def make_phi_hist_with_noise(rid, is_parallel, hist_dim, noise_stdev,
normalize):
"""Makes a histogram of the phi angle with added noise."""
hist = TH1D("blurredHist" + str(rid), "blurredHist" + str(rid),
hist_dim[0], hist_dim[1], hist_dim[2])
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
rid) + "_" + letter + ".root"
f = ROOT.TFile.Open(file_path)
phi_list = []
for event in f.hitTree:
phi_list.append(gRandom.Gaus(event.phi, noise_stdev))
mean = srkmisc.reduce_periodics(phi_list)
stdev = srkmisc.careful_std(phi_list)
for x in phi_list:
if normalize:
hist.Fill((x - mean) / stdev)
else:
hist.Fill(x)
f.Close()
ROOT.SetOwnership(hist, True)
return hist
def make_alpha_vs_phi_plot(run_id, is_parallel):
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
alpha_list = []
radius = srkdata.get_data_for_rids_from_database([run_id],
"ChamberRadius")[0][0]
print radius
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
alpha = get_alpha_angle_2d(radius, hit_tree.pos0, hit_tree.vel0)
alpha_list.append(alpha)
root_file.Close()
phi_mean = srkmisc.reduce_periodics(phi_list)
delta_phi_list = map(lambda x: x - phi_mean, phi_list)
plt.scatter(alpha_list, delta_phi_list)
def calc_delta_stats_same_tracks(run_id, use_wrapping=True):
"""Calculates differences in orientation of fields when tracks are the same"""
hit_trees = []
root_files = []
for is_parallel in [True, False]:
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_files.append(TFile(file_path, "READ"))
hit_trees.append(gDirectory.Get('hitTree'))
gROOT.cd()
delta_phi_list = []
for i in xrange(hit_trees[0].GetEntries()):
hit_trees[0].GetEntry(i)
hit_trees[1].GetEntry(i)
delta_phi = hit_trees[0].phi - hit_trees[1].phi
delta_phi_list.append(delta_phi)
if use_wrapping:
delta_phi_mean = srkmisc.reduce_periodics(delta_phi_list)
else:
delta_phi_mean = srkmisc.careful_mean(delta_phi_list)
delta_phi_std = srkmisc.careful_std(delta_phi_list)
root_files[0].Close()
root_files[1].Close()
return [delta_phi_mean, delta_phi_std]
def make_alpha_vs_phi_plot(run_id, is_parallel):
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
alpha_list = []
radius = srkdata.get_data_for_rids_from_database([run_id], "ChamberRadius")[0][0]
print radius
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
alpha = get_alpha_angle_2d(radius, hit_tree.pos0, hit_tree.vel0)
alpha_list.append(alpha)
root_file.Close()
phi_mean = srkmisc.reduce_periodics(phi_list)
delta_phi_list = map(lambda x: x - phi_mean, phi_list)
plt.scatter(alpha_list, delta_phi_list)
def make_phi_hist_with_noise(rid, is_parallel, hist_dim, noise_stdev, normalize):
"""Makes a histogram of the phi angle with added noise."""
hist = TH1D("blurredHist" + str(rid), "blurredHist" + str(rid), hist_dim[0], hist_dim[1], hist_dim[2])
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(rid) + "_" + letter + ".root"
f = ROOT.TFile.Open(file_path)
phi_list = []
for event in f.hitTree:
phi_list.append(gRandom.Gaus(event.phi, noise_stdev))
mean = srkmisc.reduce_periodics(phi_list)
stdev = srkmisc.careful_std(phi_list)
for x in phi_list:
if normalize:
hist.Fill((x - mean) / stdev)
else:
hist.Fill(x)
f.Close()
ROOT.SetOwnership(hist, True)
return hist
def calc_delta_stats_same_tracks(run_id, use_wrapping=True):
"""Calculates differences in orientation of fields when tracks are the same"""
hit_trees = []
root_files = []
for is_parallel in [True, False]:
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_files.append(TFile(file_path, "READ"))
hit_trees.append(gDirectory.Get('hitTree'))
gROOT.cd()
delta_phi_list = []
for i in xrange(hit_trees[0].GetEntries()):
hit_trees[0].GetEntry(i)
hit_trees[1].GetEntry(i)
delta_phi = hit_trees[0].phi - hit_trees[1].phi
delta_phi_list.append(delta_phi)
if use_wrapping:
delta_phi_mean = srkmisc.reduce_periodics(delta_phi_list)
else:
delta_phi_mean = srkmisc.careful_mean(delta_phi_list)
delta_phi_std = srkmisc.careful_std(delta_phi_list)
root_files[0].Close()
root_files[1].Close()
return [delta_phi_mean, delta_phi_std]
def make_sz_prob_dist(run_id, is_parallel, use_wrapping=True):
"""Make a spin direction (after flip) detection probability histogram"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return []
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
theta_list = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
theta_list.append(hit_tree.theta)
root_file.Close()
if use_wrapping:
phi_mean = srkmisc.reduce_periodics(phi_list)
else:
phi_mean = srkmisc.careful_mean(phi_list)
phi_std = srkmisc.careful_std(phi_list)
theta_std = srkmisc.careful_std(theta_list)
comb_std = np.sqrt(phi_std * phi_std + theta_std * theta_std)
print "Combined Standard deviation: %e" % (comb_std)
x = np.linspace(-30, 30, num=60)
y = []
for num_std_dev in x:
sz_det_prob = 0
for phi, theta in zip(phi_list, theta_list):
sz_det_prob += calc_opposite_spin_prob(
phi - phi_mean + comb_std * num_std_dev, theta)
sz_det_prob /= num_events
y.append(1 - sz_det_prob)
return x, y
def make_sz_prob_dist(run_id, is_parallel, use_wrapping=True):
"""Make a spin direction (after flip) detection probability histogram"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return []
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
num_events = hit_tree.GetEntries()
phi_list = []
theta_list = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
phi_list.append(hit_tree.phi)
theta_list.append(hit_tree.theta)
root_file.Close()
if use_wrapping:
phi_mean = srkmisc.reduce_periodics(phi_list)
else:
phi_mean = srkmisc.careful_mean(phi_list)
phi_std = srkmisc.careful_std(phi_list)
theta_std = srkmisc.careful_std(theta_list)
comb_std = np.sqrt(phi_std * phi_std + theta_std * theta_std)
print "Combined Standard deviation: %e" % (comb_std)
x = np.linspace(-30, 30, num=60)
y = []
for num_std_dev in x:
sz_det_prob = 0
for phi, theta in zip(phi_list, theta_list):
sz_det_prob += calc_opposite_spin_prob(phi - phi_mean + comb_std * num_std_dev, theta)
sz_det_prob /= num_events
y.append(1 - sz_det_prob)
return x, y
def calc_stats_for_results_file(file_path, runtype="nedm", use_wrapping=False):
"""Calculates summary stats for a SRK results ROOT file."""
stats = srkdata.default_file_stats(runtype)
# Check if file exists and is valid
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
stats['NumEventsRun'] = hit_tree.GetEntries()
# Get arrays for phi and theta from file
phi_array = np.empty(hit_tree.GetEntries())
theta_array = np.empty(hit_tree.GetEntries())
for i in xrange(stats['NumEventsRun']):
hit_tree.GetEntry(i)
phi_array[i] = hit_tree.phi
theta_array[i] = hit_tree.theta
root_file.Close()
# Calculate the mean phi's and thetas. Depending on purpose, wrap around at two pi depending on mean location.
if use_wrapping:
stats['PhiMean'] = srkmisc.reduce_periodics(phi_array)
stats['ThetaMean'] = srkmisc.reduce_periodics(theta_array)
else:
stats['PhiMean'] = srkmisc.careful_mean(phi_array)
stats['ThetaMean'] = srkmisc.careful_mean(theta_array)
if runtype != "g2":
# Calculate probability of detecting the spin in the opposite direction
stats['SZDetProb'] = 0.
for phi, theta in zip(phi_array, theta_array):
stats['SZDetProb'] += calc_opposite_spin_prob(
phi - stats['PhiMean'], theta)
stats['SZDetProb'] /= stats['NumEventsRun']
# Calculate other summary info
stats['PhiStDev'] = srkmisc.careful_std(phi_array)
stats['ThetaStDev'] = srkmisc.careful_std(theta_array)
stats['PhiError'] = stats['PhiStDev'] / math.sqrt(len(phi_array))
stats['ThetaError'] = stats['ThetaStDev'] / math.sqrt(len(theta_array))
if runtype != "g2":
stats['PhiKurtosis'] = kurtosis(phi_array)
stats['ThetaKurtosis'] = kurtosis(theta_array)
stats['PhiSkewness'] = skew(phi_array)
stats['ThetaSkewness'] = skew(theta_array)
print "Phi Kurtosis: %f" % stats['PhiKurtosis']
percentile_lower, percentile_upper = np.percentile(
phi_array, (16.5, 83.50))
percentile_width = percentile_upper - percentile_lower
stats['PhiPercentileWidth'] = percentile_width
percentile_lower, percentile_upper = np.percentile(
theta_array, (16.5, 83.5))
percentile_width = percentile_upper - percentile_lower
stats['ThetaPercentileWidth'] = percentile_width
# Fit phi to Tsallis q-Gaussian function (old form)
power, error = make_tsallis_fit(phi_array, stats['PhiMean'],
stats['PhiStDev'])
stats['PhiTsallisPower'] = power
stats['PhiTsallisPowerError'] = error
# Fit theta to Tsallis q-Gaussian function (old form)
power, error = make_tsallis_fit(theta_array, stats['ThetaMean'],
stats['ThetaStDev'])
stats['ThetaTsallisPower'] = power
stats['ThetaTsallisPowerError'] = error
# Fit phi to Tsallis q-Gaussian function (new form)
power, error = make_qgaussian_fit(phi_array, stats['PhiMean'],
stats['PhiStDev'])
stats['PhiQGaussianQ'] = power
stats['PhiQGaussianQError'] = error
# Fit theta to Tsallis q-Gaussian function (new form)
power, error = make_qgaussian_fit(theta_array, stats['ThetaMean'],
stats['ThetaStDev'])
stats['ThetaQGaussianQ'] = power
stats['ThetaQGaussianQError'] = error
return stats
def calc_stats_for_results_file(file_path, use_wrapping=False):
"""Calculates summary stats for a SRK results ROOT file."""
stats = srkdata.default_file_stats()
# Check if file exists and is valid
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return {}
root_file = TFile(file_path, "READ")
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
stats['NumEventsRun'] = hit_tree.GetEntries()
# Get arrays for phi and theta from file
phi_array = np.empty(hit_tree.GetEntries())
theta_array = np.empty(hit_tree.GetEntries())
for i in xrange(stats['NumEventsRun']):
hit_tree.GetEntry(i)
phi_array[i] = hit_tree.phi
theta_array[i] = hit_tree.theta
root_file.Close()
# Calculate the mean phi's and thetas. Depending on purpose, wrap around at two pi depending on mean location.
if use_wrapping:
stats['PhiMean'] = srkmisc.reduce_periodics(phi_array)
stats['ThetaMean'] = srkmisc.reduce_periodics(theta_array)
else:
stats['PhiMean'] = srkmisc.careful_mean(phi_array)
stats['ThetaMean'] = srkmisc.careful_mean(theta_array)
# Calculate probability of detecting the spin in the opposite direction
stats['SZDetProb'] = 0.
for phi, theta in zip(phi_array, theta_array):
stats['SZDetProb'] += calc_opposite_spin_prob(phi - stats['PhiMean'], theta)
stats['SZDetProb'] /= stats['NumEventsRun']
# Calculate other summary info
stats['PhiStDev'] = srkmisc.careful_std(phi_array)
stats['ThetaStDev'] = srkmisc.careful_std(theta_array)
stats['PhiError'] = stats['PhiStDev'] / math.sqrt(len(phi_array))
stats['ThetaError'] = stats['ThetaStDev'] / math.sqrt(len(theta_array))
stats['PhiKurtosis'] = kurtosis(phi_array)
stats['ThetaKurtosis'] = kurtosis(theta_array)
stats['PhiSkewness'] = skew(phi_array)
stats['ThetaSkewness'] = skew(theta_array)
print "Phi Kurtosis: %f" % stats['PhiKurtosis']
percentile_lower, percentile_upper = np.percentile(phi_array, (16.5, 83.50))
percentile_width = percentile_upper - percentile_lower
stats['PhiPercentileWidth'] = percentile_width
percentile_lower, percentile_upper = np.percentile(theta_array, (16.5, 83.5))
percentile_width = percentile_upper - percentile_lower
stats['ThetaPercentileWidth'] = percentile_width
# Fit phi to Tsallis q-Gaussian function (old form)
power, error = make_tsallis_fit(phi_array, stats['PhiMean'], stats['PhiStDev'])
stats['PhiTsallisPower'] = power
stats['PhiTsallisPowerError'] = error
# Fit theta to Tsallis q-Gaussian function (old form)
power, error = make_tsallis_fit(theta_array, stats['ThetaMean'], stats['ThetaStDev'])
stats['ThetaTsallisPower'] = power
stats['ThetaTsallisPowerError'] = error
# Fit phi to Tsallis q-Gaussian function (new form)
power, error = make_qgaussian_fit(phi_array, stats['PhiMean'], stats['PhiStDev'])
stats['PhiQGaussianQ'] = power
stats['PhiQGaussianQError'] = error
# Fit theta to Tsallis q-Gaussian function (new form)
power, error = make_qgaussian_fit(theta_array, stats['ThetaMean'], stats['ThetaStDev'])
stats['ThetaQGaussianQ'] = power
stats['ThetaQGaussianQError'] = error
return stats