def zig_zag_fits(label, null_fit, null_fit_more, params):
six_term_best_min = scitbx.math.gaussian_fit.fit_with_golay_starts(
label=label,
null_fit=null_fit,
null_fit_more=null_fit_more,
params=params)
results = []
n_term_best_min = six_term_best_min
have_all_points_in_previous = True
while 1:
while 1:
if (n_term_best_min.final_gaussian_fit.n_terms() == 1):
existing_gaussian = null_fit
else:
decr_best_min = scitbx.math.gaussian_fit.decremental_fit(
existing_gaussian=n_term_best_min.final_gaussian_fit,
params=params)
assert decr_best_min is not None
print "Decremental:",
decr_best_min.show_summary()
existing_gaussian = decr_best_min.final_gaussian_fit
if (n_term_best_min.max_error <= params.negligible_max_error
and have_all_points_in_previous):
break
incr_best_min = find_max_x_multi(
null_fit=null_fit,
existing_gaussian=existing_gaussian,
target_powers=params.target_powers,
minimize_using_sigmas=params.minimize_using_sigmas,
n_repeats_minimization=params.n_repeats_minimization,
shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n,
b_min=params.b_min,
max_max_error=params.max_max_error,
n_start_fractions=params.n_start_fractions)
assert incr_best_min is not None
print "Incremental:",
incr_best_min.show_summary()
if (existing_gaussian is null_fit):
break
if (not incr_best_min.is_better_than(n_term_best_min)):
break
n_term_best_min = incr_best_min
print " Settled on:",
n_term_best_min.show_summary()
results.append(
xray_scattering.fitted_gaussian(
stol=n_term_best_min.final_gaussian_fit.table_x()[-1],
gaussian_sum=n_term_best_min.final_gaussian_fit,
max_error=n_term_best_min.max_error))
if (existing_gaussian is null_fit):
break
have_all_points_in_previous = (
n_term_best_min.final_gaussian_fit.table_x().size() ==
decr_best_min.final_gaussian_fit.table_x().size())
n_term_best_min = decr_best_min
print
return results
def zig_zag_fits(label, null_fit, null_fit_more, params):
six_term_best_min = scitbx.math.gaussian_fit.fit_with_golay_starts(
label=label,
null_fit=null_fit,
null_fit_more=null_fit_more,
params=params)
results = []
n_term_best_min = six_term_best_min
have_all_points_in_previous = True
while 1:
while 1:
if (n_term_best_min.final_gaussian_fit.n_terms() == 1):
existing_gaussian = null_fit
else:
decr_best_min = scitbx.math.gaussian_fit.decremental_fit(
existing_gaussian=n_term_best_min.final_gaussian_fit,
params=params)
assert decr_best_min is not None
print "Decremental:",
decr_best_min.show_summary()
existing_gaussian = decr_best_min.final_gaussian_fit
if (n_term_best_min.max_error <= params.negligible_max_error
and have_all_points_in_previous):
break
incr_best_min = find_max_x_multi(
null_fit=null_fit,
existing_gaussian=existing_gaussian,
target_powers=params.target_powers,
minimize_using_sigmas=params.minimize_using_sigmas,
n_repeats_minimization=params.n_repeats_minimization,
shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n,
b_min=params.b_min,
max_max_error=params.max_max_error,
n_start_fractions=params.n_start_fractions)
assert incr_best_min is not None
print "Incremental:",
incr_best_min.show_summary()
if (existing_gaussian is null_fit):
break
if (not incr_best_min.is_better_than(n_term_best_min)):
break
n_term_best_min = incr_best_min
print " Settled on:",
n_term_best_min.show_summary()
results.append(xray_scattering.fitted_gaussian(
stol=n_term_best_min.final_gaussian_fit.table_x()[-1],
gaussian_sum=n_term_best_min.final_gaussian_fit,
max_error=n_term_best_min.max_error))
if (existing_gaussian is null_fit):
break
have_all_points_in_previous = (
n_term_best_min.final_gaussian_fit.table_x().size()
== decr_best_min.final_gaussian_fit.table_x().size())
n_term_best_min = decr_best_min
print
return results
def incremental_fits(label, null_fit, params=None, plots_dir=None, verbose=0):
if (params is None): params = fit_parameters()
f0 = null_fit.table_y()[0]
results = []
previous_n_points = 0
existing_gaussian = xray_scattering.gaussian([], [])
while (existing_gaussian.n_terms() < params.max_n_terms):
if (previous_n_points == null_fit.table_x().size()):
print "%s: Full fit with %d terms. Search stopped." % (
label, existing_gaussian.n_terms())
print
break
n_terms = existing_gaussian.n_terms() + 1
best_min = find_max_x_multi(
null_fit=null_fit,
existing_gaussian=existing_gaussian,
target_powers=params.target_powers,
minimize_using_sigmas=params.minimize_using_sigmas,
n_repeats_minimization=params.n_repeats_minimization,
shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n,
b_min=params.b_min,
max_max_error=params.max_max_error,
n_start_fractions=params.n_start_fractions)
if (best_min is None):
print "Warning: No fit: %s n_terms=%d" % (label, n_terms)
print
break
if (previous_n_points > best_min.final_gaussian_fit.table_x().size()):
print "Warning: previous fit included more sampling points."
previous_n_points = best_min.final_gaussian_fit.table_x().size()
show_fit_summary("Best fit", label, best_min.final_gaussian_fit,
best_min.max_error)
show_literature_fits(
label=label,
n_terms=n_terms,
null_fit=null_fit,
n_points=best_min.final_gaussian_fit.table_x().size(),
e_other=best_min.max_error)
best_min.final_gaussian_fit.show()
best_min.show_minimization_parameters()
existing_gaussian = best_min.final_gaussian_fit
print
show_minimize_multi_histogram()
sys.stdout.flush()
if (plots_dir):
write_plots(plots_dir=plots_dir,
label=label + "_%d" % n_terms,
gaussian_fit=best_min.final_gaussian_fit)
g = best_min.final_gaussian_fit
results.append(
xray_scattering.fitted_gaussian(stol=g.table_x()[-1],
gaussian_sum=g))
return results
def incremental_fits(label, null_fit, params=None, plots_dir=None, verbose=0):
if (params is None): params = fit_parameters()
f0 = null_fit.table_y()[0]
results = []
previous_n_points = 0
existing_gaussian = xray_scattering.gaussian([],[])
while (existing_gaussian.n_terms() < params.max_n_terms):
if (previous_n_points == null_fit.table_x().size()):
print "%s: Full fit with %d terms. Search stopped." % (
label, existing_gaussian.n_terms())
print
break
n_terms = existing_gaussian.n_terms() + 1
best_min = find_max_x_multi(
null_fit=null_fit,
existing_gaussian=existing_gaussian,
target_powers=params.target_powers,
minimize_using_sigmas=params.minimize_using_sigmas,
n_repeats_minimization=params.n_repeats_minimization,
shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n,
b_min=params.b_min,
max_max_error=params.max_max_error,
n_start_fractions=params.n_start_fractions)
if (best_min is None):
print "Warning: No fit: %s n_terms=%d" % (label, n_terms)
print
break
if (previous_n_points > best_min.final_gaussian_fit.table_x().size()):
print "Warning: previous fit included more sampling points."
previous_n_points = best_min.final_gaussian_fit.table_x().size()
show_fit_summary(
"Best fit", label, best_min.final_gaussian_fit, best_min.max_error)
show_literature_fits(
label=label,
n_terms=n_terms,
null_fit=null_fit,
n_points=best_min.final_gaussian_fit.table_x().size(),
e_other=best_min.max_error)
best_min.final_gaussian_fit.show()
best_min.show_minimization_parameters()
existing_gaussian = best_min.final_gaussian_fit
print
show_minimize_multi_histogram()
sys.stdout.flush()
if (plots_dir):
write_plots(
plots_dir=plots_dir,
label=label+"_%d"%n_terms,
gaussian_fit=best_min.final_gaussian_fit)
g = best_min.final_gaussian_fit
results.append(xray_scattering.fitted_gaussian(
stol=g.table_x()[-1], gaussian_sum=g))
return results
