fil = '200-100-363-histbins.npy'
x = np.load(fil)
hist, bins = x[:, 0], x[:, 1]
start = len(bins) - np.sum(bins > 1500)
amax = np.argmax(hist[start:])
window = 12 #in bins
if fittype == 0:
with PdfPages(fil[:-12] + '-gauss.pdf') as pdf:
mini, minj, minchi = 100., 100., 100.
for i in np.linspace(0.2, 1., 9):
for j in np.linspace(0.2, 1., 5):
beg, end = bins[start + amax] - window * 5 * i, bins[
start + amax] + window * 5 * j
fitbins = bins[pd.land(bins > beg, bins < end)]
fithist = hist[pd.land(bins > beg, bins < end)]
fithist[fithist == 0] = 1
weights = np.sqrt(fithist)
pars = [hist[start + amax], bins[start + amax], 30]
pars = curve_fit(gauss,
fitbins,
fithist,
p0=pars,
sigma=weights,
maxfev=120000)[0]
chisq = np.sum(
((gauss(fitbins, *pars) - fithist) / weights)**2.)
chisq = chisq / (len(fitbins) - len(pars))
if chisq < minchi and chisq > 0.8:
mini, minj, minchi = i, j, chisq
import matplotlib.pyplot as plt
import numpy as np
import fileread as fr
import predefined as pd
bd,ch=4,3
ebins,erange=1000,[0,5000]
rbins,rrange=100,[0,100]
x=fr.gen_output('./testing/Run_131-200-70-all.dat')[0]
x=pd.precuts(x)
x=pd.single_pixel(x,board=bd,channel=ch)
fsize=25
plt.figure(figsize=(20,20))
for i in np.linspace(0,2000,5,dtype=int):
beg,end=i+0.,i+500.
hist,bins=np.histogram(x['risetime'][pd.land(x['energy']>beg,x['energy']<end)],rbins,rrange)
bins=pd.cbins(bins)
plt.plot(bins,hist,ls='steps',label='%0.0d < E (ADC) < %0.0d' %(beg,end) )
plt.legend(fontsize=fsize)
plt.title(pd.pixel(bd,ch),fontsize=fsize)
plt.tick_params(labelsize=fsize)
plt.xlabel('Risetime (4ns timebins)',fontsize=fsize)
plt.xlim((0,100))
plt.yscale('log')
plt.savefig('./testing/rises'+pd.pixel(bd,ch))
if run > 63:
loc = '/lustre/haven/gamma/neutrons/ca45_data/2017/disk1/'
data = fr.trig(loc + 'Run_' + str(run) + '.trig')[0]
data = data[data['energy'] > 6000]
i = 0
for pixel in [11, 12, 35]:
bd, ch = int(pixel / 8), int(pixel % 8)
h, b = np.histogram(pd.single_pixel(data, bd, ch)['energy'],
bins=305,
range=[0, 20000])
b = pd.cbins(b)
mx = np.amax(h)
mu = b[np.argmax(h)]
window = 1000
beg, end = mu - window, mu + window
fithist = h[pd.land(b > beg, b < end)]
fitbins = b[pd.land(b > beg, b < end)]
pars, vrs = curve_fit(gauss,
fitbins,
fithist,
p0=[mx, mu, 200],
bounds=[0, np.inf])
vrs = np.sqrt(np.diag(vrs))
runlog[run]['mu'][i] = pars[1]
runlog[run]['muerror'][i] = vrs[1]
runlog[run]['sigma'][i] = pars[2]
runlog[run]['sigmaerror'][i] = vrs[2]
i += 1
except FileNotFoundError:
continue
name = 'Run_' + run + '_' + str(i) + '.bin'
print('Checking ' + name + ' for timestamps')
numwaves = (os.stat(inpath + name).st_size - 8) / (1 + 12 + 16 + 4 +
2 * length)
for i in range(numwaves / chunk):
rem = 0
if i == numwaves / chunk:
rem = numwaves % chunk
tot = chunk + rem
data = fr.raw(inpath + name,
length=length,
numwaves=tot,
row=i * chunk)
for j in range(len(timestamps)):
x = data[pd.land(
pd.land(data['timestamp'] == timestamps[j]['timestamp'],
data['board'] == timestamps[j]['board']),
data['channel'] == timestamps[j]['channel'])]
count += len(x)
if len(x) > 0:
# print x['board'],x['channel'],x['timestamp']
temp[count - 1] = x
print(temp['board'])
wo.baseline_restore(temp, 600)
tbins = np.arange(3500)
#for i in range(len(temp)):
with PdfPages('fallwaves.pdf') as pdf:
for j in range(int(portion), len(timestamps)):
# print temp[temp['timestamp']==timestamps[j]['timestamp']]['wave'][0]
if timestamps[j]['board'] > 2:
plt.plot(
tbins,