def two_level_threshold(data, number_of_bins=40) -> dict:
""" Determine threshold for separation of two-level signal
Typical examples of such a signal are an RTS signal or Elzerman readout.
Args:
traces: Two dimensional array with single traces
number_of_bins: Number of bins to use for calculation of double histogram
Returns:
Dictionary with results. The key readout_threshold contains the calculated threshold
"""
counts, bins = np.histogram(data, bins=number_of_bins)
bin_centres = np.array([(bins[i] + bins[i + 1]) / 2
for i in range(0,
len(bins) - 1)])
_, result_dict = fit_double_gaussian(bin_centres, counts)
result_dict = refit_double_gaussian(result_dict, bin_centres, counts)
result = {
'signal_threshold': result_dict['split'],
'double_gaussian_fit': result_dict,
'separation': result_dict['separation'],
'histogram': {
'counts': counts,
'bins': bins,
'bin_centres': bin_centres
}
}
return result
def test_fit_double_gaussian(self):
x_data = np.arange(-4, 4, .05)
initial_parameters = [10, 20, 1, 1, -2, 2]
y_data = double_gaussian(x_data, initial_parameters)
fitted_parameters, _ = fit_double_gaussian(x_data, y_data)
parameter_diff = np.abs(fitted_parameters - initial_parameters)
self.assertTrue(np.all(parameter_diff < 1e-3))
def test_refit_double_gaussian(self):
dataset = qtt.data.load_example_dataset('double_gaussian_dataset.json')
x_data = np.array(dataset.signal)
y_data = np.array(dataset.counts)
_, result_dict = fit_double_gaussian(x_data, y_data)
result_dict = refit_double_gaussian(result_dict, x_data, y_data)
self.assertAlmostEqual(result_dict['left'][0], -1.23, places=1)
self.assertAlmostEqual(result_dict['right'][0], -0.77, places=1)
self.assertAlmostEqual(result_dict['left'][2], 162.8, places=0)
self.assertAlmostEqual(result_dict['right'][2], 1971, places=-1)