import numpy as np
import spinmob as sm
from matplotlib import pyplot as plt
from spectrum import get_spectrum_chn, add_spectra
# bins used by Maestro
bins = np.arange(0, 2048)
# data used for calibration
pulse_heights_calib_old = np.array([260, 284, 304, 330, 430]) # 380 is all weird-looking
pulse_heights_calib_new = np.array([110, 180, 250, 360]) # 320 '' '' '' - ''
# for differential pressure (stopping power) measurement
fnames_pulse_calib_old = ["data/calib_old/calib" + str(height) + ".Chn"
for height in pulse_heights_calib_old]
spectra_pulse_calib_old = [get_spectrum_chn(fname)
for fname in fnames_pulse_calib_old]
# for energy, halflife, and branching ratio measurements
fnames_pulse_calib_new = ["data/calib_new/calib_" + str(height) + ".Chn"
for height in pulse_heights_calib_new]
spectra_pulse_calib_new = [get_spectrum_chn(fname)
for fname in fnames_pulse_calib_new]
# for differential pressure (stopping power) measurement
spectrum_Am_calib_old = get_spectrum_chn("data/calib_old/calib_Am_25mTorr.Chn")
# for energy, halflife, and branching ratio measurements
spectrum_Am_calib_new = get_spectrum_chn("data/calib_new/calib_Am.Chn")
fnames_Tr = ["data/long_charge/tr_%03d.Chn" % (n,) for n in range(288)]
# -*- coding: utf-8 -*-
import numpy as np
import spinmob as sm
from spectrum import get_spectrum_chn
# bins used by Maestro
bins = np.arange(0, 2048)
# data used for calibration
pulse_heights_calib = np.array([260, 284, 304, 330, 430]) # 380 is all weird-looking
fnames_pulse_calib = ["data/calib/calib" + str(height) + ".Chn"
for height in pulse_heights_calib]
spectra_pulse_calib = [get_spectrum_chn(fname)
for fname in fnames_pulse_calib]
spectrum_Am_calib = get_spectrum_chn("data/calib/calib_Am_25mTorr.Chn")
fnames_short_charge = ["data/short_charge/Tr%03d.Chn" % (n,)
for n in range(270)]
spectra_short_charge = [get_spectrum_chn(fname)
for fname in fnames_short_charge]
fnames_long_charge = ["data/long_charge/Tr%03d_long.Chn" % (n,)
for n in range(264)]
spectra_long_charge = [get_spectrum_chn(fname)
for fname in fnames_long_charge]
def _gaussian(x, sigma):
norm_factor = 1 / (sigma * np.sqrt(2 * np.pi))
