[cctbxbb] [git/cctbx] master: re-defining the shift/est convergence limit in terms of crystallographic rather than independent parameters to make it easier to relate to the convergence reported in the CIF; also caching the shifts for further analysis and reporting; (9339fd5f7)

[Nicholas Sauter]

I'm worried about making changes like this to a fundamental toolbox
component like normal equations solving.  We use this for processing xfel
data, and would like the opportunity to test any changes through a pull
request, prior to having them committed to the main branch.

Nick Sauter

Nicholas K. Sauter, Ph. D.
Senior Scientist, Molecular Biophysics & Integrated Bioimaging Division
Lawrence Berkeley National Laboratory
1 Cyclotron Rd., Bldg. 33R0345
Berkeley, CA 94720
(510) 486-5713

On Wed, Sep 5, 2018 at 5:29 AM, CCTBX commit <
diamondlightsource.jenkins at gmail.com> wrote:

> Repository : ssh://g18-sc-serv-04.diamond.ac.uk/cctbx
> On branch  : master
>
> ------------------------------
>
>
> commit 9339fd5f7d240f0d6f85dd57ce42e796d19608f1
> Author: pcxod <oleg_dolomanov at hotmail.com>
> Date:   Wed Sep 5 13:29:29 2018 +0100
>
>     re-defining the shift/est convergence limit in terms of
> crystallographic rather than independent parameters to make it easier to
> relate to the convergence reported in the CIF; also caching the shifts for
> further analysis and reporting;
>
>
> ------------------------------
>
>
> 9339fd5f7d240f0d6f85dd57ce42e796d19608f1
> scitbx/lstbx/normal_eqns_solving.py | 17 +++++++++++------
> 1 file changed, 11 insertions(+), 6 deletions(-)
>
> diff --git a/scitbx/lstbx/normal_eqns_solving.py
> b/scitbx/lstbx/normal_eqns_solving.py
> index 660304eab..45f070622 100644
> --- a/scitbx/lstbx/normal_eqns_solving.py
> +++ b/scitbx/lstbx/normal_eqns_solving.py
> @@ -140,15 +140,20 @@ class iterations(object):
>      a = self.non_linear_ls.normal_matrix_packed_u()
>      a.matrix_packed_u_diagonal_add_in_place(value*a.
> matrix_packed_u_diagonal())
>
> -  def do_scale_shifts(self, max_shift_over_esd):
> +  def do_scale_shifts(self, limit_shift_over_su):
>      x = self.non_linear_ls.step()
>      esd = self.non_linear_ls.covariance_matrix().matrix_packed_u_
> diagonal()
> -    x_over_esd = flex.abs(x/flex.sqrt(esd))
> -    max_val = flex.max(x_over_esd)
> -    if max_val < self.convergence_as_shift_over_esd:
> +    ls_shifts_over_su = flex.abs(x/flex.sqrt(esd))
> +    #max shift for the LS
> +    self.max_ls_shift_over_su = flex.max(ls_shifts_over_su)
> +    jac_tr = self.non_linear_ls.actual.\
> +      reparametrisation.jacobian_transpose_matching_grad_fc()
> +    self.shifts_over_su = jac_tr.transpose() * ls_shifts_over_su
> +    self.max_shift_over_su = flex.max(self.shifts_over_su)
> +    if self.max_shift_over_su < self.convergence_as_shift_over_esd:
>        return True
> -    if max_val > max_shift_over_esd:
> -      shift_scale = max_shift_over_esd/max_val
> +    if self.max_ls_shift_over_su > limit_shift_over_su:
> +      shift_scale = limit_shift_over_su/self.max_ls_shift_over_su
>        x *= shift_scale
>      return False
>
>
> ------------------------------
>
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