def test_parameter_json(self):
data_settings = fit2x.DataSettings(time_axis=x,
data=irf_y,
irf_histogram=irf_y)
lifetime_settings = fit2x.LifetimeSettings(lifetime_spectrum=[1, 4])
convolution_settings = fit2x.ConvolutionSettings()
scatter_settings = fit2x.PatternSettings()
background_settings = fit2x.PatternSettings()
pileup_settings = fit2x.PileupSettings()
linearization_settings = fit2x.LinearizationSettings()
scaling_settings = fit2x.ScalingSettings()
score_settings = fit2x.ScoreSettings()
dc = fit2x.Decay(data_settings=data_settings,
lifetime_settings=lifetime_settings,
scatter_settings=scatter_settings,
convolution_settings=convolution_settings,
background_settings=background_settings,
pileup_settings=pileup_settings,
linearization_settings=linearization_settings,
scaling_settings=scaling_settings,
score_settings=score_settings)
dc.update()
# Test filling, reading and writing. Fill with random data
fill_random(dc)
# JSON parameter comparison
json_str = dc.to_json()
p_json = json.loads(json_str)
p_obj = dc.parameter
self.assertEqual(compare_parameter(p_json, p_obj), True)
# Write to JSON file and restore Decay object from JSON
fh, fn = tempfile.mkstemp(".json")
fp = open(fh, "w")
fp.write(json_str)
fp.close()
decay_from_json_str = fit2x.Decay.from_json(json_str)
decay_from_json_fn = fit2x.Decay.from_json(pathlib.Path(fn))
self.assertEqual(
compare_parameter(decay_from_json_str.parameter, p_obj), True)
self.assertEqual(
compare_parameter(decay_from_json_fn.parameter, p_obj), True)
def test_decay_convolution_lifetime(self):
np.random.seed(42)
x = np.linspace(0, 20, 32)
irf_position = 2.0
irf_width = 0.25
irf_y = scipy.stats.norm.pdf(x, loc=irf_position, scale=irf_width)
data_settings = fit2x.DataSettings(time_axis=x,
data=irf_y * 100000,
irf_histogram=irf_y)
lifetime_settings = fit2x.LifetimeSettings(lifetime_spectrum=[1, 2])
convolution_settings = fit2x.ConvolutionSettings()
background_settings = fit2x.PatternSettings()
pileup_settings = fit2x.PileupSettings()
linearization_settings = fit2x.LinearizationSettings()
scaling_settings = fit2x.ScalingSettings()
score_settings = fit2x.ScoreSettings()
dc = fit2x.Decay(data_settings=data_settings,
lifetime_settings=lifetime_settings,
convolution_settings=convolution_settings,
background_settings=background_settings,
pileup_settings=pileup_settings,
linearization_settings=linearization_settings,
scaling_settings=scaling_settings,
score_settings=score_settings)
dc.update()
dc.data = np.random.poisson(1000 / max(dc.y) * dc.y)
scores = list()
lts = np.linspace(0.0, 4, 11)
for lt in lts:
dc.lifetime_spectrum = [1, lt]
scores.append(dc.score)
scores_ref = [
3641.4913153320936, 3023.436739258023, 1648.7875155218765,
731.3338936915842, 190.31400668610308, 23.229701466698423,
377.664429861769, 1529.1599392280614, 3840.3130365516517,
7698.247948184009, 13453.340913686912
]
self.assertEqual(np.allclose(scores, scores_ref), True)
def test_file_io(self):
dc = fit2x.Decay()
dc.update()
fill_random(dc)
# JSON parameter comparison
json_str = dc.to_json()
# Write to JSON file and restore Decay object from JSON
fh, fn = tempfile.mkstemp(".json")
fp = open(fh, "w")
fp.write(json_str)
fp.close()
decay_from_json_str = fit2x.Decay.from_json(json_str)
decay_from_json_fn = fit2x.Decay.from_json(pathlib.Path(fn))
self.assertEqual(
compare_parameter(decay_from_json_str.parameter, dc.parameter),
True)
self.assertEqual(
compare_parameter(decay_from_json_fn.parameter, dc.parameter),
True)
data_settings = fit2x.DataSettings(time_axis=x,
data=irf_y * 100000,
irf_histogram=irf_y)
lifetime_settings = fit2x.LifetimeSettings(lifetime_spectrum=[1, 4])
convolution_settings = fit2x.ConvolutionSettings()
background_settings = fit2x.PatternSettings()
pileup_settings = fit2x.PileupSettings()
linearization_settings = fit2x.LinearizationSettings()
scaling_settings = fit2x.ScalingSettings()
score_settings = fit2x.ScoreSettings()
dc = fit2x.Decay(data_settings=data_settings,
lifetime_settings=lifetime_settings,
convolution_settings=convolution_settings,
background_settings=background_settings,
pileup_settings=pileup_settings,
linearization_settings=linearization_settings,
scaling_settings=scaling_settings,
score_settings=score_settings)
dc.update()
# lifetime
import pylab as plt
dc.lifetime_spectrum = [1, 4]
plt.semilogy(dc.x, dc.y, label="lt 4")
dc.lifetime_spectrum = [1, 2]
dc.update()
plt.semilogy(dc.x, dc.y, label="lt 2")
plt.legend()
plt.show()
n_ph_max=5,
macro_time_calibration=data.header.macro_time_resolution,
invert=True)
high_count_selection = data_ch0[cr_selection]
data_decay, _ = np.histogram(high_count_selection.micro_times, bins=n_bins)
time_axis = np.arange(
0, n_bins
) * data.header.micro_time_resolution * 4096 * 1e9 / n_bins # in [ns]
###################################
# Make decay object
###################################
decay = fit2x.Decay(data=data_decay.astype(np.float64),
irf_histogram=irf.astype(np.float64),
time_axis=time_axis,
scale_model_to_data=True,
excitation_period=max(time_axis),
lifetime_spectrum=[1., 1.2, 1., 3.5],
abs_lifetime_spectrum=True,
convolution_method=fit2x.ConvFastPeriodicTime)
decay.update_model()
# A minimum number of photons should be in each channel
# as no MLE is used and Gaussian distributed errors are assumed
sel = np.where(data_decay > 1)[0]
x_min = 2 #int(min(sel))
# The BH card are not linear at the end of the TAC. Thus
# fit not the full range
x_max = 110 #max(sel)
# Set some initial values for the fit