def _get_pedestal(file1, N1):
f1 = File(file1)
ped = PedestalSimple()
for i in range(0, N1):
ev = next(f1.Events)
Ev = Event(ev)
Ev.read()
ped.fill_pedestal_event(Ev)
ped.finalize_pedestal()
return ped
def subtract_pedestal(file1, file2, N1=10000, N2=3000):
ped = _get_pedestal(file1, N1)
f2 = File(file2)
event_hi_gain_before_remove_pedestal = np.zeros((N2, 8, 40))
event_hi_gain_after_remove_pedestal = np.zeros((N2, 8, 40))
event_low_gain_before_remove_pedestal = np.zeros((N2, 8, 40))
event_low_gain_after_remove_pedestal = np.zeros((N2, 8, 40))
for i in range(0, N2):
ev = next(f2.Events)
Ev = Event(ev)
Ev.read()
event_hi_gain_before_remove_pedestal[i, :, :] = Ev.samples_high_gain
event_low_gain_before_remove_pedestal[i, :, :] = Ev.samples_low_gain
remove_pedestal(Ev, ped)
event_hi_gain_after_remove_pedestal[i, :, :] = Ev.samples_high_gain
event_low_gain_after_remove_pedestal[i, :, :] = Ev.samples_low_gain
plot_hist(event_hi_gain_before_remove_pedestal, "Hi Gain")
plot_hist(event_hi_gain_after_remove_pedestal, "Hi Gain")
plot_hist(event_low_gain_before_remove_pedestal, "Low Gain")
plot_hist(event_low_gain_after_remove_pedestal, "Low Gain")
from protozfits.simple import File
from enum import Enum
import matplotlib.pyplot as plt
f = File("Run019_Periodic1kHz.fits.fz")
for row in f.RunHeader:
pass
row._asdict()
print(f.Events.header)
#32678 / Offset for uint16
event = next(f.Events)
event._asdict()
print(event._fields)
#higain = event.hiGain.waveforms.samples
print(event.cameraCounters)
print(event.cameraCounters.counters)
#lo_gain_samples = toNumPyArray(
# event.loGain.waveforms.samples
#).reshape(-1, event.loGain.waveforms.num_samples)
t = []
time = []
oldtime = []
import matplotlib.pyplot as plt
import numpy as np
from protozfits.simple import File
from pedestal import PedestalSimple, remove_pedestal
from event import Event
plt.rcParams.update({'font.size': 32})
N1 = 20000
f1 = File("../data/Run021.1.fits.fz")
ped = PedestalSimple()
for i in range(0, N1):
ev = next(f1.Events)
Ev = Event(ev)
Ev.read()
ped.fill_pedestal_event(Ev)
ped.finalize_pedestal()
N2 = 1000
event_hi_gain_before_remove_pedestal = np.zeros((N2, 8, 40))
event_hi_gain_after_remove_pedestal = np.zeros((N2, 8, 40))
for i in range(0, N2):
ev = next(f1.Events)
Ev = Event(ev)
Ev.read()
event_hi_gain_before_remove_pedestal[i, :, :] = Ev.samples_high_gain
remove_pedestal(Ev, ped)
event_hi_gain_after_remove_pedestal[i, :, :] = Ev.samples_high_gain
high = 0
low = 1
fc = np.zeros((2, 8))
for i in [0, 2, 4, 6]:
fc[high][i] = first_capacitor[i]
fc[high][i + 1] = first_capacitor[i]
for j in [1, 3, 5, 7]:
fc[low][j - 1] = first_capacitor[j]
fc[low][j] = first_capacitor[j]
return fc
f = File("Run021.1.fits.fz")
N = 15000
n_channels = 8
n_modules = 19
RoI = 40
size4drs = 4 * 1024
hi_gain_samples = np.zeros((N, n_channels, RoI))
low_gain_samples = np.zeros((N, n_channels, RoI))
channel_pedestal = np.zeros((n_channels, 2, size4drs))
number_pedestal_event = np.zeros((n_channels, 2, size4drs))
for i in range(0, N):
event = next(f.Events)
hiGain, lowGain = translate_fits(event)
fc = read_first_capacitor(event)