def processdata(self):
for files in self.filelist:
wavfile = files['wavfile']
simfile = files['simfile']
templfile = files['templfile']
if not os.path.exists(simfile):
directory, _ = os.path.split(simfile)
os.makedirs(directory, exist_ok=True)
data = readwav.readwav(wavfile)
template = _template.Template.load(templfile)
kw = dict(
batch=100,
pbar=True,
trigger=np.full(len(data),
savetemplate.defaulttrigger(wavfile)),
filtlengths=[32, 64, 128, 256, 512, 1024, 2048],
)
sim = afterpulse.AfterPulse(data, template, **kw)
print(f'save {simfile}...')
sim.save(simfile)
def savenp():
filename = 'darksidehd/merged_000886.root'
channel = 'adc_W201_Ch00'
# look at PDMadcCh.png to match the channel to the wav file
print(f'reading {filename}, channel {channel}...')
root = uproot.open(filename)
tree = root['midas_data']
noise = tree.array(channel)
filename = 'darksidehd/nuvhd_lf_3x_tile57_77K_64V_6VoV_1.wav'
data = readwav.readwav(filename, mmap=False)
ignore = readwav.spurious_signals(data)
print(f'ignore {np.count_nonzero(ignore)} events due to spurious signals')
baseline = data[~ignore, 0, :8900]
kw = dict() # arguments for signal.periodogram()
print('computing noise file spectrum...')
counts = np.unique(noise.counts)
assert len(counts) == 2 and counts[0] == 0
noise_array = noise._content.reshape(-1, counts[-1])
f1, s1s = signal.periodogram(noise_array, 125e6, **kw)
s1 = np.median(s1s, axis=0)
s1 *= (2 / 2**14)**2 # 2 Vpp, 14 bit
print('computing signal file baseline spectrum...')
f2, s2s = signal.periodogram(baseline, 1e9, **kw)
s2 = np.median(s2s, axis=0)
s2 *= (1 / 2**10)**2 # 1 Vpp, 10 bit
print(f'save {npfile}')
np.savez(npfile, f1=f1, f2=f2, s1=s1, s2=s2)
def savetemplate(source, plot='show'):
dest = templatepath(source)
destbase = templatebasepath(source)
data = readwav.readwav(source)
print(f'computing template...')
if data.shape[1] == 1:
trigger = defaulttrigger(source)
else:
trigger = None
fig1 = plt.figure(num='savetemplate1', clear=True)
templ = template.Template.from_lngs(data,
7 * 512,
trigger=trigger,
fig=fig1)
print(f'write {dest}...')
templ.save(dest)
fig2 = plt.figure(num='savetemplate2', clear=True, figsize=[10, 7])
templateplot.templateplot(dest, fig=fig2)
for i, fig in enumerate([fig1, fig2]):
fig.tight_layout()
if plot == 'show':
fig.show()
elif plot == 'save':
destplot = f'{destbase}-{i + 1}.png'
print(f'write {destplot}...')
fig.savefig(destplot)
else:
raise KeyError(plot)
def apload(vov):
filelist = vovdict[vov]['files']
datalist = [
readwav.readwav(files['wavfile'])
for files in filelist
]
simlist = [
afterpulse.AfterPulse.load(files['simfile'])
for files in filelist
]
simcat = afterpulse.AfterPulse.concatenate(simlist)
return datalist, simcat
def __init__(self,
filename='darksidehd/nuvhd_lf_3x_tile57_77K_64V_6VoV_1.wav'):
"""
The wav file is read at initialization.
Parameters
----------
filename : str
The path of an LNGS wav. Default tile 57 6 VoV.
"""
self.data = readwav.readwav(filename, mmap=False)
self.ignore = readwav.spurious_signals(self.data)
print(
f'ignoring {np.sum(self.ignore)} events with signals in baseline zone'
)
_, name = os.path.split(filename)
base, _ = os.path.splitext(name)
templfile = 'templates/' + base + '-template.npz'
print(f'read {templfile}...')
self.template = _template.Template.load(templfile)
"""
Plot an event from an LNGS wav and the moving average filter output.
"""
import numpy as np
from matplotlib import pyplot as plt
import readwav
import fighelp
filename = 'darksidehd/nuvhd_lf_3x_tile57_77K_64V_6VoV_1.wav'
data = readwav.readwav(filename, maxevents=1, mmap=False)
def moving_average(x, n):
s = np.concatenate([[0], np.cumsum(x)])
return (s[n:] - s[:-n]) / n
signal = data[0, 0]
trigger = data[0, 1]
ma = moving_average(signal, 1000)
fig = fighelp.figwithsize([8.21, 5.09], resetfigcount=True)
ax = fig.subplots(1, 1)
ax.plot(signal, '-', color='red', label='signal', linewidth=1)
ax.plot(trigger, '-', color='blue', label='trigger', linewidth=1)
ax.plot(np.arange(999, len(signal)),
ma,
'-',
import numpy as np
from matplotlib import pyplot as plt
import toy
import figlatex
import readwav
import template as _template
prefix = 'nuvhd_lf_3x_tile57_77K_64V_6VoV_1'
length = 1024
###########################
data = readwav.readwav('darksidehd/' + prefix + '.wav',
maxevents=1,
mmap=False)
signal = data[:, 0]
template = _template.Template.load('templates/' + prefix + '-template.npz')
templ, offset = template.matched_filter_template(length,
timebase=1,
aligned='trigger')
filt = toy.Filter(signal, template.baseline)
fsignal = filt.all(templ)[:, 0]
print(f'filter length = {length} ns')
fig, ax = plt.subplots(num='figfilters', clear=True, figsize=[6.4, 3.5])
ax.plot(fsignal[0], color='#f55', linewidth=1, label=toy.Filter.name(0))
import os
import numpy as np
from scipy import stats
import afterpulse
import readwav
import template as _template
prefix = 'nuvhd_lf_3x_tile57_77K_64V_6VoV_1'
data = readwav.readwav('darksidehd/' + prefix + '.wav')
template = _template.Template.load(f'templates/{prefix}-template.npz')
savedir = 'afterpulse_tile57'
os.makedirs(savedir, exist_ok=True)
simfile = f'{savedir}/sim.npz'
if not os.path.exists(simfile):
sim = afterpulse.AfterPulse(data, template, batch=100, pbar=True)
sim.save(simfile)
sim = afterpulse.AfterPulse.load(simfile)
cond = 'good&(mainpos>=0)&(length==512)&(npe>0)&(npe<8)'
fig = sim.hist('mainheight/npe', cond)
fig.savefig(f'{savedir}/fig1.png')
m = sim.getexpr('median(mainheight/npe)', cond)
print(f'fig1 median = {m}')
cond = 'good&(mainpos>=0)&(length==512)&(npe==1)'
fig = sim.scatter('minorpos-mainpos', 'minorheight', cond)
plot_mf_template
plot_signals
plot_filters
plot_localization
"""
import numpy as np
from matplotlib import pyplot as plt
import toy
import readwav
generator = np.random.default_rng(202011111750)
filename = 'nuvhd_lf_3x_tile57_77K_64V_6VoV_1.wav'
data = readwav.readwav(filename, mmap=False)
ignore = readwav.spurious_signals(data)
print(f'ignoring {np.sum(ignore)} events with signals in baseline zone')
def plot_mf_template():
"""
BROKEN
Plot a cross correlation filter template.
"""
template = toy.Template()
template.make(data, 4096, ~ignore)
fig = plt.figure('runtoy-mf-template')
fig.clf()
def apload(vov):
things = vovdict[vov]
data = readwav.readwav(things['wavfile'])
sim = afterpulse.AfterPulse.load(things['simfile'])
return data, sim
path, name = os.path.split(wavfile)
prefix = name.replace('.wav', '')
if '0VoV' in wavfile:
templfile = 'templates/LF_TILE15_77K_59V_2VoV_1-template.npz'
else:
templfile = f'templates/{prefix}-template.npz'
simfile = f'{savedir}/{prefix}.npz'
vov, = re.search(r'(\d)VoV', wavfile).groups()
vov = int(vov)
vovdict[vov] = dict(simfile=simfile, wavfile=wavfile, templfile=templfile)
if not os.path.exists(simfile):
data = readwav.readwav(wavfile)
template = _template.Template.load(templfile)
filtlength = 2048
kw = dict(batch=100,
pbar=True,
filtlengths=filtlength,
ptlength=filtlength)
sim = afterpulse.AfterPulse(data, template, **kw)
print(f'save {simfile}...')
sim.save(simfile)
def apload(vov):
def datalist(self):
"""
List of memory mapped source files.
"""
return [readwav.readwav(files['wavfile']) for files in self.filelist]
import numpy as np
from matplotlib import pyplot as plt
import figlatex
import readwav
import firstbelowthreshold
archive = 'figthesis/figtriggerhist.npz'
if not os.path.exists(archive):
files = list(sorted(glob.glob('darksidehd/*.wav')))
outfiles = []
hists = []
for file in files:
data = readwav.readwav(file)
if data.shape[1] != 2:
continue
trigger = firstbelowthreshold.firstbelowthreshold(data[:, 1], 600)
hists.append(np.bincount(trigger))
outfiles.append(file)
length = max(len(h) for h in hists)
hists = [np.pad(h, (0, length - len(h))) for h in hists]
print(f'save {archive}...')
np.savez_compressed(archive, files=outfiles, hists=hists)
print(f'load {archive}...')
with np.load(archive) as arch:
files = arch['files']
import runsliced
filename = sys.argv[1]
maxevents = 1000
length = 2000
start = 0
cmap = None
try:
maxevents = int(sys.argv[2])
length = int(sys.argv[3])
start = int(sys.argv[4])
cmap = sys.argv[5]
except IndexError:
pass
data = readwav.readwav(filename, mmap=True)
nevents = min(len(data), maxevents)
h = np.zeros((length, 1024), int)
@numba.njit(cache=True)
def accumhist(hist, data, start, length):
for event in data:
signal = event[0]
trigger = event[1]
for itrig, sample in enumerate(trigger):
if sample < 600:
break
for i in range(length):
isample = itrig + start + i
def read(filespec, maxevents=None, quiet=False, mmap=True, swapch='auto', return_trigger=True, firstevent=None):
"""
Read an LNGS laser wav or a Proto0 root.
Parameters
----------
filespec : str
The file path. For root files, there must be an additional channel
specification separated by a column. Examples:
'XXX.root:adc_W200_Ch00', 'XXX.root:57'.
maxevents : int
The maximum number of events read from the file.
quiet : bool
If True, do not print 'reading <file>...'.
mmap : bool
(wav-specific) return a memmap to the file.
swapch : str, bool
(wav-specific) If True the signal and trigger channels are swapped in
the file. 'auto' (default) tries to deduce it from the file name.
return_trigger : bool
If True (default) return the trigger position for each event.
firstevent : int, optional
The first event to read.
Return
------
array : int array (nevents, nsamples)
The array with the signal waveforms.
trigger : array (nevents,) or None
The trigger position for each event in samples. Returned only if
`return_trigger` is True. `None` if there's no trigger information. For
Proto0 files the trigger position is constant, read from metadata, and
not reliable.
freq : scalar
The sampling frequency in samples per second.
ndigit : int
The number of ADC values.
"""
path, last = os.path.split(filespec)
comp = last.split(':')
if len(comp) == 1 or comp[1] == '':
name = comp[0]
ch = None
else:
name = ':'.join(comp[:-1])
ch = comp[-1]
if name.endswith('.wav'):
data = readwav.readwav(filespec, quiet=quiet, mmap=True, swapch=swapch)
if firstevent is not None:
data = data[firstevent:]
if maxevents is not None:
data = data[:maxevents]
array = data[:, 0]
if not mmap:
array = np.copy(array)
trigger = None
if return_trigger and data.shape[1] == 2:
trigger = readwav.firstbelowthreshold(data[:, 1], 600)
freq = 1e9
ndigit = 2 ** 10
elif name.endswith('.root'):
if ch is None:
raise ValueError(f'missing channel for file {name}')
if not ch.startswith('adc'):
ch = int(ch)
if path != '':
path += '/'
array, trigger, freq = readroot.readroot(path + name, ch, maxevents=maxevents, quiet=quiet, firstevent=firstevent)
if return_trigger:
trigger = np.full(array.shape[0], trigger)
ndigit = 2 ** 14
else:
raise ValueError(f'unrecognized extension for file {name}')
if return_trigger:
return array, trigger, freq, ndigit
else:
return array, freq, ndigit
