def runge_phenomenon(self, n=41, nt=35, print_it=False):
x_e = 2.0 * (flex.double(xrange(n)) / float(n - 1) - 0.5)
y_e = 1 / (1 + x_e * x_e * 25)
fit_e = chebyshev_lsq_fit.chebyshev_lsq_fit(
nt,
x_e,
y_e,
)
fit_e = chebyshev_polynome(nt, fit_e.low_limit, fit_e.high_limit,
fit_e.coefs)
x_c = chebyshev_lsq_fit.chebyshev_nodes(n, -1, 1, True)
y_c = 1 / (1 + x_c * x_c * 25)
fit_c = chebyshev_lsq_fit.chebyshev_lsq_fit(
nt,
x_c,
y_c,
)
fit_c = chebyshev_polynome(nt, fit_c.low_limit, fit_c.high_limit,
fit_c.coefs)
x_plot = 2.0 * (flex.double(xrange(3 * n)) / float(3 * n - 1) - 0.5)
y_plot_e = fit_e.f(x_plot)
y_plot_c = fit_c.f(x_plot)
y_id = 1 / (1 + x_plot * x_plot * 25)
if print_it:
for x, y, yy, yyy in zip(x_plot, y_id, y_plot_e, y_plot_c):
print x, y, yy, yyy
def runge_phenomenon(self,n=41,nt=35,print_it=False):
x_e = 2.0*(flex.double( xrange(n) )/float(n-1)-0.5)
y_e = 1/(1+x_e*x_e*25)
fit_e = chebyshev_lsq_fit.chebyshev_lsq_fit(nt,
x_e,
y_e,
)
fit_e = chebyshev_polynome(
nt, fit_e.low_limit, fit_e.high_limit, fit_e.coefs)
x_c = chebyshev_lsq_fit.chebyshev_nodes(n, -1, 1, True)
y_c = 1/(1+x_c*x_c*25)
fit_c = chebyshev_lsq_fit.chebyshev_lsq_fit(nt,
x_c,
y_c,
)
fit_c = chebyshev_polynome(
nt, fit_c.low_limit, fit_c.high_limit, fit_c.coefs)
x_plot = 2.0*(flex.double( xrange(3*n) )/float(3*n-1)-0.5)
y_plot_e = fit_e.f( x_plot )
y_plot_c = fit_c.f( x_plot )
y_id = 1/(1+x_plot*x_plot*25)
if print_it:
for x,y,yy,yyy in zip(x_plot,y_id,y_plot_e,y_plot_c):
print x,y,yy,yyy
def __init__(self,
miller_obs,
miller_calc,
r_free_flags,
kernel_width_free_reflections=None,
kernel_width_d_star_cubed=None,
kernel_in_bin_centers=False,
kernel_on_chebyshev_nodes=True,
n_sampling_points=20,
n_chebyshev_terms=10,
use_sampling_sum_weights=False,
make_checks_and_clean_up=True):
assert [kernel_width_free_reflections, kernel_width_d_star_cubed].count(None) == 1
self.miller_obs = miller_obs
self.miller_calc = abs(miller_calc)
self.r_free_flags = r_free_flags
self.kernel_width_free_reflections = kernel_width_free_reflections
self.kernel_width_d_star_cubed = kernel_width_d_star_cubed
self.n_chebyshev_terms = n_chebyshev_terms
if make_checks_and_clean_up:
self.miller_obs = self.miller_obs.map_to_asu()
self.miller_calc = self.miller_calc.map_to_asu()
self.r_free_flags = self.r_free_flags.map_to_asu()
assert self.r_free_flags.indices().all_eq(
self.miller_obs.indices() )
self.miller_calc = self.miller_calc.common_set(
self.miller_obs )
assert self.r_free_flags.indices().all_eq(
self.miller_calc.indices() )
assert self.miller_obs.is_real_array()
if self.miller_obs.is_xray_intensity_array():
self.miller_obs = self.miller_obs.f_sq_as_f()
assert self.miller_obs.observation_type() is None or \
self.miller_obs.is_xray_amplitude_array()
if self.miller_calc.observation_type() is None:
self.miller_calc = self.miller_calc.set_observation_type(
self.miller_obs)
# get normalized data please
self.normalized_obs_f = absolute_scaling.kernel_normalisation(
self.miller_obs, auto_kernel=True)
self.normalized_obs =self.normalized_obs_f.normalised_miller_dev_eps.f_sq_as_f()
self.normalized_calc_f = absolute_scaling.kernel_normalisation(
self.miller_calc, auto_kernel=True)
self.normalized_calc =self.normalized_calc_f.normalised_miller_dev_eps.f_sq_as_f()
# get the 'free data'
if(self.r_free_flags.data().count(True) == 0):
self.r_free_flags = self.r_free_flags.array(
data = ~self.r_free_flags.data())
self.free_norm_obs = self.normalized_obs.select( self.r_free_flags.data() )
self.free_norm_calc= self.normalized_calc.select( self.r_free_flags.data() )
if self.free_norm_obs.data().size() <= 0:
raise RuntimeError("No free reflections.")
if (self.kernel_width_d_star_cubed is None):
self.kernel_width_d_star_cubed=sigmaa_estimator_kernel_width_d_star_cubed(
r_free_flags=self.r_free_flags,
kernel_width_free_reflections=self.kernel_width_free_reflections)
self.sigma_target_functor = ext.sigmaa_estimator(
e_obs = self.free_norm_obs.data(),
e_calc = self.free_norm_calc.data(),
centric = self.free_norm_obs.centric_flags().data(),
d_star_cubed = self.free_norm_obs.d_star_cubed().data() ,
width=self.kernel_width_d_star_cubed)
d_star_cubed_overall = self.miller_obs.d_star_cubed().data()
self.min_h = flex.min( d_star_cubed_overall )
self.max_h = flex.max( d_star_cubed_overall )
self.h_array = None
if (kernel_in_bin_centers):
self.h_array = flex.double( range(1,n_sampling_points*2,2) )*(
self.max_h-self.min_h)/(n_sampling_points*2)+self.min_h
else:
self.min_h *= 0.99
self.max_h *= 1.01
if kernel_on_chebyshev_nodes:
self.h_array = chebyshev_lsq_fit.chebyshev_nodes(
n=n_sampling_points,
low=self.min_h,
high=self.max_h,
include_limits=True)
else:
self.h_array = flex.double( range(n_sampling_points) )*(
self.max_h-self.min_h)/float(n_sampling_points-1.0)+self.min_h
assert self.h_array.size() == n_sampling_points
self.sigmaa_array = flex.double()
self.sigmaa_array.reserve(self.h_array.size())
self.sum_weights = flex.double()
self.sum_weights.reserve(self.h_array.size())
for h in self.h_array:
stimator = sigmaa_point_estimator(self.sigma_target_functor, h)
self.sigmaa_array.append( stimator.sigmaa )
self.sum_weights.append(
self.sigma_target_functor.sum_weights(d_star_cubed=h))
# fit a smooth function
reparam_sa = -flex.log( 1.0/self.sigmaa_array -1.0 )
if (use_sampling_sum_weights):
w_obs = flex.sqrt(self.sum_weights)
else:
w_obs = None
fit_lsq = chebyshev_lsq_fit.chebyshev_lsq_fit(
n_terms=self.n_chebyshev_terms,
x_obs=self.h_array,
y_obs=reparam_sa,
w_obs=w_obs)
cheb_pol = chebyshev_polynome(
self.n_chebyshev_terms,
self.min_h,
self.max_h,
fit_lsq.coefs)
def reverse_reparam(values): return 1.0/(1.0 + flex.exp(-values))
self.sigmaa_fitted = reverse_reparam(cheb_pol.f(self.h_array))
self.sigmaa_miller_array = reverse_reparam(cheb_pol.f(d_star_cubed_overall))
assert flex.min(self.sigmaa_miller_array) >= 0
assert flex.max(self.sigmaa_miller_array) <= 1
self.sigmaa_miller_array = self.miller_obs.array(data=self.sigmaa_miller_array)
self.alpha = None
self.beta = None
self.fom_array = None
def __init__(self,
miller_array,
kernel_width=None,
n_bins=23,
n_term=13,
d_star_sq_low=None,
d_star_sq_high=None,
auto_kernel=False,
number_of_sorted_reflections_for_auto_kernel=50):
## Autokernel is either False, true or a specific integer
if kernel_width is None:
assert (auto_kernel is not False)
if auto_kernel is not False:
assert (kernel_width==None)
assert miller_array.size()>0
## intensity arrays please
work_array = None
if not miller_array.is_real_array():
raise RuntimeError("Please provide real arrays only")
## I might have to change this upper condition
if miller_array.is_xray_amplitude_array():
work_array = miller_array.f_as_f_sq()
if miller_array.is_xray_intensity_array():
work_array = miller_array.deep_copy()
work_array = work_array.set_observation_type(miller_array)
## If type is not intensity or amplitude
## raise an execption please
if not miller_array.is_xray_intensity_array():
if not miller_array.is_xray_amplitude_array():
raise RuntimeError("Observation type unknown")
## declare some shorthands
I_obs = work_array.data()
epsilons = work_array.epsilons().data().as_double()
d_star_sq_hkl = work_array.d_spacings().data()
d_star_sq_hkl = 1.0/(d_star_sq_hkl*d_star_sq_hkl)
## Set up some limits
if d_star_sq_low is None:
d_star_sq_low = flex.min(d_star_sq_hkl)
if d_star_sq_high is None:
d_star_sq_high = flex.max(d_star_sq_hkl)
## A feeble attempt to determine an appropriate kernel width
## that seems to work reasonable in practice
self.kernel_width=kernel_width
if auto_kernel is not False:
## get the d_star_sq_array and sort it
sort_permut = flex.sort_permutation(d_star_sq_hkl)
##
if auto_kernel==True:
number=number_of_sorted_reflections_for_auto_kernel
else:
number=int(auto_kernel)
if number > d_star_sq_hkl.size():
number = d_star_sq_hkl.size()-1
self.kernel_width = d_star_sq_hkl[sort_permut[number]]-d_star_sq_low
assert self.kernel_width > 0
## Making the d_star_sq_array
assert (n_bins>1) ## assure that there are more then 1 bins for interpolation
self.d_star_sq_array = chebyshev_lsq_fit.chebyshev_nodes(
n=n_bins,
low=d_star_sq_low,
high=d_star_sq_high,
include_limits=True)
## Now get the average intensity please
##
## This step can be reasonably time consuming
self.mean_I_array = scaling.kernel_normalisation(
d_star_sq_hkl = d_star_sq_hkl,
I_hkl = I_obs,
epsilon = epsilons,
d_star_sq_array = self.d_star_sq_array,
kernel_width = self.kernel_width
)
self.var_I_array = scaling.kernel_normalisation(
d_star_sq_hkl = d_star_sq_hkl,
I_hkl = I_obs*I_obs,
epsilon = epsilons*epsilons,
d_star_sq_array = self.d_star_sq_array,
kernel_width = self.kernel_width
)
self.var_I_array = self.var_I_array - self.mean_I_array*self.mean_I_array
self.weight_sum = self.var_I_array = scaling.kernel_normalisation(
d_star_sq_hkl = d_star_sq_hkl,
I_hkl = I_obs*0.0+1.0,
epsilon = epsilons*0.0+1.0,
d_star_sq_array = self.d_star_sq_array,
kernel_width = self.kernel_width
)
eps = 1e-16 # XXX Maybe this should be larger?
self.bin_selection = (self.mean_I_array > eps)
sel_pos = self.bin_selection.iselection()
# FIXME rare bug: this crashes when the majority of the data are zero,
# e.g. because resolution limit was set too high and F/I filled in with 0.
# it would be good to catch such cases in advance by inspecting the binned
# values, and raise a different error message.
assert sel_pos.size() > 0
if (sel_pos.size() < self.mean_I_array.size() / 2) :
raise Sorry("Analysis could not be continued because more than half "+
"of the data have values below 1e-16. This usually indicates either "+
"an inappropriately high resolution cutoff, or an error in the data "+
"file which artificially creates a higher resolution limit.")
self.mean_I_array = self.mean_I_array.select(sel_pos)
self.d_star_sq_array = self.d_star_sq_array.select(sel_pos)
self.var_I_array = flex.log( self.var_I_array.select( sel_pos ) )
self.weight_sum = self.weight_sum.select(sel_pos)
self.mean_I_array = flex.log( self.mean_I_array )
## Fit a chebyshev polynome please
normalizer_fit_lsq = chebyshev_lsq_fit.chebyshev_lsq_fit(
n_term,
self.d_star_sq_array,
self.mean_I_array )
self.normalizer = chebyshev_polynome(
n_term,
d_star_sq_low,
d_star_sq_high,
normalizer_fit_lsq.coefs)
var_lsq_fit = chebyshev_lsq_fit.chebyshev_lsq_fit(
n_term,
self.d_star_sq_array,
self.var_I_array )
self.var_norm = chebyshev_polynome(
n_term,
d_star_sq_low,
d_star_sq_high,
var_lsq_fit.coefs)
ws_fit = chebyshev_lsq_fit.chebyshev_lsq_fit(
n_term,
self.d_star_sq_array,
self.weight_sum )
self.weight_sum = chebyshev_polynome(
n_term,
d_star_sq_low,
d_star_sq_high,
ws_fit.coefs)
## The data wil now be normalised using the
## chebyshev polynome we have just obtained
self.mean_I_array = flex.exp( self.mean_I_array)
self.normalizer_for_miller_array = flex.exp( self.normalizer.f(d_star_sq_hkl) )
self.var_I_array = flex.exp( self.var_I_array )
self.var_norm = flex.exp( self.var_norm.f(d_star_sq_hkl) )
self.weight_sum = flex.exp( self.weight_sum.f(d_star_sq_hkl))
self.normalised_miller = None
self.normalised_miller_dev_eps = None
if work_array.sigmas() is not None:
self.normalised_miller = work_array.customized_copy(
data = work_array.data()/self.normalizer_for_miller_array,
sigmas = work_array.sigmas()/self.normalizer_for_miller_array
).set_observation_type(work_array)
self.normalised_miller_dev_eps = self.normalised_miller.customized_copy(
data = self.normalised_miller.data()/epsilons,
sigmas = self.normalised_miller.sigmas()/epsilons)\
.set_observation_type(work_array)
else:
self.normalised_miller = work_array.customized_copy(
data = work_array.data()/self.normalizer_for_miller_array
).set_observation_type(work_array)
self.normalised_miller_dev_eps = self.normalised_miller.customized_copy(
data = self.normalised_miller.data()/epsilons)\
.set_observation_type(work_array)
def __init__(
self,
miller_obs,
miller_calc,
r_free_flags,
kernel_width_free_reflections=None,
kernel_width_d_star_cubed=None,
kernel_in_bin_centers=False,
kernel_on_chebyshev_nodes=True,
n_sampling_points=20,
n_chebyshev_terms=10,
use_sampling_sum_weights=False,
make_checks_and_clean_up=True,
):
assert [kernel_width_free_reflections, kernel_width_d_star_cubed].count(None) == 1
self.miller_obs = miller_obs
self.miller_calc = abs(miller_calc)
self.r_free_flags = r_free_flags
self.kernel_width_free_reflections = kernel_width_free_reflections
self.kernel_width_d_star_cubed = kernel_width_d_star_cubed
self.n_chebyshev_terms = n_chebyshev_terms
if make_checks_and_clean_up:
self.miller_obs = self.miller_obs.map_to_asu()
self.miller_calc = self.miller_calc.map_to_asu()
self.r_free_flags = self.r_free_flags.map_to_asu()
assert self.r_free_flags.indices().all_eq(self.miller_obs.indices())
self.miller_calc = self.miller_calc.common_set(self.miller_obs)
assert self.r_free_flags.indices().all_eq(self.miller_calc.indices())
assert self.miller_obs.is_real_array()
if self.miller_obs.is_xray_intensity_array():
self.miller_obs = self.miller_obs.f_sq_as_f()
assert self.miller_obs.observation_type() is None or self.miller_obs.is_xray_amplitude_array()
if self.miller_calc.observation_type() is None:
self.miller_calc = self.miller_calc.set_observation_type(self.miller_obs)
# get normalized data please
self.normalized_obs_f = absolute_scaling.kernel_normalisation(self.miller_obs, auto_kernel=True)
self.normalized_obs = self.normalized_obs_f.normalised_miller_dev_eps.f_sq_as_f()
self.normalized_calc_f = absolute_scaling.kernel_normalisation(self.miller_calc, auto_kernel=True)
self.normalized_calc = self.normalized_calc_f.normalised_miller_dev_eps.f_sq_as_f()
# get the 'free data'
if self.r_free_flags.data().count(True) == 0:
self.r_free_flags = self.r_free_flags.array(data=~self.r_free_flags.data())
self.free_norm_obs = self.normalized_obs.select(self.r_free_flags.data())
self.free_norm_calc = self.normalized_calc.select(self.r_free_flags.data())
if self.free_norm_obs.data().size() <= 0:
raise RuntimeError("No free reflections.")
if self.kernel_width_d_star_cubed is None:
self.kernel_width_d_star_cubed = sigmaa_estimator_kernel_width_d_star_cubed(
r_free_flags=self.r_free_flags, kernel_width_free_reflections=self.kernel_width_free_reflections
)
self.sigma_target_functor = ext.sigmaa_estimator(
e_obs=self.free_norm_obs.data(),
e_calc=self.free_norm_calc.data(),
centric=self.free_norm_obs.centric_flags().data(),
d_star_cubed=self.free_norm_obs.d_star_cubed().data(),
width=self.kernel_width_d_star_cubed,
)
d_star_cubed_overall = self.miller_obs.d_star_cubed().data()
self.min_h = flex.min(d_star_cubed_overall)
self.max_h = flex.max(d_star_cubed_overall)
self.h_array = None
if kernel_in_bin_centers:
self.h_array = (
flex.double(xrange(1, n_sampling_points * 2, 2)) * (self.max_h - self.min_h) / (n_sampling_points * 2)
+ self.min_h
)
else:
self.min_h *= 0.99
self.max_h *= 1.01
if kernel_on_chebyshev_nodes:
self.h_array = chebyshev_lsq_fit.chebyshev_nodes(
n=n_sampling_points, low=self.min_h, high=self.max_h, include_limits=True
)
else:
self.h_array = (
flex.double(range(n_sampling_points)) * (self.max_h - self.min_h) / float(n_sampling_points - 1.0)
+ self.min_h
)
assert self.h_array.size() == n_sampling_points
self.sigmaa_array = flex.double()
self.sigmaa_array.reserve(self.h_array.size())
self.sum_weights = flex.double()
self.sum_weights.reserve(self.h_array.size())
for h in self.h_array:
stimator = sigmaa_point_estimator(self.sigma_target_functor, h)
self.sigmaa_array.append(stimator.sigmaa)
self.sum_weights.append(self.sigma_target_functor.sum_weights(d_star_cubed=h))
# fit a smooth function
reparam_sa = -flex.log(1.0 / self.sigmaa_array - 1.0)
if use_sampling_sum_weights:
w_obs = flex.sqrt(self.sum_weights)
else:
w_obs = None
fit_lsq = chebyshev_lsq_fit.chebyshev_lsq_fit(
n_terms=self.n_chebyshev_terms, x_obs=self.h_array, y_obs=reparam_sa, w_obs=w_obs
)
cheb_pol = chebyshev_polynome(self.n_chebyshev_terms, self.min_h, self.max_h, fit_lsq.coefs)
def reverse_reparam(values):
return 1.0 / (1.0 + flex.exp(-values))
self.sigmaa_fitted = reverse_reparam(cheb_pol.f(self.h_array))
self.sigmaa_miller_array = reverse_reparam(cheb_pol.f(d_star_cubed_overall))
assert flex.min(self.sigmaa_miller_array) >= 0
assert flex.max(self.sigmaa_miller_array) <= 1
self.sigmaa_miller_array = self.miller_obs.array(data=self.sigmaa_miller_array)
self.alpha = None
self.beta = None
self.fom_array = None
def __init__(self,
miller_array,
kernel_width=None,
n_bins=23,
n_term=13,
d_star_sq_low=None,
d_star_sq_high=None,
auto_kernel=False,
number_of_sorted_reflections_for_auto_kernel=50):
## Autokernel is either False, true or a specific integer
if kernel_width is None:
assert (auto_kernel is not False)
if auto_kernel is not False:
assert (kernel_width == None)
assert miller_array.size() > 0
## intensity arrays please
work_array = None
if not miller_array.is_real_array():
raise RuntimeError("Please provide real arrays only")
## I might have to change this upper condition
if miller_array.is_xray_amplitude_array():
work_array = miller_array.f_as_f_sq()
if miller_array.is_xray_intensity_array():
work_array = miller_array.deep_copy()
work_array = work_array.set_observation_type(miller_array)
## If type is not intensity or amplitude
## raise an execption please
if not miller_array.is_xray_intensity_array():
if not miller_array.is_xray_amplitude_array():
raise RuntimeError("Observation type unknown")
## declare some shorthands
I_obs = work_array.data()
epsilons = work_array.epsilons().data().as_double()
d_star_sq_hkl = work_array.d_spacings().data()
d_star_sq_hkl = 1.0 / (d_star_sq_hkl * d_star_sq_hkl)
## Set up some limits
if d_star_sq_low is None:
d_star_sq_low = flex.min(d_star_sq_hkl)
if d_star_sq_high is None:
d_star_sq_high = flex.max(d_star_sq_hkl)
## A feeble attempt to determine an appropriate kernel width
## that seems to work reasonable in practice
self.kernel_width = kernel_width
if auto_kernel is not False:
## get the d_star_sq_array and sort it
sort_permut = flex.sort_permutation(d_star_sq_hkl)
##
if auto_kernel == True:
number = number_of_sorted_reflections_for_auto_kernel
else:
number = int(auto_kernel)
if number > d_star_sq_hkl.size():
number = d_star_sq_hkl.size() - 1
self.kernel_width = d_star_sq_hkl[
sort_permut[number]] - d_star_sq_low
assert self.kernel_width > 0
## Making the d_star_sq_array
assert (n_bins > 1
) ## assure that there are more then 1 bins for interpolation
self.d_star_sq_array = chebyshev_lsq_fit.chebyshev_nodes(
n=n_bins,
low=d_star_sq_low,
high=d_star_sq_high,
include_limits=True)
## Now get the average intensity please
##
## This step can be reasonably time consuming
self.mean_I_array = scaling.kernel_normalisation(
d_star_sq_hkl=d_star_sq_hkl,
I_hkl=I_obs,
epsilon=epsilons,
d_star_sq_array=self.d_star_sq_array,
kernel_width=self.kernel_width)
self.var_I_array = scaling.kernel_normalisation(
d_star_sq_hkl=d_star_sq_hkl,
I_hkl=I_obs * I_obs,
epsilon=epsilons * epsilons,
d_star_sq_array=self.d_star_sq_array,
kernel_width=self.kernel_width)
self.var_I_array = self.var_I_array - self.mean_I_array * self.mean_I_array
self.weight_sum = self.var_I_array = scaling.kernel_normalisation(
d_star_sq_hkl=d_star_sq_hkl,
I_hkl=I_obs * 0.0 + 1.0,
epsilon=epsilons * 0.0 + 1.0,
d_star_sq_array=self.d_star_sq_array,
kernel_width=self.kernel_width)
eps = 1e-16 # XXX Maybe this should be larger?
self.bin_selection = (self.mean_I_array > eps)
sel_pos = self.bin_selection.iselection()
# FIXME rare bug: this crashes when the majority of the data are zero,
# e.g. because resolution limit was set too high and F/I filled in with 0.
# it would be good to catch such cases in advance by inspecting the binned
# values, and raise a different error message.
assert sel_pos.size() > 0
if (sel_pos.size() < self.mean_I_array.size() / 2):
raise Sorry(
"Analysis could not be continued because more than half " +
"of the data have values below 1e-16. This usually indicates either "
+
"an inappropriately high resolution cutoff, or an error in the data "
+ "file which artificially creates a higher resolution limit.")
self.mean_I_array = self.mean_I_array.select(sel_pos)
self.d_star_sq_array = self.d_star_sq_array.select(sel_pos)
self.var_I_array = flex.log(self.var_I_array.select(sel_pos))
self.weight_sum = self.weight_sum.select(sel_pos)
self.mean_I_array = flex.log(self.mean_I_array)
## Fit a chebyshev polynome please
normalizer_fit_lsq = chebyshev_lsq_fit.chebyshev_lsq_fit(
n_term, self.d_star_sq_array, self.mean_I_array)
self.normalizer = chebyshev_polynome(n_term, d_star_sq_low,
d_star_sq_high,
normalizer_fit_lsq.coefs)
var_lsq_fit = chebyshev_lsq_fit.chebyshev_lsq_fit(
n_term, self.d_star_sq_array, self.var_I_array)
self.var_norm = chebyshev_polynome(n_term, d_star_sq_low,
d_star_sq_high, var_lsq_fit.coefs)
ws_fit = chebyshev_lsq_fit.chebyshev_lsq_fit(n_term,
self.d_star_sq_array,
self.weight_sum)
self.weight_sum = chebyshev_polynome(n_term, d_star_sq_low,
d_star_sq_high, ws_fit.coefs)
## The data wil now be normalised using the
## chebyshev polynome we have just obtained
self.mean_I_array = flex.exp(self.mean_I_array)
self.normalizer_for_miller_array = flex.exp(
self.normalizer.f(d_star_sq_hkl))
self.var_I_array = flex.exp(self.var_I_array)
self.var_norm = flex.exp(self.var_norm.f(d_star_sq_hkl))
self.weight_sum = flex.exp(self.weight_sum.f(d_star_sq_hkl))
self.normalised_miller = None
self.normalised_miller_dev_eps = None
if work_array.sigmas() is not None:
self.normalised_miller = work_array.customized_copy(
data=work_array.data() / self.normalizer_for_miller_array,
sigmas=work_array.sigmas() /
self.normalizer_for_miller_array).set_observation_type(
work_array)
self.normalised_miller_dev_eps = self.normalised_miller.customized_copy(
data = self.normalised_miller.data()/epsilons,
sigmas = self.normalised_miller.sigmas()/epsilons)\
.set_observation_type(work_array)
else:
self.normalised_miller = work_array.customized_copy(
data=work_array.data() /
self.normalizer_for_miller_array).set_observation_type(
work_array)
self.normalised_miller_dev_eps = self.normalised_miller.customized_copy(
data = self.normalised_miller.data()/epsilons)\
.set_observation_type(work_array)
