DOC, MAINT: remove `solver='lsq'` from doc, add todo
@@ -62,11 +62,10 @@ def xloci(cube, angle_list, fwhm=4, metric='manhattan', dist_threshold=50,
Number of processes for parallel computing. If None the number of
processes will be set to (cpu_count()/2). By default the algorithm works
in single-process mode.
- solver : {'lstsq', 'nnls', 'lsq'}, str optional
+ solver : {'lstsq', 'nnls'}, str optional
Choosing the solver of the least squares problem. ``lstsq`` uses the
standard scipy least squares solver. ``nnls`` uses the scipy
- non-negative least-squares solver. ``lsq`` uses the linear least-squares
- solver.
+ non-negative least-squares solver.
tol : float, optional
Valid when ``solver`` is set to lstsq. Sets the cutoff for 'small'
singular values; used to determine effective rank of a. Singular values
@@ -239,7 +238,7 @@ def _leastsq_patch(array, nseg, indices, indices_opt, angles, pa_threshold,
coef = sp.linalg.lstsq(A.T, b, cond=tol)[0] # SVD method
elif solver == 'nnls':
coef = sp.optimize.nnls(A.T, b)[0]
- elif solver == 'lsq':
+ elif solver == 'lsq': # TODO
coef = sp.optimize.lsq_linear(A.T, b, bounds=(0, 1),
method='trf',
lsq_solver='lsmr')['x']
