def get_data(name, plot=0):
analysis_mgr = jspec.dssd_analysis_manager(name, '../../storage/',
(32, 32), [1, 1, 1])
data = analysis_mgr.load_dill('dl_estimates')
num_sources = len(data)
num_dets = len(data[0][0])
ret = meas.empty((num_sources, num_dets))
for i in range(num_sources):
for d in range(num_dets):
tmp = data[i][0][d]
ret[i, d] = meas.meas(np.nanmean(tmp.x), np.nanstd(tmp.x))
if plot:
f, axarr = plt.subplots(num_sources, num_dets, squeeze=0)
for d in range(num_dets):
for i in range(num_sources):
tmp = data[i][0][d]
print(d, i)
print(ret[i, d])
axarr[i, d].errorbar(range(32), tmp.x, tmp.dx, ls='none')
axarr[i, d].axhline(ret[i, d].x, c='r')
plt.show()
plt.close('all')
return ret
num_peaks_to_detect = 6
if cut_strips:
hitmap_label = None
else:
hitmap_label = 'no_cuts'
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' ] :
analysis_mgr = spec.dssd_analysis_manager(name, '../../../storage/', (32, 32),
[1, 1, 1])
# primary_peaks = spec.write_peakdetect( analysis_mgr, primary_peak_detector, data_retriever,
# num_peaks_to_detect )
primary_peaks = analysis_mgr.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 = analysis_mgr.compute_primary_peaks( peak_group_ranges, thresholds,
# load = 0, plot = 1 )
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st
from sklearn.neighbors.kde import KernelDensity
import statsmodels.api as sm
# data_name = 'full_bkgd_tot_cut'
data_name = 'full_bkgd_tot'
coords = (0, 16, 16)
y_bottom = 1e-6
hist_bin_size = 10
kde_bandwidth = 1
mgr = spec.dssd_analysis_manager(data_name, '../../../storage', (32, 32),
[1, 1, 1])
# coords = (0, 27, 16 )
data = mgr.get_data(*coords)
print(data)
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))
import jspectroscopy as spec
import numpy as np
import matplotlib.pyplot as plt
import colorcet
from mpl_toolkits.axes_grid1 import make_axes_locatable
plt.rcParams["font.family"] = "Arial"
plt.rcParams['mathtext.default'] = 'regular'
savepath = '../../../../plots_important/sample_hitmaps.eps'
analysis_mgr = spec.dssd_analysis_manager('full_bkgd_tot',
'../../../../storage', (32, 32),
[1, 1, 1])
hitmaps = analysis_mgr.load_dill('hitmaps')
source_names = [r'$^{148}Gd$', r'$^{264}Cm$']
f, axarr = plt.subplots(2, 1, figsize=(4, 8))
f.suptitle('Source Hitmaps', fontsize=20)
f.subplots_adjust(hspace=0.5)
for i in range(2):
idx = i + 1
axarr[i].set_title(source_names[i], fontsize=15)
im = axarr[i].imshow(hitmaps[idx][0][3], cmap=colorcet.m_rainbow)
divider = make_axes_locatable(axarr[i])
cax = divider.append_axes("right", size="5%", pad=0.05)
f.colorbar(im, cax=cax)
for coords in data_to_remove :
# print( coords )
dssd[ i,j,0, coords ] = meas.nan
# np.set_printoptions(threshold=np.nan)
# print( det_sectheta[0][0] )
# sys.exit(0)
analysis_mgr = spec.dssd_analysis_manager( data_name, storage_path, (1,1), [1,1,1] )
cf249_energies = np.array( [ 5704, 5759.5, 5783.3, 5813.3, 5849.3 ] )
cf249_probs = np.array( [ 0.0003, 0.0469, 0.0026, 0.822, 0.01430 ] )
cf249_probs /= np.sum( cf249_probs )
# pu238_energies = np.array( [ 5704, 5759.5, 5783.3, 5813.3, 5849.3 ] )
# pu238_probs = np.array( [ 0.0003, 0.0469, 0.0026, 0.822, 0.01430 ] )
# pu238_probs /= np.sum( pu238_probs )
pu240_energies = np.array( [ 5123.68, 5168.17 ] )
pu240_probs = np.array( [ 0.2710, 0.7280 ] )
import jspectroscopy as spec
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st
from sklearn.neighbors.kde import KernelDensity
import statsmodels.api as sm
use_kde = 0
y_bottom = 1e-6
hist_bin_size = 20
highres_bin_size = 2
kde_bandwidth = 1
db = spec.dssd_analysis_manager('full_bkgd_tot_cuts', '../../../storage',
[1, 1, 1], (32, 32))
coords = (0, 16, 16)
if not use_kde:
data = db.get_data(*coords)
x = np.arange(0, 3000, hist_bin_size)
y, bin_edges = np.histogram(data, x)
x = x[0:-1]
x_fine = np.arange(0, 3000, highres_bin_size)
y_fine, bin_edges_fine = np.histogram(data, x_fine)
x_fine = x_fine[0:-1]
# ax = plt.axes()
det_stopping_power_interp = None
model_params = dl_estimator.deadlayer_model_params(
disable_sources=0,
vary_det_deadlayer=1,
interp_det_stopping_power=1,
interp_source_stopping_powers=0,
fstrips_requested=np.arange(1, 31),
bstrips=np.arange(15, 28),
fix_source_deadlayers=None,
one_source_constant=0,
det_thickness=0,
vary_source_thickness=0,
constant_energy_offset=0)
analysis_mgr = spec.dssd_analysis_manager('full_bkgd_tot', storage_path,
(32, 32), [1, 1, 1])
# channels = analysis_mgr.load_dill( 'peaks' )
# peak_indices = [ [1], [0,1] ]
# num_peaks_per_source = [ len( x ) for x in peak_indices ]
channels = analysis_mgr.load_dill('means')[1:]
# peak_indices = [ [0], [0] ]
peak_indices = None
det_sectheta = exp2_geometry.get_secant_matrices()[1:]
source_sectheta = det_sectheta
if peak_indices is not None:
