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))