def main(input_file,limits,save=False,plot=False,dofit=False):
data = import_local_csv(input_file,limits[0],limits[1])
integrated = sum(data)
if save:
np.savetxt('output.dat',integrated, delimiter='\n')
if plot:
fig = plt.figure()
fig.patch.set_facecolor('white')
plt.title('Spectra')
plt.xlabel('channels')
plt.ylabel('counts')
plt.xlim(limits[0],limits[1])
x = list(range(limits[0],len(integrated)+limits[0]))
plt.plot(x,integrated,'b:',label='data')
plt.yscale('log')
plt.show()
if dofit:
mean,sigma,amp = fitter.single_peak_fit(integrated,limits[0],limits[1],20.0,50,True)
print('mean = {}, sigma = {}, amp = {}'.format(mean,sigma,amp))
peak_counts = fitter.get_peak_counts(mean[0],sigma[0],amp[0])
raw_counts = fitter.get_gross_counts(integrated,0,5000)
print('peak counts = {}'.format(peak_counts))
print('gross counts = {}'.format(raw_counts))
def main(input_file,limits,save=False,plot=False,dofit=False):
data = import_local_csv(input_file,limits[0],limits[1])
integrated = sum(data)
if save:
np.savetxt('output.dat',integrated, delimiter='\n')
if plot:
fig = plt.figure()
fig.patch.set_facecolor('white')
plt.title('Spectra')
plt.xlabel('channels')
plt.ylabel('counts')
plt.xlim(limits[0],limits[1])
x = list(range(limits[0],len(integrated)+limits[0]))
plt.plot(x,integrated,'b:',label='data')
plt.yscale('log')
plt.show()
if dofit:
mean,sigma,amp = fitter.single_peak_fit(integrated,limits[0],limits[1],20.0,50,True)
print('mean = {}, sigma = {}, amp = {}'.format(mean,sigma,amp))
peak_counts = fitter.get_peak_counts(mean[0],sigma[0],amp[0])
raw_counts = fitter.get_gross_counts(integrated,0,5000)
print('peak counts = {}'.format(peak_counts))
print('gross counts = {}'.format(raw_counts))
def main(times, spectra, nhours, stationID=0, wtimes=[], temps=[]):
#---------------------------------------------------------------------#
# Get fit results for ndays integrating over nhours for Potassium
#---------------------------------------------------------------------#
# single_peak_fit args: channel lims, expo offset, plot flag
#args = [210,310,100,False]
#args = [180,280,100,True]
args = [360, 780, 7.0, 100, False, 'K']
calibs, calib_err = get_calibrations(spectra, fitter.single_peak_fit, args)
E_spectra, bin_times = calibrate_spectra(spectra, calibs, times, nhours)
args = [180, 390, 7.0, 100, False, 'K']
K_peaks, K_sigmas, K_amps = get_peak_fits(E_spectra, \
fitter.single_peak_fit,args)
#-------------------------------------------------------------------------#
# Varify and break apart mean,sigma,amp values and uncertainties
#-------------------------------------------------------------------------#
K_ch, K_ch_errs = get_arrays(K_peaks)
K_sig = [i[0] for i in K_sigmas]
K_A = [i[0] for i in K_amps]
K_ch_ave, K_ch_var = get_stats(K_ch)
K_counts = fitter.get_peak_counts(K_ch, K_sig, K_A)
K_count = cut_outliers(K_counts)
K_mean, K_var = get_stats(np.asarray(K_counts))
for i in range(len(K_ch)):
if abs(K_ch[i] - K_ch_ave) > 3 * K_ch_var:
print('Bad K-40 fit: peak channel = {}'.format(K_ch[i]))
#---------------------------------------------------------------------#
# Do the same for Bizmuth-214
#---------------------------------------------------------------------#
# double_peak_fit args: channel lims, gaus index, expo offset, plot flag
#args = [50,130,1,1,True]
if stationID == 0:
args = [50, 130, 1, 1, False, 'Bi']
Bi_peaks,Bi_sigmas,Bi_amps = get_peak_fits(E_spectra, \
fitter.double_peak_fit,args)
if stationID == 1:
args = [90, 150, 5.0, 1, False, 'Bi']
Bi_peaks,Bi_sigmas,Bi_amps = get_peak_fits(E_spectra, \
fitter.single_peak_fit,args)
Bi_ch, Bi_ch_errs = get_arrays(Bi_peaks)
Bi_sig = [i[0] for i in Bi_sigmas]
Bi_A = [i[0] for i in Bi_amps]
B_ch_ave, B_ch_var = get_stats(Bi_ch)
#-------------------------------------------------------------------------#
# Process channel data using fit results
#-------------------------------------------------------------------------#
Bi_counts = fitter.get_peak_counts(Bi_ch, Bi_sig, Bi_A)
Bi_counts = cut_outliers(Bi_counts)
Bi_mean, Bi_var = get_stats(np.asarray(Bi_counts))
print('K-40 <channel> = {} +/- {}'.format(K_ch_ave, K_ch_var))
print('K-40 <N> = {} +/- {}'.format(K_mean, K_var))
print('Bi-214 <channel> = {} +/- {}'.format(B_ch_ave, B_ch_var))
print('Bi-214 <N> = {} +/- {}'.format(Bi_mean, Bi_var))
#-------------------------------------------------------------------------#
# Process weather data
#-------------------------------------------------------------------------#
# LBL weather station
#location = 'KCABERKE89'
#location = 'KCABERKE86'
#wtimes,temps = get_weather_data(location,nhours,tstart,tstop)
times_both, counts, temps = merge_data(bin_times, Bi_counts, wtimes, temps)
#-------------------------------------------------------------------------#
# Plots of everything we are interested in!
#-------------------------------------------------------------------------#
make_plot(bin_times,K_counts,np.sqrt(K_counts), \
'Time','counts','K-40 counts vs Time','go','g')
fig_name = '/Users/alihanks/Google Drive/NQUAKE_analysis/D3S/K_counts_{}_5-8.png'.format(
stationID)
plt.savefig(fig_name)
make_plot(times,calibs,calib_err, \
'Time','keV/channel','keV/channel vs Time','bo','b', \
2.4,2.6)
fig_name = '/Users/alihanks/Google Drive/NQUAKE_analysis/D3S/calibs_{}_5-8.png'.format(
stationID)
plt.savefig(fig_name)
make_plot(bin_times,Bi_counts,np.sqrt(Bi_counts), \
'Time','counts','Bi-214 counts vs Time','go','g')
fig_name = '/Users/alihanks/Google Drive/NQUAKE_analysis/D3S/Bi_counts_{}_5-8.png'.format(
stationID)
plt.savefig(fig_name)
#make_plot(Ktimes,K_ch,K_ch_errs, \
# 'Time','1460 center channel','1460 channel vs Time','ro','r')
#make_plot(times,Bi_ch,Bi_ch_errs, \
# 'Time','609 center channel','609 channel vs Time','ro','r', \
# B_ch_ave-10*B_ch_var,B_ch_ave+10*B_ch_var)
make_plot(temps,counts,np.sqrt(counts), \
'Temp (F)','Bi-214 counts','Bi-214 counts vs Temp (F)','ro','r')
fig_name = '/Users/alihanks/Google Drive/NQUAKE_analysis/D3S/Bi_counts_vs_T_{}_5-8.png'.format(
stationID)
plt.savefig(fig_name)
plt.show()
def main(times,spectra,nhours,stationID=0,wtimes=[],temps=[]):
#---------------------------------------------------------------------#
# Get fit results for ndays integrating over nhours for Potassium
#---------------------------------------------------------------------#
# single_peak_fit args: channel lims, expo offset, plot flag
#args = [210,310,100,False]
#args = [180,280,100,True]
args = [360,780,7.0,100,False,'K']
calibs,calib_err = get_calibrations(spectra, fitter.single_peak_fit,args)
E_spectra, bin_times = calibrate_spectra(spectra,calibs,times,nhours)
args = [180,390,7.0,100,False,'K']
K_peaks, K_sigmas, K_amps = get_peak_fits(E_spectra, \
fitter.single_peak_fit,args)
#-------------------------------------------------------------------------#
# Varify and break apart mean,sigma,amp values and uncertainties
#-------------------------------------------------------------------------#
K_ch, K_ch_errs = get_arrays(K_peaks)
K_sig = [i[0] for i in K_sigmas]
K_A = [i[0] for i in K_amps]
K_ch_ave, K_ch_var = get_stats(K_ch)
K_counts = fitter.get_peak_counts(K_ch,K_sig,K_A)
K_count = cut_outliers(K_counts)
K_mean, K_var = get_stats(np.asarray(K_counts))
for i in range(len(K_ch)):
if abs(K_ch[i]-K_ch_ave) > 3*K_ch_var:
print('Bad K-40 fit: peak channel = {}'.format(K_ch[i]))
#---------------------------------------------------------------------#
# Do the same for Bizmuth-214
#---------------------------------------------------------------------#
# double_peak_fit args: channel lims, gaus index, expo offset, plot flag
#args = [50,130,1,1,True]
if stationID==0:
args = [50,130,1,1,False,'Bi']
Bi_peaks,Bi_sigmas,Bi_amps = get_peak_fits(E_spectra, \
fitter.double_peak_fit,args)
if stationID==1:
args = [90,150,5.0,1,False,'Bi']
Bi_peaks,Bi_sigmas,Bi_amps = get_peak_fits(E_spectra, \
fitter.single_peak_fit,args)
Bi_ch, Bi_ch_errs = get_arrays(Bi_peaks)
Bi_sig = [i[0] for i in Bi_sigmas]
Bi_A = [i[0] for i in Bi_amps]
B_ch_ave,B_ch_var = get_stats(Bi_ch)
#-------------------------------------------------------------------------#
# Process channel data using fit results
#-------------------------------------------------------------------------#
Bi_counts = fitter.get_peak_counts(Bi_ch,Bi_sig,Bi_A)
Bi_counts = cut_outliers(Bi_counts)
Bi_mean, Bi_var = get_stats(np.asarray(Bi_counts))
print('K-40 <channel> = {} +/- {}'.format(K_ch_ave,K_ch_var))
print('K-40 <N> = {} +/- {}'.format(K_mean,K_var))
print('Bi-214 <channel> = {} +/- {}'.format(B_ch_ave,B_ch_var))
print('Bi-214 <N> = {} +/- {}'.format(Bi_mean,Bi_var))
#-------------------------------------------------------------------------#
# Process weather data
#-------------------------------------------------------------------------#
# LBL weather station
#location = 'KCABERKE89'
#location = 'KCABERKE86'
#wtimes,temps = get_weather_data(location,nhours,tstart,tstop)
times_both,counts,temps = merge_data(bin_times,Bi_counts,wtimes,temps)
#-------------------------------------------------------------------------#
# Plots of everything we are interested in!
#-------------------------------------------------------------------------#
make_plot(bin_times,K_counts,np.sqrt(K_counts), \
'Time','counts','K-40 counts vs Time','go','g')
fig_name = '/Users/alihanks/Google Drive/NQUAKE_analysis/D3S/K_counts_{}_5-8.png'.format(stationID)
plt.savefig(fig_name)
make_plot(times,calibs,calib_err, \
'Time','keV/channel','keV/channel vs Time','bo','b', \
2.4,2.6)
fig_name = '/Users/alihanks/Google Drive/NQUAKE_analysis/D3S/calibs_{}_5-8.png'.format(stationID)
plt.savefig(fig_name)
make_plot(bin_times,Bi_counts,np.sqrt(Bi_counts), \
'Time','counts','Bi-214 counts vs Time','go','g')
fig_name = '/Users/alihanks/Google Drive/NQUAKE_analysis/D3S/Bi_counts_{}_5-8.png'.format(stationID)
plt.savefig(fig_name)
#make_plot(Ktimes,K_ch,K_ch_errs, \
# 'Time','1460 center channel','1460 channel vs Time','ro','r')
#make_plot(times,Bi_ch,Bi_ch_errs, \
# 'Time','609 center channel','609 channel vs Time','ro','r', \
# B_ch_ave-10*B_ch_var,B_ch_ave+10*B_ch_var)
make_plot(temps,counts,np.sqrt(counts), \
'Temp (F)','Bi-214 counts','Bi-214 counts vs Temp (F)','ro','r')
fig_name = '/Users/alihanks/Google Drive/NQUAKE_analysis/D3S/Bi_counts_vs_T_{}_5-8.png'.format(stationID)
plt.savefig(fig_name)
plt.show()