def Recon_WF(wfs, spe, dn, up, h_init):
for wf in wfs:
Recon_wf=np.zeros(len(wf))
Real_Recon_wf=np.zeros(len(wf))
t=[]
blw=np.sqrt(np.mean(wf[:150]**2))
WF=WaveForm(100, blw)
find_hits(WF, wf)
init=sorted(filter(lambda hit: hit.height>h_init, WF.hits), key=lambda hit: hit.init)[0].init
while len(WF.hits)>0:
if len(WF.hits[0].groups)==0:
Recon_wf[WF.hits[0].init:WF.hits[0].fin]+=(wf-Recon_wf)[WF.hits[0].init:WF.hits[0].fin]
else:
i=WF.hits[0].groups[0].maxi
if i<init or (wf-Recon_wf)[i]>0.5*np.amin(spe):
Recon_wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].right]+=np.array(wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].right]-
Recon_wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].right])
N=0
J=-1
else:
recon_wf=np.zeros(1000)
Chi2=1e17
J=i
N=1
for j in range(np.amax((init, WF.hits[0].groups[0].left)), np.amin((WF.hits[0].groups[0].maxi+5,999))):
if (wf-Recon_wf)[j]>0.5*np.amin(spe):
temp=1
else:
if j>np.argmin(spe):
recon_wf[j-np.argmin(spe):]=spe[:len(spe)-(j-np.argmin(spe))]
else:
recon_wf[:len(recon_wf)-(np.argmin(spe)-j)]=spe[np.argmin(spe)-j:]
n=1
chi2=np.mean((recon_wf[j-dn:j-up]-(wf-Recon_wf)[j-dn:j-up])**2)
if chi2<Chi2:
Chi2=chi2
J=j
N=n
if J>np.argmin(spe):
Real_Recon_wf[J-np.argmin(spe):]+=spe[:len(spe)-(J-np.argmin(spe))]
Recon_wf[J-np.argmin(spe):]+=spe[:len(spe)-(J-np.argmin(spe))]
else:
Real_Recon_wf[:len(recon_wf)-(np.argmin(spe)-J)]+=spe[np.argmin(spe)-J:]
Recon_wf[:len(recon_wf)-(np.argmin(spe)-J)]+=spe[np.argmin(spe)-J:]
for i in range(N):
t.append(J)
WF.hits=[]
find_hits(WF, wf-Recon_wf)
yield [Real_Recon_wf, np.sum(((Real_Recon_wf-wf)[init:])**2), np.histogram(t, bins=1000, range=[-0.5, 999.5])[0]]
def Show_Recon_WF(wf, spe, dn, up, h_init, p):
Recon_wf = np.zeros(len(wf))
Real_Recon_wf = np.zeros(len(wf))
t = []
blw = np.sqrt(np.mean(wf[:150]**2))
WF = WaveForm(100, blw)
find_hits(WF, wf)
if len(list(filter(lambda hit: hit.height > h_init, WF.hits))) == 0:
return Real_Recon_wf, np.sum(((Real_Recon_wf - wf))**2), np.zeros(1000)
init = sorted(filter(lambda hit: hit.height > h_init, WF.hits),
key=lambda hit: hit.init)[0].init
while len(WF.hits) > 0:
if len(WF.hits[0].groups) == 0:
Recon_wf[WF.hits[0].init:WF.hits[0].fin] += (
wf - Recon_wf)[WF.hits[0].init:WF.hits[0].fin]
else:
i = WF.hits[0].groups[0].maxi
if i < init or (wf - Recon_wf)[i] > 0.5 * np.amin(spe):
Recon_wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].
right] += np.array(
wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].
right] - Recon_wf[WF.hits[0].groups[0].left:WF.
hits[0].groups[0].right])
N = 0
J = -1
else:
recon_wf = np.zeros(1000)
Chi2 = 1e17
J = i
N = 1
for j in range(np.amax((init, WF.hits[0].groups[0].left)),
np.amin((WF.hits[0].groups[0].maxi + 5, 999))):
if (wf - Recon_wf)[j] > 0.5 * np.amin(spe):
temp = 1
else:
if j > np.argmin(spe):
recon_wf[j -
np.argmin(spe):] = spe[:len(spe) -
(j -
np.argmin(spe))]
else:
recon_wf[:len(recon_wf) -
(np.argmin(spe) -
j)] = spe[np.argmin(spe) - j:]
n = 1
chi2 = np.mean((recon_wf[j - dn:j - up] -
(wf - Recon_wf)[j - dn:j - up])**2)
if chi2 < Chi2:
Chi2 = chi2
J = j
N = n
x = np.arange(1000)
peak = np.zeros(1000)
if J > np.argmin(spe):
Real_Recon_wf[J -
np.argmin(spe):] += spe[:len(spe) -
(J - np.argmin(spe))]
Recon_wf[J - np.argmin(spe):] += spe[:len(spe) -
(J - np.argmin(spe))]
peak[J - np.argmin(spe):] += spe[:len(spe) -
(J - np.argmin(spe))]
if p == 1:
plt.figure()
plt.plot(x, wf, 'k.-', label='wf')
plt.plot(x,
Real_Recon_wf,
'r.-',
label='Real_Recon_wf')
plt.plot(x, Recon_wf, 'g.-', label='Recon_wf')
plt.plot(x, wf - Recon_wf, 'b.-', label='fit')
plt.plot(x, peak, 'c.-', label='peak')
plt.legend()
plt.show()
else:
Real_Recon_wf[:len(recon_wf) -
(np.argmin(spe) - J)] += spe[np.argmin(spe) -
J:]
Recon_wf[:len(recon_wf) -
(np.argmin(spe) - J)] += spe[np.argmin(spe) - J:]
peak[:len(recon_wf) -
(np.argmin(spe) - J)] += spe[np.argmin(spe) - J:]
if p == 1:
plt.figure()
plt.plot(x, wf, 'k.-', label='wf')
plt.plot(x,
Real_Recon_wf,
'r.-',
label='Real_Recon_wf')
plt.plot(x, Recon_wf, 'g.-', label='Recon_wf')
plt.plot(x, wf - Recon_wf, 'b.-', label='fit')
plt.plot(x, peak, 'c.-', label='peak')
plt.legend()
plt.show()
for i in range(N):
t.append(J)
WF.hits = []
find_hits(WF, wf - Recon_wf)
return Real_Recon_wf, np.sqrt(np.mean(
(Real_Recon_wf - wf)[init:]**2)), np.histogram(t,
bins=1000,
range=[-0.5, 999.5])[0]