
main()