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
def decremental_fits(label,
null_fit,
full_fit=None,
params=None,
plots_dir=None,
verbose=0):
if (params is None): params = fit_parameters()
results = []
last_fit = scitbx.math.gaussian.fit(null_fit.table_x(), null_fit.table_y(),
null_fit.table_sigmas(), full_fit)
while (last_fit.n_terms() > 1):
good_min = scitbx.math.gaussian_fit.decremental_fit(
existing_gaussian=last_fit, params=params)
if (good_min is None):
print "%s n_terms=%d: No successful minimization. Aborting." % (
label, last_fit.n_terms() - 1)
break
show_fit_summary("Best fit", label, good_min.final_gaussian_fit,
good_min.max_error)
show_literature_fits(
label=label,
n_terms=good_min.final_gaussian_fit.n_terms(),
null_fit=null_fit,
n_points=good_min.final_gaussian_fit.table_x().size(),
e_other=good_min.max_error)
good_min.final_gaussian_fit.show()
good_min.show_minimization_parameters()
last_fit = good_min.final_gaussian_fit
print
show_minimize_multi_histogram()
sys.stdout.flush()
if (plots_dir):
write_plots(plots_dir=plots_dir,
label=label +
"_%d" % good_min.final_gaussian_fit.n_terms(),
gaussian_fit=good_min.final_gaussian_fit)
g = good_min.final_gaussian_fit
results.append(
xray_scattering.fitted_gaussian(stol=g.table_x()[-1],
gaussian_sum=g))
return results
def decremental_fits(label, null_fit, full_fit=None, params=None,
plots_dir=None, verbose=0):
if (params is None): params = fit_parameters()
results = []
last_fit = scitbx.math.gaussian.fit(
null_fit.table_x(),
null_fit.table_y(),
null_fit.table_sigmas(),
full_fit)
while (last_fit.n_terms() > 1):
good_min = scitbx.math.gaussian_fit.decremental_fit(
existing_gaussian=last_fit,
params=params)
if (good_min is None):
print "%s n_terms=%d: No successful minimization. Aborting." % (
label, last_fit.n_terms()-1)
break
show_fit_summary(
"Best fit", label, good_min.final_gaussian_fit, good_min.max_error)
show_literature_fits(
label=label,
n_terms=good_min.final_gaussian_fit.n_terms(),
null_fit=null_fit,
n_points=good_min.final_gaussian_fit.table_x().size(),
e_other=good_min.max_error)
good_min.final_gaussian_fit.show()
good_min.show_minimization_parameters()
last_fit = good_min.final_gaussian_fit
print
show_minimize_multi_histogram()
sys.stdout.flush()
if (plots_dir):
write_plots(
plots_dir=plots_dir,
label=label+"_%d"%good_min.final_gaussian_fit.n_terms(),
gaussian_fit=good_min.final_gaussian_fit)
g = good_min.final_gaussian_fit
results.append(xray_scattering.fitted_gaussian(
stol=g.table_x()[-1], gaussian_sum=g))
return results
