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Improved gaussian2d estimator #37

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77e7d56
Using a most likelihood estimator to improve performance of the estim…
Geegee Nov 20, 2022
5b1d06c
Small bugfix in the determination in the stepsize in y.
Geegee Nov 21, 2022
cc8149d
Remove logging
Geegee Nov 21, 2022
3a2d116
Merge branch 'main' into improved_gaussian2d_estimator
Neverhorst Jun 18, 2026
9632606
Remove unused logger from gaussian.py
Neverhorst Jun 18, 2026
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59 changes: 38 additions & 21 deletions src/qudi/util/fit_models/gaussian.py
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Original file line number Diff line number Diff line change
Expand Up @@ -364,29 +364,46 @@ def _model_function(x, offset, amplitude, center_x, center_y, sigma_x, sigma_y,
@estimator('Peak')
def estimate_peak(self, data, x):
# ToDo: Not properly implemented, yet
x_range = abs(np.max(x[0]) - np.min(x[0]))
y_range = abs(np.max(x[1]) - np.min(x[1]))
stepsize_x = xy[0][1,0] - xy[0][0,0]
stepsize_y = xy[1][0,1] - xy[1][0,0]
x_axis = xy[0].flatten()
y_axis = xy[1].flatten()

# TODO: Make good estimate for theta

amplitude = float(data.max() - data.min())

# By calculating the log likelihood of the 2D gaussian pdf, one obtain for
# the minimization of the center_x or center_y values the following formula
# (which are in fact just the expectation/mean value formula):
norm = np.sum(data)
center_x = np.sum(x_axis * data) / norm
center_y = np.sum(y_axis * data) / norm
exp_of_xsquared = np.sum(x_axis ** 2 * data) / norm
exp_of_ysquared = np.sum(y_axis ** 2 * data) / norm
sigma_x = np.sqrt(exp_of_xsquared - center_x ** 2)
sigma_y = np.sqrt(exp_of_ysquared - center_y ** 2)
xrange = x_axis.max() - x_axis.min()
yrange = y_axis.max() - y_axis.min()
theta = 0.0
offset = float(data.min())

# populate the parameter container:
params = self.make_params()
params['amplitude'].set(value=amplitude, min=100, max=1e7)
params['sigma_x'].set(value=sigma_x, min=1*stepsize_x,
max=3*xrange)
params['sigma_y'].set(value=sigma_y, min=1*stepsize_y,
max=3*yrange)
params['center_x'].set(value=center_x, min=center_x - xrange,
max=center_x + xrange)
params['center_y'].set(value=center_y, min=center_y - yrange,
max=center_y + yrange)
params['theta'].set(value=theta, min=0, max=np.pi)
params['offset'].set(value=offset, min=0, max=1e7)
return params

amplitude = np.max(data)
center_x = x_range / 2 + np.min(x[0])
center_y = y_range / 2 + np.min(x[1])
sigma_x = x_range / 10
sigma_y = y_range / 10
theta = 0

estimate = self.make_params()
estimate['offset'].set(value=np.mean(data), min=-np.inf, max=np.max(data))
estimate['amplitude'].set(value=amplitude, min=0, max=amplitude * 2)
estimate['center_x'].set(value=center_x,
min=np.min(x[0]) - x_range / 2,
max=np.max(x[0]) + x_range / 2)
estimate['center_y'].set(value=center_y,
min=np.min(x[1]) - y_range / 2,
max=np.max(x[1]) + y_range / 2)
estimate['sigma_x'].set(value=sigma_x, min=x_range / (x[0].shape[0] - 1), max=x_range)
estimate['sigma_y'].set(value=sigma_y, min=y_range / (x[0].shape[1] - 1), max=y_range)
estimate['theta'].set(value=theta, min=-np.pi, max=np.pi)
return estimate

@estimator('Dip')
def estimate_dip(self, data, x):
Expand Down