statistics — 2D Gaussian fitting¶
- foregrounds_diffusion.statistics.gaussian(height, center_x, center_y, width_x, width_y)[source]¶
Return a 2D Gaussian function with the given parameters.
- Parameters:
height (float) – Peak amplitude.
center_x, center_y (float) – Centre coordinates.
width_x, width_y (float) – Standard deviations along x and y.
- Returns:
callable – Function
f(x, y)evaluating the Gaussian at (x, y).
- foregrounds_diffusion.statistics.moments(data)[source]¶
Estimate 2D Gaussian parameters from image moments.
- Parameters:
data (ndarray, shape (ny, nx)) – 2D image.
- Returns:
tuple –
(height, x, y, width_x, width_y)
- foregrounds_diffusion.statistics.fitgaussian(data)[source]¶
Fit a 2D Gaussian to an image using least squares.
- Parameters:
data (ndarray, shape (ny, nx)) – 2D image.
- Returns:
tuple –
(height, x, y, width_x, width_y)— best-fit parameters.
- foregrounds_diffusion.statistics.fitting_func(p, p0, xgrid, ygrid, tmap, lbounds=None, ubounds=None, fixed=None, return_fit=0)[source]¶
Evaluate or fit a 2D Gaussian model on a pixel grid.
Used internally by
get_mask_using_gaussian_fitting().- Parameters:
p (array_like) – Current parameter vector
[baseline, amp, x_cen, y_cen, width, ...].p0 (array_like) – Reference parameter vector (used to restore fixed parameters).
xgrid, ygrid (ndarray) – Pixel coordinate grids.
tmap (ndarray) – Target map (used as fall-back return on bound violations).
lbounds, ubounds (array_like, optional) – Lower and upper parameter bounds.
fixed (array_like of int, optional) – Indices of parameters to hold fixed at p0.
return_fit (int) – If 1, return the model image; if 0, return the residual vector.
- Returns:
ndarray – Residual vector (when
return_fit=0) or model image (whenreturn_fit=1).