def minimize(args):
f_index, g_index = args
pol_f_index = f_index[-2:]
pol_g_index = g_index[-2:]
data = dataFromShared()
f = data[f_index]
g = data[g_index]
df, dg = dArraysFromShared()
df = df[pol_f_index]
dg = dg[pol_g_index]
fmin = lambda tau: -correlate(tau, f, g, df, dg)
appr = -f.argmax() + g.argmax()
halfwidth = np.sum(f < f.max() / 2) / 2
try:
return brent(fmin, brack=(appr - halfwidth, appr, appr + halfwidth))
except ValueError:
return -123.4
import time
chan = 180
f = np.loadtxt("/home/gleb/code/C/fit_spectra/sp.dat")
pos = np.unravel_index(np.argmax(f), f.shape)
print pos, f.shape
findex = 123
gindex = 13
shift = 13
g = np.roll(f[gindex], shift)
f = f[findex]
print f.shape
df = np.diff(f)
dg = np.diff(g)
fmin = lambda tau: -correlate(tau, f, g, df, dg)
halfwidth = np.sum(f < f.max() / 2) / 2
appr = -f.argmax() + g.argmax()
t = time.time()
res = brent(fmin, brack=(appr-halfwidth,appr,appr+halfwidth))
t2 = time.time()
print t2 - t
res2 = leastsq(fmin, x0=[appr], disp=0)
print time.time() - t2
# res = pool.map(minimize, TASKS, chunksize=340)
print res, res2
# fit = np.poly1d(np.polyfit(np.arange(len(f[0])), f[0], 12))
plt.plot(f, label='f')
plt.plot(g, label='g')
plt.plot(np.roll(g, int(-res)), label='res')
