def getPeakTimes(n = [],
ybins = [],
expected_tdelay = 76.,
refl_peak_width = 5.,
plot= False,
debug = False):
# ycenters
ycenters = (ybins[1:] + ybins[:-1])/2.
# bin width
bin_width = ybins[1]-ybins[0]
# Find the highest peak
prompt_peak_index = n.argmax()
t1 = ybins[prompt_peak_index]
smooth_coeff = 1./bin_width
nsmooth = ndimage.filters.gaussian_filter1d(n, sigma=smooth_coeff)
nsmooth[nsmooth<=0] = 1
# Select a range where the second peak can be
refl_peak_index = prompt_peak_index + int(expected_tdelay/bin_width)
refl_si = refl_peak_index - int(refl_peak_width/bin_width)
refl_ei = refl_peak_index + int((refl_peak_width*2)/bin_width)
refl_ei = np.min([refl_ei, ybins.size-1])
# Peak finding algorithm
peaks = detect_peaks(nsmooth[refl_si:refl_ei],
mpd = 10*smooth_coeff,
edge='rising',show=False, mph=1.1)
if peaks.size == 0:
t2 = -1
else:
t2 = ybins[refl_si:refl_ei][peaks].max()
terr1 = terr2 = 1.
if plot:
fig = plt.figure(figsize=(7,4))
ax1 = fig.add_subplot(111)
jplot.unfilledBar(ybins, n, color='0.7')
jplot.unfilledBar(ybins, nsmooth)
plt.yscale('log')
plt.plot((ybins[refl_si:refl_ei][peaks]+ybins[refl_si:refl_ei][peaks-1])/2.,
nsmooth[refl_si:refl_ei][peaks], 'xg')
plt.ylabel('N hits')
plt.axvline(x = t2, ymin=0, ymax = n.max()*1.2,
linestyle='--', color='k')
plt.axvline(x = t1, ymin=0, ymax = n.max()*1.2,
linestyle='--', color='k')
print t1, terr1, t2, terr2
return t1, terr1, t2, terr2
def getGausTime(n=[], ybins=[], prompt_peak_width=4., plot=False, debug=False):
# ycenters
ycenters = (ybins[1:] + ybins[:-1]) / 2.
# bin width
bin_width = ybins[1] - ybins[0]
# Find the highest peak
prompt_peak_index = n.argmax()
if n[prompt_peak_index] < 20:
return -1, -1
# First peak range (in bins)
fit_s = prompt_peak_index - int(prompt_peak_width / bin_width)
fit_e = prompt_peak_index + int(prompt_peak_width / bin_width)
# Fit a gaussian +/- prompt_peak_width ns around the peak
popt = [n[prompt_peak_index] / 2., ycenters[prompt_peak_index], 3.]
try:
popt, pcov = optimize.curve_fit(
analysis.gaus,
ycenters[fit_s:fit_e + 1],
n[fit_s:fit_e + 1],
popt,
)
perr = np.sqrt(np.diag(pcov))
terr1 = perr[1]
t1 = popt[1]
except:
terr1 = -1
t1 = -1
if plot:
fig = plt.figure(figsize=(7, 4))
ax1 = fig.add_subplot(111)
jplot.unfilledBar(ybins, n, color='0.7')
newx = np.linspace(ycenters[fit_s] - 3., ycenters[fit_e] + 3., 101)
plt.plot(newx, analysis.gaus(newx, *popt), 'r')
plt.yscale('log')
plt.ylabel('N hits')
plt.ylim([1., n[prompt_peak_index] * 1.3])
return fig
#raw_input()
return t1, terr1
def arrayDiagnostics(values, name, ax_lim = [None, None]):
print 'Diagnostics', name
print values.min(),values.max(), values.mean()
fig = plt.figure()
ax = fig.add_subplot(111)
if ax_lim[0] == None:
ax_lim[0] = 0.
if ax_lim[1] == None:
ax_lim[1] = np.median((values[values>0]))*10
xaxis = np.linspace(ax_lim[0], ax_lim[1], 100)
b, x = np.histogram(values, xaxis)
jplot.unfilledBar(x,b)
plt.yscale('log')
plt.xlabel(name)
return fig, ax
def plotOccupancy(pmt_list, pmt_info, hit_lim=[-1, -1]):
pmt_list = pmt_list[:pmt_info['phi'].size]
print pmt_list.shape, pmt_info['phi'].size
pmt_radii = np.sqrt(np.sum(pmt_info['xyz']**2, axis=1))
pmt_bool = (pmt_radii < 8500.) * (pmt_list > 0.)
print hit_lim
if hit_lim[0] < 0:
aux = pmt_list.mean() - 2.5 * pmt_list.std()
if aux < 0:
hit_lim[0] = 0.
else:
hit_lim[0] = aux
if hit_lim[1] < 0:
hit_lim[1] = pmt_list.mean() + 1.5 * pmt_list.std()
print hit_lim
# Print the histogram of the occupancy
f1 = plt.figure()
xaxis = np.linspace(hit_lim[0], hit_lim[1], 201)
b, x = np.histogram(pmt_list[pmt_bool], xaxis)
jplot.unfilledBar(x, b)
plt.xlabel('Nhits')
plt.ylabel('Number of PMTs')
plt.show()
f2 = plt.figure(figsize=(12, 7))
ax = f2.add_subplot(111)
plt.scatter(pmt_info['phi'][pmt_bool] / np.pi,
pmt_info['costheta'][pmt_bool],
c=pmt_list[pmt_bool],
cmap='jet',
vmin=hit_lim[0],
vmax=hit_lim[1],
marker='o',
s=18,
lw=0)
plt.colorbar()
plt.xlim([-1, 1])
plt.ylim([-1, 1])
plt.xlabel('Phi / pi')
plt.ylabel('cos(theta)')
plt.show()
figs = []
colors = ['b', 'r', 'c', 'g']
fig = plt.figure(figsize=(9, 18))
recoz_edges = aehist['reco_cosZ_edges']
recoe_edges = aehist['reco_logE_edges']
plotlims = [0.9, 1.1]
counter = 1
for eband in range(aehist['nue_histo'].shape[0] - 1):
fig.add_subplot(aehist['nue_histo'].shape[0], 2, counter)
for i, hkey in enumerate(mchist.keys()):
hratio = norm * aehist[hkey] / mchist[hkey][:, ::-1]
jplot.unfilledBar(recoz_edges,
hratio[eband, :],
color=colors[i],
label=hkey)
counter += 1
plt.ylim(plotlims)
plt.xlabel('cos(zenith)')
fig.add_subplot(aehist['nue_histo'].shape[1], 2, counter)
for i, hkey in enumerate(mchist.keys()):
hratio = norm * aehist[hkey] / mchist[hkey][:, ::-1]
jplot.unfilledBar(recoe_edges,
hratio[:, eband],
color=colors[i],
label=hkey)
counter += 1
#plt.legend(loc=0)
plt.ylim(plotlims)
def plotTOA( data = None,
position = np.array([0,0,0]),
theta_bins = 6,
time_window = 120.,
time_rebin=1,
av_reference = False,
color = None,
label=None,
newfig = True):
time_bin = data['time_edges'][1]-data['time_edges'][0]
# Time rebinning
time_nbins = int(np.ceil(time_window/time_bin))
nbins = 5
theta_edges = np.linspace(0, 180, theta_bins+1)
if av_reference:
a = psup_r
b = mynorm(position)
c = mynorm(position-pmt_info['xyz'], axis=1)
mod_ct = (a**2 + b**2 - c**2)/(2*a*b)
mod_theta = np.rad2deg(np.arccos(mod_ct))
else:
mod_pos = pmt_info['xyz']-position
mod_ct = mod_pos[:,2]/pmt_info['r']
mod_theta = np.rad2deg(np.arccos(mod_ct))
if newfig:
fig = plt.figure(figsize=(12,7))
#Rebin?
time_edges = data['time_edges']
if time_rebin > 1:
new_time = time_edges[::time_rebin][:-1]
time_edges = new_time
for i in range(len(theta_edges)-1):
mybool = pmtbool*(mod_theta>theta_edges[i])*(mod_theta<theta_edges[i+1])
norm=1.
hits = data['toa'][:len(mybool),:][mybool].sum(axis=0)*1.
if time_rebin > 1:
hout = hits.cumsum()[(time_rebin-1)::time_rebin]
hdiff = hout[1:] - hout[:-1]
hits = hdiff
#print hits
imax = hits.argmax()
#print imax
hits /= hits[imax-5:imax+5].sum()
#print hits
hits /= (2**i)
if color == None:
this_color= cs[i]
else:
this_color = color
if label == None:
mylabel = "%i" % theta_edges[i] + ' - ' + "%i" % theta_edges[i+1]
else:
mylabel = label
jplot.unfilledBar(time_edges - time_edges[imax], #+t0[i], ##-data_t0,
hits*norm,#*1./real_data['toa'].sum(),
color = this_color,
label = label)
plt.legend(bbox_to_anchor=(1.15, 1.),
loc='upper right')
plt.yscale('log')
plt.xlim(-10, 100)
#plt.ylim(10,400000)
plt.ylabel('Hits')
plt.xlabel('Corrected time residual (ns)')
if this_time < 0:
continue
toa_series_manip[iPMT] = one_pmt.GetTime() - travel_time_manip
toa_series_fit[iPMT] = one_pmt.GetTime() - travel_time_fit
all_qhl[counter] += one_pmt.GetQHL()
all_qhs[counter] += one_pmt.GetQHS()
# First easy check - do the median
all_median_manip[counter] = np.median(toa_series_manip[toa_series_manip>0])
all_median_fit[counter] = np.median(toa_series_fit[toa_series_fit>0])
if np.abs(all_median_manip[counter]-310) > median_tolerance:
xaxis = np.arange(100, 325, 0.25)
counts, x = np.histogram(toa_series_manip, xaxis)
jplot.unfilledBar(xaxis, counts)
plt.show()
raw_input()
if counter %1000 == 0:
print counter
counter += 1
if counter == max_events:
print 'Max number of events reached. Exiting!'
exit_loop = True
if exit_loop:
break
if this_time < 0:
continue
toa_series_manip[iPMT] = one_pmt.GetTime() - travel_time_manip
toa_series_fit[iPMT] = one_pmt.GetTime() - travel_time_fit
all_qhl[counter] += one_pmt.GetQHL()
all_qhs[counter] += one_pmt.GetQHS()
# First easy check - do the median
all_median_manip[counter] = np.median(toa_series_manip[toa_series_manip>0])
all_median_fit[counter] = np.median(toa_series_fit[toa_series_fit>0])
if np.abs(all_median_manip[counter]-310) > median_tolerance:
xaxis = np.arange(100, 325, 0.25)
counts, x = np.histogram(toa_series_manip, xaxis)
jplot.unfilledBar(xaxis, counts)
plt.show()
raw_input()
if counter %1000 == 0:
print counter
counter += 1
if counter == max_events:
print 'Max number of events reached. Exiting!'
exit_loop = True
if exit_loop:
break
def collectResults(options):
indir = os.path.join(user.scans_dir, options.NAME)
scan_info = pickle.load(open(os.path.join(indir,'OscFit_ScanSettings.pckl')))
total_jobs = len(scan_info['theta23_map'])
dm2_list = scan_info['dm31_list']
theta23_list = scan_info['theta23_list']
fits_dir = os.path.join(indir,'fits')
infile_list = os.listdir(fits_dir)
infile_list.sort()
if len(infile_list) == 0:
print '\n **** LLH scan: No files were found! Exiting ****'
exit()
fit_results = ['dm31','theta23','theta13','mix_angle','norm_e',
'atmmu_f','noise_f', 'norm_nu',
'norm_tau','numubar_e2','numubar_z2','gamma','axm_qe','axm_res',
'pid_bias','domeff','hole_ice','had_escale','norm_nc']
skip_params = ['theta23', 'dm31','mix_angle']
# Initialize scan results
scan_results = {}
for fit_key in fit_results:
scan_results[fit_key] = []
theta23_injected = []
dm31_injected = []
missing_files = []
nan_files = []
index = 0
error = 0
counter = 0
# Lazy coding. Just look for the number
for dm31_i in range(0, len(scan_info['dm31_list'])):
if len(infile_list) == 0:
break
for theta23_i in range(0, len(scan_info['theta23_list'])):
if len(infile_list) == 0:
break
required_task = 'Task_' + "%06i" % index + '.pckl'
if required_task in infile_list:
one_fit = pickle.load(open(os.path.join(fits_dir, required_task)))
infile_list.pop(infile_list.index(required_task))
if one_fit['llh'] == None or isnan(one_fit['llh']):# or not one_fit['successful_fit']:
#print one_fit['llh']
nan_files.append(index+1)
else:
for fit_key in fit_results:
scan_results[fit_key].append( one_fit[fit_key])
dm31_injected.append(dm2_list[dm31_i])
theta23_injected.append(theta23_list[theta23_i])
else:
missing_files.append(index+1)
index += 1
theta23_injected = np.array(theta23_injected)
dm31_injected = np.array(dm31_injected)
print theta23_injected.shape
print '\nLLH scan: Missing files -',len(missing_files)
print 'LLH scan: NAN files -', len(nan_files)
missing_files = missing_files+ nan_files
if len(missing_files) > 0 and options.DOMISSING:
print 'LLH scan: Trying to reprocess missing files with mode ', options.MODE, '. Continue?'
raw_input()
submit_string = ' '.join(['./run_mctest_scan.py -n', options.NAME,'-m',options.MODE,'-j ']) + \
','.join(["%i" % x for x in missing_files])
print submit_string
os.system(submit_string)
exit()
# Produce histograms and figures here
# Theta23-Dm31 map
fig = plt.figure()
if scan_info['data_settings']['oscMode'] == 'TwoNeutrino':
t23diff = np.array(theta23_injected) - np.array(scan_results['mix_angle'])
plt.plot(theta23_injected, dm31_injected*10**3, 'xr')
plt.plot(scan_results['mix_angle'], np.array(scan_results['dm31'])*10**3, '.b')
plt.xlabel(r'$\sin^2(2\theta_{23})$', fontsize = 'large')
t23key = 'Mixing angle'
else:
t23diff = np.sin(theta23_injected)**2 - np.sin(scan_results['theta23'])**2
plt.plot(np.sin(theta23_injected)**2, dm31_injected*10**3, 'xr')
plt.plot(np.sin(scan_results['theta23'])**2, np.array(scan_results['dm31'])*10**3, '.b')
plt.xlabel(r'$\sin^2(\theta_{23})$', fontsize = 'large')
t23key = 'sin(theta)**2'
fig.savefig(os.path.join(indir,'FullScan.png'))
fig = plt.figure()
xaxis = np.linspace(np.min(t23diff), np.max(t23diff), 22)*1.1
n, x = np.histogram(t23diff, xaxis)
jplot.unfilledBar(x, n)
plt.ylabel('Counts')
plt.xlabel(t23key)
fig.savefig(os.path.join(indir, 'Theta23.png'))
fig = plt.figure()
dm31diff = (dm31_injected - np.array(scan_results['dm31']))*10**3
xaxis = np.linspace(np.min(dm31diff), np.max(dm31diff), 22)*1.1
n, x = np.histogram(dm31diff, xaxis)
jplot.unfilledBar(x, n)
plt.ylabel('Counts')
plt.xlabel('DM31 (10**3 eV)')
fig.savefig(os.path.join(indir, 'DM31.png'))
# Parameter-wise figures
for one_key in fit_results:
if one_key in skip_params:
continue
pdiff = np.array(scan_results[one_key]) - scan_info['data_settings'][one_key]
if np.mean(pdiff) == 0:
continue
xaxis = np.linspace(np.min(pdiff), np.max(pdiff), 22)*1.1
n, x = np.histogram(pdiff, xaxis)
fig = plt.figure()
jplot.unfilledBar(x, n)
plt.xlabel(one_key)
plt.ylabel('Counts')
fig.savefig(os.path.join(indir, one_key + '.png'))
def getDPeakTimes(n = [],
ybins = [],
expected_tdelay = 76.,
refl_peak_width=5.,
plot= False,
debug = False):
if n.sum() == 0:
return -1, -1, -1, -1
# ycenters
ycenters = (ybins[1:] + ybins[:-1])/2.
# bin width
bin_width = ybins[1]-ybins[0]
smooth_coeff = 1./bin_width
nsmooth = ndimage.filters.gaussian_filter1d(n, sigma=smooth_coeff)
nsmooth[nsmooth<=0] = 1
# Finding the peaks of the derivative
dx = (ybins[1]-ybins[0])/2.
ydn = ybins[1:-1]
# Derivating, and smoothing over it
dnsmooth = np.diff(np.log10(nsmooth))/dx
dnsmooth = ndimage.filters.gaussian_filter1d(dnsmooth,
sigma=smooth_coeff)
# Highest peak is easy
prompt_minus = int(10./bin_width)
prompt_region = [n.argmax()-prompt_minus, n.argmax()]
try:
prompt_peak_index = prompt_region[0]+dnsmooth[prompt_region[0]:prompt_region[1]].argmax()
except:
# Errors appear here when the histogram is too empty
# print 'Not using this PMT'
return -2, -2, -2, -2
t1 = ydn[prompt_peak_index]
refl_peak_index = prompt_peak_index + int(expected_tdelay/bin_width)
refl_si = refl_peak_index - int(refl_peak_width/bin_width)
refl_ei = refl_peak_index + int((refl_peak_width*2)/bin_width)
refl_ei = np.min([refl_ei, ybins.size-1])
#print refl_si, refl_ei
# Finding the peaks
dpeaks = detect_peaks(dnsmooth[refl_si:refl_ei],
mpd = 10*smooth_coeff,
edge='rising',show=False, mph=0.001)
if dpeaks.size == 0:
t2 = -1
else:
t2 = ydn[refl_si:refl_ei][dpeaks].max()
terr1 = terr2 = 1.
if plot:
fig = plt.figure(figsize=(7,4))
ax1 = fig.add_subplot(111)
print 'Peaks', dpeaks
jplot.unfilledBar(ybins, np.log10(n)*dnsmooth.max()/np.log10(n.max()), color='0.7')
jplot.plot(ydn, dnsmooth)
jplot.plot([ydn[refl_si],ydn[refl_ei]], [1.,1.], 'or')
if len(dpeaks) > 0:
plt.plot((ydn[refl_si:refl_ei][dpeaks]+ydn[refl_si:refl_ei][dpeaks-1])/2.,
dnsmooth[refl_si:refl_ei][dpeaks], 'xg')
plt.ylabel('N hits')
plt.axvline(x = t2, ymin=0, ymax = dnsmooth.max()*1.2,
linestyle='--', color='k')
plt.axvline(x = t1, ymin=0, ymax = dnsmooth.max()*1.2,
linestyle='--', color='k')
print t1, terr1, t2, terr2
return t1, terr1, t2, terr2
def volumeAnalysis(data,
volume=[],
radius_range=[0, np.inf],
extra_conditions={},
theta_bins=41,
phi_bins=61,
plot_mode='histogram',
plot_nstd=2.,
radius_plot=True):
if type(volume) != list:
volume = [volume]
radius_range = np.array(radius_range)
mybool = np.array([False] * len(data['end_volume']))
for one_volume in volume:
mybool = mybool | (data['end_volume'] == one_volume)
# Additional conditions
for one_key in extra_conditions.keys():
mybool *= data[one_key] >= extra_conditions[one_key].min()
mybool *= data[one_key] <= extra_conditions[one_key].max()
positions = data['end_position'][mybool, :]
# Converting to spherical coordinates
r = np.linalg.norm(positions, axis=1)
rbool = (r > radius_range.min()) * (r < radius_range.max())
phi = np.arctan2(positions[rbool, 1], positions[rbool, 0])
theta = -1 * (np.arccos(positions[rbool, 2] / r[rbool]) - np.pi / 2.)
# Plotting the events as function of r
raxis = np.linspace(0, 8000., 201)
raxis_eff = deepcopy(raxis)
raxis_eff[-1] = np.inf
if radius_plot:
b, x = np.histogram(r, raxis_eff)
fig1 = plt.figure(figsize=figsize_small)
jplot.unfilledBar(raxis, b)
jplot.unfilledBar(raxis,
b.max() * np.cumsum(b) * 1. / b.sum(),
color='green')
plt.xlabel('Radius (mm)')
plt.ylabel('Photons absorbed')
if radius_range.max() < np.inf:
# Do the vertical lines here
plt.axvline(x=radius_range.min(),
ymax=b.max(),
linestyle='--',
color='r')
plt.axvline(x=radius_range.max(),
ymax=b.max(),
linestyle='--',
color='r')
# Axes for the projection
paxis = np.linspace(-np.pi, np.pi, phi_bins)
taxis = np.arccos(np.linspace(-1, 1, theta_bins))[::-1] - np.pi / 2.
# Plotting the events in a projection - histogrammed
fig2 = plt.figure(figsize=figsize)
plt.title('Photons absorbed')
ax = fig2.add_subplot(111, projection='mollweide')
if plot_mode == 'histogram' or plot_mode == 'combo':
b, x, y = np.histogram2d(theta, phi, [taxis, paxis])
nonzero = b > 0
bmean = b[nonzero].mean()
bstd = b[nonzero].std()
print 'Bin stats', b[nonzero].mean(), b[nonzero].std()
plt.pcolor(paxis,
taxis,
b,
vmin=bmean - plot_nstd * bstd,
vmax=bmean + plot_nstd * bstd)
plt.colorbar()
return b, x, y
if plot_mode == 'scatter' or plot_mode == 'combo':
plt.plot(phi, theta, '.', color='black')
native['time_edges'].max() - native_t0
])
plt.ylim([-50, 150])
plt.xlabel('PMT ID')
plt.ylabel('TOA - t0 (ns)')
plt.colorbar()
sum_data = np.log10(native['toa'].sum(axis=1) + 1)
nonzeroa = (sum_data > 0)
nonzerob = nonzeroa[:pmt_info['phi'].size]
occ = np.log10((native['toa'].sum(axis=1) + 1) / mc_triggers)
fig = plt.figure(figsize=figsize)
x = np.linspace(2, 3.5, 41)
b, x = np.histogram(sum_data[nonzeroa], x)
jplot.unfilledBar(x, b)
plt.xlabel('log(Nhit)')
plt.ylabel('PMTs')
fig = plt.figure(figsize=figsize)
#ax = fig.add_subplot(111, projection='mollweide')
plt.scatter(pmt_info['phi'][nonzerob],
pmt_info['costheta'][nonzerob],
s=10.,
c=sum_data[nonzeroa],
edgecolor='',
vmin=2.,
vmax=3.2)
plt.xlabel('phi')
plt.ylabel('cos(theta)')
time_array = np.concatenate((time_array,
this_array[this_array > 0]))
except:
print 'PMT problematic', one_pmt, new_list[isoc]
continue
if ipmt % 500 == 0:
print ipmt
# PMT ready. Make histogram and store time delay
time_residuals[:, one_pmt], pmt_delays[one_pmt] = makeHistogram(time_array)
data['time_residuals'] = time_residuals
data['pmt_delays'] = pmt_delays
pickle.dump(data, open(outfile_name,'w'))
for reader in reader_list:
reader.close()
# Verification for one file
fig = plt.figure()
colors = ['b','r','g','m']
for iTest, pmtid in enumerate(test_pmts):
jplot.unfilledBar(residual_axis + data['pmt_delays'][pmtid],
data['time_residuals'][:,pmtid], color = colors[iTest])
plt.yscale('log')
fig.savefig(outfile_name.rstrip('.pckl') + '.png')
def getGausTimes(n = [],
ybins = [],
expected_tdelay = 76.,
prompt_peak_width = 4.,
refl_peak_width = 4.5,
second_gaus = True,
plot = False,
debug = False):
# ycenters
ycenters = (ybins[1:] + ybins[:-1])/2.
# bin width
bin_width = ybins[1]-ybins[0]
# Find the highest peak
prompt_peak_index = n.argmax()
# First peak range (in bins)
fit_s = prompt_peak_index - int(prompt_peak_width/bin_width)
fit_e = prompt_peak_index + int(prompt_peak_width/bin_width)
# Fit a gaussian +/- prompt_peak_width ns around the peak
popt = [n[prompt_peak_index]/2., ycenters[prompt_peak_index],3.]
try:
popt, pcov = optimize.curve_fit(analysis.gaus,
ycenters[fit_s:fit_e+1],
n[fit_s:fit_e+1],
popt,
)
perr = np.sqrt(np.diag(pcov))
terr1 = perr[1]
t1 = popt[1]
except:
terr1 = -1
t1 = -1
# Fit a gaussian around the second peak, expected_tdelay-refl_peak_width+2*refl_peak_width
refl_peak_index = prompt_peak_index + int(expected_tdelay/bin_width)
fit_s2 = refl_peak_index - int(refl_peak_width/bin_width)
fit_e2 = refl_peak_index + int((refl_peak_width)/bin_width)
fit_e2 = np.min([fit_e2, ybins.size-1])
if refl_peak_index > (ybins.size-1):
terr2 = -1
t2 = -1
else:
if second_gaus:
try:
popt2 = [n[refl_peak_index]/2.,ycenters[refl_peak_index],3.]
popt2, pcov2 = optimize.curve_fit(analysis.gaus,
ycenters[fit_s2:fit_e2+1],
n[fit_s2:fit_e2+1],
popt2)
perr2 = np.sqrt(np.diag(pcov2))
terr2 = perr2[1]
t2 = popt2[1]
except:
# There are errors here when the ToA histogram is empty
terr2 = -1
t2 = -1
else:
t2 = ycenters[fit_s2 + n[fit_s2:].argmax()]
terr2 = -1
if plot:
print 'Times', t1, terr1, t2, terr2
fig = plt.figure(figsize=(7,4))
ax1 = fig.add_subplot(111)
jplot.unfilledBar(ybins, n, color='0.7')
newx = np.linspace(ycenters[fit_s],
ycenters[fit_e], 101)
plt.plot(newx, analysis.gaus(newx, *popt), 'r')
print t1+expected_tdelay
plt.axvline(x = t1 + expected_tdelay-refl_peak_width,
ymin=0, ymax = n.max()*1.2,
linestyle='--', color = 'k')
plt.axvline(x = t1 + expected_tdelay+refl_peak_width,
ymin=0, ymax = n.max()*1.2,
linestyle='--', color = 'k')
if second_gaus:
try:
newx = np.linspace(ycenters[fit_s2],
ycenters[fit_e2], 101)
plt.plot(newx, analysis.gaus(newx, *popt2), 'r')
plt.xlim([ycenters[prompt_peak_index]-20., ycenters[refl_peak_index] + 20])
except:
print 'Could not plot second gaus (out of bounds)'
else:
plt.axvline(x = t2, ymin =0, ymax = n.max()*1.2)
plt.yscale('log')
plt.ylabel('N hits')
plt.ylim([1., n[prompt_peak_index]*1.3])
return fig
#raw_input()
return t1, terr1, t2, terr2
print 'logE(reco_E)', logE_edges[logE], logE_edges[logE+1]
# This is a histogram of the bin weights
if doPlots and len(logw < 10) > 0:
fig = plt.figure(figsize=(12,6))
fig_title = flabel + '_' + \
"%0.2f" % cosZ_edges[cosZ] + '_' + "%0.2f" % cosZ_edges[cosZ+1] + '___' + \
"%0.2f" % logE_edges[logE] + '_' + "%0.2f" % logE_edges[logE+1]
# Distribution of event weights
fig.add_subplot(1,2,1)
xhist = np.linspace(0, 10, 101)
xhist[-1] = np.inf
bhist, x = np.histogram(logw, xhist)
jplot.unfilledBar(xhist, bhist)
plt.axvline(x=logw.mean()+2*logw.std(), lw=2, color='r', linestyle='-')
plt.axvline(x=logw.mean()+3*logw.std(), lw=2, color='r', linestyle='--')
plt.ylabel('N events')
plt.xlabel('weight (a.u.)')
plt.yscale('log')
# Effective area for one bin
fig.add_subplot(1,2,2)
plt.pcolor(trueE_edges, trueZ_edges, effarea_onebin, cmap='Blues')
plt.xlim([trueE_edges.min(), trueE_edges.max()])
plt.axvspan(logE_edges[logE], logE_edges[logE+1], lw=2,
facecolor='0.3', alpha=0.2)
plt.axhspan(cosZ_edges[cosZ], cosZ_edges[cosZ+1], lw=2,
facecolor='0.3', alpha=0.2)
def compareTracking(set_list=[],
costheta_range=[-1, 1],
outdir='',
pltlabels=[]):
reference = set_list[0]
if len(pltlabels) == 0:
pltlabels = generic_labels[:len(set_list)]
set_bool = []
for one_set in set_list:
set_bool.append((one_set['costheta_dir'] >= costheta_range[0])*\
(one_set['costheta_dir'] <= costheta_range[1]))
# Do the reference set first
ref_gen = len(reference['end_volume'][set_bool[0]])
labels_ref, values_ref = zip(
*collections.Counter(reference['end_volume'][set_bool[0]]).items())
labels_ref = np.array(labels_ref)
values_ref = np.array(values_ref)
sort_ref = np.argsort(labels_ref)
labels_ref = labels_ref[sort_ref]
values_ref = values_ref[sort_ref]
values_list = []
for i in range(1, len(set_list)):
set_gen = len(set_list[i]['end_volume'][set_bool[i]])
#print 'Difference in photons produced', ref_gen - set_gen, np.sqrt(ref_gen)
labels, values = zip(*collections.Counter(set_list[i]['end_volume'][
set_bool[i]]).items())
# Check if there are differences in the fields
diff_labels = list(set(labels_ref) - set(labels))
if len(diff_labels) > 0:
#print 'Labels missing ', diff_labels
labels = list(labels) + diff_labels
values = list(values) + [0] * len(diff_labels)
labels = np.array(labels)
values = np.array(values)
onesort = np.argsort(labels)
labels = labels[onesort]
values = values[onesort]
values_list.append(values)
fig1 = plt.figure(figsize=(7, 7))
ax1 = fig1.add_subplot(211)
plt.title('cos(theta) = [' + "%.2f" % costheta_range[0] + ', ' +
"%.2f" % costheta_range[1] + ']')
indices = np.arange(len(labels_ref) + 1)
jplot.unfilledBar(indices, values_ref, label=pltlabels[0])
plt.xticks(indices + 0.5, labels_ref)
plt.yscale('log')
plt.xlim([0, indices[-1]])
plt.ylabel('Photons')
for i in range(len(values_list)):
jplot.unfilledBar(indices,
values_list[i],
color=color_list[i],
label=pltlabels[i + 1])
plt.legend(loc=0)
ax2 = fig1.add_subplot(212, sharex=ax1)
#fig2 = plt.figure()
#plt.title('cos(theta) = [' + "%.2f" % costheta_range[0] + ', '+"%.2f" % costheta_range[1] + ']')
plt.ylabel('Ratio')
plt.plot([0, indices[-1]], [1, 1], '--k')
ref_error = np.sqrt(values_ref) / values_ref
for i in range(len(values_list)):
ratio = values_list[i] * 1. / values_ref
error = np.sqrt(2) * ratio * ref_error
jplot.unfilledBar(indices, ratio, color=color_list[i])
jplot.errorMarkVert(indices * 1.,
ratio,
yerror=error,
color=color_list[i])
print ratio
plt.xticks(indices + 0.5, labels_ref)
plt.ylim([0.9, 1.1])
#plt.ylim([0.65, 1.35])
plt.xlim([0, indices[-1]])
plt.subplots_adjust(hspace=0)
fig3 = plt.figure(figsize=(7, 7))
ax3 = fig3.add_subplot(211)
plt.title('cos(theta) = [' + "%.2f" % costheta_range[0] + ', ' +
"%.2f" % costheta_range[1] + ']')
plt.ylabel('Photons')
plt.xlabel('Steps taken')
myx = np.arange(0.5, 17, 1)
#myx[-1] = np.inf
ref_steps, x = np.histogram(reference['n_steps'][set_bool[0]] - 1, myx)
jplot.unfilledBar(myx, ref_steps, label=pltlabels[0])
for i in range(1, len(set_list)):
n_steps, x = np.histogram(set_list[i]['n_steps'][set_bool[i]] - 1, myx)
jplot.unfilledBar(myx,
n_steps,
color=color_list[i - 1],
label=pltlabels[i])
plt.yscale('log')
plt.legend(loc=0)
ax4 = fig3.add_subplot(212, sharex=ax3)
plt.ylabel('Ratio')
plt.xlabel('Steps taken')
for i in range(1, len(set_list)):
n_steps, x = np.histogram(set_list[i]['n_steps'][set_bool[i]] - 1, myx)
ratio = n_steps * 1. / ref_steps
error = np.sqrt(2) * ratio * np.sqrt(ref_steps) / ref_steps
jplot.unfilledBar(myx, ratio, color=color_list[i - 1])
jplot.errorMarkVert(myx, ratio, yerror=error, color=color_list[i - 1])
print ratio
plt.ylim([0.9, 1.1])
#plt.ylim([0.65, 1.35])
plt.subplots_adjust(hspace=0)
if len(outdir) > 0:
theta_str = "%.2f" % costheta_range[0] + '_' + "%.2f" % costheta_range[
1]
theta_str = theta_str.replace('.', '')
fig1.savefig(os.path.join(outdir, 'EndPhotons_' + theta_str + '.pdf'),
dpi=200)
#fig2.savefig(os.path.join(outdir, 'EndPhotonsRatio_'+theta_str+'.pdf'), dpi=200)
fig3.savefig(os.path.join(outdir, 'Steps_' + theta_str + '.pdf'),
dpi=200)
#fig4.savefig(os.path.join(outdir, 'StepsRatio_'+theta_str+'.pdf'), dpi=200)
return
def plotComparison(data=None,
datasets=[],
datakey='',
scale_factor=[],
labels=[],
figname='',
xaxis=np.arange(0, 100, 2),
xlabel='Nhits',
ylabel='Entries',
minall=-1,
outdir=None,
verbose=False):
if len(scale_factor) == 0:
scale_factor = [1.] * len(datasets)
if len(labels) == 0:
labels = datasets
# Loop over the different levels
for k, level in enumerate(['top', 'middle', 'bottom', 'all']):
# Reading each of the files one by one
if verbose: print '\n****', level, '*****'
nbins = []
myfig = plt.figure(figsize=(8, 5))
if level == 'all':
if minall < 0:
xaxis = np.arange(1.3 * xaxis.max(), 2.3 * xaxis.max(),
xaxis[1] - xaxis[0])
else:
xaxis = np.arange(minall, 2.3 * xaxis.max(),
xaxis[1] - xaxis[0])
for i, one_set in enumerate(datasets):
n, x = np.histogram(data[one_set][datakey][:, k], xaxis)
nbins.append(n)
if verbose:
print '\n', labels[i]
print 'SUM ', data[one_set][datakey][:,
k].sum() * scale_factor[i]
print 'Mean ', data[one_set][datakey][:, k].mean()
print 'Std ', data[one_set][datakey][:, k].std()
if i == 0:
baseline = data[one_set][datakey][:, k].mean()
if i > 0:
print one_set, level, 1 - data[one_set][datakey][:, k].mean(
) / baseline
jplot.unfilledBar(xaxis,
nbins[-1] * scale_factor[i],
color=mycolors[i])
jplot.errorMark(xaxis, nbins[-1]*scale_factor[i],
error=np.sqrt(nbins[-1])*scale_factor[i], color=mycolors[i],
label =labels[i] + '\n' + \
r'$\mu$' + '=' + "%.2f" % data[one_set][datakey][:,k].mean() + '\n'+\
r'$\sigma$' + '=' + "%.2f" % data[one_set][datakey][:,k].std())
plt.title(level)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
#plt.ylim([0,])
plt.legend(loc=0, ncol=1)
if len(figname) > 0:
myfig.savefig(os.path.join(outdir, figname + '_' + level + '.png'),
dpi=300)
myfig.savefig(os.path.join(outdir, figname + '_' + level + '.pdf'))
if iPhoton % 10000 == 0:
print iPhoton
if iPhoton == stop_at:
print 'Momentum'
print good_mom[iPhoton]
print 'Position'
print good_pos[iPhoton]
break
data['nsteps'][iPhoton] = len(good_pos[iPhoton])
# In[ ]:
b, x = np.histogram(data['delta_t'], np.linspace(0, 3.))
jplot.unfilledBar(x, b)
plt.xlabel('Time delay (ns)')
plt.show()
# In[ ]:
data['r'] = np.sqrt(data['y']**2 + data['x']**2)
phidiff = (data['phi_f'] - data['phi_i'])
phidiff[phidiff < 0] += np.pi * 2
data['phidiff'] = phidiff
# In[ ]:
print data['phi_i'][:10]
print data['phi_f'][:10]