def update_rec(shift, spread, names, det_no, exp_cal_term, database_dir):
''' takes the mean slope (spread) and intercept (shift) terms and applies the linear calibration to the data
'''
print ' saving to:', numpy_dir + 'raw_cleaned/'
runs = get_run_names((names,), database_dir, run_database) # all files to parse
groomed_arrays = []
# individual angular run names from run_database.txt
for i, r in enumerate(runs):
print ' ', r
cal_names = ('cs', 'na', 'co')
if any(cal_name in r for cal_name in cal_names):
continue
np_files = get_numpy_arr(database_dir, run_database, r, numpy_dir, prefix, True)
# individual np files for a given rotation
for f in np_files:
data = np.load(numpy_dir + f)
rec = data['data']
cal_det_index = np.where(rec['det_no'] == det_no)[0]
# remove rough calibration for qs and ql
rec['ql'][cal_det_index] = remove_temp_cal(rec['ql'][cal_det_index], exp_cal_term)
rec['qs'][cal_det_index] = remove_temp_cal(rec['qs'][cal_det_index], exp_cal_term)
# apply calibration for qs and ql
rec['ql'][cal_det_index] = (rec['ql'][cal_det_index] - shift)/spread
rec['qs'][cal_det_index] = (rec['qs'][cal_det_index] - shift)/spread
tmp = f.split('.')
name = tmp[:-1][0]
np.savez_compressed(numpy_dir + 'raw_cleaned/' + name + '_raw', data=rec)
print ' ', name, 'saved'
def plot(database_dir, names, det_no):
run = get_run_names(names, database_dir, run_database)
plt.figure()
colors = cm.viridis(np.linspace(0, 1, len(run)))
for i, r in enumerate(run):
if 'na_' in r:
continue
print r
data = get_numpy_arr(database_dir, run_database, r, numpy_dir, prefix,
True)
ql = []
for data_index, datum in enumerate(data):
print data_index, datum
f_in = np.load(numpy_dir + datum)
data = f_in['data']
ql_det = data['ql'][np.where(
(data['det_no'] == det_no
))] # pat/plastic det 0, bvert det 1, cpvert det 2
ql.extend(ql_det)
plt.hist(ql,
bins=1000,
histtype='step',
label=r,
normed=True,
color=colors[i])
plt.plot([0.476] * 10,
np.linspace(0, 4, 10),
'k--',
linewidth=0.5,
alpha=0.25)
plt.xlim(0, 1.3)
plt.title(r)
plt.legend()
def cal_interp(shift_terms, spread_terms, start_file, stop_file, run_names,
database_dir):
''' Interpolates between calibrations to get calibration for each measurement.
Pass start_file (beginning calibration) and stop_file (end_calibration).
returns numpy array with groomed numpy file name and calibration factor.
'''
#print run_names
run = get_run_names([run_names], database_dir,
run_database) # all files to parse
groomed_arrays = []
#print '\nFiles between start and stop:'
for i, r in enumerate(run):
#print r
cal_names = ('cs', 'na', 'co')
if any(cal_name in r for cal_name in cal_names):
continue
groomed_arrays.extend(
get_numpy_arr(database_dir, run_database, r, numpy_dir, prefix,
True))
num_files = float(len(groomed_arrays))
print '\nshift_terms, spread_terms:', shift_terms, spread_terms
if shift_terms[0] == shift_terms[
1]: # accounts from end of 11MeV run with no end calibration
#print [shift_terms[0]]*len(groomed_arrays)
return [shift_terms[0]] * len(groomed_arrays), [
spread_terms[0]
] * len(groomed_arrays), groomed_arrays
else:
step_shift = (max(shift_terms) - min(shift_terms)) / (num_files - 1)
interp_shift = np.arange(min(shift_terms),
max(shift_terms) + step_shift / 2, step_shift)
# account for positive or negative change in shift parameter
if shift_terms[0] > shift_terms[1]:
interp_shift = interp_shift[::-1]
step_spread = (max(spread_terms) - min(spread_terms)) / (num_files - 1)
interp_spread = np.arange(min(spread_terms),
max(spread_terms) + step_spread / 2,
step_spread)
if spread_terms[0] > spread_terms[1]:
interp_spread = interp_spread[::-1]
print 'interp shift[0], shift[-1]:', interp_shift[0], interp_shift[-1]
print 'interp spread[0], spread[-1]:', interp_spread[0], interp_spread[
-1]
return interp_shift, interp_spread, groomed_arrays
def get_coinc_counts(names, database_dir, run_database, numpy_dir, window):
runs = get_run_names(names, database_dir, run_database)
for index, r in enumerate(runs):
print '\n', r
#cal_files = ['cs', 'na', 'co']
#if any(cal in r for cal in cal_files):
# print 'Skipping calibration file:', r
# continue
# parse data for ql and tof
print numpy_dir + r
data_files = get_numpy_arr(database_dir,
run_database,
r,
numpy_dir,
'comp_coinc_',
groomed=True)
ql_dets, del_t_dets, qs_dets, backing_idx_dets = [], [], [], []
for data_index, datum in enumerate(data_files):
print data_index, datum
f_in = np.load(numpy_dir + datum)
data = f_in['data']
scatter_index = np.where(data['det_no'] == 0)[0]
det_index = np.where(data['det_no'] == 1)
det_data = data[det_index[0]]
backing = det_index[0]
# search for scatter to backing hits with delta t < 100ns
j = 0
i = 0
delta_t, ql, qs, backing_idx = [], [], [], []
while j < (len(scatter_index) - 1) and i < (len(backing) - 1):
if (scatter_index[j] < backing[i]):
coincidence = data['trig_offset'][backing[i]] + data['trig_time'][backing[i]] - \
data['trig_offset'][scatter_index[j]] - data['trig_time'][scatter_index[j]]
if coincidence < window:
delta_t.append(coincidence)
ql.append(data['ql'][scatter_index[j]])
j += 1
else:
i += 1
ql_dets.extend(ql)
del_t_dets.extend(delta_t)
np.save(save_dir + 'coinc_data_' + str(window) + 'ns',
(ql_dets, del_t_dets))
def simultaneous(adc_ranges, det_no, names, database_dir, sim_files, sim_numpy_files, exp_cal_term, spread,
beam_4_mev, print_info, show_plots):
''' simultaneously fits spectra '''
res_arr = []
for name in names:
runs = get_run_names(name, database_dir, run_database)
if any('co' in r for r in runs):
co_cal = True
else:
co_cal = False
# adc ranges
cs_range = adc_ranges[0]
na_range = adc_ranges[1]
co_range = adc_ranges[2]
for idx, run in enumerate(runs):
if 'cpvert_11MeV_neg15tiltend' in run:
continue
# check if co in runs
tmp1 = run.split('_')
if len(tmp1) > 5:
continue
print '\n---------------------------------------------------'
print run
if 'cs_' in run:
sim_data_cs = get_sim_data((sim_files[0],sim_numpy_files[0]))
meas_data_full_cs = get_meas_data(det_no, run, database_dir, exp_cal_term, beam_4_mev)
meas_data_cs = fit_range(cs_range[0], cs_range[1], meas_data_full_cs,'meas_data_cs') #0.35,0.7
cs_data = [sim_data_cs, meas_data_full_cs, meas_data_cs]
continue
if 'na_' in run:
sim_data_na = get_sim_data((sim_files[2],sim_numpy_files[2]))
meas_data_full_na = get_meas_data(det_no, run, database_dir, exp_cal_term, beam_4_mev)
meas_data_na = fit_range(na_range[0], na_range[1], meas_data_full_na,'meas_data_na') #0.95,1.5
na_data = [sim_data_na, meas_data_full_na, meas_data_na]
fit_params = assign_fit_params(spread)
fit_params.add('y_scale_2', value=0.00, min=1e-5, max=0.1, vary=False)
if 'co_' in run:
print 'found co'
sim_data_co = get_sim_data((sim_files[1],sim_numpy_files[1]))
meas_data_full_co = get_meas_data(det_no, run, database_dir, exp_cal_term, beam_4_mev)
meas_data_co = fit_range(co_range[0], co_range[1], meas_data_full_co, 'meas_data_co') #0.9,1.5
co_data = [sim_data_co, meas_data_full_co, meas_data_co]
if idx == 1:
# no na22 case
fit_params = assign_fit_params(spread)
fit_params.add('y_scale_2', value=0.00, min=1e-5, max=0.1, vary=False)
shift, spread, res = spectra_fit(fit_params, cs_data, co_data, print_info=print_info, show_plots=show_plots)
else:
# cs, na, and co case
fit_params.add('alpha_3', value=0.0, min=0., max=20., vary=False)
fit_params.add('beta_3', value=0.03, min=0, max=0.05, vary=True)
fit_params.add('gamma_3', value=0.0, min=0, max=20, vary=False)
fit_params.add('c1_3', value=0.01, vary=True)
fit_params.add('c2_3', value=-1, vary=True)
fit_params.add('y_scale_3', value=0.00, min=1e-5, max=0.1, vary=False)
res = spectra_fit(fit_params, cs_data, na_data, co_data, print_info=print_info, show_plots=show_plots)
res_arr.append(res)
if co_cal == False:
# cs, na case
res = spectra_fit(fit_params, cs_data, na_data, print_info=print_info, show_plots=show_plots)
# 4 mev no co spectra
res_arr.append(res)
if show_plots:
plt.show()
return res_arr
('cs_bvert_4MeV_neg15tilt', 'na_bvert_4MeV_neg15tilt', 'cs_cpvert_4MeV_15tilt', 'na_cpvert_4MeV_15tilt'),
('cs_cpvert_4MeV_15tilt', 'na_cpvert_4MeV_15tilt', 'cs_cpvert_4MeV_neg15tilt', 'na_cpvert_4MeV_neg15tilt'),
('cs_cpvert_4MeV_neg15tilt', 'na_cpvert_4MeV_neg15tilt', 'cs_bvert_cpvert_end', 'na_bvert_cpvert_end', 'co_bvert_cpvert_end') ]
#names = [('na_bvert_4MeV_0tilt',)]
spread = 25000
adc_ranges = ((7500, 14000), (19000, 30000), (16000, 32000))
'''
sim_files = [
'cs_spec_polimi.log', 'co_spec_polimi.log', 'na_spec_polimi.log'
]
sim_numpy_files = ['cs_spec.npy', 'co_spec.npy',
'na_spec.npy'] # numpy files with data from sim_files
for exp_cal_term, det_no, name in zip(exp_cal_terms, det_nos, names):
run = get_run_names(name, database_dir, run_database)
#single(adc_ranges, det_no, name, database_dir, sim_files, sim_numpy_files, run, exp_cal_term, spread, beam_4mev, print_info=True, show_plots=True)
simultaneous(adc_ranges,
det_no,
name,
database_dir,
sim_files,
sim_numpy_files,
run,
exp_cal_term,
spread,
beam_4mev,
save_arrays=False,
print_info=True,
show_plots=True)
