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Covariances.py
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Covariances.py
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# -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
import PSim
import pickle
import csv
import simplex
fit_type = 'global'
scale = 0.003
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
def scalar_min(p, data):
xdata, ydata, ysim = data[0]
xdata_250, ydata_250, ysim_250 = data[1]
scaled_ysim = ysim * p[0]
scaled_ysim_250 = ysim_250 * p[0]
err_20 = 0
err_250 = 0
num_points = 0
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
for dat, sim in zip(ydata_250[:-1], scaled_ysim_250[:-1]):
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
err = np.sqrt(err_250*err_20)
if np.isnan(err):
err = 7e20
fitness = err * 100
return fitness
def evaluate(p):
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[9] * sim.step
sim.scalar = 1
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[9] * sim_250.step
sim_250.scalar = 1
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]
# Use a simplex minimization to find the best scalar
scalar0 = np.array([3e-26])
ranges = scalar0*0.1
s = simplex.Simplex(scalar_min, scalar0, ranges)
values, fitness, iter = s.minimize(epsilon=0.00001, maxiters=500,
monitor=0, data=data)
scalar = values[0]
#p[-1] = scalar
if scalar < 0:
fitness = 1e30
return fitness
def main():
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))
dim = 11
pi = np.ones(dim)
for i, n in enumerate([0,1,2,3,4,5,6,7,8,9,10]):
pi[i] = p0[n]
ps1 = np.ndarray([dim, dim, dim])
ps2 = np.ndarray([dim, dim, dim])
fitness1 = np.ndarray([dim, dim])
fitness2 = np.ndarray([dim, dim])
differences = scale*pi
for i in range(dim):
for j in range(dim):
for k in range(dim):
val1 = pi[k]
val2 = pi[k]
if i == k or j == k:
val1 = val1 + differences[k]
val2 = val2 - differences[k]
ps1[i][j][k] = val1
ps2[i][j][k] = val2
for i in range(dim):
for j in range(i, dim):
fitness1[i][j] = evaluate(ps1[i][j])
fitness1[j][i] = fitness1[i][j]
fitness2[i][j] = evaluate(ps2[i][j])
fitness2[j][i] = fitness2[i][j]
error0 = evaluate(pi)
data = {'fitness1': fitness1,
'fitness2': fitness2,
'differences': differences,
'error0': error0}
with open("covariance_data_{}.p".format(scale), "wb") as file:
pickle.dump(data, file)
hessian = np.ndarray([dim, dim])
for i in range(dim):
for j in range(dim):
if i == j:
d2i = differences[i]
df1 = (fitness1[i][j] - error0) / d2i
df2 = (error0 - fitness2[i][j]) / d2i
hessian[i][j] = (df1 - df2) / (d2i)
else:
df1di1 = (fitness1[i][i] - error0) / differences[i]
df1di2 = (fitness1[i][j] - fitness1[j][j]) / differences[i]
dff1didj = (df1di2 - df1di1) / differences[j]
df2di1 = (error0 - fitness2[i][i]) / differences[i]
df2di2 = (fitness2[j][j] - fitness2[i][j]) / differences[i]
dff2didj = (df2di2 - df2di1) / differences[j]
hessian[i][j] = (dff1didj + dff2didj) / 2
hessian[j][i] = hessian[i][j]
with open("hessian_{}.p".format(scale), "wb") as file:
pickle.dump(hessian, file)
m_hessian = np.matrix(hessian)
covariance = np.linalg.inv(m_hessian)
cv_array = np.array(covariance)
paramaters=['Traps', 'EH_Decay', 'E_Trap', 'TH_loss', 'G_Decay', 'G2_Decay', 'G3_Decay', 'GH_Decay', 'G_Escape', 'G3_Loss']
for i in range(dim):
print('{}{}: {} +- {}'.format(' ' * (8-len(paramaters[i])), paramaters[i], p0[i], np.sqrt(cv_array[i][i])))
with open('Parameters_{}.txt'.format(scale), 'w') as f:
writer = csv.writer(f, dialect="excel-tab")
for i in range(10):
error = np.sqrt(cv_array[i][i])
relerror = error / pi[i] * 100
words = '{}{}: {} +- {} ({}%)'.format(' ' * (8-len(paramaters[i])), paramaters[i], pi[i], error, relerror)
print(words)
writer.writerow([words])
if __name__ == '__main__':
main()