def all_spectra( db_names ) :
constrain_det_params = { 'a' : 1 }
time_estimator = jutils.time_estimator( len(db_names) * 4 * 32 * 32, 20 )
for name in db_names :
# dets_used = bpt.dets_used( bpt_data_path, name )
db = spec.spectrum_db( name, '../../../storage/', (32,32),
peak_types, constrain_det_params )
spec.auto_fit_many_spectra( db, (4,4),
fit_bounds_guesser,
peak_params_guesser, det_params_guesses,
fit_acceptor = fit_acceptor,
params_shuffler = params_shuffler,
rel_plot_bounds = rel_plot_bounds,
logscale = 1,
time_estimator = time_estimator,
print_output = 0,
debug_peaks = 0,
overwrite = 0,
mask = none )
# mu_path = '../../storage/mu_values/%s_%d_mu_values.bin' % ( name, detnum )
db.disconnect()
def one_spectrum(db_name, detnum, x, y):
db = spec.spectrum_db(db_name, '../../../storage/')
global debug
debug = 1
chans, counts, dcounts = db.get_histo(detnum, x, y)
dcounts = np.sqrt(counts)
dcounts[(dcounts == 0)] = 1
plt.figure(figsize=(10, 12))
ax = plt.axes()
primary_peaks = db.load_dill('primary_peaks')
primary_peaks = primary_peaks[:, detnum, x, y]
db.disconnect()
# peak_params_guesses = [ peak_params_guesser( i, detnum, x, y )
# for i in range(2) ]
spec.auto_fit_spectrum(chans,
counts,
dcounts,
fit_bounds_guesser,
peak_params_guesser,
det_params_guesses,
fit_acceptor=fit_acceptor,
params_shuffler=params_shuffler,
primary_peaks=primary_peaks,
rel_plot_bounds=rel_plot_bounds,
ax=ax,
print_output=1)
# spec.auto_fit_spectrum( x, y, dy,
# group_ranges, peak_locations,
# num_peaks_to_detect, primary_peak_detector,
# peak_sizes_guesses, peak_width_guesses,
# det_params_guesses, peak_mu_offset,
# fit_acceptor = fit_acceptor,
# params_shuffler = params_shuffler,
# ax = ax,
# rel_plot_bounds = rel_plot_bounds,
# print_output = 1)
plt.show()
def all_spectra(db_names):
constrain_det_params = {'a': 1}
time_estimator = jutils.time_estimator(len(db_names) * 4 * 32 * 32, 20)
for name in db_names:
# dets_used = bpt.dets_used( bpt_data_path, name )
db = spec.spectrum_db(name, '../../../storage/', (32, 32), peak_types,
constrain_det_params)
mask = np.zeros((3, 32, 32))
mask[0, 0:15, :] = 1
mask[0, 23:, :] = 1
spec.auto_fit_many_spectra(
db,
(4, 4),
# group_ranges, peak_locations,
# num_peaks_to_detect, primary_peak_detector,
# peak_sizes_guesses, peak_width_guesses,
# det_params_guesses,
fit_bounds_guesser,
peak_params_guesser,
det_params_guesses,
fit_acceptor=fit_acceptor,
params_shuffler=params_shuffler,
rel_plot_bounds=rel_plot_bounds,
logscale=1,
time_estimator=time_estimator,
print_output=0,
debug_peaks=0,
overwrite=0,
mask=mask)
# mu_path = '../../storage/mu_values/%s_%d_mu_values.bin' % ( name, detnum )
db.disconnect()
num_peaks_to_detect = 6
exp1_secant_matrices = exp1_geometry.get_secant_matrices()
peak_group_ranges = [ [ -np.inf, 2700 ], [ 2700, 2900 ], [ 2900, np.inf ] ]
thresholds = [ 20, 20, 20 ]
# for name in [ 'centered', 'moved', 'flat', 'angled' ] : # , 'det3_moved', 'det3_cent' ] :
for name in [ 'moved' ] :
db = spec.spectrum_db( name, '../../../storage/' )
secant_matrices = exp1_secant_matrices[ name ]
# primary_peaks = spec.write_peakdetect( db, primary_peak_detector, data_retriever,
# num_peaks_to_detect )
primary_peaks = db.compute_primary_peaks( group_ranges,
primary_peak_detector,
num_peaks_to_detect = num_peaks_to_detect,
primary_peak_detector = primary_peak_detector,
plot = 0, load = 1 )
# primary_peaks = db.compute_primary_peaks( peak_group_ranges, thresholds,
# load = 0, plot = 1 )
vary_det_deadlayer=0,
interp_det_stopping_power=1,
interp_source_stopping_powers=0,
fstrips_requested=np.arange(2, 30),
# fstrips_requested = np.arange(32),
bstrips=np.arange(2, 30),
fix_source_deadlayers=None,
one_source_constant=0,
det_thickness=0,
vary_source_thickness=1)
num_dbs = len(db_names)
db_path = '../../../storage/databases/'
dbs = [spec.spectrum_db(db_path + db_names[i]) for i in range(len(db_names))]
channels = [
dbs[i].read_values('../../../storage/peak_values/' + db_names[i] +
'_peak_values.bin')[0] for i in range(len(db_names))
]
# print( channels )
# channels = [ dbs[i].read_mu_values( '../../storage/peak_values/'
# + db_names[i]
# + '_mu_values.bin' )
# for i in range( len( db_names ) ) ]
num_sources = len(channels[0])
print(x, y)
ax.text(x, y, text, fontsize=16, ha='center')
labels = [r'$^8$Li$\to\alpha\alpha\beta^-$', r'$^{148}$Gd', r'$^{244}$Cm']
guesses = [763, 1600, 2903]
num_labels = len(labels)
y_bottom = 1e-6
hist_bin_size = 10
kde_bandwidth = 1
savepath = '../../../../plots_important/'
db = spec.spectrum_db('full_bkgd_tot', '../../../../storage')
# coords = (0, 27, 16 )
coords = (0, 16, 16)
data = db.get_data(*coords)
x = np.arange(0, 3000, hist_bin_size)
y, bin_edges = np.histogram(data, x)
x = x[0:-1]
# ax = plt.axes()
f = plt.figure(figsize=(6, 7))
f.suptitle('Uncalibrated $^8$Li Decay Spectrum\nwith Calibration Sources',
fontsize=20)
import jspectroscopy as spec
db_name = 'full_bkgd_tot'
db = spec.spectrum_db('../../storage/databases/' + db_name)
success_rate = db.compute_fit_success_rate()
print(success_rate)
db.plot_success_pixels()
db.disconnect()
import jspectroscopy as spec
import time
test_db = spec.spectrum_db( 'test_db', None, ( 32, 32 ), [ 'a', 'a' ] )
start_time = time.time()
for i in range( 100 ) :
test_db.insert_fit_data( -1, 31, 31, 0, None )
stop_time = time.time()
print( start_time - stop_time )
