def make_txt_hist_from_root_file(file_path_txt,
rid,
is_parallel,
draw_string,
hist_dim,
cut_string="",
option_string="",
titles=("", "", "")):
"""Given a draw/cut string, take an SRK ROOT results file and create a histogram from it."""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
rid) + "_" + 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()
histogram = TH1D("tempHist", "tempHist", hist_dim[0], hist_dim[1],
hist_dim[2])
total_draw_string = draw_string + " >> tempHist"
hit_tree.Draw(total_draw_string, cut_string, option_string)
make_txt_from_hist(file_path_txt, histogram, titles)
root_file.Close()
return
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_txt_hist_from_root_file(file_path_txt, rid, is_parallel, draw_string, hist_dim, cut_string="",
option_string="", titles=("", "", "")):
"""Given a draw/cut string, take an SRK ROOT results file and create a histogram from it."""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(rid) + "_" + 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()
histogram = TH1D("tempHist", "tempHist", hist_dim[0], hist_dim[1], hist_dim[2])
total_draw_string = draw_string + " >> tempHist"
hit_tree.Draw(total_draw_string, cut_string, option_string)
make_txt_from_hist(file_path_txt, histogram, titles)
root_file.Close()
return
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 get_result_data(leaf_names, run_id, is_parallel):
"""Get result data directly from root files based on leaf names"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + letter + ".root"
print "Opening ", file_path
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()
data = []
for leaf_name in leaf_names:
temp_data = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
temp_data.append(
hit_tree.GetBranch(leaf_name).GetListOfLeaves()[0].GetValue())
data.append(temp_data)
root_file.Close()
return data
def get_result_data(leaf_names, run_id, is_parallel):
"""Get result data directly from root files based on leaf names"""
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"
print "Opening ", file_path
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()
data = []
for leaf_name in leaf_names:
temp_data = []
for i in xrange(num_events):
hit_tree.GetEntry(i)
temp_data.append(hit_tree.GetBranch(leaf_name).GetListOfLeaves()[0].GetValue())
data.append(temp_data)
root_file.Close()
return data
def make_root_plot_from_results_file(rid,
is_parallel,
draw_string,
cut_string="",
option_string=""):
"""Makes a ROOT plot from results file based on a draw and cut string."""
# gStyle.SetOptStat("iMRKS")
if is_parallel:
letter = 'P'
else:
letter = 'A'
file_path = srkglobal.results_dir + "Results_RID" + str(
rid) + "_" + 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()
c1 = rootnotes.canvas("Canvas", (1200, 800))
hit_tree.Draw(draw_string, cut_string, option_string)
root_file.Close()
return c1
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_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 check_user_info_tree(run_id, post_fix):
"""Prints use info tree (contains used macro commands)."""
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + post_fix + ".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')
u = hit_tree.GetUserInfo()
u.Print()
root_file.Close()
return
def check_user_info_tree(run_id, post_fix):
"""Prints use info tree (contains used macro commands)."""
file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
run_id) + "_" + post_fix + ".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')
u = hit_tree.GetUserInfo()
u.Print()
root_file.Close()
return
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_and_run(srk_settings, run_settings, computer="work_laptop"):
"""Adds run to database, makes macro, and runs it (remotely if on optima)"""
prev_comp = srkglobal.computer
srkglobal.set_computer(computer)
rid = make_macro_and_add_to_database(srk_settings, run_settings)
if computer == "optima":
run_macro_optima(rid)
else:
run_macro_local(rid, True)
results_path = srkglobal.results_dir + 'Results_RID' + str(rid) + '.root'
# time.sleep(5) #To wait for SRK to finish. Maybe later somewhere else, or have return value of SRK?
if not srkmisc.file_exits_and_not_zombie(results_path):
print "No results file created. Check log."
else:
update_database({"ResultsFilePath": results_path}, "Run=" + str(rid))
srkglobal.set_computer(prev_comp)
def calc_step_tree(file_path, inp_periodic_stop_time):
"""Calculate time and spin detection probabilities from step tree."""
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return []
print "Calculating step tree for {}".format(file_path)
root_file = TFile(file_path, "READ")
step_tree = gDirectory.Get('stepTree')
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
user_info_list = hit_tree.GetUserInfo()
periodic_stop_time = inp_periodic_stop_time
for i in xrange(user_info_list.GetEntries()):
par = user_info_list.At(i)
if par.GetName() == 'PeriodicStopTime':
periodic_stop_time = float(par.GetTitle())
break
num_events = hit_tree.GetEntries()
num_steps_per_event = step_tree.GetEntries() / num_events
time_data = np.zeros(num_steps_per_event)
sx_prob_data = np.zeros(num_steps_per_event)
# Set times
for j in xrange(num_steps_per_event):
time_data[j] = ((j + 1) * periodic_stop_time)
# Add data
for i in xrange(num_events):
if i % 100 == 0:
print "Stepping for Event {} in {}".format(i, file_path)
for j in xrange(num_steps_per_event):
step_tree.GetEntry(i * num_steps_per_event + j)
sx_prob_data[j] += step_tree.sxProb
# Normalize
sx_prob_data /= float(num_events)
root_file.Close()
return zip(time_data, sx_prob_data)
def calc_step_tree(file_path, inp_periodic_stop_time):
"""Calculate time and spin detection probabilities from step tree."""
if not srkmisc.file_exits_and_not_zombie(file_path):
print file_path + " doesn't exist or is zombie."
return []
print "Calculating step tree for {}".format(file_path)
root_file = TFile(file_path, "READ")
step_tree = gDirectory.Get('stepTree')
hit_tree = gDirectory.Get('hitTree')
gROOT.cd()
user_info_list = hit_tree.GetUserInfo()
periodic_stop_time = inp_periodic_stop_time
for i in xrange(user_info_list.GetEntries()):
par = user_info_list.At(i)
if par.GetName() == 'PeriodicStopTime':
periodic_stop_time = float(par.GetTitle())
break
num_events = hit_tree.GetEntries()
num_steps_per_event = step_tree.GetEntries() / num_events
time_data = np.zeros(num_steps_per_event)
sx_prob_data = np.zeros(num_steps_per_event)
# Set times
for j in xrange(num_steps_per_event):
time_data[j] = ((j + 1) * periodic_stop_time)
# Add data
for i in xrange(num_events):
if i % 100 == 0:
print "Stepping for Event {} in {}".format(i, file_path)
for j in xrange(num_steps_per_event):
step_tree.GetEntry(i * num_steps_per_event + j)
sx_prob_data[j] += step_tree.sxProb
# Normalize
sx_prob_data /= float(num_events)
root_file.Close()
return zip(time_data, sx_prob_data)
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 make_tree_to_array(run_id, query_list=default_params):
"""Makes the step_tree of result files into a numpy array"""
#Currently only for one particle!
# print query_list
results_path = srkglobal.results_dir + "Results_RID" + str(
run_id) + ".root"
if not srkmisc.file_exits_and_not_zombie(results_path):
print "No results file found for RunID " + str(run_id) + ". Aborting."
return
else:
root_file = r.TFile(results_path, "READ")
step_tree = r.gDirectory.Get('stepTree')
# r.gROOT.cd()
return_arr = np.empty((len(query_list), step_tree.GetEntries() + 2))
# tree_dict = {
# "posX":step_tree.pos.x(),
# "posZ":step_tree.pos.z(),
# "velX":step_tree.vel.x(),
# "velZ":step_tree.vel.z(),
# "phi":step_tree.phi,
# "theta":step_tree.theta()
# }
for i in range(step_tree.GetEntries()):
step_tree.GetEntry(i)
return_arr[0, i + 1] = step_tree.pos.x()
return_arr[1, i + 1] = step_tree.pos.y()
return_arr[2, i + 1] = step_tree.pos.z()
return_arr[3, i + 1] = step_tree.vel.x()
return_arr[4, i + 1] = step_tree.vel.y()
return_arr[5, i + 1] = step_tree.vel.z()
return_arr[6, i + 1] = step_tree.M.x()
return_arr[7, i + 1] = step_tree.M.y()
return_arr[8, i + 1] = step_tree.M.z()
return_arr[9, i + 1] = step_tree.magM
return_arr[10, i + 1] = step_tree.projection
#return_arr[6,i+1] = step_tree.BFieldStrenth.x()
#return_arr[7,i+1] = step_tree.BFieldStrenth.y()
#return_arr[8,i+1] = step_tree.BFieldStrenth.z()
# for j in range(len(query_list)):
# return_arr[j,i]=tree_dict[query_list[j]]
hit_tree = r.gDirectory.Get('hitTree')
hit_tree.GetEntry(0)
return_arr[0, 0] = hit_tree.pos0.x()
return_arr[1, 0] = hit_tree.pos0.y()
return_arr[2, 0] = hit_tree.pos0.z()
return_arr[3, 0] = hit_tree.vel0.x()
return_arr[4, 0] = hit_tree.vel0.y()
return_arr[5, 0] = hit_tree.vel0.z()
return_arr[6, 0] = hit_tree.M0.x()
return_arr[7, 0] = hit_tree.M0.y()
return_arr[8, 0] = hit_tree.M0.z()
return_arr[9, 0] = hit_tree.magM0
return_arr[10, 0] = hit_tree.projection0
return_arr[0, -1] = hit_tree.pos.x()
return_arr[1, -1] = hit_tree.pos.y()
return_arr[2, -1] = hit_tree.pos.z()
return_arr[3, -1] = hit_tree.vel.x()
return_arr[4, -1] = hit_tree.vel.y()
return_arr[5, -1] = hit_tree.vel.z()
return_arr[6, -1] = hit_tree.M.x()
return_arr[7, -1] = hit_tree.M.y()
return_arr[8, -1] = hit_tree.M.z()
return_arr[9, -1] = hit_tree.magM
return_arr[10, -1] = hit_tree.projection
root_file.Close()
return return_arr
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
